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AI Affordances and EFL Learners' Speaking Engagement: The Moderating Roles of Gender and Learner Type

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Title: AI Affordances and EFL Learners' Speaking Engagement: The Moderating Roles of Gender and Learner Type
Language: English
Authors: Fang Huang (ORCID 0000-0001-6706-8251), Dingyang Peng, Timothy Teo
Source: European Journal of Education. 2025 60(1).
Availability: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Peer Reviewed: Y
Page Count: 14
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Learner Engagement, Second Language Learning, Second Language Instruction, English (Second Language), Artificial Intelligence, Teaching Methods, Technology Uses in Education, Speech Communication, Gender Differences, Predictor Variables, Computer Software, Instructional Design, Student Attitudes, College Students, On the Job Training, Foreign Countries
Geographic Terms: China
DOI: 10.1111/ejed.70041
ISSN: 0141-8211
1465-3435
Abstract: Contextualised in the AI--supported English-speaking learning, this study examined the roles of AI affordances in influencing EFL learners' emotional, cognitive, and behavioural speaking engagement, and explored the moderating roles of gender and learner types (on-campus vs. on-job) in influencing AI-supported English-speaking engagement. Data collected from 332 Chinese EFL learners (159 on-campus and 173 on-job learners) were analysed by using structural equation modelling. Results indicated that Chinese EFL learners perceived AI affordances to be significant in influencing their emotional, cognitive and behavioural engagement in practicing their spoken English. The results from the PLS-SEM model revealed that AI affordances accounted for 54.7%, 52.4% and 56.0% of the variance in emotional engagement, cognitive engagement and behavioural engagement, respectively. Learner type was not found to significantly moderate the relationships between AI affordances and speaking engagement. Gender was found to be a significant moderator for the AI affordances--behavioural engagement and AI affordance--cognitive engagement relationships. These findings enrich existing literature about AI--empowered speaking engagement and provide practical implications for English teachers to design effective speaking-teaching models.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1461380
Database: ERIC
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  Value: <anid>AN0183654458;eje01mar.25;2025Mar17.06:27;v2.2.500</anid> <title id="AN0183654458-1">AI Affordances and EFL Learners' Speaking Engagement: The Moderating Roles of Gender and Learner Type </title> <p>Contextualised in the AI–supported English‐speaking learning, this study examined the roles of AI affordances in influencing EFL learners' emotional, cognitive, and behavioural speaking engagement, and explored the moderating roles of gender and learner types (on‐campus vs. on‐job) in influencing AI‐supported English‐speaking engagement. Data collected from 332 Chinese EFL learners (159 on‐campus and 173 on‐job learners) were analysed by using structural equation modelling. Results indicated that Chinese EFL learners perceived AI affordances to be significant in influencing their emotional, cognitive and behavioural engagement in practicing their spoken English. The results from the PLS‐SEM model revealed that AI affordances accounted for 54.7%, 52.4% and 56.0% of the variance in emotional engagement, cognitive engagement and behavioural engagement, respectively. Learner type was not found to significantly moderate the relationships between AI affordances and speaking engagement. Gender was found to be a significant moderator for the AI affordances–behavioural engagement and AI affordance–cognitive engagement relationships. These findings enrich existing literature about AI–empowered speaking engagement and provide practical implications for English teachers to design effective speaking‐teaching models.</p> <p>Keywords: AI affordances; gender; learner type; learning engagement; spoken English</p> <hd id="AN0183654458-2">Introduction</hd> <p>Learning engagement indicates how learners act, think and feel in learning activities (Oga‐Baldwin [<reflink idref="bib77" id="ref1">77</reflink>]), and it influences the quality and intensity of efforts that learners put into their learning (Wang and Xue [<reflink idref="bib103" id="ref2">103</reflink>]; Wang et al. [<reflink idref="bib104" id="ref3">104</reflink>]). Learning engagement encompasses behavioural engagement (BE), emotional engagement (EE) and cognitive engagement (CE) (Fredricks et al. [<reflink idref="bib34" id="ref4">34</reflink>]). To be specific, BE refers to learners' physical involvement in learning activities; EE reflects learners' affective states (e.g., joy, anxiety, happiness) towards learning, schools, peers and teachers and CE indicates the strategies taken by learners to achieve their learning goals (Zimmerman [<reflink idref="bib114" id="ref5">114</reflink>]), either deep (e.g., meaningful processing actions) or shallow (e.g., mechanical memorization strategies) (Ravindran et al. [<reflink idref="bib81" id="ref6">81</reflink>]). As the 'holy grail' of learning (Sinatra et al. [<reflink idref="bib90" id="ref7">90</reflink>]), learning engagement has received wide attention among scholars, given its important roles in learning effectiveness (e.g., Bakır‐Yalçın and Usluel [<reflink idref="bib4" id="ref8">4</reflink>]; Huang et al. [<reflink idref="bib50" id="ref9">50</reflink>]; Su et al. [<reflink idref="bib92" id="ref10">92</reflink>]). Learning engagement is shaped by various factors (e.g., teacher care, rapport, sociocultural values) (Derakhshan et al. [<reflink idref="bib25" id="ref11">25</reflink>]) and interplay with them in language learning (Salas‐Pilco et al. [<reflink idref="bib85" id="ref12">85</reflink>]). Learners who are sufficiently engaged in learning are advantageous to academic, social and economic achievements, whereas disengagement will result in underachievement, dropout, and boredom (Fredricks et al. [<reflink idref="bib35" id="ref13">35</reflink>]; Wang and Eccles [<reflink idref="bib101" id="ref14">101</reflink>]).</p> <p>With technology being pervasively adopted in informal learning settings to improve language learning in recent years, scholars further examined students' engagement in the online learning context (e.g., Bedi [<reflink idref="bib8" id="ref15">8</reflink>]; Liu et al. [<reflink idref="bib70" id="ref16">70</reflink>]). In language learning, scholars suggested that learning engagement has played a critical role in improving students' spoken English (Lo [<reflink idref="bib71" id="ref17">71</reflink>]; Saqr et al. [<reflink idref="bib86" id="ref18">86</reflink>]), such as motivation in speaking (Zhang and Zhang [<reflink idref="bib112" id="ref19">112</reflink>]), speaking clarity (Robillos [<reflink idref="bib84" id="ref20">84</reflink>]) and better pronunciation (Asratie et al. [<reflink idref="bib3" id="ref21">3</reflink>]).</p> <p>Although online learning improves EFL learners' English learning, mastering spoken English is perceived as more challenging compared to other language skills such as reading, listening and writing (Fan and Chen [<reflink idref="bib30" id="ref22">30</reflink>]; Islam and Stapa [<reflink idref="bib55" id="ref23">55</reflink>]). English speaking calls for extensive attention and effort devotion (Ericsson et al. [<reflink idref="bib29" id="ref24">29</reflink>]; Swain [<reflink idref="bib93" id="ref25">93</reflink>]) because it is cognitively, mentally and socially demanding (Burns [<reflink idref="bib11" id="ref26">11</reflink>]). Researchers suggested some Chinese EFL learners were reluctant or unwilling to devote themselves to speaking activities (e.g., Hau and Ho [<reflink idref="bib46" id="ref27">46</reflink>]; Zhang and Zhang [<reflink idref="bib112" id="ref28">112</reflink>]), which led to a deficiency in English speaking (Gan [<reflink idref="bib37" id="ref29">37</reflink>]). For those who are willing to engage in speaking activities, the limited opportunity to speak to native English speakers and insufficient practice opportunities are existing barriers, as noted by previous studies (Fathi et al. [<reflink idref="bib31" id="ref30">31</reflink>]; Tran et al. [<reflink idref="bib97" id="ref31">97</reflink>]).</p> <p>Therefore, some EFL learners have resorted to informal learning opportunities such as extracurricular activities or online learning to produce fluent English (e.g., Huang and Zou [<reflink idref="bib53" id="ref32">53</reflink>]; Lin [<reflink idref="bib67" id="ref33">67</reflink>]). They have adopted diverse conversation–based online systems or AI tools (Duolinguo; Flipgrid; YouGlish) to facilitate English speaking (Huang and Zou [<reflink idref="bib53" id="ref34">53</reflink>]; Robillos [<reflink idref="bib84" id="ref35">84</reflink>]), given that these tools enable them to access more practice opportunities (Huang et al. [<reflink idref="bib52" id="ref36">52</reflink>]; Huang et al. [<reflink idref="bib51" id="ref37">51</reflink>]), and notably, they can record speaking and practice repeatedly to enhance self‐regulated learning (Dai and Wu [<reflink idref="bib22" id="ref38">22</reflink>]; Henry and Thorsen [<reflink idref="bib47" id="ref39">47</reflink>]). It is noticeable that, besides on‐campus student learners, many on‐job learners also use technological tools to improve English, as in many workplaces, such as international company branches, English proficiency to a certain degree determines their competitiveness and work efficiency.</p> <p>AI‐empowered technology enables immediate assessment and provides specialised support (Chen et al. [<reflink idref="bib16" id="ref40">16</reflink>]). EFL learners' engagement with speaking by using AI are still suggested as peripheral (Huang and Zou [<reflink idref="bib53" id="ref41">53</reflink>]). Understanding to what extent do AI affordances influence EFL learners' speaking learning engagement has yet to be achieved by both EFL learners and academic researchers. A review of the existing literature suggested that the majority of studies placed their foci on describing AI tools and teachers' or students' AI acceptance by examining factors that influence EFL learners' attitudes and intentions to use AI (e.g., Chai et al. [<reflink idref="bib13" id="ref42">13</reflink>]; Huang and Zou [<reflink idref="bib53" id="ref43">53</reflink>]). Among them, very few have tried to unpack learners' engagement with AI and how EFL learners perceive AI affordances to influence their speaking engagement (Fathi et al. [<reflink idref="bib31" id="ref44">31</reflink>]). What's more, no previous study has ever tried to uncover the roles of gender and learner types (on‐campus learner versus on‐job learner) on the relationships between AI affordances and diverse learning engagement.</p> <p>Given the above‐mentioned research gap, this study aims to unpack the roles of AI affordances in influencing EFL learners' speaking learning engagement, and particularly, examine the moderating roles of gender and learner types in influencing their AI usage. Research questions are as follows: (<reflink idref="bib1" id="ref45">1</reflink>) What are EFL learners' perceptions of AI affordances in speaking learning? (<reflink idref="bib2" id="ref46">2</reflink>) To what extent do AI affordances influence their learning engagement? (<reflink idref="bib3" id="ref47">3</reflink>) How do gender and learner types moderate the relationship between perceived AI affordances and speaking engagement?</p> <p>As affordances' features and EFL learners' engagement vary depending on the context, and learners interpret and perceive AI affordances differently (Grgecic et al. [<reflink idref="bib40" id="ref48">40</reflink>]), the roles of AI affordances in influencing EFL learners' engagement deserve further examination (Dong et al. [<reflink idref="bib26" id="ref49">26</reflink>]; Huang and Zou [<reflink idref="bib53" id="ref50">53</reflink>]). In addition, this study contributes to the existing knowledge by being among the first to examine the moderating roles of learner type and gender in the relationship between AI affordances and engagement. Results of the study will inform scholars and practitioners of the impact of AI on EFL learners' speaking engagement, with a particular focus on revealing the moderating effects of gender and learner types on the mechanism of AI‐speaking engagement.