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Effects of Design Thinking STEAM Instruction on AI Learning and Creativity

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Title: Effects of Design Thinking STEAM Instruction on AI Learning and Creativity
Language: English
Authors: Ming-Yu Lin, Yu-Shan Chang (ORCID 0000-0002-5594-6528)
Source: International Journal of Technology and Design Education. 2025 35(5):2025-2047.
Availability: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
Peer Reviewed: Y
Page Count: 23
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: STEM Education, Art Education, Design, Thinking Skills, Artificial Intelligence, Teaching Methods, Creativity, College Students, Teacher Education Programs, Public Colleges, Technology Uses in Education, Student Attitudes
DOI: 10.1007/s10798-025-09977-y
ISSN: 0957-7572
1573-1804
Abstract: This study investigated the effects of design thinking STEAM (DT-STEAM) education on artificial intelligence (AI) learning and creativity. A total of 59 university students enrolled in two courses as part of a teacher education program at a public university were recruited. A nonequivalent group pretest and posttest design was used to perform a teaching experiment. The main conclusions of the study were as follows: DT-STEAM instruction improved the breadth and depth of understanding of the AI concept, particularly in terms of relational connections, hierarchies, and cross-connections; DT-STEAM instruction had a positive effect on attitude toward AI, particularly the AI process; and DT-STEAM had a positive effect on AI design creativity in terms of elaboration, usability, values and, in particular, novelty.
Abstractor: As Provided
Entry Date: 2026
Accession Number: EJ1493015
Database: ERIC
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  Value: <anid>AN0189055441;ogv01nov.25;2025Nov05.03:57;v2.2.500</anid> <title id="AN0189055441-1">Effects of design thinking STEAM instruction on AI learning and creativity </title> <p>This study investigated the effects of design thinking STEAM (DT-STEAM) education on artificial intelligence (AI) learning and creativity. A total of 59 university students enrolled in two courses as part of a teacher education program at a public university were recruited. A nonequivalent group pretest and posttest design was used to perform a teaching experiment. The main conclusions of the study were as follows: DT-STEAM instruction improved the breadth and depth of understanding of the AI concept, particularly in terms of relational connections, hierarchies, and cross-connections; DT-STEAM instruction had a positive effect on attitude toward AI, particularly the AI process; and DT-STEAM had a positive effect on AI design creativity in terms of elaboration, usability, values and, in particular, novelty.</p> <p>Keywords: STEAM; AI; Creativity</p> <hd id="AN0189055441-2">Research background and motivation</hd> <p>Against a background of rapid technological advances and social changes, innovation is having a decisive effect on the survival and growth of enterprises (Puriwat & Hoonsopon, [<reflink idref="bib52" id="ref1">52</reflink>]), and is essential to national competitiveness. The European Union established the European Institute of Innovation and Technology ([<reflink idref="bib61" id="ref2">61</reflink>]) to enhance the competitiveness of Europe in nine emerging technology industries. Meanwhile, the Biden administration in the United States implemented the US Innovation and Competition Act, and made substantial investments in areas such as artificial intelligence (AI), computer chips, lithium batteries (used in smart devices), and electric vehicles, to promote innovation and competitiveness (White House, [<reflink idref="bib63" id="ref3">63</reflink>]). In addition, the promotion of AI research, development, and education is a major focus (Chiu, [<reflink idref="bib18" id="ref4">18</reflink>]). Currently, many countries, such as the United States, Australia, China, India, and South Korea, are actively promoting AI education, using AI technology or Five Big Ideas as the framework (AI4K12, [<reflink idref="bib3" id="ref5">3</reflink>]; Kim et al., [<reflink idref="bib34" id="ref6">34</reflink>]). Thus, the implementation of AI education and enhancement of creative and innovative capabilities are major targets of countries worldwide.</p> <p>As early as 2001, the National Science Foundation (NSF) proposed the concept of science, technology, engineering, and mathematics (STEM), thereby emphasizing the importance of science and technology education (Lathan, [<reflink idref="bib36" id="ref7">36</reflink>]). Subsequently, to alleviate the lack of science and technology talent, the United States, particularly under the Obama administration, heavily promoted STEM education (The White House Office of Science and Technology Policy, [<reflink idref="bib62" id="ref8">62</reflink>]). According to various institutions, such as the Europass Teacher Academy ([<reflink idref="bib24" id="ref9">24</reflink>]), Australia's Brocklesby Public School ([<reflink idref="bib10" id="ref10">10</reflink>]), University of Connecticut (STEAM at UConn, [<reflink idref="bib57" id="ref11">57</reflink>]), New Zealand's Ministry of Education digital learning community, Te Kete Ipurangi ([<reflink idref="bib60" id="ref12">60</reflink>]), and Canada's University of Calgary ([<reflink idref="bib11" id="ref13">11</reflink>]), science, technology, engineering, arts, and mathematics (STEAM) is an educational method that guides students in terms of exploration, integration, and critical thinking as they pertain to STEM (Wannapiroon & Pimdee, [<reflink idref="bib66" id="ref14">66</reflink>]). STEAM has also received increasing attention and been promoted in various other countries.</p> <p>Studies have demonstrated that STEAM instruction can improve students' learning in the domains of science (Abueita et al., [<reflink idref="bib1" id="ref15">1</reflink>]; Priantari et al., [<reflink idref="bib51" id="ref16">51</reflink>]), technology (Skowronek et al., [<reflink idref="bib56" id="ref17">56</reflink>]), and creative and critical thinking (Abueita et al., [<reflink idref="bib1" id="ref18">1</reflink>]; Priantari et al., [<reflink idref="bib51" id="ref19">51</reflink>]; Wannapiroon & Pimdee, [<reflink idref="bib66" id="ref20">66</reflink>]). In the present study, we evaluated whether design thinking (DT) STEAM instruction is effective for AI teaching and creative design teaching.</p> <hd id="AN0189055441-3">Literature review</hd> <p></p> <hd id="AN0189055441-4">STEAM education and DT</hd> <p>After the heavy promotion of STEM education in the United States (The White House Office of Science and Technology Policy, [<reflink idref="bib62" id="ref21">62</reflink>]), the integration of arts disciplines (including arts and crafts, language, sports, and social science) has attracted attention, leading to the proposal of teaching frameworks, such as the STEAM pedagogy pyramid (STEAM Education, [<reflink idref="bib58" id="ref22">58</reflink>]; Fig. 1). The main factor distinguishing STEM from STEAM is not the inclusion of arts, but rather the incorporation and integration of DT and liberal arts-related capabilities (i.e., creativity, active attitude, and interpersonal collaboration) into STEM (Lathan, [<reflink idref="bib36" id="ref23">36</reflink>]). Active exploration, interdisciplinary integration, critical thinking, and creativity are key goals of STEAM education in various countries (Brocklesby Public School, [<reflink idref="bib10" id="ref24">10</reflink>]; STEAM at UConn, [<reflink idref="bib57" id="ref25">57</reflink>]; Te Kete Ipurangi, [<reflink idref="bib60" id="ref26">60</reflink>]; University of Calgary, [<reflink idref="bib64" id="ref27">64</reflink>]; Wannapiroon & Pimdee, [<reflink idref="bib66" id="ref28">66</reflink>]; Yakman, [<reflink idref="bib71" id="ref29">71</reflink>]). In the future, professional expertise acquired through higher education in a single discipline will not be sufficient; instead, STEAM learning that encompasses social and economic interdisciplinary skills will be required to meet societal needs (Carter et al., [<reflink idref="bib12" id="ref30">12</reflink>]).</p> <p>Graph: Fig. 1 STEAM pedagogy pyramid.</p> <p>The practical value of STEAM lies in its emphasis on experiential learning and scientific inquiry in real-world environments, and its focus on learning, discovering, and constructing knowledge. STEAM design innovation places emphasis on DT and creative innovation in actual situations (Wannapiroon & Pimdee, [<reflink idref="bib66" id="ref31">66</reflink>]), focusing on innovative knowledge application and problem-solving (Chang & Weng, [<reflink idref="bib16" id="ref32">16</reflink>]). Knowledge learning, discovery, application, and innovation mutually support each other; this echoes the concept of the <emph>taijitu</emph> in ancient Chinese philosophy, which symbolizes mutual growth and application in a continuous cycle (Wikipedia, [<reflink idref="bib68" id="ref33">68</reflink>]; Fig. 2). In higher education, there is a strong emphasis on research and innovation, transforming new knowledge gained from creative research and development into economic innovations (Carter et al., [<reflink idref="bib12" id="ref34">12</reflink>]; Perales & Aróstegui, [<reflink idref="bib49" id="ref35">49</reflink>]). This aligns with the concept of "practical inquiry—innovative design" (Fig. 2).</p> <p>Graph: Fig. 2 Integration of scientific and technological aspects of STEAM.</p> <p>The concept of the <emph>Taiji</emph> ("Supreme Ultimate") is described in both Taoist and Confucian philosophies, and represents the fusion of yin and yang into a single, ultimate state based on the dynamic relationship of the two component forces (Chang & Weng, [<reflink idref="bib16" id="ref36">16</reflink>]; Wikipedia, [<reflink idref="bib68" id="ref37">68</reflink>]). Similar to <emph>Taijitu</emph>, STEAM education emphasizes the combination of teaching themes and life situations. Through thinking, inquiry, practice, and innovation, knowledge, capabilities, and attitude are integrated and applied for contextual learning, enabling the mutual growth of practical inquiry and design innovation (Chang & Weng, [<reflink idref="bib16" id="ref38">16</reflink>]; Wikipedia, [<reflink idref="bib69" id="ref39">69</reflink>]; Fig. 3). Therefore, the concept of <emph>Taiji</emph> can be applied to the integration of inquiry and design into DT-STEAM instruction. In higher education, bridging the gap between specialized knowledge and social humanities involves applying inquiry-based learning content to real-world situations, such as design or project-based learning (Carter et al., [<reflink idref="bib12" id="ref40">12</reflink>]; MacDonald et al., [<reflink idref="bib41" id="ref41">41</reflink>]; Perales & Aróstegui, [<reflink idref="bib49" id="ref42">49</reflink>]). This approach is effectively conveyed by the concept of "Innovative Design & Practical Inquiry in STEAM" (Fig. 3) presented in this study.