Continuities and Transformations in Identifying Gifted Students: The Role of Technology and Equity
Abstract
Gifted education has historically relied on narrow definitions of ability, often equating giftedness with high IQ scores and standardised test performance. While these measures provide clear benchmarks, they have been widely criticised for overlooking creativity, socio-emotional skills, and culturally diverse expressions of talent. The purpose of this research was to explore the continuities and transformations in gifted identification, with particular attention to the role of technology and equity. Specifically, the research asked how traditional markers of giftedness have persisted, how technological innovations have transformed identification practices, and what ethical and inclusivity challenges accompany these changes. Methodologically, the research adopted a qualitative desk research approach. A systematic review of scholarly publications, policy documents, and empirical studies published between 2015 and 2025 was conducted, complemented by key historical works. Thematic analysis was used to trace patterns of continuity, innovation, and ethical concern. The findings revealed that cognitive ability, creativity, problem-solving, and teacher judgment remain central in identification practices, reflecting their deep institutional embeddedness. At the same time, dynamic assessments, AI-driven adaptive platforms, and psychometric modelling represent a significant shift toward more individualised and process-oriented approaches. However, without safeguards, these innovations risk perpetuating inequities. It was concludes that gifted identification must be reframed as both a technical and ethical endeavour. Its contribution lies in offering a conceptual framework that balances innovation with equity, guiding policymakers and educators toward more inclusive and culturally responsive practices.
References
Alamri, H., Lowell, V., Watson, W., & Watson, S. L. (2020). Using personalised learning as an instructional approach to motivate learners in online higher education: Learner self-determination and intrinsic motivation. Journal of Research on Technology in Education, 52(3), 322-352.
Alexander, L. (2025). Does Universal Screening Increase the Proportion of Culturally, Linguistically, and Economically Diverse Students in Gifted Education? (Doctoral dissertation, University of Northern Colorado).
Alshamsi, A. (2024). Conceptions of Intelligence, Giftedness, and Talent Over Time. Sino-US English Teaching, 21(12), 559-565.
Ateş, H. K., & Afat, N. (2018). A case study investigating the language development process, early literacy experiences and educational problems of a gifted child. Journal for the Education of Gifted Young Scientists, 6(4), 36-71.
Au, W. (2022). Unequal by design: High-stakes testing and the standardisation of inequality. Routledge.
Barak, M., & Levenberg, A. (2016). Flexible thinking in learning: An individual differences measure for learning in technology-enhanced environments. Computers & education, 99, 39-52.
Barr, A. (2025). Eradicating Educational Disparities: Challenging Deficit Thinking in Educators (Doctoral dissertation, Western Carolina University).
Butterfield, T. (2021). Teacher perceptions of gifted culturally, linguistically, and economically diverse (CLED) students (Doctoral dissertation, Northcentral University).
Cross, T. L., & Olszewski-Kubilius, P. (Eds.). (2021). Conceptual frameworks for giftedness and talent development: Enduring theories and comprehensive models in gifted education. Routledge.
Dai, D. Y. (2020). Assessing and accessing high human potential: A brief history of giftedness and what it means to school psychologists. Psychology in the Schools, 57(10), 1514-1527.
Davis, D. S., & Willson, A. (2015). Practices and commitments of test‐centric literacy instruction: Lessons from a testing transition. Reading Research Quarterly, 50(3), 357-379.
Demartini, C. G., Sciascia, L., Bosso, A., & Manuri, F. (2024). Artificial intelligence bringing improvements to adaptive learning in education: A case study. Sustainability, 16(3), 1347.
Desvaux, T., Danna, J., Velay, J. L., & Frey, A. (2024). From gifted to high potential and twice exceptional: A state-of-the-art meta-review. Applied Neuropsychology: Child, 13(2), 165-179.
Diamond, R., & Persson, P. (2016). The long-term consequences of teacher discretion in grading of high-stakes tests (No. w22207). National Bureau of Economic Research.
Fair, D. A., Dosenbach, N. U., Moore, A. H., Satterthwaite, T. D., & Milham, M. P. (2021). Developmental cognitive neuroscience in the era of networks and big data: strengths, weaknesses, opportunities, and threats. Annual Review of Developmental Psychology, 3, 249-275.
Fugate, C. M., Behrens, W. A., Boswell, C., & Davis, J. L. (Eds.). (2021). Culturally responsive teaching in gifted education: Building cultural competence and serving diverse student populations. Routledge.
Gajda, A., Karwowski, M., & Beghetto, R. A. (2017). Creativity and academic achievement: A meta-analysis. Journal of Educational Psychology, 109(2), 269.
Gil Jr, T. O. (2025). Gifted education: A survey on the theories and models of giftedness. Inovasi Kurikulum, 22(3), 1961-1976.
Golinkoff, R. M., & Hirsh-Pasek, K. (2016). Becoming brilliant: What science tells us about raising successful children. American Psychological Association.
Jung, J. Y. (2022). Physical giftedness/talent: A systematic review of the literature on identification and development. Frontiers in psychology, 13, 961624.
Kaufman, A. S., Choi, D., Kapoor, H., & Kaufman, J. C. (2022). A brief history of IQ testing: Fixed vs. malleable intelligence. In Intelligence in context: The cultural and historical foundations of human intelligence (pp. 59-92). Cham: Springer International Publishing.
