Overview

This literature framework supports a paper/poster focused on generative AI, teacher education, gifted education, and teacher practice in AI-rich learning environments. The framework is organized around five strands: (1) AI and learning/cognitive engagement, (2) AI literacy and responsible use, (3) gifted education and talent development, (4) teacher education and teacher practice, and (5) integrative literature that links these areas into a coherent teacher-practice model.

Central integrative claim: Generative AI can improve tool-supported performance, but it does not automatically deepen learning. The key mediating factor is teacher practice. In gifted education, teachers must design thinking environments that calibrate when AI is withheld, when it supports learning, and when it extends synthesis, creativity, and perspective-taking. In this way, teacher education becomes central to preparing educators who can develop learners that use AI safely, ethically, effectively, and with human judgment.

Organizing Structure

Strand	Core Question	Contribution to the Framework
1. AI and Learning / Cognitive Engagement	How does AI affect thinking, effort, inquiry, and durable learning?	Builds the argument that performance with AI is not the same as durable learning and that productive struggle and metacognition matter.
2. AI Literacy and Responsible Use	What do students and teachers need to know and do to use AI safely, ethically, and effectively?	Supports the emphasis on disclosure, verification, evaluation, human agency, and ethical judgment.
3. Gifted Education and Talent Development	What kinds of challenge, complexity, and creativity should gifted education preserve?	Ensures the framework remains oriented toward advanced learning, originality, and meaningful talent development rather than mere acceleration.
4. Teacher Education and Teacher Practice	What must teachers know and be able to do in AI-rich classrooms?	Positions teachers as designers of thinking environments who calibrate AI use and maintain human-centered instruction.
5. Integrative / Bridging Literature	How can these strands be combined into a coherent framework?	Provides the bridge from separate literatures to a unified teacher-practice framework for AI-rich gifted education.

Suggested Use in the Paper or Poster

• Use Strand 1 to establish the central tension: AI may support performance while weakening depth, effort, or transfer if tasks are poorly designed.
• Use Strand 2 to argue that AI-rich learning requires explicit instruction in safe, ethical, and effective use—not just access to tools.
• Use Strand 3 to connect the framework to gifted education’s long-standing commitments to complexity, creativity, challenge, and contribution.
• Use Strand 4 to support the claim that teacher education must prepare educators to calibrate AI use, make thinking visible, and preserve human judgment.
• Use Strand 5 to synthesize the literature into the proposed framework: teachers as designers of thinking environments in AI-rich gifted education.

1. AI and Learning / Cognitive Engagement

These sources help explain the distinction between tool-supported performance and durable learning, including cognitive offloading, metacognition, self-regulation, and productive struggle.

Flavell, J. H. (1979). Metacognition and cognitive monitoring: A new area of cognitive-developmental inquiry. American Psychologist, 34(10), 906–911. https://doi.org/10.1037/0003-066X.34.10.906

Gerlich, M. (2025). AI tools in society: Impacts on cognitive offloading and the future of critical thinking. Societies, 15(1), Article 6. https://doi.org/10.3390/soc15010006

Jonassen, D. H. (1997). Instructional design models for well-structured and ill-structured problem-solving learning outcomes. Educational Technology Research and Development, 45(1), 65–94. https://doi.org/10.1007/BF02299613

Jose, B., Cherian, J., Verghis, A. M., Varghise, S. M., S, M., & Joseph, S. (2025). The cognitive paradox of AI in education: Between enhancement and erosion. Frontiers in Psychology, 16, 1550621. https://doi.org/10.3389/fpsyg.2025.1550621

Kosmyna, N., Hauptmann, E., Yuan, Y. T., Situ, J., Liao, X.-H., Beresnitzky, A. V., Braunstein, I., & Maes, P. (2025). Your brain on ChatGPT: Accumulation of cognitive debt when using an AI assistant for essay writing task. arXiv. https://arxiv.org/abs/2506.08872

Stadler, M., Bannert, M., & Sailer, M. (2024). Cognitive ease at a cost: LLMs reduce mental effort but compromise depth in student scientific inquiry. Computers in Human Behavior, 160, Article 108386. https://doi.org/10.1016/j.chb.2024.108386

Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41(2), 64–70. https://doi.org/10.1207/S15430421TIP4102_2

2. AI Literacy and Responsible Use

These sources support the paper’s emphasis on safe, ethical, and effective AI use, including disclosure, verification, privacy, bias recognition, and human agency.

