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Augmenting Gifted Education through Artificial Intelligence: A Four-Dimensional Framework for AI Tutoring Integration

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Title: Augmenting Gifted Education through Artificial Intelligence: A Four-Dimensional Framework for AI Tutoring Integration
Language: English
Authors: Josh Ecker (ORCID 0000-0001-5164-5122), Greg Eckert (ORCID 0000-0002-7759-9272), Erin Cummings (ORCID 0009-0004-8063-3699)
Source: Journal of Advanced Academics. 2025 36(4):879-896.
Availability: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
Peer Reviewed: Y
Page Count: 18
Publication Date: 2025
Document Type: Journal Articles
Reports - Descriptive
Descriptors: Gifted Education, Artificial Intelligence, Tutoring, Technology Integration, Individualized Instruction, Learner Engagement
DOI: 10.1177/1932202X251356326
ISSN: 1932-202X
2162-9536
Abstract: This theoretical work presents a conceptual framework for integrating artificial intelligence (AI) tutoring within gifted education programming. Drawing upon established gifted education frameworks, student-centered learning practices, and recent advances in AI technology, the proposed framework leverages AI capabilities across four key dimensions: affect, content, process, and product. The framework addresses persistent challenges in gifted education, including the need for personalized instruction, advanced content delivery, and meaningful mentor relationships. By augmenting traditional gifted programming with AI tutoring, this approach enables more scalable differentiation while supporting student autonomy and engagement. The integration of AI across these dimensions represents a significant advancement in how educators can approach gifted instruction, moving beyond traditional constraints to create dynamic learning environments. Through systematic integration of AI tools across these dimensions, educators can provide more personalized, challenging, and engaging learning experiences that better align with gifted students' accelerated developmental needs in modern educational environments.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1485844
Database: ERIC
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  Value: <anid>AN0188422896;[261p]01nov.25;2025Oct06.06:29;v2.2.500</anid> <title id="AN0188422896-1">Augmenting Gifted Education Through Artificial Intelligence: A Four-Dimensional Framework for AI Tutoring Integration </title> <p>This theoretical work presents a conceptual framework for integrating artificial intelligence (AI) tutoring within gifted education programming. Drawing upon established gifted education frameworks, student-centered learning practices, and recent advances in AI technology, the proposed framework leverages AI capabilities across four key dimensions: affect, content, process, and product. The framework addresses persistent challenges in gifted education, including the need for personalized instruction, advanced content delivery, and meaningful mentor relationships. By augmenting traditional gifted programming with AI tutoring, this approach enables more scalable differentiation while supporting student autonomy and engagement. The integration of AI across these dimensions represents a significant advancement in how educators can approach gifted instruction, moving beyond traditional constraints to create dynamic learning environments. Through systematic integration of AI tools across these dimensions, educators can provide more personalized, challenging, and engaging learning experiences that better align with gifted students' accelerated developmental needs in modern educational environments.</p> <p>Keywords: gifted education; artificial intelligence; tutoring; student-centered learning; educational technology; theoretical</p> <p>Gifted students often develop asynchronously from their nongifted peers, where their intellectual talents develop at a faster rate when compared to their social and emotional maturity ([<reflink idref="bib1" id="ref1">1</reflink>]; [<reflink idref="bib14" id="ref2">14</reflink>]; [<reflink idref="bib39" id="ref3">39</reflink>]). This asynchronous development can lead to complications with gifted students' internal experiences as well as how they externally behave across environments ([<reflink idref="bib29" id="ref4">29</reflink>]). Within the regular education classroom, gifted students may display frustration when they are not academically challenged or have ample opportunity for intellectual growth ([<reflink idref="bib2" id="ref5">2</reflink>]). These students may exhibit emotional intensity and cognitive rigidity ([<reflink idref="bib52" id="ref6">52</reflink>]) as well as socioemotional challenges such as interpersonal conflicts ([<reflink idref="bib13" id="ref7">13</reflink>]; [<reflink idref="bib24" id="ref8">24</reflink>]) and isolation from their peers ([<reflink idref="bib50" id="ref9">50</reflink>]).</p> <p>The unique socioemotional characteristics of gifted learners necessitate intentional approaches to socioemotional support. In educational environments where socioemotional support is not adequately addressed, gifted students may face an increased risk of anxiety, perfectionism, and difficulties with emotional regulation ([<reflink idref="bib7" id="ref10">7</reflink>]). To address these needs, effective social-emotional learning (SEL) interventions for gifted students should specifically target their unique psychosocial needs, including issues of identity development, managing expectations, and navigating asynchronous development ([<reflink idref="bib40" id="ref11">40</reflink>]). Implementation of targeted SEL programming has been theorized and found to support positive outcomes for gifted students' overall wellbeing, including improvements in emotional resilience, feelings of social support, and academic engagement when schools incorporate explicit instruction in coping strategies and emotional intelligence tailored to the gifted population ([<reflink idref="bib27" id="ref12">27</reflink>]; [<reflink idref="bib30" id="ref13">30</reflink>]; [<reflink idref="bib54" id="ref14">54</reflink>]).