</p> <p>Graph: Fig. 3 Innovative design and practical enquiry in STEAM.</p> <p>Studies have reported that STEAM education can enhance the creativity of university students through instruction in the processes of investigation, discovery, making connections, creativity, and reflection. (Abueita et al., [<reflink idref="bib1" id="ref43">1</reflink>]; Priantari et al., [<reflink idref="bib51" id="ref44">51</reflink>]; Wannapiroon & Pimdee, [<reflink idref="bib66" id="ref45">66</reflink>]). Empirical studies also revealed that STEAM-based robotic learning can improve the academic achievement and creative thinking of eighth grade students (Abueita et al., [<reflink idref="bib1" id="ref46">1</reflink>]; Priantari et al., [<reflink idref="bib51" id="ref47">51</reflink>]). In addition, an effect of STEAM on learning about high-technology AI was demonstrated (Skowronek et al., [<reflink idref="bib56" id="ref48">56</reflink>]). These studies were inquiry-, design-, and topic-oriented (Zayyinah et al., [<reflink idref="bib75" id="ref49">75</reflink>]), with an emphasis on the effect on acquiring subject knowledge and high-level thinking skills, such as critical and creative thinking (Aguilera & Ortiz-Revilla, [<reflink idref="bib2" id="ref50">2</reflink>]; Conradty & Bogner, [<reflink idref="bib20" id="ref51">20</reflink>]; Wannapiroon & Petsangsri, [<reflink idref="bib65" id="ref52">65</reflink>]; Wannapiroon & Pimdee, [<reflink idref="bib66" id="ref53">66</reflink>]). These findings can serve as a basis for design evaluation and interpretation of the results of the present study.</p> <hd id="AN0189055441-5">AI education</hd> <p>Traditional technology-oriented production concepts have gradually transformed into a combination of augmented human and machine intelligence (Yang et al., [<reflink idref="bib72" id="ref54">72</reflink>]). AI refers to machine intelligence, which allows them to demonstrate rational, human-like thinking and actions through computer program operations (Schwendicke et al., [<reflink idref="bib55" id="ref55">55</reflink>]). In addition, using AI algorithms, big data, and cloud computing, AI can interpret and learn from external data, and apply the acquired knowledge flexibly to achieve specific goals (Bozkurt et al., [<reflink idref="bib9" id="ref56">9</reflink>]). AI must possess four major capabilities, namely the ability to perceive, comprehend, act, and learn. These capabilities depend on sensor and AI technologies, including natural language processing, computer vision, image recognition, machine learning, and deep learning (Bozkurt et al., [<reflink idref="bib9" id="ref57">9</reflink>]). The correlation of various AI technologies is illustrated in Table 1.</p> <p>Table 1 Input–process–output model of AI</p> <p> <ephtml> <table rules="groups"><thead><tr><th align="left" rowspan="2" /><th align="left" rowspan="2"><p>Natural intelligence</p></th><th align="left" colspan="2"><p>Artificial intelligence</p></th><th align="left" rowspan="2" colspan="2"><p>Key technologies</p><p>Five big ideas</p></th></tr><tr><th align="left"><p>Machine learning</p></th><th align="left"><p>Deep learning</p></th></tr></thead><tbody><tr><td align="left"><p>Input</p></td><td align="left"><p>Perception</p></td><td align="left"><p>Data & outcomes</p><p>Engineered features</p></td><td align="left"><p>Data & outcomes</p></td><td align="left"><p>Big data</p><p>Sensing component</p><p>AIoT</p></td><td align="left"><p>Perception</p></td></tr><tr><td align="left" rowspan="2"><p>Process</p></td><td align="left" rowspan="2"><p>Interpretation</p></td><td align="left"><p>Feature mapping</p></td><td align="left"><p>Feature learning & mapping</p></td><td align="left"><p>Algorithms</p><p>Cloud computing</p><p>Natural language</p></td><td align="left" rowspan="2"><p>Representation and reasoning</p><p>Learning</p><p>Natural interaction</p></td></tr><tr><td align="left"><p>Interpretation</p></td><td align="left"><p>Interpretation</p></td><td align="left" /></tr><tr><td align="left"><p>Output</p></td><td align="left"><p>Response</p></td><td align="left" colspan="2"><p>Actions & applications</p></td><td align="left"><p>Decision support</p><p>Robot</p></td><td align="left"><p>Societal impact</p></td></tr></tbody></table> </ephtml> </p> <p> <emph>Source</emph> Modified from Schwendicke et al. ([<reflink idref="bib55" id="ref58">55</reflink>]) and Kim et al. ([<reflink idref="bib34" id="ref59">34</reflink>])</p> <p></p> <p>Many countries are actively promoting AI education, in particular K-12 AI education in developed countries (AI4K12, [<reflink idref="bib3" id="ref60">3</reflink>]; Kim et al., [<reflink idref="bib34" id="ref61">34</reflink>]). These AI education programs primarily focus on the learning and application of techniques and skills; however, they often lack a defined curriculum structure that serves as a basis for teaching. Chiu ([<reflink idref="bib18" id="ref62">18</reflink>]) proposed an AI curriculum model based on qualitative research, which included the following six key components: AI knowledge, AI processes, the impact of AI (contents and products), relevance to students, teacher–student communication, and flexibility (processes and praxis). In addition, AI for K-12 initiative (AI4K12), jointly sponsored by the NSF, Association for the Advancement of Artificial Intelligence, and Computer Science Teachers Association, proposed an AI curriculum that consists of the Five Big Ideas, namely perception, representation and reasoning, learning, natural interaction, and societal impact (AI4K12, [<reflink idref="bib3" id="ref63">3</reflink>]). The model of the Five Big Ideas has been adopted by many scholars and countries. The correspondence between the Five Big Ideas and AI technology processes is presented in Table 1.</p> <p>Previous research showed that incorporating AI into high school students' daily lives, current events, and real-world problems can improve their understanding of the AI concept and engagement therewith (Lee et al., [<reflink idref="bib38" id="ref64">38</reflink>]). Hsu et al., ([<reflink idref="bib29" id="ref65">29</reflink>]) observed that male and female students exhibited different AI learning behaviors. Lin et al. ([<reflink idref="bib39" id="ref66">39</reflink>]) stated that integrating AI technology into STEAM courses (e.g., using image recognition to control the direction of self-propelled vehicles) can effectively improve students' AI literacy, particularly their awareness of ethics. The aforementioned integration model can also be used to impart AI knowledge and skills (Lin et al., [<reflink idref="bib39" id="ref67">39</reflink>]). Thus, this research examines effects on AI concept learning (knowledge of AI, including big data, sensing component, AIoT, algorithms, cloud computing, natural language, decision support, and robot, etc.) and on attitudes toward AI caused by DT-STEAM instruction. Hypotheses 1 and Hypotheses 2 are as follows.</p> <p> <emph>H1:</emph> DT-STEAM instruction has a significant effect on the learning of the AI concept.</p> <p> <emph>H2:</emph> DT-STEAM instruction has a significant effect on attitudes toward AI.</p> <hd id="AN0189055441-6">Creativity</hd> <p>Creativity is the ability to generate novel and useful ideas and products (Esling & Devis, [<reflink idref="bib23" id="ref68">23</reflink>]; Mikalef & Gupta, [<reflink idref="bib43" id="ref69">43</reflink>]), whereas design creativity represents a series of problem-solving processes (Sameti et al., [<reflink idref="bib54" id="ref70">54</reflink>]). Creative evaluation must account for both the process and results (Esling & Devis, [<reflink idref="bib23" id="ref71">23</reflink>]). The process of design creativity involves generating innovative ideas to address problems and needs. The novelty, number, and categories of ideas are indicators of creativity (Han et al., [<reflink idref="bib28" id="ref72">28</reflink>]).</p> <p>For enterprises, product novelty and appropriateness are crucial (Yi et al., [<reflink idref="bib73" id="ref73">73</reflink>]). New and useful ideas (creativity) facilitate product innovation (Puriwat & Hoonsopon, [<reflink idref="bib52" id="ref74">52</reflink>]). Enterprises emphasize the novelty and meaningfulness of creative products, as they generate economic value and promote favorable market performance (Yi et al., [<reflink idref="bib73" id="ref75">73</reflink>]). The creative indicators of product design creativity include novelty, value, and intentionality (Weisberg et al., [<reflink idref="bib67" id="ref76">67</reflink>]). Han et al. ([<reflink idref="bib28" id="ref77">28</reflink>]) proposed that creativity, novelty, and surprise belong to the same dimension, whereas functionality and usefulness belong to another dimension. A multinational study reported that both the judges and winners of a design competition emphasized that, with respect to novel products, usability and function must be considered to meet the needs of users and benefit enterprises (Sameti et al., [<reflink idref="bib54" id="ref78">54</reflink>]). Accordingly, novelty, functionality, and aesthetics are the three essential performance indicators for creative products (Besemer, [<reflink idref="bib7" id="ref79">7</reflink>]; Han et al., [<reflink idref="bib28" id="ref80">28</reflink>]; Mazerant et al., [<reflink idref="bib42" id="ref81">42</reflink>]; Weisberg et al., [<reflink idref="bib67" id="ref82">67</reflink>]).