</p> <hd id="AN0183654458-3">Literature Review</hd> <p></p> <hd id="AN0183654458-4">Learning Engagement</hd> <p>Learning engagement refers to students' active participation in various learning activities or engagement in the learning process, who think deeply and respond energetically to challenges and frustrations (Christenson et al. [<reflink idref="bib18" id="ref51">18</reflink>]). Engagement influences and determines the efficiency of learning in formal and informal settings (Tam and Reynolds [<reflink idref="bib94" id="ref52">94</reflink>]).</p> <p>As a widely explored topic in the field of foreign language acquisition, engagement is divided into cognitive, emotional and behavioural components, according to Fredricks et al. ([<reflink idref="bib34" id="ref53">34</reflink>]). Learning engagement encompasses BE, CE, and EE. Contextualised in speaking learning, BE is reflected through learners' physical behaviours in the speaking learning process; CE indicates learners' self‐regulation and cognitive adjustments in the speaking learning procedure and EE indicates learners' affective response, such as curiosity and aversion in speaking (Fredricks et al. [<reflink idref="bib34" id="ref54">34</reflink>]; Wang and Reynolds [<reflink idref="bib102" id="ref55">102</reflink>]; Zimmerman [<reflink idref="bib114" id="ref56">114</reflink>]; Zhou and Han [<reflink idref="bib113" id="ref57">113</reflink>]).</p> <p>In the technology‐assisted EFL context where teachers' and peers' monitoring became weak, the functions of technology that help learners to engage in EFL learning are particularly important for individual learners because they ensure learners' attention, efforts, commitment and further determined, to a large degree, their learning success (Yu et al. [<reflink idref="bib111" id="ref58">111</reflink>]). Studies suggested mobile‐assisted EFL learning increased students' motivation and self‐regulation (Baldock et al. [<reflink idref="bib6" id="ref59">6</reflink>]), and ultimately uplifted learning engagement (Chang [<reflink idref="bib14" id="ref60">14</reflink>]). Learners' anonymity, perceived satisfaction of learning experience in technology environment, contributed to their learning engagement (Ge [<reflink idref="bib38" id="ref61">38</reflink>]; Tan et al. [<reflink idref="bib95" id="ref62">95</reflink>]). Contrarily, irrational learning design with technology increased learners' cognitive and mental load and thus reduced learning engagement and performance (Chu [<reflink idref="bib20" id="ref63">20</reflink>]). In short, learning engagement plays a critical role in either formal or informal English learning, and facilitating technology adoption enhances students' learning engagement.</p> <hd id="AN0183654458-5">Theoretical Framework: Technology Affordances</hd> <p>The theoretical framework used in this study is the technology affordance theory, which is derived from affordance theory in ecology. Affordances are the action possibilities created by the given environment for agents, and in the educational context, it provides learners with opportunities to act, explore and utilise resources in it (Gibson [<reflink idref="bib39" id="ref64">39</reflink>]). Derived from affordance theory, technology affordances refer to the technology components that effectively and efficiently support students' learning. Before actual technology adoption, learners perceive technology affordances and these perceptions facilitate further technology adoption (Gibson [<reflink idref="bib39" id="ref65">39</reflink>]; Mettler and Wulf [<reflink idref="bib73" id="ref66">73</reflink>]).</p> <p>In technology–assisted learning literature, the affordance theory is widely used to describe how learners perceive and interact with the technology. The typical example is the human–computer interaction (Oliver [<reflink idref="bib78" id="ref67">78</reflink>]), suggesting learning possibilities afforded by technology as well as the effectiveness and outcomes that result from appropriate technology use (Lee et al. [<reflink idref="bib63" id="ref68">63</reflink>]; Leonardi [<reflink idref="bib64" id="ref69">64</reflink>]). In other words, considering the technology affordances (e.g., discussion forum), scholars suggested the extent to which learners perceive affordances is critical in their learning engagement and outcomes (Dubé and McEwen [<reflink idref="bib27" id="ref70">27</reflink>]; Li and Song [<reflink idref="bib66" id="ref71">66</reflink>]).</p> <p>Studies in the EFL learning context suggested that it is the technology affordances, rather than the technology itself and its parameters, that facilitated EFL learners' engagement (Yu et al. [<reflink idref="bib111" id="ref72">111</reflink>]). Failure to perceive technology affordances may lead to either technology non‐use or low‐level use (Huang and Zou [<reflink idref="bib53" id="ref73">53</reflink>]), which would affect the realisation of long‐term educational affordances (Huang et al. [<reflink idref="bib52" id="ref74">52</reflink>]).</p> <p>Language learning incorporates learners' perceptions and actions in the learning environment (Van Lier [<reflink idref="bib98" id="ref75">98</reflink>]), in which learners continuously recognise and use the environmental resource in the meaning‐making activities to reach learning objectives (Kukulska‐Hulme and Viberg [<reflink idref="bib61" id="ref76">61</reflink>]; Van Lier [<reflink idref="bib98" id="ref77">98</reflink>]). Thus, it is rational that perceptions of technology affordances may lead to EFL learners' technology uptake to facilitate their engagement in learning so as to attain learning goals.</p> <p>Learners show positive engagement when using technology, such as responding to comments and answering questions (Darhower [<reflink idref="bib23" id="ref78">23</reflink>]; Gromik [<reflink idref="bib41" id="ref79">41</reflink>]). Willis et al. ([<reflink idref="bib106" id="ref80">106</reflink>]) suggested that technology facilitates active speaking, especially for those who feel anxious in the face‐to‐face speaking context. Certainly, insufficient technology affordance makes them feel down, leading to a failure in learning engagement (Yang and Gong [<reflink idref="bib107" id="ref81">107</reflink>]). Therefore, affordances of technology fostered learning engagement by providing learners with action possibilities, eliciting either positive or negative responses (Shin [<reflink idref="bib88" id="ref82">88</reflink>]).</p> <p>This study examined EFL learners' perceptions of affordances provided by LAIX, a widely used AI‐driven speaking learning app. On the basis of Fu et al. ([<reflink idref="bib36" id="ref83">36</reflink>]), the four‐aspect functions include accurate automatic speech recognition, later on accurate speech recognition (ASR), benefit of multimodal evaluation (BOME), social presence (SP) and peer influence (PI). ASR indicates the degree to which LAIX catches learners' speech and responses to questions (Hagen et al. [<reflink idref="bib43" id="ref84">43</reflink>]). BOME indicates that LAIX instantly evaluates pronunciations from multiple perspectives, including accuracy, fluency and rhythm (Cheng et al. [<reflink idref="bib17" id="ref85">17</reflink>]). As a crucial component in online learning, SP refers to the degree to which learners perceive AI as teachers, providing them with physical presence and psychological support in the AI‐mediated communication (Gunawardena [<reflink idref="bib42" id="ref86">42</reflink>]). As LAIX allows learners to review their scores and compare their performance rankings with those of other users, learners might perceive themselves as competitors in a digital learning environment, and thus, PI captures the competition vibe that stimulates competence, self‐improvement and desire to succeed (Christy and Fox [<reflink idref="bib19" id="ref87">19</reflink>]).</p> <p>On the basis of the above rationales, we proposed the hypotheses that learner‐perceived AI affordances significantly influence their learning engagement. To specify its influence on the three dimensions of engagement, this study examined its influence on EE, CE and BE, respectively.</p> <hd id="AN0183654458-6">Moderators: Learner Types and Gender</hd> <p>As EFL learners comprise a variety of learner types (on‐campus learners and on‐job learners), the way they perceive AI affordances and the degree to which they engage in learning may differ. This study employs the term 'learner type' to characterise the two learner identities that reflect varying relationships between the individual and the social world.</p> <p>Norton ([<reflink idref="bib75" id="ref88">75</reflink>]) suggested that language learners' efforts and investment are determined by their identity shifts. Technological advancements enable both on‐campus students and determined learners who are employed (on‐job learners) to leverage technology to improve their knowledge and English language skills. On‐job staff improve English to advance personal development and their careers, and curious citizens use online courses as public libraries (Chen et al. [<reflink idref="bib15" id="ref89">15</reflink>]). People who work in international organisations or companies seek lifelong development; some senior staff express the need to speak English at work (Kanno and Kangas [<reflink idref="bib57" id="ref90">57</reflink>]). In addition to career development, some may learn English due to personal interests or hobbies. Lin et al. ([<reflink idref="bib68" id="ref91">68</reflink>]) noted that learner type influences affordance perceptions, which further influence their learning engagement. On‐campus learners think differently from on‐job learners because they have various expectations, preferences, learning experiences and achievement goals, which lead to diverse engagement patterns. As Veletsianos et al. ([<reflink idref="bib99" id="ref92">99</reflink>]) suggested, the learning engagement of full‐time employees (on‐job learners) was high, with a strong focus on specific skills, leading to spurts of advancement. Thus, this study proposed learner type as a moderator to examine if the relationship between perceived AI affordances and engagement varies by learner type.</p> <p>Gender, as one of individual differences, poses a subtle but long‐term impact on the foreign language learning field, including learning engagement (Ellis [<reflink idref="bib28" id="ref93">28</reflink>]; Oga‐Baldwin [<reflink idref="bib77" id="ref94">77</reflink>]). When using technology, people with different genders perceive technology affordances differently, which may further influence their engagement in using technology (Cisek [<reflink idref="bib21" id="ref95">21</reflink>]). Existing studies have not reached a consensus regarding the role of gender in language learning (Lee et al. [<reflink idref="bib62" id="ref96">62</reflink>]). Stoet and Geary ([<reflink idref="bib91" id="ref97">91</reflink>]) suggested that girls tend to outperform boys in academic areas that require language skills. Additionally, girls are often found to be more engaged in learning and less disaffected towards academic pursuits compared to boys (King [<reflink idref="bib58" id="ref98">58</reflink>]). While in the online learning environment, males were found to be more positive and self‐assured (Li and Kirkup [<reflink idref="bib65" id="ref99">65</reflink>]). Recent studies in the digital language learning context suggest female and male learners have similar engagement patterns (Almusharraf [<reflink idref="bib1" id="ref100">1</reflink>]; Korkmaz and Öz [<reflink idref="bib60" id="ref101">60</reflink>]). Given the inconsistent evidence about the impact of gender on learning, it is necessary to re‐examine the role of gender in the AI‐assisted speaking learning context. This study proposed gender as a moderator that influences the relationship between AI affordances and engagement. On the basis of the above we have mentioned, the research model is provided in Figure 1.</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/EJE/01mar25/ejed70041-fig-0001.jpg?ephost1=dGJyMNXb4kSepq84yOvqOLCmsE6epq5Srqa4SK6WxWXS" alt="ejed70041-fig-0001.jpg" title="1 Conceptual model. ASR, accurate speech recognition; BE, behavioural engagement; BOME, benefit of multidimensional evaluation; CE, cognitive engagement; EE, emotional engagement; PAA, perceived AI affordances; PI, peer influence; SP, social presence." /> </p> <p></p> <hd id="AN0183654458-8">Method</hd> <p></p> <hd id="AN0183654458-9">The LAIX and Participants of the Current Study</hd> <p>The present study was contextualised in using LAIX (learning + AI with [X] infinite possibilities), an English‐speaking practice tool, to improve English speaking. LAIX (流利说in Chinese) is a popular mobile application for EFL learners in China, and it creates a learning community for English learners, which boasts over 200 million users in China. LAIX provides a range of features to enhance pronunciation learning, including instant feedback, classified scores for each phoneme, a score leaderboard and peer‐to‐peer pronunciation tutoring. It offers a wealth of video clips, words and phrases, providing EFL learners with an immersive experience that makes them feel as if they are in an authentic English‐speaking context. A speech recognition system has the capability to correct pronunciations and evaluate learners' speaking performance from multiple dimensions. Besides, the ranking list or leaderboard stimulates peer competition. Its AI teacher is capable of interacting with learners in a manner that mimics the experience of a real teacher, which fosters a sense of SP in the learning environment. Figure 2 illustrates the interface of LAIX.