</p> <p>Even though gifted students have high academic potential, low achievement is a common topic of discussion in gifted education. [<reflink idref="bib33" id="ref15">33</reflink>] characterize this complex phenomenon as "a severe discrepancy between expected achievement (as measured by standardized achievement test scores or cognitive or intellectual ability assessments) and actual achievement (as measured by class grades and teacher evaluations)" (p. 157). Alarmingly, [<reflink idref="bib37" id="ref16">37</reflink>] asserted that close to 50% of gifted students underachieve at some point during their educational trajectory. Although there is little consensus in gifted education about the root cause of underachievement, researchers point to a complex interplay of interpersonal, family, and school-related factors ([<reflink idref="bib15" id="ref17">15</reflink>]; [<reflink idref="bib34" id="ref18">34</reflink>]; [<reflink idref="bib37" id="ref19">37</reflink>]). While this issue of underachievement has been a topic of interest since the 1950s ([<reflink idref="bib6" id="ref20">6</reflink>]), classroom teachers are expected to understand ways to address the needs of gifted learners.</p> <p>The unique characteristics of gifted learners also create specific instructional demands that many educators find challenging to address in a regular education classroom. Enhanced instruction such as differentiation relies heavily upon the quality of instruction students receive ([<reflink idref="bib47" id="ref21">47</reflink>]). Regular education teachers cite a lack of content knowledge and adequate planning time as well as limited knowledge of modifying curriculum and meeting the needs of diverse learners ([<reflink idref="bib48" id="ref22">48</reflink>]). In a national survey of U.S. teachers, [<reflink idref="bib11" id="ref23">11</reflink>] reported that low-achieving students receive the most attention from teachers (63%) when compared to just 7% with gifted students and 13% with average students. This lack of attention on gifted students also influences teacher preparation. In their study of preservice teachers. [<reflink idref="bib3" id="ref24">3</reflink>] found that the teachers-in-training lacked a true understanding of the unique needs of gifted students because of a focus on serving the needs of their nongifted peers.</p> <p>These challenges, including asynchronous development, socioemotional difficulties, underachievement, and teacher training/preparation, require creative solutions that can be crafted through the integration of student-centered learning ([<reflink idref="bib20" id="ref25">20</reflink>]) and artificial intelligence (AI), the latter of which has been theorized to improve learning for gifted students ([<reflink idref="bib21" id="ref26">21</reflink>]) and supported with empirical findings ([<reflink idref="bib41" id="ref27">41</reflink>]). The purpose of this theoretical work is to develop a framework to address these challenges for gifted learners through integration of innovative approaches to teaching and learning.</p> <hd id="AN0188422896-2">Methodology</hd> <p>This study employs theoretical framework development as its primary methodological approach, combining elements of conceptual analysis and theoretical synthesis to construct a new integrated model. This process began by identifying persistent challenges in gifted education through comprehensive literature review. Existing frameworks of gifted programming were then analyzed—particularly the Levels of Service model—to identify conceptual foundations that could support AI integration. The development process involved systematic mapping of AI capabilities to established educational dimensions (affect, content, process, and product), guided by theoretical coherence and practical applicability principles. Rather than testing hypotheses through empirical methods, this theoretical work focuses on constructing a coherent conceptual structure that synthesizes knowledge from multiple domains to address practical challenges in gifted education. The resulting framework serves as both an explanatory tool and a foundation for future empirical research that can test its assumptions and effectiveness in educational settings.</p> <hd id="AN0188422896-3">Literature Review</hd> <p>The noted challenges in meeting gifted students' needs have led educators to explore alternative instructional approaches that can better accommodate diverse learning profiles. Student-centered learning methodologies have emerged as a promising framework for addressing the unique characteristics of gifted learners while supporting teachers in providing differentiated instruction. To understand how AI might address these challenges in gifted education, it is essential to first examine the current state of student-centered learning approaches.</p> <hd id="AN0188422896-4">Student-Centered Learning</hd> <p>Student-centered learning (SCL), sometimes referred to as personalized learning, represents a paradigm shift from traditional approaches to content delivery to a more adaptive learning environment that empowers students to take ownership of their learning pathway ([<reflink idref="bib32" id="ref28">32</reflink>]). This approach accounts for and emphasizes differentiated learning needs, moving beyond standardized instructional approaches designed for typical learners ([<reflink idref="bib31" id="ref29">31</reflink>]). As a result of this paradigm and related changes in practices, educators shift from being primarily facilitating direct instruction to being coaches and expert learning mentors for their students.