</p> <p>Commonly used instruments for rating product creativity include the Creative Product Semantic Scale (Besemer, [<reflink idref="bib7" id="ref83">7</reflink>]; Han et al., [<reflink idref="bib28" id="ref84">28</reflink>]; Mazerant et al., [<reflink idref="bib42" id="ref85">42</reflink>]) and Student Product Assessment Form (Reis & Renzulli, [<reflink idref="bib53" id="ref86">53</reflink>]). Techniques frequently used for assessing product creativity include the Consensual Assessment Technique (Nazzal & Kaufman, [<reflink idref="bib45" id="ref87">45</reflink>]; Stemler & Kaufman, [<reflink idref="bib59" id="ref88">59</reflink>]) and 5-point scales (Mazerant et al., [<reflink idref="bib42" id="ref89">42</reflink>]; Weisberg et al., [<reflink idref="bib67" id="ref90">67</reflink>]). These techniques and scales have demonstrated reliability and validity (Chang et al., [<reflink idref="bib14" id="ref91">14</reflink>]), and were therefore used in this study to collect data on conceptual and product creativity. Hypotheses 3 is as follows.</p> <p> <emph>H3:</emph> DT-STEAM instruction has a significant effect on AI design creativity.</p> <hd id="AN0189055441-7">Methods</hd> <p></p> <hd id="AN0189055441-8">Study participants</hd> <p>This study recruited a total of 59 students enrolled in two courses within a teaching education program at a public university in Taipei. They possessed basic information literacy skills typical of university students, such as software application and online learning. However, most of the students had not received any prior instruction on AI. The two classes, one with 29 participants and the other with 30 participants, were randomly assigned to the experimental and comparison groups, respectively.</p> <p>Since AI is a subdiscipline of computer science (Ertel, [<reflink idref="bib22" id="ref92">22</reflink>]), this study collected and analyzed the grades of both groups from the previous semester's Introduction to Computer Science course using a t-test. The results showed no significant difference between the experimental group (M = 89.10, SD = 4.95) and the comparison group (M = 88.66, SD = 3.47), t = 0.39, <emph>p</emph> = 0.69 > 0.05. In other words, the two groups had equivalent computer science abilities.</p> <hd id="AN0189055441-9">Independent variable</hd> <p>The independent variable in this study was the teaching strategy. STEAM instruction was the strategy for the experimental group, whereas conventional didactic teaching and operation experience was the strategy for the comparison group. As an experiential teaching aid, we used a model of an AI elevator, which delivered goods different floors; the goods included images of indigenous Taiwanese people based on the results of image recognition (Fig. 4). Totems and culture of indigenous Taiwanese people could be integrated into this learning activity, and included humanity and art knowledge. Taiwan's indigenous culture is a significant asset for the country. Therefore, this study uses Taiwan's indigenous culture as the background for designing AI image recognition teaching aids. We hope this approach will encourage students to engage with cultural and life issues and foster greater empathy and creativity.</p> <p>Graph: Fig. 4 Teaching aid: AI elevator performing image recognition of images of indigenous Taiwanese people</p> <p>The teaching methods for the comparison group included lecturing and discussions. The teaching experiment followed the instructional procedures designed based on the theoretical model proposed in this study (Practical Enquiry & Innovative Design, see Figs. 2 and 3). In addition to AI explanations and demonstrations (Practical Enquiry), the teaching sessions for the experimental group included creative design activities, which required empathy, problem identification, objective determination, creative development, and prototype production, testing, and modification (Innovative Design). The equipment for the practical activities included the image recognition lens module (Pixetto), which was used to establish a neural network image model, and a controller (Arduino), which received the recognition signal and controlled the motor. The teaching processes of the two groups are described in Table 2.</p> <p>Table 2 Main teaching procedure</p> <p> <ephtml> <table rules="groups"><thead><tr><th align="left" /><th align="left"><p>Phase</p></th><th align="left"><p>Stage</p></th><th align="left"><p>Experimental group</p></th><th align="left"><p>Comparison group</p></th><th align="left"><p>Hours</p></th></tr></thead><tbody><tr><td align="left"><p>1</p></td><td align="left"><p>Practical enquiry</p></td><td align="left"><p>Preliminary understanding of AI</p></td><td align="left" colspan="2"><p>Multimedia lectures</p><p>AI-related trivia challenge</p><p>Introduction to AI (weak and strong AI)</p><p>Explanation of important AI terms (e.g., machine learning, neural network, and deep learning)</p></td><td align="left"><p>1</p></td></tr><tr><td align="left"><p>2</p></td><td align="left" rowspan="2"><p>Innovative design</p></td><td align="left"><p>Introduction to indigenous people (empathy)</p></td><td align="left"><p>Lecture and discussion on the current developments in AI</p><p>Explore the relationship between AI and the cultural practices of Taiwan's indigenous peoples</p><p>Discuss challenges that may arise in preserving indigenous cultures and daily life</p></td><td align="left"><p>Lecture and discussion on the current developments in AI</p><p>Explore the relationship between AI, various industries, and daily life</p><p>Discuss potential challenges people may encounter in daily life and industries</p></td><td align="left"><p>1</p></td></tr><tr><td align="left"><p>3</p></td><td align="left"><p>Problem definition</p><p>and creative development</p></td><td align="left"><p>Watch videos about AI-controlled devices</p><p>Observe the operation of the indigenous elevator</p><p>Introduce machine learning and AI image recognition</p><p>Conduct online research to identify and discuss the most urgent issues related to indigenous culture and daily life</p><p>Formulate ideas based on the identified themes</p></td><td align="left"><p>Watch videos about AI-controlled devices</p><p>Introduce machine learning and AI image recognition</p><p>Conduct online research to identify and discuss the most pressing issues that need to be addressed</p><p>Formulate ideas based on the identified themes</p></td><td align="left"><p>1</p></td></tr><tr><td align="left" /><td align="left"><p>Practical enquiry</p></td><td align="left"><p>Understanding of AI</p></td><td align="left"><p>Operate the Pixetto Indigenous Totem Image Recognition Module</p><p>Input Indigenous totem images</p><p>Train the model</p><p>Conduct recognition testing</p><p>Operate the Indigenous Elevator using Arduino</p><p>Practice servo motor control for the Indigenous Elevator</p><p>Programming</p></td><td align="left"><p>Operate the Pixetto Image Recognition Module</p><p>Input randomly selected images</p><p>Train the model</p><p>Conduct recognition testing</p><p>Practice with Arduino components</p><p>Assemble servo motors and LEDs</p><p>Programming</p></td><td align="left"><p>2</p></td></tr><tr><td align="left"><p>4</p></td><td align="left" rowspan="3"><p>Innovative design</p></td><td align="left"><p>Prototype production</p></td><td align="left"><p>Assemble the structure of the Indigenous Elevator</p><p>Install the Pixetto image recognition camera</p><p>Operate the Indigenous Elevator</p><p>Establish an AI image recognition model</p></td><td align="left"><p>Introduce different image recognition models</p><p>Operate mobile applications of image recognition</p><p>Establish an AI image recognition model</p></td><td align="left"><p>2</p></td></tr><tr><td align="left"><p>5</p></td><td align="left"><p>Testing and modification</p></td><td align="left"><p>Operate the Indigenous Elevator</p><p>Test the functionality of the Indigenous Elevator using various Indigenous totems</p><p>Discuss how AI image recognition works</p></td><td align="left"><p>Test and modify the AI model</p><p>Discuss how AI image recognition works</p></td><td align="left"><p>1</p></td></tr><tr><td align="left"><p>6</p></td><td align="left"><p>Presentation of results</p></td><td align="left" colspan="2"><p>Present and discuss AI work</p><p>Learn about concept mapping</p><p>Draw an AI concept map</p></td><td align="left"><p>1</p></td></tr></tbody></table> </ephtml> </p> <p>In the first hour of instruction, both groups adopted a practical inquiry approach (as shown on the right side of Fig. 3). After the first hour, the experimental group transitioned to STEAM design thinking processes (as illustrated on the left side of Fig. 3), while the comparison group continued with lecture-based instruction and hands-on practice. The primary distinction between the two methods lies in the experimental group's integration of interdisciplinary STEAM design thinking processes, incorporating indigenous culture and daily life into AI image recognition learning at every stage. In contrast, the comparison group focused solely on understanding and practicing AI operations.</p> <hd id="AN0189055441-10">Dependent variables</hd> <p>The dependent variables in this study were the degree of understanding of the AI concept, attitude toward AI, and design creativity. The understanding of the AI concept was evaluated based on the concept maps drawn by the students, and the assessment items pertained to conjunctions, hierarchies, cross-conjunctions, and examples. Correlative conjunctions were used to represent the connections between conceptual terms, with 1 point assigned to each valid connection. Hierarchy refers to the upper and lower hierarchical relationships between knowledge concepts, with 5 points being assigned to each significant relationship. Cross-conjunction refers to the relationships between conceptual terms of different hierarchies, with 10 points being assigned to each cross-connection. Regarding examples, 1 point was assigned for each demonstration of knowledge of a particular domain (Llinás et al., [<reflink idref="bib40" id="ref93">40</reflink>]). Reliability analysis revealed a Cronbach's alpha of 0.85 and the correlation coefficient was 0.81, indicating good consistency (Novak & Gowin, [<reflink idref="bib48" id="ref94">48</reflink>]; Plummer, [<reflink idref="bib50" id="ref95">50</reflink>]).