</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/EJE/01mar25/ejed70041-fig-0002.jpg?ephost1=dGJyMNXb4kSepq84yOvqOLCmsE6epq5Srqa4SK6WxWXS" alt="ejed70041-fig-0002.jpg" title="2 The interface of LAIX." /> </p> <p></p> <p>The participants of the study are LAIX users who utilise the platform between 3 and 5 times a week, dedicating approximately 30 min of practice each session. They actively participated in assessing their speaking skills, making personalised learning plans based on the assessment reports and engaging in tailored speaking training practice with AI assistance. After completing the training courses, they were provided with corresponding course reports, learning suggestions and improvement plans. In addition, learners can choose various speaking topics based on their interests and needs, such as career English, cosmetics, social intercourse and culture.</p> <p>Data were collected from 397 LAIX users (159 on‐campus vs. 173 on‐job) to inquire about their perceptions of AI affordances and speaking learning engagement, of which 332 were found to be valid answers. Among them, 52.7% were females and 47.2% were males, and their ages ranged from 14 to 51 years with the average age being 26.67 (SD = 34.042). Among the on‐campus students, the majority are university students (<emph>n</emph> = 153) and only six are high school students. To protect their privacy, the participants were not required to provide the names of their schools. For on‐job learners, 91 (27.4%) are company employees, 34 (10.2%) are professionals (lawyers, doctors, and teachers, etc.), 25 (7.5%) are government officers and 23 (6.9%) are service staff and freelancers (see Table 1).</p> <p>1 TABLE Demographic information of participants (N  = 332).</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left" /><th align="center">Category</th><th align="center">Number</th><th align="center">Percentage</th></tr></thead><tbody valign="top"><tr><td align="left">Gender</td><td align="center">Female</td><td align="center">175</td><td align="center">52.7%</td></tr><tr><td align="center">Male</td><td align="center">157</td><td align="center">47.3%</td></tr><tr><td align="left">Learner types</td><td align="center">On‐campus students</td><td align="center">159</td><td align="center">47.9%</td></tr><tr><td align="center">–High School</td><td align="center">6</td><td align="center">1.8%</td></tr><tr><td align="center">–Junior college</td><td align="center">18</td><td align="center">5.4%</td></tr><tr><td align="center">–Bachelor</td><td align="center">118</td><td align="center">35.5%</td></tr><tr><td align="center">–Master</td><td align="center">17</td><td align="center">5.1%</td></tr><tr><td align="center">On‐job learners</td><td align="center">173</td><td align="center">52%</td></tr><tr><td align="center">–Company employees</td><td align="center">91</td><td align="center">27.4%</td></tr><tr><td align="center">–Professionals</td><td align="center">34</td><td align="center">10.2%</td></tr><tr><td align="center">–Government officers—</td><td align="center">25</td><td align="center">7.5%</td></tr><tr><td align="center">Service staff and freelancers</td><td align="center">23</td><td align="center">6.9%</td></tr></tbody></table> </ephtml> </p> <hd id="AN0183654458-11">Research Instrument and Data Collection</hd> <p>In this study, an online questionnaire was adopted to examine EFL learners' perception of AI affordances and their speaking engagement when using LAIX. The questionnaire consists of two parts: the first part is the demographic information, including gender, age, educational background and learner types; the second part investigated their perceptions of AI affordances and learning engagement when using the LAIX. Items indicating constructs were adapted from diverse sources to suit to the study context (see Appendix A). Items on emotional, cognitive and BE were adapted from Kahn ([<reflink idref="bib56" id="ref102">56</reflink>]) and Ni et al. ([<reflink idref="bib74" id="ref103">74</reflink>]), ASR and BOME were from Cheng et al. ([<reflink idref="bib17" id="ref104">17</reflink>]), SP and PI were from Weidlich and Bastiaens ([<reflink idref="bib105" id="ref105">105</reflink>]) and Christy and Fox ([<reflink idref="bib19" id="ref106">19</reflink>]). We used 5‐point Likert scale to measure participants' responses to these items.</p> <p>Online questionnaires were distributed to 397 participants. The data points resulting from abnormal entries, such as malicious filling or repetitive IP addresses, were excluded, leaving 332 valid data points for analysis. Participants were informed of the research purpose and their rights to withdraw at any time. Generally, they spent about 15 min to fill in the questionnaire.</p> <hd id="AN0183654458-12">Data Analysis</hd> <p>The data were analysed using SPSS 26 and SmartPLS4. Descriptive statistics analysis was conducted using SPSS 26 to suggest participants' gender, age and occupation. The normality of the data was indicated by the skewness and kurtosis. In the data analysis, the first step is to examine the reflective measurement model, involving indicator loading, composite reliability, convergent validity and discriminant validity. When the measurement model results are satisfactory, the next step is to assess the structural model, including the coefficient of determination (R<sups>2</sups>), the Stone–Geisser or <emph>Q</emph><sups>2</sups> test, the statistical significance and relevance of the path coefficients (Becker et al. [<reflink idref="bib7" id="ref107">7</reflink>]). Binary moderators were handled by multigroup analysis (MGA) in PLS to evaluate the moderation effect of gender and learner types, following the steps of generating corresponding subgroups, testing measurement invariance and multigroup comparisons. As for the concept of model fit in CB‐SEM, it is not required to be addressed in PLS‐SEM (Hair et al. [<reflink idref="bib44" id="ref108">44</reflink>]).</p> <hd id="AN0183654458-13">Results</hd> <p></p> <hd id="AN0183654458-14">EFL Learners' Perceptions of AI Affordances and Speaking Learning Engagement</hd> <p>The results of descriptive analyses and normal distribution are shown in Table 2. According to Kline ([<reflink idref="bib59" id="ref109">59</reflink>]), when the values of skewness and kurtosis are within |3| and |8|, the normal distribution is achieved. In this study, the values of skewness and kurtosis of the dimensions ranged from −1.064 to −0.749 and from 0.035 to 2.508, respectively, indicating a normal distribution. Different from covariance‐based SEM, PLS‐SEM poses a relatively low standard on residual distributions, measurement scales and sample size (Hair et al. [<reflink idref="bib44" id="ref110">44</reflink>]).</p> <p>2 TABLE Descriptive statistics of constructs.</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left">Constructs</th><th align="center">Mean</th><th align="center">SD</th><th align="center">Skewness</th><th align="center">Kurtosis</th></tr></thead><tbody valign="top"><tr><td align="left">Peer influence</td><td align="center">4.113</td><td align="center">0.357</td><td align="center">−1.064</td><td align="center">2.194</td></tr><tr><td align="left">Benefit of multidimensional evaluation</td><td align="center">4.055</td><td align="center">0.315</td><td align="center">−0.989</td><td align="center">2.508</td></tr><tr><td align="left">Accurate speech recognition</td><td align="center">3.957</td><td align="center">0.432</td><td align="center">−0.752</td><td align="center">0.402</td></tr><tr><td align="left">Cognitive engagement</td><td align="center">3.853</td><td align="center">0.514</td><td align="center">−0.823</td><td align="center">0.396</td></tr><tr><td align="left">Behavioural engagement</td><td align="center">3.600</td><td align="center">0.818</td><td align="center">−0.903</td><td align="center">0.188</td></tr><tr><td align="left">Social presence</td><td align="center">3.718</td><td align="center">0.592</td><td align="center">−0.749</td><td align="center">0.035</td></tr><tr><td align="left">Emotional engagement</td><td align="center">3.936</td><td align="center">0.361</td><td align="center">−0.968</td><td align="center">1.782</td></tr></tbody></table> </ephtml> </p> <p>As shown in Table 2, EFL learners generally hold positive perceptions of the four dimensions of AI affordance, with the mean values being 4.113 (SD = 0.357) for PI, 4.055 (SD = 0.315) for the BOME, 3.957 (SD = 0.432) for ASR and 3.718 (SD = 0.592) for SP. As for learning engagement, EFL learners had the highest score in EE (<emph>M</emph> = 3.936, SD = 0.361), followed by CE (<emph>M</emph> = 3.853, SD = 0.514) and BE (<emph>M</emph> = 3.600, SD = 0.818).</p> <hd id="AN0183654458-15">Perceptions of AI Affordance and Learning Engagement</hd> <p>This study used Smart PLS 4 to test the hypothesized model. First, the Cronbach's alphas of PI, BOME, CE, BE, SP and EE are all above 0.600, which are satisfying when measurement scale has < 6 items (Hair Jr. et al. [<reflink idref="bib45" id="ref111">45</reflink>]). The Cronbach's alpha of ASR is above 0.500, which is also acceptable (Nunnaly and Bernstein [<reflink idref="bib76" id="ref112">76</reflink>]). The values of composite reliability (CR) of all the dimensions are within the range of 0.786–0.904 (see Table 3), meeting the recommended values of reliability (Hair Jr. et al. [<reflink idref="bib45" id="ref113">45</reflink>]). For the validity, the convergent and discriminant validities were evaluated. The average variance extracted (AVE) and factor loadings are over 0.500 and 0.600, respectively, showing acceptable convergent validity. The AVE of EE is 0.480, but its CR is over 0.600, so it still has its convergent validity (Fornell and Larcker [<reflink idref="bib33" id="ref114">33</reflink>]). The Fornell–Larcker criterion and cross‐loadings are deployed to verify discriminant validity. In validating high‐order constructs in PLS‐SEM, it should be noted that the high‐order component (PAA) should be assessed from its low‐order components; otherwise, the violation of discriminant validity will occur (Sarstedt et al. [<reflink idref="bib87" id="ref115">87</reflink>]). Discriminant validity is confirmed when the square root of AVE exceeds each latent variable's interconstruct correlation coefficient (Fornell and Larcker [<reflink idref="bib33" id="ref116">33</reflink>]) (see Table 4). If most factors' indicators meet the established standards, it is permissible to accept individual factors that fall slightly below these criteria (Hair Jr. et al. [<reflink idref="bib45" id="ref117">45</reflink>]), that is, the relationship between PAA and EE in this study. Besides, each item loading exceeds its cross‐loadings, which also confirmed the discriminant validity.</p> <p>3 TABLE Factor loadings, reliabilities and validities.