</p> <p>Student-centered learning has many potential and realized benefits. For example, it is essential to prepare students for modern life by helping them develop the self-direction and goal-setting skills necessary to thrive in the quickly evolving landscape of the twenty-first century ([<reflink idref="bib18" id="ref30">18</reflink>]). Additionally, this approach allows for the level of differentiation required in increasingly diverse classroom settings ([<reflink idref="bib8" id="ref31">8</reflink>]). This is reflected in evidence that SCL has positively impacted student learning and motivation across a range of ability levels ([<reflink idref="bib26" id="ref32">26</reflink>]; [<reflink idref="bib28" id="ref33">28</reflink>]; [<reflink idref="bib51" id="ref34">51</reflink>]). These flexible learning pathways also have the potential to increase student engagement, as they have more opportunities to identify personally relevant connections within learning experiences.</p> <p>The benefits of student-centered learning are particularly relevant for groups whose needs are not captured in core educational programming, such as gifted learners. Student-centered approaches allow gifted students to deepen their learning through open-ended projects, and accelerate their learning through self-direction, teacher coaching, and more challenging content ([<reflink idref="bib16" id="ref35">16</reflink>]). With teacher support, gifted students of any age can learn to self-direct their learning experiences to maximize learning outcomes and engagement. While student-centered learning provides a theoretical foundation for meeting gifted students' needs, recent technological advances offer new possibilities for implementation. AI is particularly useful for supporting student-centered, personalized learning ([<reflink idref="bib22" id="ref36">22</reflink>]; [<reflink idref="bib38" id="ref37">38</reflink>]).</p> <hd id="AN0188422896-5">Artificial Intelligence in Education</hd> <p>The current era of AI, known collectively as generative AI, is often considered to have started with the release of OpenAI's ChatGPT in 2022, and the impact of this and similar tools has been compared to the industrial revolution ([<reflink idref="bib49" id="ref38">49</reflink>]). These modern tools are part of the broader machine-learning research that has occurred over the past 50 years. However, what makes these modern tools unique is their ability to generate novel content across modalities ([<reflink idref="bib55" id="ref39">55</reflink>]). Although various forms of AI have been integrated into education for decades ([<reflink idref="bib53" id="ref40">53</reflink>]), recent developments in AI technology have had and will likely continue to have major impacts ([<reflink idref="bib17" id="ref41">17</reflink>]). There is already evidence to this point, as generative AI has forced education systems to reconsider how they conduct a variety of tasks and facilitate effective teaching and learning ([<reflink idref="bib12" id="ref42">12</reflink>]; [<reflink idref="bib36" id="ref43">36</reflink>]). In response to this rapid and drastic shift, the U.S. Department of Education and other organizations have released various sets of guidance to help education leaders as they navigate the management of schools in an AI-driven world, and how these tools can be utilized to improve teaching and learning ([<reflink idref="bib45" id="ref44">45</reflink>]).</p> <p>Educational institutions must carefully consider several significant challenges when implementing AI technologies<emph>.</emph> These challenges encompass concerns about inappropriate content, over-reliance on technology, potential academic integrity issues, data privacy, and the risk of dehumanizing the learning experience ([<reflink idref="bib9" id="ref45">9</reflink>]; [<reflink idref="bib12" id="ref46">12</reflink>]; [<reflink idref="bib17" id="ref47">17</reflink>]; [<reflink idref="bib36" id="ref48">36</reflink>]; [<reflink idref="bib38" id="ref49">38</reflink>]). Additionally, occasional hallucinations (i.e., instances in which AI tools fabricate information and convey it as factual) can lead to misunderstandings when students do not have the time or skill to critically evaluate each claim ([<reflink idref="bib49" id="ref50">49</reflink>]). This is particularly harmful with regards to proliferating bias ([<reflink idref="bib55" id="ref51">55</reflink>]). The [<reflink idref="bib45" id="ref52">45</reflink>] frames many of these challenges as a push/pull, with the ideal solution likely being somewhere in the middle: (a) collecting information for personalization versus excess information collection and surveillance; (b) teachers augmenting their expertise with AI versus AI automating instructional decisions; (c) context-specific interventions versus scalable AI solutions for all students. Despite these challenges, the potential upside of generative AI for education makes it worth further exploration and careful implementation to mitigate the noted risks.</p> <hd id="AN0188422896-6">AI Tutoring</hd> <p>As a general instructional practice, tutoring is highly effective for student learning, and advanced AI can provide a scalable solution to educators' limited time available to spend working one-on-one with students ([<reflink idref="bib17" id="ref53">17</reflink>]). These AI powered tutoring systems have the ability to quickly adapt support in response to student needs ([<reflink idref="bib17" id="ref54">17</reflink>]; [<reflink idref="bib36" id="ref55">36</reflink>]), allowing gifted learners to work at a level that meets their unique skill sets. There is evidence to indicate that even relatively primitive smart tutoring systems (i.e., pregenerative AI) can be highly efficacious, comparable to human tutors ([<reflink idref="bib5" id="ref56">5</reflink>]; [<reflink idref="bib46" id="ref57">46</reflink>]). These advances in smart tutoring can allow for true one-to-one learning and unlock other learning models that are more personalized, such as flipped classrooms and project-based learning ([<reflink idref="bib25" id="ref58">25</reflink>]).