</p> <p>The attitude toward AI was determined by subjecting the written thoughts of students to word segmentation. After removing irrelevant words and merging synonyms, a word cloud application (https://wordart.com/create?fbclid=IwAR2PosYhHh8cg6er8y5ZDB9edTr4NT8hYq9MylgRemg6UkGgPZ4ISgYQRoI) was used for plotting the data. Word clouds are useful for data visualization and aid effective computing (Bilro et al., [<reflink idref="bib8" id="ref96">8</reflink>]; Chintalapudi et al., [<reflink idref="bib17" id="ref97">17</reflink>]). In this study, three students studying for a doctorate in the field of technology education reviewed the analysis procedure and reached a consensus regarding its validity (Chintalapudi et al., [<reflink idref="bib17" id="ref98">17</reflink>]).</p> <p>Design creativity refers to the innovativeness of DT implementation and solutions proposed based on AI. The design task used in this study, "AI application to daily life," also served as the posttest for design creativity. Students were required to propose an innovative design solution that creatively applies AI to solve a problem in daily life. This design solution was then evaluated for its creativity. The assessment items pertained to novelty (materials, shape, and structure), newness (originality and unusualness), feasibility (specificity and completeness), and value (aesthetic, performance, and versatility; Besemer, [<reflink idref="bib7" id="ref99">7</reflink>]; Chang & Yu, [<reflink idref="bib13" id="ref100">13</reflink>]; Mazerant et al., [<reflink idref="bib42" id="ref101">42</reflink>]; Weisberg et al., [<reflink idref="bib67" id="ref102">67</reflink>]), which had correlation coefficients of 0.93, 0.76, 0.80, and 0.86, respectively, according to the scores of two senior teachers; thus, high reliability was demonstrated (Mugenda & Mugenda, [<reflink idref="bib44" id="ref103">44</reflink>]).</p> <hd id="AN0189055441-11">Research design and implementation of the experimental procedure</hd> <p>As the experiment was part of a formal elective university course, the students in the two classes were randomly assigned to the experimental and comparison groups to ensure that the results were due to the experimental treatment (Gopalan et al., [<reflink idref="bib26" id="ref104">26</reflink>]). To eliminate the impact of initial learning behaviors, a nonequivalent group pretest–posttest design was adopted (Krishnan, [<reflink idref="bib35" id="ref105">35</reflink>]). A design creativity pretest was conducted, followed by a teaching experiment that spanned three weeks with three-hour sessions each week, totaling nine hours. The experimental group used STEAM teaching strategies integrated with the design thinking process, while the comparison group employed conventional didactic teaching and operational experience as the main teaching strategies. After the teaching sessions, both groups completed the "AI application to daily life" design task, which served as the posttest for design creativity. Students in both the experimental and comparison groups were encouraged to demonstrate creativity and propose innovative solutions (Barlex, [<reflink idref="bib6" id="ref106">6</reflink>]). Additionally, assessments were conducted on understanding of the AI concept and attitude toward AI.</p> <hd id="AN0189055441-12">Data analysis</hd> <p>In this study, quantitative concept map (Novak, [<reflink idref="bib47" id="ref107">47</reflink>]), word cloud (Bilro et al., [<reflink idref="bib8" id="ref108">8</reflink>]; Chintalapudi et al., [<reflink idref="bib17" id="ref109">17</reflink>]), and design creativity data were obtained, after which covariate analysis and a <emph>t</emph> test were used to compare performance between the experimental and comparison groups.</p> <hd id="AN0189055441-13">Ethical considerations</hd> <p>All participants were anonymously identified by codes so that their personal identities were not disclosed. For each participant, we obtained an informed consent. The collected data did not pertain to an investigation on the individual characteristics of each participant student. The data were collected and processed anonymously and exclusively at group level, and they were subject to scientific communication (oral and written). A scientific report would be provided at the end of the study to share the results.</p> <hd id="AN0189055441-14">Results</hd> <p></p> <hd id="AN0189055441-15">Understanding of the AI concept</hd> <p>DT-STEAM instruction had a significant effect on the students' understanding of the AI concept, thereby supporting H1. The <emph>t</emph> tests of the concept map scores showed that the experimental group performed significantly better in terms of conjunctions (<emph>t</emph> = 4.88, <emph>p</emph> < 0.01), hierarchies (<emph>t</emph> = 4.32, <emph>p</emph> < 0.01), cross-conjunctions (<emph>t</emph> = 2.85, <emph>p</emph> < 0.01), and total scores (<emph>t</emph> = 4.32, <emph>p</emph> < 0.01) compared to the comparison group; however, there was no significant difference for the examples parameter (<emph>t</emph> = 1.86, <emph>p</emph> = 0.06) (Fig. 5). Figure 6 depicts an example AI concept map drawn by one of the students.</p> <p>Graph: Fig. 5 Group comparison of the mean scores for understanding of the AI concept</p> <p>Graph: Fig. 6 AI concept maps drawn by participant students. Note The left one is by the experimental group, and the right one is by the comparison group. These two images represent the best AI concept maps from each group</p> <hd id="AN0189055441-16">Attitude toward AI</hd> <p>DT-STEAM instruction had a significant effect on attitudes toward AI, thereby supporting H2. The word frequency analysis demonstrated that the average numbers of positive mentions of AI contents (5.75 > 4.06, <emph>t</emph> = 2.41, <emph>p</emph> < 0.05) and processes (2.06 > 1.33, <emph>t</emph> = 2.26, <emph>p</emph> < 0.05) were significantly higher in the experimental group compared to the comparison group (Fig. 7). The word cloud revealed that the students in the experimental group mentioned more advanced technologies, such as machine learning, and also provided more in-depth descriptions of output-related content, such as intelligence and license plate recognition. By contrast, the content mentioned by students in the comparison group was relatively simple (e.g., simple technologies and robots); the descriptions of output-related content were also rather nonspecific and pertained only to applications to daily life (Figs. 8 and 9).</p> <p>Graph: Fig. 7 Group comparison of the mean scores for attitude toward AI</p> <p>Graph: Fig. 8 AI word cloud of the students in the experimental group</p> <p>Graph: Fig. 9 AI word cloud of the students in the comparison group</p> <hd id="AN0189055441-17">AI design creativity</hd> <p>DT-STEAM instruction had a significant effect on AI design creativity, thereby supporting H3. Neither the homogeneity of variance test nor that for regression achieved statistical significance (p > 0.05); therefore, multivariate analysis of covariance (MANCOVA) was conducted. MANCOVA indicated a large effect size (Wilks' lambda = 0.680, <emph>p</emph> < 0.01; Eta squared = 0.320) (Cohen, [<reflink idref="bib19" id="ref110">19</reflink>]). MANCOVA revealed significant differences in the novelty, elaboration, usability, and value AI design creativity parameters between the two groups (<emph>F</emph> = 12.95–23.84, <emph>p</emph> < 0.01; Eta squared = 0.188–0.299). The experimental group had superior results for all design creativity parameters (Table 3, Fig. 10). In particular, the experimental group exhibited a significantly higher score for novelty than the comparison group (3.647 vs. 2.657; <emph>F</emph> = 23.84, <emph>p</emph> < 0.01; Eta squared = 0.299). Figure 11 provides an example of a student design.</p> <p>Table 3 MANCOVA results for the creativity components</p> <p> <ephtml> <table rules="groups"><thead><tr><th align="left"><p>Item</p></th><th align="left"><p>Source</p></th><th align="left"><p>Sum of squares</p></th><th align="left"><p>df</p></th><th align="left"><p>Mean square</p></th><th align="left"><p>F</p></th><th align="left"><p><italic>p</italic></p></th><th align="left"><p>Eta squared</p></th></tr></thead><tbody><tr><td align="left" rowspan="2"><p>Novelty</p></td><td align="left"><p>Experimental processing</p></td><td char="." align="char"><p>227.82</p></td><td char="." align="char"><p>1</p></td><td char="." align="char"><p>227.82</p></td><td char="." align="char"><p>23.84**</p></td><td char="." align="char"><p>.000</p></td><td char="." align="char"><p>.299</p></td></tr><tr><td align="left"><p>Error</p></td><td char="." align="char"><p>535.16</p></td><td char="." align="char"><p>56</p></td><td char="." align="char"><p>9.55</p></td><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /></tr><tr><td align="left" rowspan="2"><p>Elaboration</p></td><td align="left"><p>Experimental treatment</p></td><td char="." align="char"><p>12.03</p></td><td char="." align="char"><p>1</p></td><td char="." align="char"><p>12.03</p></td><td char="." align="char"><p>14.23**</p></td><td char="." align="char"><p>.000</p></td><td char="." align="char"><p>.203</p></td></tr><tr><td align="left"><p>Error</p></td><td char="." align="char"><p>47.37</p></td><td char="." align="char"><p>56</p></td><td char="." align="char"><p>.84</p></td><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /></tr><tr><td align="left" rowspan="2"><p>Usability</p></td><td align="left"><p>Experimental treatment</p></td><td char="." align="char"><p>16.37</p></td><td char="." align="char"><p>1</p></td><td char="." align="char"><p>16.37</p></td><td char="." align="char"><p>13.98**</p></td><td char="." align="char"><p>.000</p></td><td char="." align="char"><p>.200</p></td></tr><tr><td align="left"><p>Error</p></td><td char="." align="char"><p>65.58</p></td><td char="." align="char"><p>56</p></td><td char="." align="char"><p>1.17</p></td><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /></tr><tr><td align="left" rowspan="2"><p>Value</p></td><td align="left"><p>Experimental treatment</p></td><td char="." align="char"><p>15.86</p></td><td char="." align="char"><p>1</p></td><td char="." align="char"><p>15.86</p></td><td char="." align="char"><p>12.95**</p></td><td char="." align="char"><p>.001</p></td><td char="." align="char"><p>.188</p></td></tr><tr><td align="left"><p>Error</p></td><td char="." align="char"><p>68.56</p></td><td char="." align="char"><p>56</p></td><td char="." align="char"><p>1.22</p></td><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /></tr></tbody></table> </ephtml> </p> <p> <sups>**</sups> <emph>p</emph> < 0.1, *<emph>p</emph> < 0.05</p> <p>Graph: Fig. 10 Group comparison of the mean scores for design creativity</p> <p>Graph: Fig. 11 Example student design of AI applications to daily life. Note The left one is by the experimental group, and the right one is by the comparison group. These two images represent the best AI application design from each group</p> <p>Written description of left one: "AI can recognize faces, judge health and skin tone, and provide makeup suggestions through image recognition. It can also identify body shape and provide suggestions for appropriate clothing."