</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left">Constructs</th><th align="center">Items</th><th align="center">Loading</th><th align="center">Cronbach alpha</th><th align="center">CR</th><th align="center">AVE</th></tr></thead><tbody valign="top"><tr><td align="left">PI</td><td align="center">PI1</td><td align="center">0.773</td><td align="center">0.639</td><td align="center">0.806</td><td align="center">0.581</td></tr><tr><td align="center">PI2</td><td align="center">0.748</td><td align="center" /><td align="center" /><td align="center" /></tr><tr><td align="center">PI3</td><td align="center">0.765</td><td align="center" /><td align="center" /><td align="center" /></tr><tr><td align="left">BOME</td><td align="center">BOME1</td><td align="center">0.825</td><td align="center">0.614</td><td align="center">0.795</td><td align="center">0.566</td></tr><tr><td align="center">BOME2</td><td align="center">0.706</td><td align="center" /><td align="center" /><td align="center" /></tr><tr><td align="center">BOME3</td><td align="center">0.720</td><td align="center" /><td align="center" /><td align="center" /></tr><tr><td align="left">ASR</td><td align="center">ASR1</td><td align="center">0.862</td><td align="center">0.535</td><td align="center">0.810</td><td align="center">0.681</td></tr><tr><td align="center">ASR2</td><td align="center">0.786</td><td align="center" /><td align="center" /><td align="center" /></tr><tr><td align="left">CE</td><td align="center">C1</td><td align="center">0.790</td><td align="center">0.719</td><td align="center">0.842</td><td align="center">0.640</td></tr><tr><td align="center">C2</td><td align="center">0.802</td><td align="center" /><td align="center" /><td align="center" /></tr><tr><td align="center">C3</td><td align="center">0.808</td><td align="center" /><td align="center" /><td align="center" /></tr><tr><td align="left">BE</td><td align="center">B1</td><td align="center">0.856</td><td align="center">0.858</td><td align="center">0.904</td><td align="center">0.701</td></tr><tr><td align="center">B2</td><td align="center">0.840</td><td align="center" /><td align="center" /><td align="center" /></tr><tr><td align="center">B3</td><td align="center">0.817</td><td align="center" /><td align="center" /><td align="center" /></tr><tr><td align="center">B4</td><td align="center">0.836</td><td align="center" /><td align="center" /><td align="center" /></tr><tr><td align="left">SP</td><td align="center">SP1</td><td align="center">0.832</td><td align="center">0.836</td><td align="center">0.891</td><td align="center">0.671</td></tr><tr><td align="center">SP2</td><td align="center">0.830</td><td align="center" /><td align="center" /><td align="center" /></tr><tr><td align="center">SP3</td><td align="center">0.807</td><td align="center" /><td align="center" /><td align="center" /></tr><tr><td align="left">EE</td><td align="center">E1</td><td align="center">0.775</td><td align="center">0.642</td><td align="center">0.786</td><td align="center">0.480</td></tr><tr><td align="center">E2</td><td align="center">0.642</td><td align="center" /><td align="center" /><td align="center" /></tr><tr><td align="center">E3</td><td align="center">0.635</td><td align="center" /><td align="center" /><td align="center" /></tr><tr><td align="center">E4</td><td align="center">0.711</td><td align="center" /><td align="center" /><td align="center" /></tr></tbody></table> </ephtml> </p> <p>4 TABLE Results of discriminant validity.</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left" /><th align="center">ASR</th><th align="center">BE</th><th align="center">BOME</th><th align="center">CE</th><th align="center">EE</th><th align="center">PI</th><th align="center">PAA</th><th align="center">SP</th></tr></thead><tbody valign="top"><tr><td align="left">ASR</td><td align="center">0.825</td><td align="center" /><td align="center" /><td align="center" /><td align="center" /><td align="center" /><td align="center" /><td align="center" /></tr><tr><td align="left">BE</td><td align="center">0.597</td><td align="center">0.837</td><td align="center" /><td align="center" /><td align="center" /><td align="center" /><td align="center" /><td align="center" /></tr><tr><td align="left">BOME</td><td align="center">0.560</td><td align="center">0.546</td><td align="center">0.752</td><td align="center" /><td align="center" /><td align="center" /><td align="center" /><td align="center" /></tr><tr><td align="left">CE</td><td align="center">0.634</td><td align="center">0.731</td><td align="center">0.546</td><td align="center">0.800</td><td align="center" /><td align="center" /><td align="center" /><td align="center" /></tr><tr><td align="left">EE</td><td align="center">0.568</td><td align="center">0.588</td><td align="center">0.609</td><td align="center">0.585</td><td align="center">0.693</td><td align="center" /><td align="center" /><td align="center" /></tr><tr><td align="left">PI</td><td align="center">0.457</td><td align="center">0.442</td><td align="center">0.567</td><td align="center">0.465</td><td align="center">0.534</td><td align="center">0.762</td><td align="center" /><td align="center" /></tr><tr><td align="left">PAA</td><td align="center">—</td><td align="center">0.749</td><td align="center">—</td><td align="center">0.725</td><td align="center">0.741</td><td align="center">—</td><td align="center">0.642</td><td align="center" /></tr><tr><td align="left">SP</td><td align="center">0.610</td><td align="center">0.745</td><td align="center">0.568</td><td align="center">0.665</td><td align="center">0.656</td><td align="center">0.452</td><td align="center">—</td><td align="center">0.819</td></tr></tbody></table> </ephtml> </p> <p>1 <emph>Note:</emph> Numbers in bold are the square root of AVE. Non‐bold numbers are correlations between constructs.</p> <p>Later, we adopted a bootstrapping method to examine the path coefficient and its related <emph>p</emph>‐value. For PAA and its four lower‐order factors, the link between the higher‐order factor and the lower‐order factors is significant (<emph>p</emph>‐value < 0.001) with path coefficients being 0.881, 0.725, 0.783 and 0.815, respectively. For the role of AI affordances on learning engagement, the result revealed that the direct effect of AI affordances on learning engagement is significant and positive; the three hypotheses in this study were all supported (see Figure 3). The model had an <emph>R</emph><sups>2</sups> value of 54.7% for EE, 52.4% for CE and 56.0% for BE. These <emph>R</emph><sups>2</sups>values indicate that the model is able to account for a substantial portion of the variability in each type of engagement, suggesting its effectiveness and robustness. For predictive power, the model is equipped with predictive relevance with <emph>Q</emph><sups>2</sups> over 0 (Hair et al. [<reflink idref="bib44" id="ref118">44</reflink>]). The values of 0.02, 0.15 and 0.35 demonstrate small, medium and large predictive power (Riady et al. [<reflink idref="bib82" id="ref119">82</reflink>]). By conducting blindfolding, the following prediction sizes are reported: BE, <emph>Q</emph><sups>2</sups> = 0.369; CE, <emph>Q</emph><sups>2</sups> = 0.326 and EE, <emph>Q</emph><sups>2</sups> = 0.251, showing a large and medium predictive power.</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/EJE/01mar25/ejed70041-fig-0003.jpg?ephost1=dGJyMNXb4kSepq84yOvqOLCmsE6epq5Srqa4SK6WxWXS" alt="ejed70041-fig-0003.jpg" title="3 Final model." /> </p> <p></p> <hd id="AN0183654458-17">Moderation Effect of Learner Types and Gender by Conducting Multigroup Analysis</hd> <p>To examine if gender and learner types moderate the relationship between AI affordances and speaking engagement, participants were divided into different groups based on gender (male and female) and learner types (on‐campus and on‐job). The path coefficients of the subgroups were estimated to examine if there are significant differences. The measurement invariance of composite models is used to evaluate the group‐specific difference. The results of the configurational invariance assessment, followed by the compositional invariance assessment, and the final step, equal variance assessment, are shown in Table 5. The configurational invariance was supported automatically. Compositional invariance was confirmed because the values underlying the original correlation are greater than those underlying 5%, and the permutation <emph>p</emph>‐values were above 0.005. In the equal mean and variance assessment stages, all the confidence intervals of the variables did not include the original differences, indicating that full measurement invariance was achieved (Table 5).</p> <p>5 TABLE Results of learner types in the invariance measurement assessment.</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left">Constructs</th><th align="center">Configurational invariance (step 1)</th><th align="center">Compositional invariance (step 2)</th><th align="center">Equal mean assessment (step 3a)</th><th align="center">Equal variance assessment (step 3b)</th></tr><tr><th align="center">Original correlation</th><th align="center">5.0%</th><th align="center">Original differences</th><th align="center">Confidence interval</th><th align="center">Original differences</th><th align="center">Confidence interval</th></tr></thead><tbody valign="top"><tr><td align="left">BE</td><td align="center">Yes</td><td align="center">0.999</td><td align="center">0.998</td><td align="center">0.896</td><td align="center">[−0.173, 0.197]</td><td align="center">−0.779</td><td align="center">[−0.272, 0.266]</td></tr><tr><td align="left">CE</td><td align="center">Yes</td><td align="center">0.999</td><td align="center">0.996</td><td align="center">0.818</td><td align="center">[−0.181, 0.183]</td><td align="center">−0.659</td><td align="center">[−0.280, 0.288]</td></tr><tr><td align="left">EE</td><td align="center">Yes</td><td align="center">0.997</td><td align="center">0.988</td><td align="center">0.585</td><td align="center">[−0.188, 0.175]</td><td align="center">−0.504</td><td align="center">[−0.321, 0.379]</td></tr><tr><td align="left">PAA</td><td align="center">Yes</td><td align="center">0.999</td><td align="center">0.998</td><td align="center">0.780</td><td align="center">[−0.178, 0.188]</td><td align="center">−0.421</td><td align="center">[−0.249, 0.265]</td></tr></tbody></table> </ephtml> </p> <p>Table 6 suggests that learner type is not a significant moderator for the relationship between AI affordances and speaking engagement, as all the <emph>p</emph>‐values of Henseler's MGA are > 0.05.</p> <p>6 TABLE Results of moderation effect of learner types.</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left">Path</th><th align="center">On‐campus learners</th><th align="center">On‐job learners</th><th align="center">Differences (on‐campus vs. on‐job)</th><th align="center"><italic>p</italic>‐value Henseler's MGA</th><th align="center">Supported</th></tr></thead><tbody valign="top"><tr><td align="left">PAA – > BE</td><td align="center">0.662</td><td align="center">0.768</td><td align="center">0.105</td><td align="center">0.061</td><td align="center">No</td></tr><tr><td align="left">PAA – > CE</td><td align="center">0.630</td><td align="center">0.750</td><td align="center">0.120</td><td align="center">0.162</td><td align="center">No</td></tr><tr><td align="left">PAA – > EE</td><td align="center">0.672</td><td align="center">0.773</td><td align="center">0.101</td><td align="center">0.111</td><td align="center">No</td></tr></tbody></table> </ephtml> </p> <p>As for the test of gender's moderation effect, a partial measurement invariance was achieved because the values of the original differences of EE (0.155) were within the confidence intervals (Table 7). Table 8 suggests that males perceived higher in BE, CE and EE than females; in addition, gender was found to significantly moderate the relationships between AI affordances and BE and CE, while not significantly moderating the AI affordance–EE relationship, with <emph>p‐</emph>values being 0.014, 0.000 and 0.557, respectively.</p> <p>7 TABLE Results of gender in the invariance measurement assessment.</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left">Constructs</th><th align="center">Configurational invariance (step 1)</th><th align="center">Compositional invariance (step 2)</th><th align="center">Equal mean assessment (step 3a)</th><th align="center">Equal variance assessment (step 3b)</th></tr><tr><th align="center">Original correlation</th><th align="center">5.0%</th><th align="center">Original differences</th><th align="center">Confidence interval</th><th align="center">Original differences</th><th align="center">Confidence interval</th></tr></thead><tbody valign="top"><tr><td align="left">BE</td><td align="center">Yes</td><td align="center">0.998</td><td align="center">0.998</td><td align="center">−0.927</td><td align="center">[−0.178, 0.181]</td><td align="center">1.170</td><td align="center">[−0.274, 0.264]</td></tr><tr><td align="left">CE</td><td align="center">Yes</td><td align="center">0.996</td><td align="center">0.996</td><td align="center">−0.712</td><td align="center">[−0.182, 0.186]</td><td align="center">0.620</td><td align="center">[−0.283, 0.279]</td></tr><tr><td align="left">EE</td><td align="center">Yes</td><td align="center">0.994</td><td align="center">0.987</td><td align="center">−0.472</td><td align="center">[−0.188, 0.188]</td><td align="center">0.155</td><td align="center">[−0.366, 0.363]</td></tr><tr><td align="left">PAA</td><td align="center">Yes</td><td align="center">0.999</td><td align="center">0.998</td><td align="center">−0.651</td><td align="center">[−0.181, 0.164]</td><td align="center">0.433</td><td align="center">[−0.268, 0.244]</td></tr></tbody></table> </ephtml> </p> <p>8 TABLE Results of the moderation effect of gender.