</p> <p>To facilitate successful integration of AI tutoring in educational settings, there are several considerations to keep in mind. [<reflink idref="bib38" id="ref59">38</reflink>] notes five recommended guidelines for implementation: (a) clearly establishing an AI use scale; (b) monitoring student usage; (c) encouraging critical thinking; (d) emphasizing best practices and ethical use; and (e) providing guidance on how to effectively use AI tools. It is also recommended to start implementation on a relatively small scale, possibly with a single activity or lesson ([<reflink idref="bib9" id="ref60">9</reflink>]). Initial implementation may be teacher-facilitated, having the class participate as a whole group in prompting and practicing with a generative AI tool to simulate the one-to-one tutoring experience. It is also important to note that, with the speed at which change is occurring in this field, it is likely that currently ubiquitous AI tools will be overtaken by others with different feature sets, improved user interfaces, or other distinguishing characteristics. Regardless of which AI tools are integrated into the gifted classroom, it is essential to ground this innovation within established theoretical frameworks. Therefore, before presenting the proposed conceptual framework, it is valuable to consider how gifted programming is currently conceptualized.</p> <hd id="AN0188422896-7">Levels of Service Model</hd> <p>The Levels of Service (LoS), provides a four-level approach to meet the instructional needs of all students within an educational system (see Figure 1). The LoS framework acknowledges that learners can display talents and abilities across different domains ([<reflink idref="bib44" id="ref61">44</reflink>]). Level 1 provides foundational skills for all students; level 2 serves the needs of many students; level 3 meets the needs of some students; and level 4 responds to the needs of a few students. The LoS model was ultimately chosen as the starting point for the newly proposed framework because it emphasizes the importance of all students within any given educational system—not just gifted students—but also pivots programming away from teacher-directed learning and into self-directed learning.</p> <p>Graph: Figure 1. Levels of Service, Levels 1 to 4.</p> <hd id="AN0188422896-8">Conceptual Framework</hd> <p>The proposed framework builds upon level 4 of the LoS model with consideration for the unique capabilities of AI technology. Level 4 proposes four dimensions of talent development for gifted learners, including content, process, affect, and product, identical to those popularized by Carol Ann Tomlinson and included in her differentiation framework ([<reflink idref="bib42" id="ref62">42</reflink>]). Additionally, Level 4 of this model highlights a variety of programming options for gifted services, including seminars, mentorship, advanced work, and performance tasks. The newly proposed framework incorporates AI tutoring as an approach to support development by augmenting each of these dimensions (see Figure 2). Specifically, this framework takes the four dimensions of talent development captured in level 4, aligns them with programming and other considerations, and highlights how AI can augment learning opportunities for gifted students, meet their often challenging needs for differentiation, and improve learning outcomes.</p> <p>Graph: Figure 2. Four-Dimensional Framework for AI in Gifted Education.</p> <hd id="AN0188422896-9">Affect</hd> <p>Affect is defined as "the effect of students' emotions and feelings on their learning" ([<reflink idref="bib43" id="ref63">43</reflink>], p. 3) and can be increased by providing opportunities and means for students to self-direct the learning process ([<reflink idref="bib44" id="ref64">44</reflink>]). In level 4 of the LoS model, schools are considered to be responsible for resource location, opportunities to use resources, and identification and prioritization of those resources. AI facilitates greater student autonomy ([<reflink idref="bib25" id="ref65">25</reflink>]) in managing and directing the content, process, and product of their learning and allows them to take on some of the responsibilities previously reserved for the "school," thus improving affective response and learning outcomes ([<reflink idref="bib23" id="ref66">23</reflink>]). Though this does not absolve educators from their responsibilities to serve students, since they are ultimately responsible for their students' learning outcomes, AI tools can increase opportunities for students to make key decisions in their learning processes. For example, students may prompt a chatbot to coach them through the process of identifying learning goals, topics of interest to the student, or academic and cognitive skills they may be interested in developing. A sample prompt for this kind of engagement might be: <emph>My next project is going to be about basketball, which is my favorite sport. How can I connect a project about basketball to my science and social studies classes?</emph> The role of the teacher as a learning coach remains critically important to help students develop the technology skills necessary to use AI tools and the metacognitive and project management skills to progress through complex projects that require academically advanced skillsets.