</p> <p>Written description of right one: "AI-recognized door lock: After more than three recognition errors, notify the homeowner via IoT or connect to the security system".</p> <hd id="AN0189055441-18">Discussion</hd> <p></p> <hd id="AN0189055441-19">DT-STEAM instruction has a positive effect on understanding of the AI concept</hd> <p>The results of this study revealed that DT-STEAM instruction had a significant positive effect on understanding of the AI concept (knowledge of AI), including conjunctions, hierarchies, and cross-conjunctions. Skowronek stated that STEAM instruction allows students to acquire scientific and technological knowledge, as well as information about various technologies (e.g., energy and AI) and interdisciplinary knowledge in fields such as liberal arts (Skowronek et al., [<reflink idref="bib56" id="ref111">56</reflink>]). The STEAM "camp" organized by the University of Colorado Boulder emphasized DT-oriented teaching that incorporates technology into learning to promote knowledge integration (Norgaard, [<reflink idref="bib46" id="ref112">46</reflink>]).</p> <p>Research has also shown that a three-day STEAM camp focused on design thinking significantly impacts learning outcomes, empathy, and self-confidence (Kijima et al., [<reflink idref="bib33" id="ref113">33</reflink>]). This highlights the importance of the empathy stage in design thinking as a crucial learning phase. Conradty and Bogner ([<reflink idref="bib20" id="ref114">20</reflink>]) reported that STEAM instruction can effectively improve self-efficacy and self-confidence, which in turn increases students' willingness to tackle more difficult concepts, thereby enhancing the breadth and depth of their learning. Other studies have also reported that STEAM instruction can enhance learning motivation (Aguilera & Ortiz-Revilla, [<reflink idref="bib2" id="ref115">2</reflink>]; Wannapiroon & Pimdee, [<reflink idref="bib66" id="ref116">66</reflink>]). The results of these studies are consistent with those of the present study, demonstrating the positive effect of DT-STEAM instruction on understanding of the AI concept. In addition to enhancing learners' motivation, STEAM instruction facilitates integrated learning and knowledge acquisition. The lack of a significant effect in terms of the examples parameter in this study may be attributable to the students' lack of experience or contact with AI applications in daily life.</p> <hd id="AN0189055441-20">DT-STEAM instruction has a positive effect on attitudes toward AI</hd> <p>Attitudes toward AI are positive and/or negative affect, behavior, and cognition toward AI (Ghotbi et al., [<reflink idref="bib25" id="ref117">25</reflink>]). The results of this study revealed that DT-STEAM instruction had a significantly positive effect on attitudes toward AI in general, and AI in terms of content and processes. Regarding substantive content, the experimental group referred to more in-depth processes and outputs according to the word analysis. Following experimental STEAM instruction, middle school students in South Korea exhibited significantly improved attitudes toward physics (Lee, [<reflink idref="bib37" id="ref118">37</reflink>]). STEAM Expo learning activities improved attitudes toward science among Turkish children aged 13–24 years (Yılmaz İnce et al., [<reflink idref="bib74" id="ref119">74</reflink>]). Another study revealed that, after participating in a STEAM coding workshop, middle school students' attitudes toward programming improved significantly (Gul et al., [<reflink idref="bib27" id="ref120">27</reflink>]). These results are consistent with those of the present study, demonstrating that STEAM instruction can promote a positive attitude toward learning (Ishartono et al., [<reflink idref="bib31" id="ref121">31</reflink>]).</p> <p>Research has also demonstrated that STEAM learning through design thinking significantly impacts creative confidence and empathy (Khan et al., [<reflink idref="bib32" id="ref122">32</reflink>]; Kijima et al., [<reflink idref="bib33" id="ref123">33</reflink>]). In this study, through the inquiry process of "2. Explore the relationship between AI and the cultural practices of Taiwan's indigenous peoples" and "3. Discuss challenges that may arise in preserving indigenous cultures and daily life," (See Table 2) participants developed a deeper empathy for the significance and importance of AI in the context of indigenous culture and daily life. This led to a stronger positive attitude toward AI and the creation of more comprehensive AI word cloud maps (see Fig. 8). This suggests that, compared to other engineering design processes, the empathy stage in design thinking not only enables learners to better understand the needs of others but also enhances their confidence in their own creativity. Similar results have been validated in comparable three-day STEAM camps (Kijima et al., [<reflink idref="bib33" id="ref124">33</reflink>]).</p> <hd id="AN0189055441-21">DT-STEAM instruction has a positive effect on AI design creativity</hd> <p>The results of this study indicated that DT-STEAM instruction had significant effects on the elaboration, usability, and value AI design creativity parameters, but especially on novelty. These results are consistent with those of previous research (Conradty & Bogner, [<reflink idref="bib20" id="ref125">20</reflink>]; Skowronek et al., [<reflink idref="bib56" id="ref126">56</reflink>]; Zayyinah et al., [<reflink idref="bib75" id="ref127">75</reflink>]). Studies on professional training of teachers (Conradty & Bogner, [<reflink idref="bib20" id="ref128">20</reflink>]), and others on university students (Wannapiroon & Pimdee, [<reflink idref="bib66" id="ref129">66</reflink>]), support positive effects of STEAM instruction on cognitive processes pertaining to, and motivation for, creativity. Moreover, a meta-analysis based on a 10-year (2010–2020) literature review confirmed a positive effect of STEAM on creativity (Aguilera & Ortiz-Revilla, [<reflink idref="bib2" id="ref130">2</reflink>]). In this study, the experimental group was tasked with operating an AI elevator for indigenous people, integrating the STEAM learning process. This approach likely enhanced students' cognitive understanding and emotional attitudes toward AI applications in real-life contexts (Chang et al., [<reflink idref="bib15" id="ref131">15</reflink>]; Xoliyorova et al., [<reflink idref="bib70" id="ref132">70</reflink>]), thereby fostering unique and diverse creative designs (see Fig. 11).</p> <p>However, most of the aforementioned studies obtained data on creative thinking through questionnaires. In the present study, creativity data were acquired through design practice, which offers a more accurate reflection of creativity. Another longitudinal study, conducted from 2017 to 2021, concluded that interdisciplinary learning that integrates STEAM knowledge can enhance creativity through the process of DT (Hurley et al., [<reflink idref="bib30" id="ref133">30</reflink>]). Hurley et al. ([<reflink idref="bib30" id="ref134">30</reflink>]) primarily analyzed creativity based on creative thinking, which manifested in novel designs.</p> <hd id="AN0189055441-22">Conclusion and suggestions</hd> <p></p> <hd id="AN0189055441-23">Research conclusions and suggestions for practical application</hd> <p>The objective of this study was to investigate the effect of DT-STEAM instruction on creativity. The main conclusions are as follows:</p> <p></p> <ulist> <item> DT-STEAM instruction improved the breadth and depth of understanding of the AI concept, particularly in terms of conceptual conjunctions, hierarchies, and cross-conjunctions. These results may be attributed to increased self-efficacy, self-confidence, and learning motivation. Regarding the examples parameter, the effect of DT-STEAM was not as favorable as expected. It is suggested that teachers should provide AI learning activities for various subjects to increase students' contact and engagement with real-life AI applications.</item> <p></p> <item> DT-STEAM instruction had a positive effect on attitudes toward AI, particularly the process thereof. Thus, AI-related engineering teaching can be implemented using the DT-STEAM instruction model.</item> <p></p> <item> DT-STEAM had positive effects on AI design creativity, including in terms of elaboration, usability, and value, but especially novelty. Design practice encompasses creative thinking, professional knowledge, motivational attitude, and design processes (Amabile, [<reflink idref="bib4" id="ref135">4</reflink>]). This study demonstrated that DT-STEAM instruction effectively integrates the above elements, as reflected in improved AI design creativity.</item> </ulist> <hd id="AN0189055441-24">Research limitations and suggestions for future studies</hd> <p>In this study, DT-STEAM instruction had a significant effect on learning regarding the AI concept. In addition to learner factors, such as motivation, self-confidence, and self-efficacy, as noted in other relevant studies, the organization of knowledge and concepts is also key, and merits detailed discussion. The attitude toward AI discussed in this study was based on the input–process–output–feedback framework of AI. Future studies should further explore the affective, cognitive, and behavioral dimensions of learning (Gul et al., [<reflink idref="bib27" id="ref136">27</reflink>]). In this study, DT-STEAM had a positive effect on AI design creativity. The generation of ideas and integration of STEAM knowledge during the process of DT (ElSayary et al., [<reflink idref="bib21" id="ref137">21</reflink>]) merit further investigation.