</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left">Path</th><th align="center">Male</th><th align="center">Female</th><th align="center">Differences (male vs. female)</th><th align="center"><italic>p</italic>‐value Henseler's MGA</th><th align="center">Supported</th></tr></thead><tbody valign="top"><tr><td align="left">PAA – > BE</td><td align="center">0.818</td><td align="center">0.683</td><td align="center">−0.134</td><td align="center">0.014</td><td align="center">Yes</td></tr><tr><td align="left">PAA – > CE</td><td align="center">0.855</td><td align="center">0.603</td><td align="center">−0.252</td><td align="center">0.000</td><td align="center">Yes</td></tr><tr><td align="left">PAA – > EE</td><td align="center">0.746</td><td align="center">0.712</td><td align="center">−0.033</td><td align="center">0.557</td><td align="center">No</td></tr></tbody></table> </ephtml> </p> <hd id="AN0183654458-18">Discussion</hd> <p></p> <hd id="AN0183654458-19">The Relationship Between AI Affordances and Speaking Engagement</hd> <p>The present study examined the role of perceived AI affordances in influencing Chinese EFL learners' speaking engagement when using LAIX. Results of the study revealed that Chinese EFL learners perceived AI affordances and their speaking engagement and at satisfying levels, align with previous findings (Huang and Zou [<reflink idref="bib53" id="ref120">53</reflink>]; Shin [<reflink idref="bib89" id="ref121">89</reflink>]; Wang and Li [<reflink idref="bib100" id="ref122">100</reflink>]). ASR helps learners achieve the two‐way communication (Arora and Singh [<reflink idref="bib2" id="ref123">2</reflink>]; Riccardi and Hakkani‐Tur [<reflink idref="bib83" id="ref124">83</reflink>]) with accurate scoring and unsupervised guidance, indicating its application value of ASR in language learning (Young and Wang [<reflink idref="bib110" id="ref125">110</reflink>]). The assessments based on stress, accuracy and segments empower learners to gain insight into their pronunciation (McCrocklin [<reflink idref="bib72" id="ref126">72</reflink>]), whereas the multidimensional approach alleviates their negative emotions (Bodnar et al. [<reflink idref="bib10" id="ref127">10</reflink>]). EFL learners' perception towards SP is obviously lower than the other three dimensions, indicating that the interaction between AI teachers and real EFL learners is not robust (Qiu and Benbasat [<reflink idref="bib80" id="ref128">80</reflink>]), and the level of humaneness‐like traits exhibited by LAIX is constrained (Yeung and Yau [<reflink idref="bib108" id="ref129">108</reflink>]).</p> <p>This study suggested the significant role of AI affordances on EFL learners' speaking engagement (Hwang et al. [<reflink idref="bib54" id="ref130">54</reflink>]). To be specific, the affordances of AI significantly impacted learners' BE, CE and EE in speaking, validating the applicability of the technology affordance theory and the actualisation affordance theory within the EFL learning context (Tim et al. [<reflink idref="bib96" id="ref131">96</reflink>]).</p> <hd id="AN0183654458-20">The Moderating Roles of Learner Type and Gender</hd> <p>This study examined the moderation roles of learner type and gender in the AI affordances and speaking engagement relationship. Unlike Deng et al. ([<reflink idref="bib24" id="ref132">24</reflink>]), which specified the relationship between learner factors and engagement, the moderating role of learner type (on‐campus vs. on‐job) was not significant in this study. However, this finding aligns with the research conducted by Hood et al. ([<reflink idref="bib48" id="ref133">48</reflink>]). Be that as it may, the path coefficients of on‐job learners are greater than those of student learners (Table 6), indicating that on‐job learners perceive the affordances of AI to have a greater impact on their speaking engagement compared to on‐campus learners (Bakker [<reflink idref="bib5" id="ref134">5</reflink>]). This is understandable, considering that on‐job learners may have more limited learning opportunities and resources compared to traditional students. When they discover a useful tool that caters to learning needs, they are often more motivated to incorporate it into their learning process.</p> <p>As for the moderating role of gender, this study found that gender significantly moderates the relationship between AI affordances and both BE and CE, which contrasted with existing studies that have not found gender differences in student engagement (Yin et al. [<reflink idref="bib109" id="ref135">109</reflink>]; O'Neal et al. [<reflink idref="bib79" id="ref136">79</reflink>]). The actual behaviour of male groups outweighs that of female groups when it comes to engaging with AI affordances (Hsieh et al. [<reflink idref="bib49" id="ref137">49</reflink>]). Canchola Gonzalez and Glasserman‐Morales ([<reflink idref="bib12" id="ref138">12</reflink>]) suggested gender influenced CE, and to be specific, males presented higher CE than female groups. The discrepancy in AI engagement behaviour between male and female groups could be explained by the fact that female groups may have more difficulties in managing learning through digital tools and may be more easily distracted when using them (Bergdahl and Nouri [<reflink idref="bib9" id="ref139">9</reflink>]). Although the moderation role of gender was not significant in the relationship between AI affordances and EE, the study found that males were more emotionally engaged in speaking than females, which contrasts with the findings of Liu et al. ([<reflink idref="bib69" id="ref140">69</reflink>]). Male learners are likely to be attracted by innovative technology (Feng and Ivanov [<reflink idref="bib32" id="ref141">32</reflink>]).</p> <hd id="AN0183654458-21">Limitations and Suggestions for Further Study</hd> <p>The limitation of the current study includes the relatively small sample size that may influence the generalisability of the results. Future studies may involve a larger pool of AI users to participate in the survey so as to gain a more comprehensive understanding of the roles of AI affordances on learning engagement. Furthermore, the study did not incorporate a qualitative inquiry to explore how and why different learners perceive AI affordances and engagement in varying ways. Such an investigation could provide deeper insights into the individual experiences and perspectives of learners, and help to elucidate the complex relationships between AI affordances and engagement. Therefore, the study calls for further research that includes in‐depth qualitative inquiry, action research and classroom observations in this area of study. These methods can help to provide a more nuanced understanding of how AI affordances are perceived and used by learners, and how they impact engagement and learning outcomes. Last but not least, as this study did not test learners' actual performance, future studies may look at it so that teachers and practitioners will make informed decisions.</p> <hd id="AN0183654458-22">Conclusion</hd> <p>This study explored Chinese English‐speaking learners' perceptions of AI affordances and learning engagement, and the effect of affordance perceptions on their learning engagement. Results of the study suggested the significant role of AI affordances on speaking learning engagement. In addition, the moderation effect of learner type was not suggested as significant, whereas gender was found to significantly moderate the relationship between AI affordance and BE, as well as the relationship between AI affordance and CE.</p> <p>On the basis of the research findings, this study provides implications for educational stakeholders, including language learners, teachers and educational technologists, to design teaching and learning tasks with technology to better cater to learners' needs and help them engage in learning tasks. In addition, because male and female students demonstrate different thinking regarding the roles of AI affordances in influencing BE and CE, instructors may design diverse tasks for female and male students so that all the students would be more engaged in learning.</p> <hd id="AN0183654458-23">Acknowledgements</hd> <p>We are sincerely grateful to all the participants of this study. This study is a part of the project titled 'Examining Influencing Factors of Foreign Language Teachers' AI Acceptance Mechanism and Intervention in Shanghai Universities' (No. 2024BYY008).</p> <hd id="AN0183654458-24">Conflicts of Interest</hd> <p>The authors declare no conflicts of interest.</p> <hd id="AN0183654458-25">Data Availability Statement</hd> <p>The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.</p> <hd id="AN0183654458-26">A Appendix</hd> <p></p> <hd id="AN0183654458-27">Emotional Engagement (Adapted From Kahn 1990)</hd> <p></p> <ulist> <item> I often feel emotionally detached when using LAIX to learn oral English.</item> <p></p> <item> Practice English using LAIX stirs my emotions.</item> <p></p> <item> My own feelings are affected by how well I pronounced in LAIX.</item> <p></p> <item> I stay with the site till I am satisfied with my pronunciation score.</item> </ulist> <hd id="AN0183654458-28">Cognitive Engagement (Adapted From Kahn 1990)</hd> <p></p> <ulist> <item> I am rarely distracted when I practise English pronunciation using LAIX.</item> <p></p> <item> Practising English pronunciation using LAIX is so absorbing that I forget about other things.</item> <p></p> <item> Time passes quickly while I practise English pronunciation using LAIX.</item> </ulist> <hd id="AN0183654458-29">Behavioural Engagement (Adapted From Ni et al. 2020)</hd> <p></p> <ulist> <item> Using LAIX to practise oral English is my daily habit.</item> <p></p> <item> I use LAIX to practise my oral English whenever I have time.</item> <p></p> <item> Even if it's late, I'll check my score in the speech recognition score system defaulted in LAIX before I sleep.</item> <p></p> <item> I often use LAIX to improve my oral English.</item> </ulist> <hd id="AN0183654458-30">Accurate Speech Recognition (Adapted From Cheng et al. 2013)</hd> <p></p> <ulist> <item> I think LAIX can accurately recognize my English.</item> <p></p> <item> I think the automatic score system of LAIX can rate my English accurately.</item> </ulist> <hd id="AN0183654458-31">Benefit of Multidimensional Evaluation (Adapted From Cheng et al. 2013)</hd> <p></p> <ulist> <item> I benefit from the multidimensional evaluation of LAIX on English pronunciation.</item> <p></p> <item> LAIX enables evaluation of the phoneme of each word, which is useful to improve my English pronunciation.</item> <p></p> <item> What I learned from the automatic score system of LAIX can be put into immediate practice.</item> </ulist> <hd id="AN0183654458-32">Social Presence (Adapted From Weidlich and Bastiaens 2019)</hd> <p></p> <ulist> <item> When using LAIX, I feel as if I deal with 'real' person.</item> <p></p> <item> When using LAIX, I feel as if my tutor is a 'real' physical person.</item> <p></p> <item> The tutor in LAIX feels so 'real' that I almost believe that he/she is not a robot at all.</item> <p></p> <item> When using LAIX, I feel as if my tutor and I are in close proximity.</item> </ulist> <hd id="AN0183654458-33">Peer Influence (Adapted From Christy and Fox 2014)</hd> <p>When I use LAIX,</p> <p></p> <ulist> <item> My ranking in ranking list inspires me to improve my English pronunciation.</item> <p></p> <item> The ranking list motivates me to improve my English pronunciation since my friends score higher than me.</item> <p></p> <item> I do not want to be listed behind others, and thus, I practice English speaking to improve my score.</item> </ulist> <ref id="AN0183654458-34"> <title> Footnotes </title> <blist> <bibl id="bib1" idref="ref45" type="bt">1</bibl> <bibtext> Funding: This study was supported by the Shanghai Social Science Planning and Management Office.</bibtext> </blist> </ref> <ref id="AN0183654458-35"> <title> References </title> <blist> <bibtext> Almusharraf, N. 2023. " Incorporation of a Game‐Based Approach Into the EFL Online Classrooms: Students' Perceptions." Interactive Learning Environments 31, no. 7 : 4440 – 4453.</bibtext> </blist> <blist> <bibl id="bib2" idref="ref46" type="bt">2</bibl> <bibtext> Arora, S. J., and R. P. Singh. 2012. " Automatic Speech Recognition: A Review." International Journal of Computer Applications 60, no. 9 : 34 – 44.</bibtext> </blist> <blist> <bibl id="bib3" idref="ref21" type="bt">3</bibl> <bibtext> Asratie, M. G., B. D. Wale, and Y. T. Aylet. 2023. " Effects of Using Educational Technology Tools to Enhance EFL Students' Speaking Performance." Education and Information Technologies 28, no. 8 : 10031 – 100051.</bibtext> </blist> <blist> <bibl id="bib4" idref="ref8" type="bt">4</bibl> <bibtext> Bakır‐Yalçın, E., and Y. K. Usluel. 2023. " Investigating the Antecedents of Engagement in Online Learning: Do Achievement Emotions Matter? " Education and Information Technologies 29, no. 4 : 3759 – 3791.</bibtext> </blist> <blist> <bibl id="bib5" idref="ref134" type="bt">5</bibl> <bibtext> Bakker, A. B. 2011. " An Evidence‐Based Model of Work Engagement." Current Directions in Psychological Science 20, no. 4 : 265 – 269.