</p> <hd id="AN0188422896-10">Content</hd> <p>Content can be defined as "the knowledge, understanding, and skills (KUD) that students need to learn" ([<reflink idref="bib43" id="ref67">43</reflink>], p. 1). While student autonomy forms the foundation for effective gifted education, appropriate academic challenge remains essential. Content considerations for level 4 include "high-level, complex, and challenging" content for students ([<reflink idref="bib4" id="ref68">4</reflink>], p. 3). AI tools can be utilized in several ways to support gifted students' needs for advanced coursework. First, AI tools can help students and teachers identify advanced content aligned with student learning goals and interests. Beyond a simple web search, AI tools can engage in an extensive, conversational process of identifying content, receiving feedback from the user, and identifying more highly aligned content based on that feedback iteratively until the student has access to content that is appropriately advanced and relevant. For example, a student may make a resource request with the following prompt: <emph>I am doing a project about different historical perspectives during the American Revolution. Can you help me find five credible sources that highlight different views from the time period?</emph> After reviewing the five resources, the student identifies two that are not relevant to the project and prompts the chatbot to find two resources to replace the two that were not applicable.</p> <p>Second, AI tools can be used to differentiate the difficulty level of existing resources or create new resources of appropriate difficulty. For example, AI tools can change the reading level of a text to more appropriately challenge gifted students. An important note regarding the creation of content is the potential for AI to hallucinate or generate content with cultural or gender biases. Though students engaging with factually incorrect content on the internet is not a new problem, this new example of the problem does highlight the importance of students developing the information literacy necessary to navigate these challenges, including skills such as cross-checking references and verifying with teachers as needed. This may be connected to a teacher-guided learning focused on digital literacy and proactively preparing students to use AI tools effectively and ethically. Additionally, the teacher plays a critical role in helping students verify the AI-generated information as well as identifying alternative sources when AI content includes factually inaccurate information.</p> <hd id="AN0188422896-11">Process</hd> <p>Process refers to "how students come to understand and make sense of the content" ([<reflink idref="bib43" id="ref69">43</reflink>], p. 2). Beyond content selection and modification, the manner in which students engage with learning materials significantly impacts educational outcomes. The learning process in level 4 includes both field experiences and academic experiences that holistically create a "professional process of inquiry and problem solving that deals with real-life issues" ([<reflink idref="bib4" id="ref70">4</reflink>], p. 3) and prepare them for various performance tasks. There are various benefits to these kinds of experiences, such as engaging with field experts to help students form their identities in that field ([<reflink idref="bib10" id="ref71">10</reflink>]). Though the ideal field experience is often location-based engagement with professionals or experts in the field, opportunities for this are often limited due to safety considerations for students and resource strain. AI can alleviate this challenge by taking on the persona of a field area expert to serve as a mentor through the learning process. Though simulated, students can engage in similar conversations as they would with a human mentor, even receiving formative feedback throughout the learning process. For example, a student may prompt an AI tool to <emph>act as an expert scientist who specializes in quantum mechanics. I am your research assistant and want you to have a conversation with me about current topics in the field. However, I am new to the lab and have little experience in quantum mechanics. Let's start the simulation now; you lead the conversation in a way that's appropriate for someone without much experience to help me learn about quantum mechanics, and treat it as an interaction you would have in your lab with colleagues.</emph> Additionally, these simulated experiences could serve as practice for future interactions students have with actual field experts.</p> <p>AI tools can similarly serve to provide opportunities for students to engage with academic experts in the chosen field or topic. Although the internet contains many free, advanced courses that may be relevant for a gifted student, these courses do not allow for true interaction and discourse the way an advanced, synchronous course does. Taking on the persona of an academic, AI tools can not only provide important, advanced learning experiences through simulated seminars, but they can also engage in discussion with students, answering their questions and allowing students to dig more deeply into their own thinking on the topic. For example, an AI tool could be prompted to engage in conversation as an expert marine biologist or take on the role of a historical figure who can discuss historical events from a first-person perspective.