</p> <p>This research noted that to compare average values, it is essential to establish that the assessment results adhere to a normal distribution. The authors found it perplexing to compare the average cognitive scores between the experimental and comparison classes due to the substantial influence of outliers. It was recommended that future research should first use tests like Shapiro–Wilk or Kolmogorov–Smirnov to check for normal distribution to obtain more accurate research results. Additionally, the evaluation criteria for creative products and services could be expanded as follows (Barlex, [<reflink idref="bib5" id="ref138">5</reflink>]): 1.technology available for use, 2. social value and acceptance, 3.users' needs, and 4.market acceptance and development. These features align well with the STEAM thinking in this research, offering a broader base than novelty, functionality, and aesthetics for developing and identifying creativity.</p> <p>Although both groups of participants in this study were given the same assignment—"AI applications in everyday life design solutions"—and were only required to illustrate their design concepts and sketches without creating actual products (as shown in Fig. 11), a noticeable discrepancy is observed between Figs. 6 and 11. This difference may be attributed to the exceptional performance of individual participants. Additionally, the relatively small sample size (a total of 59 participants across both groups) could be a contributing factor. Future research is recommended to increase the number of participants to enhance the study's validity.</p> <hd id="AN0189055441-25">Acknowledgements</hd> <p>The authors thank Ministry of Science and Technology, Taiwan for funding this research.</p> <hd id="AN0189055441-26">Authors' contributions</hd> <p>All authors contributed to the paper. YC inspired the research idea and edited the manuscript. TW conducted the instruction experiment. JK, YW, and IT reviewed the literature and analyzed research data. All authors read and approved the final manuscript.</p> <hd id="AN0189055441-27">Funding</hd> <p>Open access funding provided by National Taiwan Normal University. This study was funded by Ministry of Science and Technology, Taiwan. (Grant Number NSC 110-2511-H-003 -018 -MY3).</p> <hd id="AN0189055441-28">Data availability</hd> <p>The data that support the findings of this study are available from the corresponding author upon reasonable request. In the near future, they can be available through an institutional repository.</p> <hd id="AN0189055441-29">Declarations</hd> <p></p> <hd id="AN0189055441-30">Conflict of interest</hd> <p>The authors declare that they have no conflict of interest.</p> <hd id="AN0189055441-31">Ethical approval</hd> <p>The ethics rules and regulations of the Declaration of Helsinki were followed during the experiment. All the participants were volunteers and were told that they could quit the study at any time.</p> <hd id="AN0189055441-32">Ethical considerations</hd> <p>All participants were anonymously identified by codes so that their personal identities were not disclosed. For each participant, we obtained an informed consent. The collected data would not pertain to an investigation on the individual characteristics of each participants. The data would be collected and processed anonymously and exclusively at group level, and they would be subject to scientific communication (oral and written). A scientific report would be provided at the end of the study to share the results.</p> <hd id="AN0189055441-33">Publisher's Note</hd> <p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p> <ref id="AN0189055441-34"> <title> References </title> <blist> <bibl id="bib1" idref="ref15" type="bt">1</bibl> <bibtext> Abueita JD, Fayez MQ, Alsabeelah A, Humaidat MA. The impact of (STEAM) approach on the innovative thinking and academic achievement of the educational robot subject among eighth grade students in Jordan. Journal of Educational and Social Research. 2022; 12; 1: 188-203. 10.36941/jesr-2022-0016</bibtext> </blist> <blist> <bibl id="bib2" idref="ref50" type="bt">2</bibl> <bibtext> Aguilera D, Ortiz-Revilla J. STEM versus STEAM education and student creativity: A systematic literature review. Education Science. 2021; 11: 331. 10.3390/educsci11070331</bibtext> </blist> <blist> <bibl id="bib3" idref="ref5" type="bt">3</bibl> <bibtext> AI4K12. (2020). The artificial intelligence (AI) for K-12 initiative (AI4K12) is jointly sponsored by AAAI and CSTA. https://ai4k12.org/</bibtext> </blist> <blist> <bibl id="bib4" idref="ref135" type="bt">4</bibl> <bibtext> Amabile, T. M. (2012). Componential theory of creativity.<ulink href="http://www.hbs.edu/faculty/Publication%20Files/12-096.pdf">http://www.hbs.edu/faculty/Publication%20Files/12-096.pdf</ulink></bibtext> </blist> <blist> <bibl id="bib5" idref="ref138" type="bt">5</bibl> <bibtext> Barlex DFrance B, Compton V. The young foresight project. Bringing communities together. 2012: Rotterdam; Sense Publishers. 10.1007/978-94-6091-791-2_8</bibtext> </blist> <blist> <bibl id="bib6" idref="ref106" type="bt">6</bibl> <bibtext> Barlex, D. (2003). Creativity in crisis? Nuffield Design and Technology Design and Technology Association. https://dandtfordandt.wordpress.com/wp-content/uploads/2024/02/report-pages.pdf</bibtext> </blist> <blist> <bibl id="bib7" idref="ref79" type="bt">7</bibl> <bibtext> Besemer SP. Creative product analysis mat rix: Testing the model structure and a comparison among products three novel chairs. Creativity Research Journal. 1998; 11; 4: 333-346. 10.1207/s15326934crj1104_7</bibtext> </blist> <blist> <bibl id="bib8" idref="ref96" type="bt">8</bibl> <bibtext> Bilro RG, Loureiro SMC, de Aires Angelino FJ. The role of creative communications and gamification in student engagement in higher education: A sentiment analysis approach. Journal of Creative Communications. 2021; 1: 15. 10.1177/0973258621992644</bibtext> </blist> <blist> <bibl id="bib9" idref="ref56" type="bt">9</bibl> <bibtext> Bozkurt A, Karadeniz A, Baneres D, Guerrero-Roldán AE, Rodríguez ME. Artificial intelligence and reflections from educational landscape: A review of AI studies in half a century. Sustainability. 2021; 13: 800. 10.3390/su13020800</bibtext> </blist> <blist> <bibtext> Brocklesby Public School. (2021). STEAM. https://brocklesby-p.schools.nsw.gov.au/learning-at-our-school/steam.html</bibtext> </blist> <blist> <bibtext> Canada's University of Calgary. (2022). From STEM to STEAM. https://univcan.ca/media-room/media-releases/from-stem-to-steam/</bibtext> </blist> <blist> <bibtext> Carter CE, Barnett H, Burns K, Cohen N, Durall E, Lordick D, Nack F, Newman A, Ussher S. Defining STEAM approaches for higher education. European Journal of STEM Education. 2021; 6; 1: 13. 10.20897/ejsteme/11354</bibtext> </blist> <blist> <bibtext> Chang YS, Yu KC. The relationship between perceptions of an innovative environment and creative performance in an online synchronous environment. Computers in Human Behavior. 2015; 49: 38-43. 10.1016/j.chb.2015.02.040</bibtext> </blist> <blist> <bibtext> Chang YS, Chien YH, Yu KC, Lin HC, Chen MYC. Students' innovative environmental perceptions and creative performances in cloud based m learning. Computers in Human Behavior. 2016; 63; c: 988-994. 10.1016/j.chb.2016.06.032</bibtext> </blist> <blist> <bibtext> Chang YS, Wang YY, Ku YT. Influence of online STEAM hands-on learning on AI learning, creativity, and creative emotions. Interactive Learning Environments. 2024; 32; 8: 4719-4738. 10.1080/10494820.2023.2205898</bibtext> </blist> <blist> <bibtext> Chang, Y. S, & Weng, T. H. (2022). An AI: STEAM instruction exemplar. Science Study Monthly, 60(6). https://wwwold.ntsec.gov.tw/FileAtt.ashx?id=4379</bibtext> </blist> <blist> <bibtext> Chintalapudi N, Battineni G, Canio MD, Sagaro GG, Amenta F. Text mining with sentiment analysis on seafarers' medical documents. International Journal of Information Management Data Insights. 2021; 1: 100005. 10.1016/j.jjimei.2020.100005</bibtext> </blist> <blist> <bibtext> Chiu TKF. A holistic approach to the design of artificial intelligence (AI) education for k−12 schools. TechTrends. 2021; 65: 796-807. 10.1007/s11528-021-00637-1</bibtext> </blist> <blist> <bibtext> Cohen J. Statistical power analysis. 19882; Erlbaum</bibtext> </blist> <blist> <bibtext> Conradty C, Bogner FX. STEAM instruction professional development works: Effects on students' creativity and motivation. Smart Learning Environments. 2020; 7; 1: 26. 10.1186/s40561-020-00132-9</bibtext> </blist> <blist> <bibtext> ElSayary A, Zein R, Antonio LS. Using interactive technology to develop preservice teachers' STEAM competencies in early childhood education program. EURASIA Journal of Mathematics, Science and Technology Education. 2022; 18; 2: em2079. 10.29333/ejmste/11649</bibtext> </blist> <blist> <bibtext> Ertel W. Introduction to artificial intelligence. 2024; Springer Nature</bibtext> </blist> <blist> <bibtext> Esling, P, & Devis, N. (2021). Creativity in the era of artificial intelligence. https://arxiv.org/pdf/2008.05959.pdf</bibtext> </blist> <blist> <bibtext> Europass Teacher Academy. (2022). From STEM to STEAM education: a new learning approach.https://<ulink href="http://www.teacheracademy.eu/course/steam-education/">www.teacheracademy.eu/course/steam-education/</ulink></bibtext> </blist> <blist> <bibtext> Ghotbi N, Ho MT, Mantello P. Attitude of college students towards ethical issues of artificial intelligence in an international university in Japan. AI & Society. 2022; 37: 283-290. 10.1007/s00146-021-01168-2</bibtext> </blist> <blist> <bibtext> Gopalan M, Rosinger K, Ahn JB. Use of quasi-experimental research designs in education research: Growth, promise, and challenges. Review of Research in Education. 2020; 44; 1: 218-243. 10.3102/0091732X20903302</bibtext> </blist> <blist> <bibtext> Gul D, Cetin I, Ozden MY. A scale for measuring middle school students' attitudes toward programming. Computer Applications in Engineering Education. 2022; 30: 251-258. 10.1002/cae.22454</bibtext> </blist> <blist> <bibtext> Han J, Forbes H, Schaefer D. An exploration of how creativity, functionality, and aesthetics are related in design. Research in Engineering Design. 