</bibtext> </blist> <blist> <bibl id="bib6" idref="ref59" type="bt">6</bibl> <bibtext> Baldock, B. L., A. L. Fernandez, J. Franco, B. A. Provencher, and M. R. McCoy. 2021. " Overcoming the Challenges of Remote Instruction: Using Mobile Technology to Promote Active Learning." Journal of Chemical Education 98, no. 3 : 833 – 842.</bibtext> </blist> <blist> <bibl id="bib7" idref="ref107" type="bt">7</bibl> <bibtext> Becker, J. M., J. H. Cheah, R. Gholamzade, C. M. Ringle, and M. Sarstedt. 2023. " PLS‐SEM's Most Wanted Guidance." International Journal of Contemporary Hospitality Management 35, no. 1 : 321 – 346.</bibtext> </blist> <blist> <bibl id="bib8" idref="ref15" type="bt">8</bibl> <bibtext> Bedi, A. 2023. " Keep Learning: Student Engagement in an Online Environment." Online Learning 27, no. 2 : 119 – 136.</bibtext> </blist> <blist> <bibl id="bib9" idref="ref139" type="bt">9</bibl> <bibtext> Bergdahl, N., and J. Nouri. 2020. " Student Engagement and Disengagement in TEL–the Role of Gaming, Gender and Non‐Native Students." Research in Learning Technology 28 : 2293.</bibtext> </blist> <blist> <bibtext> Bodnar, S., C. Cucchiarini, B. P. de Vries, H. Strik, and R. van Hout. 2017. " Learner Affects in Computerized L2 Oral Grammar Practice With Corrective Feedback." Computer Assisted Language Learning 30, no. 3–4 : 223 – 246.</bibtext> </blist> <blist> <bibtext> Burns, A. 2016. " Research and the Teaching of Speaking in the Second Language Classroom." In Handbook of Research in Second Language Teaching, edited by E. Hinkel and E. Hinkel, 242 – 256. Routledge.</bibtext> </blist> <blist> <bibtext> Canchola Gonzalez, J. A., and L. D. Glasserman‐Morales. 2020. " Factors That Influence Learner Engagement and Completion Rate in an XMOOC on Energy and Sustainability." Knowledge Management & E‐Learning 12, no. 2 : 129 – 146.</bibtext> </blist> <blist> <bibtext> Chai, C. S., S. Liang, and X. Wang. 2023. " A Survey Study of Chinese Teachers' Continuous Intentions to Teach Artificial Intelligence." Education and Information Technologies 29, no. 11 : 14015 – 14034.</bibtext> </blist> <blist> <bibtext> Chang, M.‐M. 2005. " Applying Self‐Regulated Learning Strategies in a Web‐Based Instruction—an Investigation of Motivation Perception." Computer Assisted Language Learning 18, no. 3 : 217 – 230.</bibtext> </blist> <blist> <bibtext> Chen, C. M., L. Ming‐Chaun, and C. P. Kuo. 2023. " A Game‐Based Learning System Based on Octalysis Gamification Framework to Promote Employees' Japanese Learning." Computers & Education 205 : 104899.</bibtext> </blist> <blist> <bibtext> Chen, X., D. Zou, H. Xie, and G. Cheng. 2021. " Twenty Years of Personalized Language Learning." Educational Technology & Society 24, no. 1 : 205 – 222.</bibtext> </blist> <blist> <bibtext> Cheng, X., L. Macaulay, and A. Zarifis. 2013. " Modeling Individual Trust Development in Computer Mediated Collaboration: A Comparison of Approaches." Computers in Human Behavior 29, no. 4 : 1733 – 1741.</bibtext> </blist> <blist> <bibtext> Christenson, S., A. L. Reschly, and C. Wylie. 2012. Handbook of Research on Student Engagement. Springer.</bibtext> </blist> <blist> <bibtext> Christy, K. R., and J. Fox. 2014. " Leaderboards in a Virtual Classroom: A Test of Stereotype Threat and Social Comparison Explanations for Women's Math Performance." Computers & Education 78 : 66 – 77.</bibtext> </blist> <blist> <bibtext> Chu, H. C. 2014. " Potential Negative Effects of Mobile Learning on Students' Learning Achievement and Cognitive Load—a Format Assessment Perspective." Educational Technology & Society 17, no. 1 : 332 – 344.</bibtext> </blist> <blist> <bibtext> Cisek, P. 2007. " Cortical Mechanisms of Action Selection: The Affordance Competition Hypothesis." Philosophical Transactions of the Royal Society, B: Biological Sciences 362, no. 1485 : 1585 – 1599.</bibtext> </blist> <blist> <bibtext> Dai, Y., and Z. Wu. 2021. " Mobile‐Assisted Pronunciation Learning With Feedback From Peers and/or Automatic Speech Recognition: A Mixed‐Methods Study." Computer Assisted Language Learning 36, no. 5–6 : 861 – 884.</bibtext> </blist> <blist> <bibtext> Darhower, M. A. 2008. " The Role of Linguistic Affordances in Telecollaborative Chat." CALICO Journal 26, no. 1 : 48 – 69.</bibtext> </blist> <blist> <bibtext> Deng, R., P. Benckendorff, and D. Gannaway. 2020. " Linking Learner Factors, Teaching Context, and Engagement Patterns With MOOC Learning Outcomes." Journal of Computer Assisted Learning 36, no. 5 : 688 – 708.</bibtext> </blist> <blist> <bibtext> Derakhshan, A., D. Doliński, K. Zhaleh, M. J. Enayat, and J. Fathi. 2022. " A Mixed‐Methods Cross‐Cultural Study of Teacher Care and Teacher‐Student Rapport in Iranian and Polish University Students' Engagement in Pursuing Academic Goals in an L2 Context." System 106 : 102790.</bibtext> </blist> <blist> <bibtext> Dong, X., T. Wang, and I. Benbasat. 2016. "IT Affordances in Online Social Commerce: Conceptualization Validation and Scale Development." In Paper Presented at the Twenty‐Second Americas Conference on Information Systems. San Diego.</bibtext> </blist> <blist> <bibtext> Dubé, A. K., and R. N. McEwen. 2017. " Abilities and Affordances: Factors Influencing Successful Child–Tablet Communication." Educational Technology Research and Development 65 : 889 – 908.</bibtext> </blist> <blist> <bibtext> Ellis, R. 2006. " Individual Differences in Second Language Learning." In The Handbook of Applied Linguistics, edited by A. Davis and C. Elder, 525 – 551. Wiley.</bibtext> </blist> <blist> <bibtext> Ericsson, E., S. Sofkova Hashemi, and J. Lundin. 2023. " Fun and Frustrating: Students' Perspectives on Practising Speaking English With Virtual Humans." Cogent Education 10, no. 1 : 2170088.</bibtext> </blist> <blist> <bibtext> Fan, T. Y., and H. L. Chen. 2023. " Developing Cooperative Learning in a Content and Language Integrated Learning Context to Enhance Elementary School Students' Digital Storytelling Performance, English Speaking Proficiency, and Financial Knowledge." Journal of Computer Assisted Learning 39, no. 4 : 1354 – 1367.</bibtext> </blist> <blist> <bibtext> Fathi, J., M. Rahimi, and A. Derakhshan. 2024. " Improving EFL Learners' Speaking Skills and Willingness to Communicate via Artificial Intelligence‐Mediated Interactions." System 121 : 103254.</bibtext> </blist> <blist> <bibtext> Feng, R., and A. Ivanov. 2022. " Gender Differences in Emotional Valence and Social Media Content Engagement Behaviors in Pandemic Diaries: An Analysis Based on Microblog Texts." Behavioral Science 13, no. 1 : 34.</bibtext> </blist> <blist> <bibtext> Fornell, C., and D. F. Larcker. 1981. " Evaluating Structural Equation Models With Unobservable Variables and Measurement Error." Journal of Marketing Research 18, no. 1 : 39 – 50.</bibtext> </blist> <blist> <bibtext> Fredricks, J. A., P. C. Blumenfeld, and A. H. Paris. 2004. " School Engagement: Potential of the Concept, State of the Evidence." Review of Educational Research 74, no. 1 : 59 – 109.</bibtext> </blist> <blist> <bibtext> Fredricks, J. A., M. Filsecker, and M. A. Lawson. 2016. " Student Engagement, Context, and Adjustment: Addressing Definitional, Measurement, and Methodological Issues." Learning and Instruction 43 : 1 – 4.</bibtext> </blist> <blist> <bibtext> Fu, S., H. Gu, and B. Yang. 2020. " The Affordances of AI‐Enabled Automatic Scoring Applications on Learners' Continuous Learning Intention: An Empirical Study in China." British Journal of Educational Technology 51, no. 5 : 1674 – 1692.</bibtext> </blist> <blist> <bibtext> Gan, Z. 2013. " Understanding English Speaking Difficulties: An Investigation of Two Chinese Populations." Journal of Multilingual and Multicultural Development 34, no. 3 : 231 – 248.</bibtext> </blist> <blist> <bibtext> Ge, Z. G. 2024. " Exploring the Impact of Different Types of E‐Learners' Anonymity on Their Learning Engagement in Competitive Gamified Language Learning." Computer Assisted Language Learning 1‐27 : 1 – 27. https://doi.org/10.1080/09588221.2024.2342885.</bibtext> </blist> <blist> <bibtext> Gibson, J. J. 1979. " The Ecological Approach to the Visual Perception of Pictures." Leonardo 11, no. 3 : 227 – 235.</bibtext> </blist> <blist> <bibtext> Grgecic, D., R. Holten, and C. Rosenkranz. 2015. " The Impact of Functional Affordances and Symbolic Expressions on the Formation of Beliefs." Journal of the Association for Information Systems 16, no. 7 : 580 – 607.</bibtext> </blist> <blist> <bibtext> Gromik, N. 2012. " Cell Phone Video Recording Feature as a Language Learning Tool: A Case Study." Computers & Education 58, no. 1 : 223 – 230.</bibtext> </blist> <blist> <bibtext> Gunawardena, C. N. 1995. " Social Presence Theory and Implications for Interaction and Collaborative Learning in Computer Conferences." International Journal of Educational Telecommunications 1, no. 2 : 147 – 166.</bibtext> </blist> <blist> <bibtext> Hagen, A., B. Pellom, and R. Cole. 2007. " Highly Accurate Children's Speech Recognition for Interactive Reading Tutors Using Subword Units." Speech Communication 49, no. 12 : 861 – 873.</bibtext> </blist> <blist> <bibtext> Hair, J. F., M. Sarstedt, and C. M. Ringle. 2019. " Rethinking Some of the Rethinking of Partial Least Squares." European Journal of Marketing 53, no. 4 : 566 – 584.</bibtext> </blist> <blist> <bibtext> Hair, J. F., Jr., G. T. M. Hult, C. Ringle, and M. Sarstedt. 2016. A Primer on Partial Least Squares Structural Equation Modeling (PLS‐SEM). Sage Publications.</bibtext> </blist> <blist> <bibtext> Hau, K.‐T., and I. T. Ho. 2010. " Chinese Students' Motivation and Achievement." In Oxford Handbook of Chinese Psychology, edited by M. H. Bond, 187 – 204. Oxford University Press.</bibtext> </blist> <blist> <bibtext> Henry, A., and C. Thorsen. 2020. " Disaffection and Agentic Engagement: 'Redesigning' Activities to Enable Authentic Self‐Expression." Language Teaching Research 24, no. 4 : 456 – 475.</bibtext> </blist> <blist> <bibtext> Hood, N., A. Littlejohn, and C. Milligan. 2015. " Context Counts: How Learners' Contexts Influence Learning in a MOOC." Computers & Education 91 : 83 – 91.</bibtext> </blist> <blist> <bibtext> Hsieh, Y. H., Y. C. Lin, and H. T. Hou. 2015. " Exploring Elementary‐School Students' Engagement Patterns in a Game‐Based Learning Environment." Journal of Educational Technology & Society 18, no. 2 : 336 – 348.</bibtext> </blist> <blist> <bibtext> Huang, F., X. Y. Ge, Y. Y. Li, and Z. Q. Tang. 2025. " Unveiling Unwillingness to Communicate Among EFL Learners in Flipped Learning: An Ecological Perspective." Innovation in Language Learning and Teaching 2458638. https://doi.org/10.1080/17501229.2025.2458638.</bibtext> </blist> <blist> <bibtext> Huang, F., T. Teo, and X. Y. Zhao. 2023. " Examining Factors Influence Chinese Ethnic Minority English Teachers' Technology Adoption: An Extension of the UTAUT Model." Computer Assisted Language Learning 304. https://doi.org/10.1080/09588221.2023.2239304.</bibtext> </blist> <blist> <bibtext> Huang, F., T. Teo, and M. Zhou. 2020. " Chinese Students' Intentions to Use the Internet for Learning." Educational Technology Research & Development 68, no. 1 : 575 – 591.</bibtext> </blist> <blist> <bibtext> Huang, F., and B. Zou. 2024. " English Speaking With Artificial Intelligence (AI): The Roles of Enjoyment, Willingness to Communicate With AI, and Innovativeness." Computers in Human Behavior 159 : 108355. https://doi.org/10.1016/j.chb.2024.</bibtext> </blist> <blist> <bibtext> Hwang, B. L., T. C. Chou, and C. H. Huang. 2021. " Actualizing the Affordance of Mobile Technology for Mobile Learning." Educational Technology & Society 24, no. 4 : 67 – 80.</bibtext> </blist> <blist> <bibtext> Islam, M. S., and M. B. Stapa. 2021. " Students' Low Proficiency in Spoken English in Private Universities in Bangladesh: Reasons and Remedies." Language Testing in Asia 11, no. 1 : 22.</bibtext> </blist> <blist> <bibtext> Kahn, W. A. 1990. " Psychological Conditions of Personal Engagement and Disengagement at Work." Academy of Management Journal 33, no. 4 : 692 – 724.</bibtext> </blist> <blist> <bibtext> Kanno, Y., and S. E. Kangas. 2024. " English Learner as an Intersectional Identity." Journal of Language, Identity & Education 23, no. 2 : 320 – 326.</bibtext> </blist> <blist> <bibtext> King, R. B. 2016. " Gender Differences in Motivation, Engagement and Achievement Are Related to Students' Perceptions of Peer—But Not of Parent or Teacher—Attitudes Toward School." Learning and Individual Differences 52 : 60 – 71.</bibtext> </blist> <blist> <bibtext> Kline, R. B. 2015. Principles and Practice of Structural Equation Modeling. Guilford Publications.</bibtext> </blist> <blist> <bibtext> Korkmaz, S., and H. Öz. 2021. " Using Kahoot to Improve Reading Comprehension of English as a Foreign Language Learner." International Online Journal of Education and Teaching 8, no. 2 : 1138 – 1150.