</p> <p>Students can leverage AI tools to generate standards-aligned rubrics for their work, guiding their learning processes with those rubric criteria as the targets. This also provides opportunities for students and teachers to engage in meaningful dialogue about what they are learning by engaging in their chosen project, helping clarify a key detail that can become nebulous over the course of a complex, multistep project. This again speaks to the importance of the teacher in this process, serving as a guide to students and providing key supports and feedback as students develop their abilities to self-direct their learning. Additionally, while the teacher may serve as the main support for gifted students struggling with emotional self-regulation and cognitive rigidity ([<reflink idref="bib52" id="ref72">52</reflink>]), there is some promise for the future of AI tools for therapeutic use cases ([<reflink idref="bib19" id="ref73">19</reflink>]), which students could access as needed while working through challenging projects.</p> <p>Lastly, incorporating AI into the learning process is essential when considering the goal of preparing students for college and career, their lives in their chosen fields. With the utility of generative AI and its ubiquity across fields and academic disciplines, it is likely to play a role in whatever contexts students choose to engage in after their K-12 experience. By using AI and increasing their AI literacy, particularly in simulated contexts relevant to their goals and interests, educators can help ensure students have the skills they need for long-term success and lifelong growth.</p> <hd id="AN0188422896-12">Product</hd> <p>Product refers to the ways "students demonstrate what they have come to know, understand, and be able to do after an extended period of learning" ([<reflink idref="bib43" id="ref74">43</reflink>], p. 2). Having established effective processes for content delivery and engagement, the framework must also consider how students demonstrate their learning. The products of student learning in level 4 include performance tasks and, often, opportunities to share "authentic products with others in their field" as a form of real-life impact or influence ([<reflink idref="bib4" id="ref75">4</reflink>], p. 3). With AI tutors, students can collaboratively brainstorm and identify opportunities to engage in the local community. AI tools can also help determine appropriate performance tasks relevant to the field and aligned with student learning goals. Beyond just determining these performance tasks, generative AI can also provide feedback on student learning products. For example, some AI tools are designed to listen to and evaluate a user's public speaking skills, a valuable resource when teacher time is limited, and they cannot listen to and provide feedback on the same presentation or speech multiple times. Even with more common generative AI tools, such as chatbots, students can provide scripts or drafts of speeches for formative feedback. In performance tasks that do not include written artifacts, students may conceptually explain their plan for the performance task to receive feedback and tips. A prompt for this kind of support could be, <emph>I'm creating a 3-minute interpretive dance to show the life cycle of a butterfly for my science project. I start as an egg curled on the floor, then gradually move through crawling, cocooning, and finally emerging as a butterfly. Can you give me feedback on whether my movements clearly represent each stage and suggest ways to make the metamorphosis more expressive or scientifically accurate?</emph> (See Table 1 for a synthesis of these sample prompts for each domain.)</p> <p>Table 1. Sample Prompts for LoS Level 4 Domains.</p> <p>Graph</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="left" /><col align="left" /></colgroup><thead><tr><th align="left">Domain</th><th align="left">AI Integration</th><th align="left">Sample Prompt</th></tr></thead><tbody><tr><td>Affect</td><td>Using AI to increase learner autonomy</td><td><italic>"My next project is going to be about basketball, which is my favorite sport. How can I connect a project about basketball to my science and social studies classes?"</italic></td></tr><tr><td>Content</td><td>Identifying advanced content; differentiating content</td><td><italic>"I am doing a project about different historical perspectives during the American Revolution. Can you help me find five credible sources that highlight different views from the time period?"</italic></td></tr><tr><td>Process</td><td>Simulated field experience and field experts; generating standards-aligned rubrics to guide student projects</td><td><italic>"Act as an expert scientist who specializes in quantum mechanics. I am your research assistant and want you to have a conversation with me about current topics in the field. However, I am new to the lab and have little experience in quantum mechanics. Let's start the simulation now; you lead the conversation in a way that's appropriate for someone without much experience to help me learn about quantum mechanics, and treat it as an interaction you would have in your lab with colleagues."</italic></td></tr><tr><td>Product</td><td>Brainstorming opportunities to engage with community; determining and providing feedback on appropriate performance tasks based on student learning needs and interests</td><td><italic>"I'm creating a 3-minute interpretive dance to show the life cycle of a butterfly for my science project. I start as an egg curled on the floor, then gradually move through crawling, cocooning, and finally emerging as a butterfly. Can you give me feedback on whether my movements clearly represent each stage and suggest ways to make the metamorphosis more expressive or scientifically accurate?"</italic></td></tr></tbody></table> </ephtml> </p> <p>1 <emph>Note</emph>. AI = Artificial Intelligence; LoS = Level of Service.