2021; 32: 289-307. 10.1007/s00163-021-00366-9</bibtext> </blist> <blist> <bibtext> Hsu TC, Abelson H, Brummelen JV. The effects on secondary school students of applying experiential learning to the conversational AI learning curriculum. International Review of Research in Open and Distributed Learning. 2022; 23; 1: 82-103. 10.19173/irrodl.v22i4.5474</bibtext> </blist> <blist> <bibtext> Hurley, M, Rhinehart, A, Bell, P, Brown, A, Price, N, & Roche, J. (2022). Designing for future action: How STEAM programming can support youth engagement in community change making projects. Connected Science Learning, 4(1).</bibtext> </blist> <blist> <bibtext> Ishartono N, Prayitno HJ, Irfan M, Waluyo M, Sufahani SFB. An investigation of Indonesian in-service mathematics teachers' perception and attitude toward STEAM education. Journal of Physics Conference Series. 2022; 1776: 012021. 10.1088/1742-6596/1776/1/012021/meta</bibtext> </blist> <blist> <bibtext> Khan MA, Kazi AS, Butt AI. Influence of design thinking in integrated STEAM inquiry based learning on primary school students' empathy and confidence. Global Sociological Review. 2023; 8; 1: 321-330. 10.31703/gsr.2023(VIII-I).32</bibtext> </blist> <blist> <bibtext> Kijima R, Yang-Yoshihara M, Maekawa MS. Using design thinking to cultivate the next generation of female STEAM thinkers. International Journal of STEM Education. 2021; 8: 1-15. 10.1186/s40594-021-00271-6</bibtext> </blist> <blist> <bibtext> Kim S, Jang Y, Choi S, Kim W, Jung H, Kim S, Kim H. Analyzing teacher competency with TPACK for k-12 AI education. KI - Künstliche Intelligenz. 2021; 35: 139-151. 10.1007/s13218-021-00731-9</bibtext> </blist> <blist> <bibtext> Krishnan P. A review of the non-equivalent control group post-test-only design. Nurse Researcher. 2019; 26; 2: 37-40. 10.7748/nr.2018.e1582</bibtext> </blist> <blist> <bibtext> Lathan, J. (2021). Why STEAM is so important to 21st century education. https://onlinedegrees.sandiego.edu/steam education in schools/?fbclid=IwAR3vLsLcxYSzkC0 Gza9jEEBzS_xpgCqg4kye09AuavneC98BWKn1GCCXv2E#STEAM</bibtext> </blist> <blist> <bibtext> Lee DJ. The effect of STEAM-based physical education classes on middle school students' attitudes toward physical education classes and self-directed learning abilities. Iranian Journal of Public Health. 2021; 50; 5: 938-948. 10.18502/ijph.v50i5.6111</bibtext> </blist> <blist> <bibtext> Lee, I, Ali, S, Zhang, H, DiPaola, D, & Breazeal, C. (2021). Developing middle school students' AI literacy. In Proceedings of the 52nd ACM Technical Symposium on Computer Science Education (SIGCSE '21), pp.191–197. https://doi.org/10.1145/3408877.3432513</bibtext> </blist> <blist> <bibtext> Lin C-H, Yu C-C, Shih P-K, Wu L-Y. STEM-based artificial intelligence learning in general education for non-engineering undergraduate students. Educational Technology & Society. 2021; 24; 3: 224-237</bibtext> </blist> <blist> <bibtext> Llinás JG, Macías FS, Márquez LMT. The use of concept maps as an assessment tool in physics classes: Can one use concept maps for quantitative evaluations?. Research in Science Education. 2020; 50; 5: 1789-1804. 10.1177/1932202X20921770</bibtext> </blist> <blist> <bibtext> MacDonald A, Wise K, Tregloan K, Fountain W, Wallis L, Holmstrom N. Designing STEAM education: Fostering relationality through design-led disruption. International Journal of Art & Design Education. 2020; 39; 1: 227-241. 10.1111/jade.12258</bibtext> </blist> <blist> <bibtext> Mazerant K, Willemsen LM, Neijens PC, van Noort G. Spot on creativity: Creativity biases and their differential effects on consumer responses in (non-)real time marketing. Journal of Interactive Marketing. 2021; 53: 15-31. 10.1016/j.intmar.2020.06.004</bibtext> </blist> <blist> <bibtext> Mikalef P, Gupta M. Artificial intelligence capability: Conceptualization, measurement calibration, and empirical study on its impact on organizational creativity and firm performance. Information & Management. 2021; 58: 103434. 10.1016/j.im.2021.103434</bibtext> </blist> <blist> <bibtext> Mugenda A, Mugenda O. Research methods: Qualitative and quantitative approaches. 20034; ACTS</bibtext> </blist> <blist> <bibtext> Nazzal LJ, Kaufman JC. The relationship of the quality of creative problem solving stages to overall creativity in engineering students. Thinking Skills and Creativity. 2020; 38: 100734. 10.1016/j.tsc.2020.100734</bibtext> </blist> <blist> <bibtext> Norgaard R. Knowledge in the making: How STEAM can transform notions of expertise. 2022; Routledge</bibtext> </blist> <blist> <bibtext> Novak JD. Concept maps and vee diagrams: Two metacognitive tools to facilitate meaningful learning. Instructional Science. 1990; 19: 29-52. 10.1007/BF00377984</bibtext> </blist> <blist> <bibtext> Novak JD, Gowin DB. Learning how to learn. 1984: New York; Cambridge University Press. 10.1017/CBO9781139173469</bibtext> </blist> <blist> <bibtext> Perales FJ, Aróstegui JL. The STEAM approach: Implementation and educational, social and economic consequences. Arts Education Policy Review. 2024; 125; 2: 59-67. 10.1080/10632913.2021.1974997</bibtext> </blist> <blist> <bibtext> Plummer, K. J. (2008). Analysis of the psychometric of two different concept map assessment tasks.https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=2351&context=etd</bibtext> </blist> <blist> <bibtext> Priantari I, Prafitasari AN, Kusumawardhani DR, Susanti S. Improving student critical thinking trough STEAM-PjBL learning. Bioeducation Journal. 2020; 4; 2: 95-103. 10.24036/bioedu.v4i2.283</bibtext> </blist> <blist> <bibtext> Puriwat W, Hoonsopon D. Cultivating product innovation performance through creativity: The impact of organizational agility and flexibility under technological turbulence. Journal of Manufacturing Technology Management. 2021. 10.1108/JMTM-10-2020-0420</bibtext> </blist> <blist> <bibtext> Reis SM, Renzulli JS. The assessment of creative products in programs for gifted and talented students. Gifted Child Quarterly. 1991; 35; 3: 128-134. 10.1177/001698629103500304</bibtext> </blist> <blist> <bibtext> Sameti A, Koslow S, Mashhady A. Are product design researchers and practitioners on the same page? The way professional product designers view creative design. Journal of Product & Brand Management. 2022; 31: 951-970. 10.1108/JPBM-07-2021-3560</bibtext> </blist> <blist> <bibtext> Schwendicke F, Samek W, Krois J. Artificial intelligence in dentistry: Chances and challenges. Journal of Dental Research. 2020; 99; 7: 769-774. 10.1177/0022034520915714</bibtext> </blist> <blist> <bibtext> Skowronek M, Gilberti RM, Petro M, Sancomb C, Maddern S, Jankovic J. Inclusive STEAM education in diverse disciplines of sustainable energy and AI. Energy and AI. 2022; 7: 100124. 10.1016/j.egyai.2021.100124</bibtext> </blist> <blist> <bibtext> STEAM at UConn. (2021). What is STEAM. https://steam.uconn.edu/#</bibtext> </blist> <blist> <bibtext> STEAM Education. (2018). STEAM pyramid history. https://steamedu.com/pyramidhistory/</bibtext> </blist> <blist> <bibtext> Stemler SE, Kaufman JC. Are creative people better than others at recognizing creative work?. Thinking Skills and Creativity. 2020; 38: 100727. 10.1016/j.tsc.2020.100727</bibtext> </blist> <blist> <bibtext> Te Kete Ipurangi, TKI. (2021). What is STEM/STEAM? https://elearning.tki.org.nz/Teaching/Future-focused-learning/STEM-STEAM</bibtext> </blist> <blist> <bibtext> The European Institute of Innovation and Technology. (2022). Vision and mission. https://eit.europa.eu/who-we-are/eit-glance/mission</bibtext> </blist> <blist> <bibtext> The White House Office of Science and Technology Policy. (2018). Summary of the 2018 White House state-federal STEM education summit.https://<ulink href="http://www.whitehouse.gov/wp-content/uploads/2018/06/Summary-of-the-2018-White-House-State-Federal-STEM-Education-Summit.pdf">www.whitehouse.gov/wp-content/uploads/2018/06/Summary-of-the-2018-White-House-State-Federal-STEM-Education-Summit.pdf</ulink></bibtext> </blist> <blist> <bibtext> The White House. (2021). Statement of president Joe Biden on senate passage of the U.S. Innovation and Competition Act.https://<ulink href="http://www.whitehouse.gov/briefing-room/statements-releases/2021/06/08/statement-of-president-joe-biden-on-senate-passage-of-the-u-s-innovation-and-competition-act/">www.whitehouse.gov/briefing-room/statements-releases/2021/06/08/statement-of-president-joe-biden-on-senate-passage-of-the-u-s-innovation-and-competition-act/</ulink></bibtext> </blist> <blist> <bibtext> University of Calgary. (2022). Education - STEM & STEAM Education.https://libguides.ucalgary.ca/c.php?g=255548</bibtext> </blist> <blist> <bibtext> Wannapiroon N, Petsangsri S. Effects of STEAMification model in flipped classroom learning environment on creative thinking and creative innovation. TEM Journal. 2020; 9; 4: 1647-1655. 10.18421/TEM94-42</bibtext> </blist> <blist> <bibtext> Wannapiroon N, Pimdee P. Thai undergraduate science, technology, engineering, arts, and math (STEAM) creative thinking and innovation skill development: A conceptual model using a digital virtual classroom learning environment. Education and Information Technologies. 2022. 10.1007/s10639-021-10849-w</bibtext> </blist> <blist> <bibtext> Weisberg R, Pichot N, Bonetto E, Pavani JB, Arciszewski T, Bonnardel N. From explicit to implicit theories of creativity and back: The relevance of naive criteria in defining Creativity. The Journal of Creative Behavior. 2021; 55; 3: 839-856. 10.1002/jocb.492</bibtext> </blist> <blist> <bibtext> Wikipedia. (2021)。太極圖。https://zh.wikipedia.org/wiki/%E5%A4%AA%E6%A5%B5%E5%9C%96</bibtext> </blist> <blist> <bibtext> Wikipedia. (2022). Tai chi. https://en.wikipedia.org/wiki/Tai_chi</bibtext> </blist> <blist> <bibtext> Xoliyorova S, Tilyabov M, Pardayev U. Explaining the basic concepts of chemistry to 7th grade students in general schools based on STEAM. Modern Science and Research. 2024; 3; 2: 362-365. 