</bibtext> </blist> <blist> <bibtext> Kukulska‐Hulme, A., and O. Viberg. 2018. " Mobile Collaborative Language Learning: State of the Art." British Journal of Educational Technology 49, no. 2 : 207 – 218.</bibtext> </blist> <blist> <bibtext> Lee, C., A. S. Yeung, and T. Ip. 2016. " Use of Computer Technology for English Language Learning: Do Learning Styles, Gender, and Age Matter? " Computer Assisted Language Learning 29, no. 5 : 1035 – 1051.</bibtext> </blist> <blist> <bibtext> Lee, Z. W., C. M. Cheung, and T. K. Chan. 2021. " Understanding Massively Multiplayer Online Role‐Playing Game Addiction: A Hedonic Management Perspective." Information Systems Journal 31, no. 1 : 33 – 61.</bibtext> </blist> <blist> <bibtext> Leonardi, P. M. 2013. " When Does Technology Use Enable Network Change in Organizations? A Comparative Study of Feature Use and Shared Affordances." MIS Quarterly 37, no. 3 : 749 – 775.</bibtext> </blist> <blist> <bibtext> Li, N., and G. Kirkup. 2007. " Gender and Cultural Differences in Internet Use: A Study of China and the UK." Computers & Education 48 : 301 – 317.</bibtext> </blist> <blist> <bibtext> Li, X., and S. Song. 2018. " Mobile Technology Affordance and Its Social Implications: A Case of 'Rain Classroom'." British Journal of Educational Technology 49, no. 2 : 276 – 291.</bibtext> </blist> <blist> <bibtext> Lin, J. 2022. " A Structural Relationship Model for L2 Oral Proficiency, L2 Interest, Perceived Importance of Speaking, and Out‐of‐Class L2 Contact." Language Teaching Research 29 : 13621688221074027.</bibtext> </blist> <blist> <bibtext> Lin, X., R. Kaufmann, S. A. Spates, K. A. Lachlan, and P. R. Spence. 2021. " Exploring Students' Perceptions of Identity and Helper Heuristics in the Online Classroom Discussion Board." Communication Education 71, no. 2 : 108 – 124.</bibtext> </blist> <blist> <bibtext> Liu, E., J. Wang, and S. Bai. 2022. " Self‐Guides, Enjoyment, Gender, and Achievement: A Survey of Chinese EFL High School Students." Journal of Multilingual and Multicultural Development 45, no. 10 : 4270 – 4287.</bibtext> </blist> <blist> <bibtext> Liu, Z., N. Zhang, X. Peng, S. Liu, and Z. Yang. 2023. " Students' Social‐Cognitive Engagement in Online Discussions." Educational Technology & Society 26, no. 1 : 1 – 15.</bibtext> </blist> <blist> <bibtext> Lo, S. 2023. " Viewing Dual‐Subtitled Videos Under Different Learning Conditions: Effects on Learners' Behavioral, Emotional, and Cognitive Engagement." Computer Assisted Language Learning : 1 – 31. https://doi.org/10.1080/09588221.2023.2219711.</bibtext> </blist> <blist> <bibtext> McCrocklin, S. 2019. " ASR‐Based Dictation Practice for Second Language Pronunciation Improvement." Journal of Second Language Pronunciation 5, no. 1 : 98 – 118.</bibtext> </blist> <blist> <bibtext> Mettler, T., and J. Wulf. 2019. " Physiolytics at the Workplace: Affordances and Constraints of Wearables Use From an Employee's Perspective." Information Systems Journal 29, no. 1 : 245 – 273.</bibtext> </blist> <blist> <bibtext> Ni, X., X. Shao, Y. Geng, R. Qu, G. Niu, and Y. Wang. 2020. " Development of the Social Media Engagement Scale for Adolescents." Frontiers in Psychology 11 : 701.</bibtext> </blist> <blist> <bibtext> Norton, B. 2000. Identity and Language Learning: Gender, Ethnicity and Educational Change. Pearson Education.</bibtext> </blist> <blist> <bibtext> Nunnaly, J. C., and I. H. Bernstein. 1994. Psychometric Theory. McGraw‐Hill.</bibtext> </blist> <blist> <bibtext> Oga‐Baldwin, W. Q. 2019. " Acting, Thinking, Feeling, Making, Collaborating: The Engagement Process in Foreign Language Learning." System 86 : 102128.</bibtext> </blist> <blist> <bibtext> Oliver, M. 2013. " Learning Technology: Theorizing the Tools We Study." British Journal of Educational Technology 44, no. 1 : 31 – 43.</bibtext> </blist> <blist> <bibtext> O'Neal, C. R., K. Meyering, L. Babaturk, and N. Gosnell. 2024. " Child Anger Regulation Measure: Psychometric Properties, Prediction of Emotional Engagement, and Gender Differences." Psychology in the Schools 61, no. 4 : 1565 – 1589.</bibtext> </blist> <blist> <bibtext> Qiu, L., and I. Benbasat. 2009. " Evaluating Anthropomorphic Product Recommendation Agents: A Social and Relational Perspective to Designing Information Systems." Journal of Management Information Systems 25, no. 4 : 145 – 182.</bibtext> </blist> <blist> <bibtext> Ravindran, B., B. A. Greene, and T. K. DeBacker. 2005. " Predicting Preservice Teachers' Cognitive Engagement With Goals and Epistemological Beliefs." Journal of Educational Research 98, no. 4 : 222 – 233.</bibtext> </blist> <blist> <bibtext> Riady, Y., T. M. Alqahtany, A. Habibi, S. Sofyan, and N. A. Albelbisi. 2022. " Factors Affecting Teachers' Social Media Use During Covid‐19." Cogent Social Science 8, no. 1 : 2115658.</bibtext> </blist> <blist> <bibtext> Riccardi, G., and D. Hakkani‐Tur. 2005. " Active Learning: Theory and Applications to Automatic Speech Recognition." IEEE Transactions on Speech and Audio Processing 13, no. 4 : 504 – 511.</bibtext> </blist> <blist> <bibtext> Robillos, R. J. 2023. " Improving Students' Speaking Performance and Communication Engagement Through Technology‐Mediated Pedagogical Approach." International Journal of Instruction 16, no. 1 : 551 – 572.</bibtext> </blist> <blist> <bibtext> Salas‐Pilco, S. Z., Y. Yang, and Z. Zhang. 2022. " Student Engagement in Online Learning in Latin American Higher Education During the COVID‐19 Pandemic: A Systematic Review." British Journal of Educational Technology 53, no. 3 : 593 – 619.</bibtext> </blist> <blist> <bibtext> Saqr, M., S. López‐Pernas, S. Helske, and S. Hrastinski. 2023. " The Longitudinal Association Between Engagement and Achievement Varies by Time, Students' Profiles, and Achievement State: A Full Program Study." Computers & Education 199 : 104787.</bibtext> </blist> <blist> <bibtext> Sarstedt, M., J. F. Hair Jr., J. H. Fredri Cheah, J. M. Becker, and C. M. Ringle. 2019. " How to Specify, Estimate, and Validate Higher‐Order Constructs in PLS‐SEM." Australasian Marketing Journal 27, no. 3 : 197 – 211.</bibtext> </blist> <blist> <bibtext> Shin, D. 2022. " The Actualization of Meta‐Affordances: Conceptualizing Affordance Actualization in the Metaverse Games." Computers in Human Behavior 133 : 1 – 13.</bibtext> </blist> <blist> <bibtext> Shin, D. H. 2017. " The Role of Affordance in the Experience of Virtual Reality Learning: Technological and Affective Affordances in Virtual Reality." Telematics and Informatics 34, no. 8 : 1826 – 1836.</bibtext> </blist> <blist> <bibtext> Sinatra, G. M., B. C. Heddy, and D. Lombardi. 2015. " The Challenges of Defining and Measuring Student Engagement in Science." Educational Psychologist 50, no. 1 : 1 – 13.</bibtext> </blist> <blist> <bibtext> Stoet, G., and D. C. Geary. 2015. " Sex Differences in Academic Achievement Are Not Related to Political, Economic, or Social Equality." Intelligence 48 : 137 – 151.</bibtext> </blist> <blist> <bibtext> Su, F., D. Zou, L. Wang, and L. Kohnke. 2023. " Student Engagement and Teaching Presence in Blended Learning and Emergency Remote Teaching." Journal of Computers in Education 11, no. 2 : 445 – 470.</bibtext> </blist> <blist> <bibtext> Swain, M. 2005. " The Output Hypothesis: Theory and Research." In Handbook of Research in Second Language Teaching and Learning, edited by E. Hinkel, 471 – 483. Erlbaum.</bibtext> </blist> <blist> <bibtext> Tam, H. I., and B. L. Reynolds. 2023. " The Relationship Between Extramural English Engagement and the Vocabulary Size of L1 Cantonese Speakers in Macau." ITL‐International Journal of Applied Linguistics 174, no. 1 : 49 – 82.</bibtext> </blist> <blist> <bibtext> Tan, S. F., L. H. Ooi, L. Y. M. Ng, and T. Y. Sim. 2024. " Undergraduates' Perception, Engagement and Learning Experience in Online Learning Amid Covid‐19 Pandemic." International Journal of Instruction 17, no. 2 : 401 – 418.</bibtext> </blist> <blist> <bibtext> Tim, Y., T. Ouyang, and D. Zeng. 2020. " Back to the Future: Actualizing Technology Affordances to Transform Emperor Qin's Terracotta Warriors Museum." Information & Management 57, no. 8 : 103271.</bibtext> </blist> <blist> <bibtext> Tran, N., D. T. N. Hoang, R. Gillespie, T. T. H. Yen, and H. Phung. 2024. " Enhancing EFL Learners' Speaking and Listening Skills Through Authentic Online Conversations With Video Conferencing Tools." Innovation in Language Learning and Teaching 1‐11 : 1 – 11. https://doi.org/10.1080/17501229.2024.2334809.</bibtext> </blist> <blist> <bibtext> Van Lier, L. 2010. " The Ecology of Language Learning: Practice to Theory, Theory to Practice." Procedia‐Social and Behavioral Sciences 3 : 2 – 6.</bibtext> </blist> <blist> <bibtext> Veletsianos, G., A. Collier, and E. Schneider. 2015. " Digging Deeper Into Learners' Experiences in MOOCs: Participation in Social Networks Outside of MOOCs, Notetaking and Contexts Surrounding Content Consumption." British Journal of Educational Technology 46, no. 3 : 570 – 587.</bibtext> </blist> <blist> <bibtext> Wang, C., and S. Li. 2021. " The Trade‐Off Between Individuals and Groups: Role Interactions Under Different Technology Affordance Conditions." International Journal of Computer‐Supported Collaborative Learning 16, no. 4 : 525 – 557.</bibtext> </blist> <blist> <bibtext> Wang, M.‐T., and J. S. Eccles. 2013. " School Context, Achievement Motivation, and Academic Engagement: A Longitudinal Study of School Engagement Using a Multidimensional Perspective." Learning and Instruction 28 : 12 – 23.</bibtext> </blist> <blist> <bibtext> Wang, X., and B. L. Reynolds. 2024. " Beyond the Books: Exploring Factors Shaping Chinese English Learners' Engagement With Large Language Models for Vocabulary Learning." Education and Science 14, no. 5 : 496.</bibtext> </blist> <blist> <bibtext> Wang, Y., and L. Xue. 2024. " Using AI‐Driven Chatbots to Foster Chinese EFL Students' Academic Engagement: An Intervention Study." Computers in Human Behavior 159 : 108353.</bibtext> </blist> <blist> <bibtext> Wang, Y. L., A. Derakhshan, F. Ghiasvand, and M. Esfandyari. 2024. " Exploring Chinese and Iranian EAP Students' Oral Communication Apprehension in English: A Cross‐Cultural Mixed‐Methods Study." System 125 : 103437.</bibtext> </blist> <blist> <bibtext> Weidlich, J., and T. J. Bastiaens. 2019. " Designing Sociable Online Learning Environments and Enhancing Social Presence: An Affordance Enrichment Approach." Computers & Education 142 : 103622.</bibtext> </blist> <blist> <bibtext> Willis, J., K. Davis, and S. Chaplin. 2013. " Sociocultural Affordances of Online Peer Engagement." Journal of Learning Design 6, no. 1 : 34 – 45.</bibtext> </blist> <blist> <bibtext> Yang, Q., and X. Gong. 2021. " The Engagement–Addiction Dilemma: An Empirical Evaluation of Mobile User Interface and Mobile Game Affordance." Internet Research 31, no. 5 : 1745 – 1768.</bibtext> </blist> <blist> <bibtext> Yeung, M. W., and A. H. Yau. 2022. " A Thematic Analysis of Higher Education Students' Perceptions of Online Learning in Hong Kong Under COVID‐19: Challenges, Strategies and Support." Education and Information Technologies 27, no. 1 : 181 – 208.</bibtext> </blist> <blist> <bibtext> Yin, T., J. Yin, and Z. Xu. 2023. " Chinese Students' Perceptions of Social Networks and Their Academic Engagement in Technology‐Enhanced Classrooms." Heliyon 9, no. 11 : e21686.</bibtext> </blist> <blist> <bibtext> Young, S. S. C., and Y. H. Wang. 2014. " The Game Embedded CALL System to Facilitate English Vocabulary Acquisition and Pronunciation." Journal of Educational Technology & Society 17, no. 3 : 239 – 251.</bibtext> </blist> <blist> <bibtext> Yu, Z., L. Yu, Q. Xu, W. Xu, and P. Wu. 2022. " Effects of Mobile Learning Technologies and Social Media Tools on Student Engagement and Learning Outcomes of English Learning." Technology, Pedagogy and Education 31, no. 3 : 381 – 398.</bibtext> </blist> <blist> <bibtext> Zhang, C., and W. Zhang. 2022. " The Impact of Social Phobia on Willingness to Communicate in a Second Language: The Mediation Effect of Ideal L2 Self." Frontiers in Psychology 13 : 1018633.</bibtext> </blist> <blist> <bibtext> Zhou, Y., and Y. Han. 2018. " Research on Learners' Learning Engagement in Blended‐Learning Activities." E‐Education Research 39 : 99 – 105.</bibtext> </blist> <blist> <bibtext> Zimmerman, B. J. 1990. " Self‐Regulated Learning and Academic Achievement: An Overview." Educational Psychologist 25, no. 1 : 3 – 17.