</p> <hd id="AN0188422896-13">Limitations and Delimitations</hd> <p>This framework, as a theoretical construct, has inherent limitations and boundaries that warrant acknowledgment. As a conceptual model without empirical testing, its practical effectiveness remains theoretical until validated through implementation research. The rapid evolution of AI technologies, particularly generative AI tools developed since 2022, means that some capabilities or challenges may have emerged between writing and publishing that are not fully addressed within this framework. Additionally, this work is intentionally delimited to focus specifically on gifted education contexts rather than general education settings, with particular emphasis on level 4 of the LoS model. The framework concentrates on AI tutoring applications rather than the comprehensive spectrum of educational AI tools, recognizing that different AI functionalities may require distinct integration approaches. These boundaries were established to maintain focused theoretical development while acknowledging opportunities for future expansion and empirical validation as discussed in the conclusion.</p> <hd id="AN0188422896-14">Conclusion</hd> <p>This integration of AI tools across the dimensions of affect, content, process, and product represents a comprehensive approach to enhancing gifted education. The integration of AI tutoring within level 4 of the LoS model represents a significant advancement in meeting the complex needs of gifted learners. This framework provides a structured approach to leveraging AI capabilities across the four key dimensions of talent development while maintaining the student-centered focus essential for gifted education. By augmenting traditional gifted programming with AI tutoring, educators can provide more personalized, challenging, and engaging learning experiences that better align with gifted students' accelerated developmental needs.</p> <p>Successful implementation of this framework depends on thoughtful attention to several critical elements. First, educators must ensure that AI integration enhances rather than replaces human interaction and instruction. Next, proper protocols must be established for monitoring AI-student interactions, verifying the accuracy of AI-generated content, and protecting student privacy. The framework's effectiveness will also depend on developing clear guidelines for when and how to implement AI tutoring across different learning contexts and student needs.</p> <p>Additionally, there are several ways to enhance and expand this framework through future work. First, detailed implementation protocols need to be developed, including specific examples of AI tutoring applications across different subject areas and learning objectives within level 4 programming. These protocols should include guidance for selecting appropriate AI tools, designing effective prompts, evaluating the impact on student learning outcomes, and monitoring AI–student interactions. Second, future research can expand the framework to include levels 1 to 3 of the LoS model, considering the varying intensities of service and student needs at each level. This expansion would create a comprehensive approach to AI integration across the full spectrum of educational services. Finally, future work should focus on developing best practices for professional development related to this framework and how to effectively support teachers through the process of integrating AI tutors and various tools into classrooms, including effective use of generative AI, data privacy practices, and supporting students in ethical use. This will be essential as generative AI is a new technology, and new technologies are often met with resistance from many potential users ([<reflink idref="bib35" id="ref76">35</reflink>]).</p> <p>To address the limitations of this as a theoretical work, empirical research can and should be conducted to further develop and improve the framework. One promising starting point for implementation research is by utilizing the six-stage LoS approach, that begins with preparation and continues through the other stages until reaching the final stage of innovation and continuous improvement ([<reflink idref="bib44" id="ref77">44</reflink>]). This would include further research and publication elaborating on how to operationalize this framework. Through systematic implementation and continuous refinement, this framework has the potential to significantly advance educators' abilities to meet the unique learning needs of gifted students in the modern educational landscape.</p> <hd id="AN0188422896-15">Acknowledgments</hd> <p>Figure 1 created by Jennifer Baron was acknowledged by the authors.</p> <ref id="AN0188422896-16"> <title> References </title> <blist> <bibl id="bib1" idref="ref1" type="bt">1</bibl> <bibtext> Aziz A. R. A., Ab Razak N. H., Sawai R. P., Kasmani M. F., Amat M. I., Shafie A. A. H. (2021). Exploration of challenges among gifted and talented children. Malaysian Journal of Social Sciences and Humanities, 6(4), 242–251. https://doi.org/10.47405/mjssh.v6i4.760</bibtext> </blist> <blist> <bibl id="bib2" idref="ref5" type="bt">2</bibl> <bibtext> Beckmann E., Minnaert A. (2018). 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All AI-generated content was thoroughly reviewed, edited, and finalized by the authors.</bibtext> </blist> </ref> <aug> <p>By Josh Ecker; Greg Eckert and Erin Cummings</p> <p>Reported by Author; Author; Author</p> <p></p> <p>Josh Ecker is a graduate from the Johns Hopkins University, School of Education. He is an educational consultant whose work and research centers on artificial intelligence, student-centered learning, and school system redesign.</p> <p>Gregory K. Eckert is a graduate from the Johns Hopkins University, School of Education. He teaches secondary English Language Arts in Pennsylvania and serves as an EdD faculty member at Johns Hopkins, School of Education. His research areas include writing instruction, educational policy, gifted education, and AI in education.