10.5281/zenodo.10966929</bibtext> </blist> <blist> <bibtext> Yakman, G. (2008). STEAM Education: An overview of creating a model of integrative education.https://<ulink href="http://www.researchgate.net/profile/Georgette-Yakman-2/publication/327351326%5fSTEAM%5fEducation%5fan%5foverview%5fof%5fcreating%5fa%5fmodel%5fof%5fintegrative%5feducation/links/5b89d6b24585151fd1403a90/STEAM-Education-an-overview-of-creating-a-model-of-integrative-education.pdf">www.researchgate.net/profile/Georgette-Yakman-2/publication/327351326%5fSTEAM%5fEducation%5fan%5foverview%5fof%5fcreating%5fa%5fmodel%5fof%5fintegrative%5feducation/links/5b89d6b24585151fd1403a90/STEAM-Education-an-overview-of-creating-a-model-of-integrative-education.pdf</ulink></bibtext> </blist> <blist> <bibtext> Yang SJH, Ogata H, Matsui T, Chen NS. Human centered artificial intelligence in education: Seeing the invisible through the visible. Computers and Education: Artificial Intelligence. 2021. 10.1016/j.caeai.2021.100008</bibtext> </blist> <blist> <bibtext> Yi HT, Amenuvor FE, Boateng H. The impact of entrepreneurial orientation on new product creativity, competitive advantage and new product performance in SMEs: The moderating role of corporate life cycle. Sustainability. 2021; 13: 3586. 10.3390/su13063586</bibtext> </blist> <blist> <bibtext> Yılmaz İnce, E, Kabul, A. & Diler, İ. (2022). The effect of science festival on participants' attitudes towards science. Journal of STEAM Education, 5(1), 88–99. https://dergipark.org.tr/en/download/article-file/2129202</bibtext> </blist> <blist> <bibtext> Zayyinah Z, Erman E, Supardi ZAI, Hariyono E, Prahani BK. STEAM-integrated project based learning models: Alternative to improve 21st century skills. Advances in Social Science, Education and Humanities Research. 2022; 627: 251-258</bibtext> </blist> </ref> <aug> <p>By Ming-Yu Lin and Yu-Shan Chang</p> <p>Reported by Author; Author</p> </aug> <nolink nlid="nl1" bibid="bib52" firstref="ref1"></nolink> <nolink nlid="nl2" bibid="bib61" firstref="ref2"></nolink> <nolink nlid="nl3" bibid="bib63" firstref="ref3"></nolink> <nolink nlid="nl4" bibid="bib18" firstref="ref4"></nolink> <nolink nlid="nl5" bibid="bib34" firstref="ref6"></nolink> <nolink nlid="nl6" bibid="bib36" firstref="ref7"></nolink> <nolink nlid="nl7" bibid="bib62" firstref="ref8"></nolink> <nolink nlid="nl8" bibid="bib24" firstref="ref9"></nolink> <nolink nlid="nl9" bibid="bib10" firstref="ref10"></nolink> <nolink nlid="nl10" bibid="bib57" firstref="ref11"></nolink> <nolink nlid="nl11" bibid="bib60" firstref="ref12"></nolink> <nolink nlid="nl12" bibid="bib11" firstref="ref13"></nolink> <nolink nlid="nl13" bibid="bib66" firstref="ref14"></nolink> <nolink nlid="nl14" bibid="bib51" firstref="ref16"></nolink> <nolink nlid="nl15" bibid="bib56" firstref="ref17"></nolink> <nolink nlid="nl16" bibid="bib58" firstref="ref22"></nolink> <nolink nlid="nl17" bibid="bib64" firstref="ref27"></nolink> <nolink nlid="nl18" bibid="bib71" firstref="ref29"></nolink> <nolink nlid="nl19" bibid="bib12" firstref="ref30"></nolink> <nolink nlid="nl20" bibid="bib16" firstref="ref32"></nolink> <nolink nlid="nl21" bibid="bib68" firstref="ref33"></nolink> <nolink nlid="nl22" bibid="bib49" firstref="ref35"></nolink> <nolink nlid="nl23" bibid="bib69" firstref="ref39"></nolink> <nolink nlid="nl24" bibid="bib41" firstref="ref41"></nolink> <nolink nlid="nl25" bibid="bib75" firstref="ref49"></nolink> <nolink nlid="nl26" bibid="bib20" firstref="ref51"></nolink> <nolink nlid="nl27" bibid="bib65" firstref="ref52"></nolink> <nolink nlid="nl28" bibid="bib72" firstref="ref54"></nolink> <nolink nlid="nl29" bibid="bib55" firstref="ref55"></nolink> <nolink nlid="nl30" bibid="bib38" firstref="ref64"></nolink> <nolink nlid="nl31" bibid="bib29" firstref="ref65"></nolink> <nolink nlid="nl32" bibid="bib39" firstref="ref66"></nolink> <nolink nlid="nl33" bibid="bib23" firstref="ref68"></nolink> <nolink nlid="nl34" bibid="bib43" firstref="ref69"></nolink> <nolink nlid="nl35" bibid="bib54" firstref="ref70"></nolink> <nolink nlid="nl36" bibid="bib28" firstref="ref72"></nolink> <nolink nlid="nl37" bibid="bib73" firstref="ref73"></nolink> <nolink nlid="nl38" bibid="bib67" firstref="ref76"></nolink> <nolink nlid="nl39" bibid="bib42" firstref="ref81"></nolink> <nolink nlid="nl40" bibid="bib53" firstref="ref86"></nolink> <nolink nlid="nl41" bibid="bib45" firstref="ref87"></nolink> <nolink nlid="nl42" bibid="bib59" firstref="ref88"></nolink> <nolink nlid="nl43" bibid="bib14" firstref="ref91"></nolink> <nolink nlid="nl44" bibid="bib22" firstref="ref92"></nolink> <nolink nlid="nl45" bibid="bib40" firstref="ref93"></nolink> <nolink nlid="nl46" bibid="bib48" firstref="ref94"></nolink> <nolink nlid="nl47" bibid="bib50" firstref="ref95"></nolink> <nolink nlid="nl48" bibid="bib17" firstref="ref97"></nolink> <nolink nlid="nl49" bibid="bib13" firstref="ref100"></nolink> <nolink nlid="nl50" bibid="bib44" firstref="ref103"></nolink> <nolink nlid="nl51" bibid="bib26" firstref="ref104"></nolink> <nolink nlid="nl52" bibid="bib35" firstref="ref105"></nolink> <nolink nlid="nl53" bibid="bib47" firstref="ref107"></nolink> <nolink nlid="nl54" bibid="bib19" firstref="ref110"></nolink> <nolink nlid="nl55" bibid="bib46" firstref="ref112"></nolink> <nolink nlid="nl56" bibid="bib33" firstref="ref113"></nolink> <nolink nlid="nl57" bibid="bib25" firstref="ref117"></nolink> <nolink nlid="nl58" bibid="bib37" firstref="ref118"></nolink> <nolink nlid="nl59" bibid="bib74" firstref="ref119"></nolink> <nolink nlid="nl60" bibid="bib27" firstref="ref120"></nolink> <nolink nlid="nl61" bibid="bib31" firstref="ref121"></nolink> <nolink nlid="nl62" bibid="bib32" firstref="ref122"></nolink> <nolink nlid="nl63" bibid="bib15" firstref="ref131"></nolink> <nolink nlid="nl64" bibid="bib70" firstref="ref132"></nolink> <nolink nlid="nl65" bibid="bib30" firstref="ref133"></nolink> <nolink nlid="nl66" bibid="bib21" firstref="ref137"></nolink>
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  Label: Title
  Group: Ti
  Data: Effects of Design Thinking STEAM Instruction on AI Learning and Creativity
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Ming-Yu+Lin%22">Ming-Yu Lin</searchLink><br /><searchLink fieldCode="AR" term="%22Yu-Shan+Chang%22">Yu-Shan Chang</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0002-5594-6528">0000-0002-5594-6528</externalLink>)
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  Data: <searchLink fieldCode="SO" term="%22International+Journal+of+Technology+and+Design+Education%22"><i>International Journal of Technology and Design Education</i></searchLink>. 2025 35(5):2025-2047.
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  Label: Availability
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  Data: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
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  Label: Peer Reviewed
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  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 23
– 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="%22STEM+Education%22">STEM Education</searchLink><br /><searchLink fieldCode="DE" term="%22Art+Education%22">Art Education</searchLink><br /><searchLink fieldCode="DE" term="%22Design%22">Design</searchLink><br /><searchLink fieldCode="DE" term="%22Thinking+Skills%22">Thinking Skills</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="%22Creativity%22">Creativity</searchLink><br /><searchLink fieldCode="DE" term="%22College+Students%22">College Students</searchLink><br /><searchLink fieldCode="DE" term="%22Teacher+Education+Programs%22">Teacher Education Programs</searchLink><br /><searchLink fieldCode="DE" term="%22Public+Colleges%22">Public Colleges</searchLink><br /><searchLink fieldCode="DE" term="%22Technology+Uses+in+Education%22">Technology Uses in Education</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Attitudes%22">Student Attitudes</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1007/s10798-025-09977-y
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 0957-7572<br />1573-1804
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This study investigated the effects of design thinking STEAM (DT-STEAM) education on artificial intelligence (AI) learning and creativity. A total of 59 university students enrolled in two courses as part of a teacher education program at a public university were recruited. A nonequivalent group pretest and posttest design was used to perform a teaching experiment. The main conclusions of the study were as follows: DT-STEAM instruction improved the breadth and depth of understanding of the AI concept, particularly in terms of relational connections, hierarchies, and cross-connections; DT-STEAM instruction had a positive effect on attitude toward AI, particularly the AI process; and DT-STEAM had a positive effect on AI design creativity in terms of elaboration, usability, values and, in particular, novelty.
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  Data: As Provided
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  Group: Date
  Data: 2026
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1493015
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      – Type: doi
        Value: 10.1007/s10798-025-09977-y
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      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 23
        StartPage: 2025
    Subjects:
      – SubjectFull: STEM Education
        Type: general
      – SubjectFull: Art Education
        Type: general
      – SubjectFull: Design
        Type: general
      – SubjectFull: Thinking Skills
        Type: general
      – SubjectFull: Artificial Intelligence
        Type: general
      – SubjectFull: Teaching Methods
        Type: general
      – SubjectFull: Creativity
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      – SubjectFull: College Students
        Type: general
      – SubjectFull: Teacher Education Programs
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      – SubjectFull: Public Colleges
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      – SubjectFull: Technology Uses in Education
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      – SubjectFull: Student Attitudes
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      – TitleFull: Effects of Design Thinking STEAM Instruction on AI Learning and Creativity
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            NameFull: Ming-Yu Lin
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            NameFull: Yu-Shan Chang
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              Type: published
              Y: 2025
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