</bibtext> </blist> </ref> <aug> <p>By Fang Huang; Dingyang Peng and Timothy Teo</p> <p>Reported by Author; Author; Author</p> </aug> <nolink nlid="nl1" bibid="bib77" firstref="ref1"></nolink> <nolink nlid="nl2" bibid="bib103" firstref="ref2"></nolink> <nolink nlid="nl3" bibid="bib104" firstref="ref3"></nolink> <nolink nlid="nl4" bibid="bib34" firstref="ref4"></nolink> <nolink nlid="nl5" bibid="bib114" firstref="ref5"></nolink> <nolink nlid="nl6" bibid="bib81" firstref="ref6"></nolink> <nolink nlid="nl7" bibid="bib90" firstref="ref7"></nolink> <nolink nlid="nl8" bibid="bib50" firstref="ref9"></nolink> <nolink nlid="nl9" bibid="bib92" firstref="ref10"></nolink> <nolink nlid="nl10" bibid="bib25" firstref="ref11"></nolink> <nolink nlid="nl11" bibid="bib85" firstref="ref12"></nolink> <nolink nlid="nl12" bibid="bib35" firstref="ref13"></nolink> <nolink nlid="nl13" bibid="bib101" firstref="ref14"></nolink> <nolink nlid="nl14" bibid="bib70" firstref="ref16"></nolink> <nolink nlid="nl15" bibid="bib71" firstref="ref17"></nolink> <nolink nlid="nl16" bibid="bib86" firstref="ref18"></nolink> <nolink nlid="nl17" bibid="bib112" firstref="ref19"></nolink> <nolink nlid="nl18" bibid="bib84" firstref="ref20"></nolink> <nolink nlid="nl19" bibid="bib30" firstref="ref22"></nolink> <nolink nlid="nl20" bibid="bib55" firstref="ref23"></nolink> <nolink nlid="nl21" bibid="bib29" firstref="ref24"></nolink> <nolink nlid="nl22" bibid="bib93" firstref="ref25"></nolink> <nolink nlid="nl23" bibid="bib11" firstref="ref26"></nolink> <nolink nlid="nl24" bibid="bib46" firstref="ref27"></nolink> <nolink nlid="nl25" bibid="bib37" firstref="ref29"></nolink> <nolink nlid="nl26" bibid="bib31" firstref="ref30"></nolink> <nolink nlid="nl27" bibid="bib97" firstref="ref31"></nolink> <nolink nlid="nl28" bibid="bib53" firstref="ref32"></nolink> <nolink nlid="nl29" bibid="bib67" firstref="ref33"></nolink> <nolink nlid="nl30" bibid="bib52" firstref="ref36"></nolink> <nolink nlid="nl31" bibid="bib51" firstref="ref37"></nolink> <nolink nlid="nl32" bibid="bib22" firstref="ref38"></nolink> <nolink nlid="nl33" bibid="bib47" firstref="ref39"></nolink> <nolink nlid="nl34" bibid="bib16" firstref="ref40"></nolink> <nolink nlid="nl35" bibid="bib13" firstref="ref42"></nolink> <nolink nlid="nl36" bibid="bib40" firstref="ref48"></nolink> <nolink nlid="nl37" bibid="bib26" firstref="ref49"></nolink> <nolink nlid="nl38" bibid="bib18" firstref="ref51"></nolink> <nolink nlid="nl39" bibid="bib94" firstref="ref52"></nolink> <nolink nlid="nl40" bibid="bib102" firstref="ref55"></nolink> <nolink nlid="nl41" bibid="bib113" firstref="ref57"></nolink> <nolink nlid="nl42" bibid="bib111" firstref="ref58"></nolink> <nolink nlid="nl43" bibid="bib14" firstref="ref60"></nolink> <nolink nlid="nl44" bibid="bib38" firstref="ref61"></nolink> <nolink nlid="nl45" bibid="bib95" firstref="ref62"></nolink> <nolink nlid="nl46" bibid="bib20" firstref="ref63"></nolink> <nolink nlid="nl47" bibid="bib39" firstref="ref64"></nolink> <nolink nlid="nl48" bibid="bib73" firstref="ref66"></nolink> <nolink nlid="nl49" bibid="bib78" firstref="ref67"></nolink> <nolink nlid="nl50" bibid="bib63" firstref="ref68"></nolink> <nolink nlid="nl51" bibid="bib64" firstref="ref69"></nolink> <nolink nlid="nl52" bibid="bib27" firstref="ref70"></nolink> <nolink nlid="nl53" bibid="bib66" firstref="ref71"></nolink> <nolink nlid="nl54" bibid="bib98" firstref="ref75"></nolink> <nolink nlid="nl55" bibid="bib61" firstref="ref76"></nolink> <nolink nlid="nl56" bibid="bib23" firstref="ref78"></nolink> <nolink nlid="nl57" bibid="bib41" firstref="ref79"></nolink> <nolink nlid="nl58" bibid="bib106" firstref="ref80"></nolink> <nolink nlid="nl59" bibid="bib107" firstref="ref81"></nolink> <nolink nlid="nl60" bibid="bib88" firstref="ref82"></nolink> <nolink nlid="nl61" bibid="bib36" firstref="ref83"></nolink> <nolink nlid="nl62" bibid="bib43" firstref="ref84"></nolink> <nolink nlid="nl63" bibid="bib17" firstref="ref85"></nolink> <nolink nlid="nl64" bibid="bib42" firstref="ref86"></nolink> <nolink nlid="nl65" bibid="bib19" firstref="ref87"></nolink> <nolink nlid="nl66" bibid="bib75" firstref="ref88"></nolink> <nolink nlid="nl67" bibid="bib15" firstref="ref89"></nolink> <nolink nlid="nl68" bibid="bib57" firstref="ref90"></nolink> <nolink nlid="nl69" bibid="bib68" firstref="ref91"></nolink> <nolink nlid="nl70" bibid="bib99" firstref="ref92"></nolink> <nolink nlid="nl71" bibid="bib28" firstref="ref93"></nolink> <nolink nlid="nl72" bibid="bib21" firstref="ref95"></nolink> <nolink nlid="nl73" bibid="bib62" firstref="ref96"></nolink> <nolink nlid="nl74" bibid="bib91" firstref="ref97"></nolink> <nolink nlid="nl75" bibid="bib58" firstref="ref98"></nolink> <nolink nlid="nl76" bibid="bib65" firstref="ref99"></nolink> <nolink nlid="nl77" bibid="bib60" firstref="ref101"></nolink> <nolink nlid="nl78" bibid="bib56" firstref="ref102"></nolink> <nolink nlid="nl79" bibid="bib74" firstref="ref103"></nolink> <nolink nlid="nl80" bibid="bib105" firstref="ref105"></nolink> <nolink nlid="nl81" bibid="bib44" firstref="ref108"></nolink> <nolink nlid="nl82" bibid="bib59" firstref="ref109"></nolink> <nolink nlid="nl83" bibid="bib45" firstref="ref111"></nolink> <nolink nlid="nl84" bibid="bib76" firstref="ref112"></nolink> <nolink nlid="nl85" bibid="bib33" firstref="ref114"></nolink> <nolink nlid="nl86" bibid="bib87" firstref="ref115"></nolink> <nolink nlid="nl87" bibid="bib82" firstref="ref119"></nolink> <nolink nlid="nl88" bibid="bib89" firstref="ref121"></nolink> <nolink nlid="nl89" bibid="bib100" firstref="ref122"></nolink> <nolink nlid="nl90" bibid="bib83" firstref="ref124"></nolink> <nolink nlid="nl91" bibid="bib110" firstref="ref125"></nolink> <nolink nlid="nl92" bibid="bib72" firstref="ref126"></nolink> <nolink nlid="nl93" bibid="bib10" firstref="ref127"></nolink> <nolink nlid="nl94" bibid="bib80" firstref="ref128"></nolink> <nolink nlid="nl95" bibid="bib108" firstref="ref129"></nolink> <nolink nlid="nl96" bibid="bib54" firstref="ref130"></nolink> <nolink nlid="nl97" bibid="bib96" firstref="ref131"></nolink> <nolink nlid="nl98" bibid="bib24" firstref="ref132"></nolink> <nolink nlid="nl99" bibid="bib48" firstref="ref133"></nolink> <nolink nlid="nl100" bibid="bib109" firstref="ref135"></nolink> <nolink nlid="nl101" bibid="bib79" firstref="ref136"></nolink> <nolink nlid="nl102" bibid="bib49" firstref="ref137"></nolink> <nolink nlid="nl103" bibid="bib12" firstref="ref138"></nolink> <nolink nlid="nl104" bibid="bib69" firstref="ref140"></nolink> <nolink nlid="nl105" bibid="bib32" firstref="ref141"></nolink>
Header DbId: eric
DbLabel: ERIC
An: EJ1461380
AccessLevel: 3
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: AI Affordances and EFL Learners' Speaking Engagement: The Moderating Roles of Gender and Learner Type
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Fang+Huang%22">Fang Huang</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-6706-8251">0000-0001-6706-8251</externalLink>)<br /><searchLink fieldCode="AR" term="%22Dingyang+Peng%22">Dingyang Peng</searchLink><br /><searchLink fieldCode="AR" term="%22Timothy+Teo%22">Timothy Teo</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22European+Journal+of+Education%22"><i>European Journal of Education</i></searchLink>. 2025 60(1).
– Name: Avail
  Label: Availability
  Group: Avail
  Data: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 14
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2025
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Research
– Name: Audience
  Label: Education Level
  Group: Audnce
  Data: <searchLink fieldCode="EL" term="%22Higher+Education%22">Higher Education</searchLink><br /><searchLink fieldCode="EL" term="%22Postsecondary+Education%22">Postsecondary Education</searchLink>
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Learner+Engagement%22">Learner Engagement</searchLink><br /><searchLink fieldCode="DE" term="%22Second+Language+Learning%22">Second Language Learning</searchLink><br /><searchLink fieldCode="DE" term="%22Second+Language+Instruction%22">Second Language Instruction</searchLink><br /><searchLink fieldCode="DE" term="%22English+%28Second+Language%29%22">English (Second Language)</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+Intelligence%22">Artificial Intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Teaching+Methods%22">Teaching Methods</searchLink><br /><searchLink fieldCode="DE" term="%22Technology+Uses+in+Education%22">Technology Uses in Education</searchLink><br /><searchLink fieldCode="DE" term="%22Speech+Communication%22">Speech Communication</searchLink><br /><searchLink fieldCode="DE" term="%22Gender+Differences%22">Gender Differences</searchLink><br /><searchLink fieldCode="DE" term="%22Predictor+Variables%22">Predictor Variables</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+Software%22">Computer Software</searchLink><br /><searchLink fieldCode="DE" term="%22Instructional+Design%22">Instructional Design</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Attitudes%22">Student Attitudes</searchLink><br /><searchLink fieldCode="DE" term="%22College+Students%22">College Students</searchLink><br /><searchLink fieldCode="DE" term="%22On+the+Job+Training%22">On the Job Training</searchLink><br /><searchLink fieldCode="DE" term="%22Foreign+Countries%22">Foreign Countries</searchLink>
– Name: Subject
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22China%22">China</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1111/ejed.70041
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 0141-8211<br />1465-3435
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Contextualised in the AI--supported English-speaking learning, this study examined the roles of AI affordances in influencing EFL learners' emotional, cognitive, and behavioural speaking engagement, and explored the moderating roles of gender and learner types (on-campus vs. on-job) in influencing AI-supported English-speaking engagement. Data collected from 332 Chinese EFL learners (159 on-campus and 173 on-job learners) were analysed by using structural equation modelling. Results indicated that Chinese EFL learners perceived AI affordances to be significant in influencing their emotional, cognitive and behavioural engagement in practicing their spoken English. The results from the PLS-SEM model revealed that AI affordances accounted for 54.7%, 52.4% and 56.0% of the variance in emotional engagement, cognitive engagement and behavioural engagement, respectively. Learner type was not found to significantly moderate the relationships between AI affordances and speaking engagement. Gender was found to be a significant moderator for the AI affordances--behavioural engagement and AI affordance--cognitive engagement relationships. These findings enrich existing literature about AI--empowered speaking engagement and provide practical implications for English teachers to design effective speaking-teaching models.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2025
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1461380
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1461380
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1111/ejed.70041
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 14
    Subjects:
      – SubjectFull: Learner Engagement
        Type: general
      – SubjectFull: Second Language Learning
        Type: general
      – SubjectFull: Second Language Instruction
        Type: general
      – SubjectFull: English (Second Language)
        Type: general
      – SubjectFull: Artificial Intelligence
        Type: general
      – SubjectFull: Teaching Methods
        Type: general
      – SubjectFull: Technology Uses in Education
        Type: general
      – SubjectFull: Speech Communication
        Type: general
      – SubjectFull: Gender Differences
        Type: general
      – SubjectFull: Predictor Variables
        Type: general
      – SubjectFull: Computer Software
        Type: general
      – SubjectFull: Instructional Design
        Type: general
      – SubjectFull: Student Attitudes
        Type: general
      – SubjectFull: College Students
        Type: general
      – SubjectFull: On the Job Training
        Type: general
      – SubjectFull: Foreign Countries
        Type: general
      – SubjectFull: China
        Type: general
    Titles:
      – TitleFull: AI Affordances and EFL Learners' Speaking Engagement: The Moderating Roles of Gender and Learner Type
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Fang Huang
      – PersonEntity:
          Name:
            NameFull: Dingyang Peng
      – PersonEntity:
          Name:
            NameFull: Timothy Teo
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 03
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-print
              Value: 0141-8211
            – Type: issn-electronic
              Value: 1465-3435
          Numbering:
            – Type: volume
              Value: 60
            – Type: issue
              Value: 1
          Titles:
            – TitleFull: European Journal of Education
              Type: main
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