AI for Education. (2026). The SEE framework: A practical guide to building generative AI literacy. https://aiforeducation.io

European Commission, Directorate-General for Education, Youth, Sport and Culture. (2022). Ethical guidelines on the use of artificial intelligence (AI) and data in teaching and learning for educators. Publications Office of the European Union.

UNESCO. (2023). Guidance for generative AI in education and research. UNESCO.

U.S. Department of Education, Office of Educational Technology. (2023). Artificial intelligence and the future of teaching and learning: Insights and recommendations. https://tech.ed.gov/ai/

Wineburg, S., & McGrew, S. (2019). Lateral reading and the nature of expertise: Reading less and learning more when evaluating digital information. Teachers College Record, 121(11), 1–40.

3. Gifted Education and Talent Development

These sources anchor the framework in gifted education’s commitments to challenge, complexity, creativity, and talent development.

Amabile, T. M. (1996). Creativity in context: Update to The social psychology of creativity. Westview Press.

Kargın, T., & Karataş, A. (2026). AI literacy with gifted children: Iterative co-design and critical multimodal practices. Reading Research Quarterly, 61, Article e70103. https://doi.org/10.1002/rrq.70103

McBee, M. T., McCoach, D. B., Peters, S. J., & Matthews, M. S. (2012). The case for a schism: A commentary on Subotnik et al. (2011). Gifted Child Quarterly, 56(4), 210–214. https://doi.org/10.1177/0016986212456075

Renzulli, J. S. (1978). What makes giftedness? Reexamining a definition. Phi Delta Kappan, 60(3), 180–184.

Runco, M. A., & Jaeger, G. J. (2012). The standard definition of creativity. Creativity Research Journal, 24(1), 92–96. https://doi.org/10.1080/10400419.2012.650092

Subotnik, R. F., Olszewski-Kubilius, P., & Worrell, F. C. (2011). Rethinking giftedness and gifted education: A proposed direction forward based on psychological science. Psychological Science in the Public Interest, 12(1), 3–54. https://doi.org/10.1177/1529100611418056

VanTassel-Baska, J., & Stambaugh, T. (2006). Comprehensive curriculum for gifted learners (3rd ed.). Pearson.

4. Teacher Education and Teacher Practice

These sources support the argument that AI-rich teaching requires professional judgment, pedagogical design, relational teaching, and explicit teacher preparation.

Guilherme, A. (2019). AI and education: The importance of teacher and student relations. AI & Society, 34, 47–54. https://doi.org/10.1007/s00146-017-0693-8

Long, L. (n.d.). Minus AI, plus AI, times AI. Substack. https://lizalong.substack.com/p/minus-ai-plus-ai-times-ai

Miao, F., & Murtlu, C. (2024). AI competency framework for teachers. UNESCO. https://unesdoc.unesco.org/ark:/48223/pf0000391104

North Carolina Department of Public Instruction. (2024). Generative AI implementation recommendations and considerations for PK–13 public schools. https://go.ncdpi.gov/AI_Guidelines

U.S. Department of Education, Office of Educational Technology. (2023). Artificial intelligence and the future of teaching and learning: Insights and recommendations. https://tech.ed.gov/ai/

5. Integrative / Bridging Literature

These sources are useful for connecting the strands above into a coherent framework about teacher practice, analytics, and AI-rich learning environments.

Alfredo, R., Echeverria, V., Jin, Y., Yan, L., Swiecki, Z., Gašević, D., & Martinez-Maldonado, R. (2024). Human-centred learning analytics and AI in education: A systematic literature review. Computers and Education: Artificial Intelligence, 6, Article 100215. https://doi.org/10.1016/j.caeai.2024.100215

Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.

Marino, M. T., Vasquez, E., Dieker, L., Basham, J., & Blackorby, J. (2023). The future of artificial intelligence in special education technology. Journal of Special Education Technology, 38(3), 404–416.

Melo-López, V.-A., Basantes-Andrade, A., Gudiño-Mejía, C.-B., & Hernández-Martínez, E. (2025). The impact of artificial intelligence on inclusive education: A systematic review. Education Sciences, 15(5), 539. https://doi.org/10.3390/educsci15050539

Stanford SCALE Initiative. (2026). The evidence base on AI in K–12 education. Stanford University.

Framing Sentence

Taken together, these literatures suggest that the central issue is not whether students use AI, but how teachers are prepared to design and calibrate AI-rich learning environments so that gifted learners develop deeper thinking, ethical judgment, creativity, and human agency.