</p> <p>Erin Cummings is an educational consultant whose work centers on educational technology and gifted education.</p> </aug> <nolink nlid="nl1" bibid="bib14" firstref="ref2"></nolink> <nolink nlid="nl2" bibid="bib39" firstref="ref3"></nolink> <nolink nlid="nl3" bibid="bib29" firstref="ref4"></nolink> <nolink nlid="nl4" bibid="bib52" firstref="ref6"></nolink> <nolink nlid="nl5" bibid="bib13" firstref="ref7"></nolink> <nolink nlid="nl6" bibid="bib24" firstref="ref8"></nolink> <nolink nlid="nl7" bibid="bib50" firstref="ref9"></nolink> <nolink nlid="nl8" bibid="bib40" firstref="ref11"></nolink> <nolink nlid="nl9" bibid="bib27" firstref="ref12"></nolink> <nolink nlid="nl10" bibid="bib30" firstref="ref13"></nolink> <nolink nlid="nl11" bibid="bib54" firstref="ref14"></nolink> <nolink nlid="nl12" bibid="bib33" firstref="ref15"></nolink> <nolink nlid="nl13" bibid="bib37" firstref="ref16"></nolink> <nolink nlid="nl14" bibid="bib15" firstref="ref17"></nolink> <nolink nlid="nl15" bibid="bib34" firstref="ref18"></nolink> <nolink nlid="nl16" bibid="bib47" firstref="ref21"></nolink> <nolink nlid="nl17" bibid="bib48" firstref="ref22"></nolink> <nolink nlid="nl18" bibid="bib11" firstref="ref23"></nolink> <nolink nlid="nl19" bibid="bib20" firstref="ref25"></nolink> <nolink nlid="nl20" bibid="bib21" firstref="ref26"></nolink> <nolink nlid="nl21" bibid="bib41" firstref="ref27"></nolink> <nolink nlid="nl22" bibid="bib32" firstref="ref28"></nolink> <nolink nlid="nl23" bibid="bib31" firstref="ref29"></nolink> <nolink nlid="nl24" bibid="bib18" firstref="ref30"></nolink> <nolink nlid="nl25" bibid="bib26" firstref="ref32"></nolink> <nolink nlid="nl26" bibid="bib28" firstref="ref33"></nolink> <nolink nlid="nl27" bibid="bib51" firstref="ref34"></nolink> <nolink nlid="nl28" bibid="bib16" firstref="ref35"></nolink> <nolink nlid="nl29" bibid="bib22" firstref="ref36"></nolink> <nolink nlid="nl30" bibid="bib38" firstref="ref37"></nolink> <nolink nlid="nl31" bibid="bib49" firstref="ref38"></nolink> <nolink nlid="nl32" bibid="bib55" firstref="ref39"></nolink> <nolink nlid="nl33" bibid="bib53" firstref="ref40"></nolink> <nolink nlid="nl34" bibid="bib17" firstref="ref41"></nolink> <nolink nlid="nl35" bibid="bib12" firstref="ref42"></nolink> <nolink nlid="nl36" bibid="bib36" firstref="ref43"></nolink> <nolink nlid="nl37" bibid="bib45" firstref="ref44"></nolink> <nolink nlid="nl38" bibid="bib46" firstref="ref57"></nolink> <nolink nlid="nl39" bibid="bib25" firstref="ref58"></nolink> <nolink nlid="nl40" bibid="bib44" firstref="ref61"></nolink> <nolink nlid="nl41" bibid="bib42" firstref="ref62"></nolink> <nolink nlid="nl42" bibid="bib43" firstref="ref63"></nolink> <nolink nlid="nl43" bibid="bib23" firstref="ref66"></nolink> <nolink nlid="nl44" bibid="bib10" firstref="ref71"></nolink> <nolink nlid="nl45" bibid="bib19" firstref="ref73"></nolink> <nolink nlid="nl46" bibid="bib35" firstref="ref76"></nolink>
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  Data: 10.1177/1932202X251356326
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 1932-202X<br />2162-9536
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This theoretical work presents a conceptual framework for integrating artificial intelligence (AI) tutoring within gifted education programming. Drawing upon established gifted education frameworks, student-centered learning practices, and recent advances in AI technology, the proposed framework leverages AI capabilities across four key dimensions: affect, content, process, and product. The framework addresses persistent challenges in gifted education, including the need for personalized instruction, advanced content delivery, and meaningful mentor relationships. By augmenting traditional gifted programming with AI tutoring, this approach enables more scalable differentiation while supporting student autonomy and engagement. The integration of AI across these dimensions represents a significant advancement in how educators can approach gifted instruction, moving beyond traditional constraints to create dynamic learning environments. Through systematic integration of AI tools across these dimensions, educators can provide more personalized, challenging, and engaging learning experiences that better align with gifted students' accelerated developmental needs in modern educational environments.
– 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: EJ1485844
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1485844
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1177/1932202X251356326
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 18
        StartPage: 879
    Subjects:
      – SubjectFull: Gifted Education
        Type: general
      – SubjectFull: Artificial Intelligence
        Type: general
      – SubjectFull: Tutoring
        Type: general
      – SubjectFull: Technology Integration
        Type: general
      – SubjectFull: Individualized Instruction
        Type: general
      – SubjectFull: Learner Engagement
        Type: general
    Titles:
      – TitleFull: Augmenting Gifted Education through Artificial Intelligence: A Four-Dimensional Framework for AI Tutoring Integration
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Josh Ecker
      – PersonEntity:
          Name:
            NameFull: Greg Eckert
      – PersonEntity:
          Name:
            NameFull: Erin Cummings
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 11
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-print
              Value: 1932-202X
            – Type: issn-electronic
              Value: 2162-9536
          Numbering:
            – Type: volume
              Value: 36
            – Type: issue
              Value: 4
          Titles:
            – TitleFull: Journal of Advanced Academics
              Type: main
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