Kim, H. (2023). Dual Track Strategies for Technologically Augmented Humans: Mitigating Societal Conflicts. MechEcology, 2(2), 13-30.
Kuznetsova, E., Liashenko, A., Zhozhikashvili, N., & Arsalidou, M. (2024). Giftedness identification and cognitive, physiological and psychological characteristics of gifted children: A systematic review. Frontiers in Psychology, 15, 1411981.
Lisle-Johnson, T., & Kohli, R. (2020). Critical Black women educators: Resisting the racial and ideological marginality of K–12 teaching through critical professional development. Theory Into Practice, 59(4), 348-357.
Lockwood, A. B., & Brown, J. (2025). Mitigating AI Bias in School Psychology: Toward Equitable and Ethical Implementation. School Psychology Review, 1-13.
Lopez-Gazpio, I. (2021). Gaining student engagement through project-based learning: A competitive 2d game construction case study. IEEE Access, 10, 1881-1892.
Lubinski, D. (2016). From Terman to today: A century of findings on intellectual precocity. Review of Educational Research, 86(4), 900-944.
Lubinski, D., & Benbow, C. P. (2021). Intellectual precocity: What have we learned since Terman?. Gifted Child Quarterly, 65(1), 3-28.
Ludolph, R., & Schulz, P. J. (2018). Debiasing health-related judgments and decision making: a systematic review. Medical Decision Making, 38(1), 3-13.
Mergen, A., Çetin-Kılıç, N., & Özbilgin, M. F. (2025). Artificial intelligence and bias towards marginalised groups: Theoretical roots and challenges. In AI and Diversity in a Datafied World of Work: Will the Future of Work be Inclusive? (Vol. 12, pp. 17-38). Emerald Publishing Limited.
Mun, R. U., Ezzani, M. D., & Lee, L. E. (2020). Culturally relevant leadership in gifted education: A systematic literature review. Journal for the Education of the Gifted, 43(2), 108-142.
Okonji, N. N., & Igwe, E. A. (2025). Prospects and Challenges of Personalised Learning through Artificial Intelligence Driven Adaptive Learning Systems in N. Journal of Education in Developing Areas, 33(1), 139-147.
Ouyang, F., Xu, W., & Cukurova, M. (2023). An artificial intelligence-driven learning analytics method to examine the collaborative problem-solving process from the complex adaptive systems perspective. International Journal of Computer-Supported Collaborative Learning, 18(1), 39-66.
Papadopoulos, D. (2020). Psychological framework for gifted children’s cognitive and socio-emotional development: A review of the research literature and implications. Journal for the Education of Gifted Young Scientists, 8(1), 305-323.
Peña Gangadharan, S., & Niklas, J. (2019). Decentering technology in discourse on discrimination. Information, Communication & Society, 22(7), 882-899.
Piirto, J. (2021). Talented children and adults: Their development and education. Routledge.
Ravand, H., & Baghaei, P. (2020). Diagnostic classification models: Recent developments, practical issues, and prospects. International Journal of Testing, 20(1), 24-56.
Renzulli, J. S. (2021). The three-ring conception of giftedness: A developmental model for promoting creative productivity 4. In Reflections on gifted education (pp. 55-90). Routledge.
Reyes, C. L. (2024). Awaken Your Potential: 10 Ways to Unlock Greatness. Simon and Schuster.
Robertson, D. (2024). The Underrepresentation of Historically Minoritised and Low Socioeconomic Students in Gifted and Talented Education Programs (Doctoral dissertation, San Diego State University).
Saputra, I., Kurniawan, A., Yanita, M., Putri, E. Y., & Mahniza, M. (2024). The evolution of educational assessment: How artificial intelligence is shaping the trends and future of learning evaluation. The Indonesian Journal of Computer Science, 13(6).
Sezgin, E., & Ulus, L. (2020). An Examination of Self-Regulation and Higher-Order Cognitive Skills as Predictors of Preschool Children's Early Academic Skills. International Education Studies, 13(7), 65-87.
Shah, M., & Sureja, N. (2025). A comprehensive review of bias in deep learning models: Methods, impacts, and future directions. Archives of Computational Methods in Engineering, 32(1), 255-267.
Shanta, S. (2022). Assessment of real-world problem-solving and critical thinking skills in a technology education classroom. In Applications of Research in Technology Education: Helping Teachers Develop Research-Informed Practice (pp. 149-163). Singapore: Springer Nature Singapore.
Shearer, C. B. (2020). Multiple intelligences in gifted and talented education: Lessons learned from neuroscience after 35 years. Roeper Review, 42(1), 49-63.
Shen, J., Zhu, K., Zhao, Z., Liang, H., Ma, Y., Qian, K., Zhang, Y. & Dong, Q. (2023). A novel intelligence evaluation framework: exploring the psychophysiological patterns of gifted students. IEEE Transactions on Computational Social Systems, 11(2), 2036-2045.
Silverman, L. K. (2018). Assessment of giftedness. In Handbook of giftedness in children: Psychoeducational theory, research, and best practices (pp. 183-207). Cham: Springer International Publishing.
Sternberg, R. J., & Desmet, O. A. (2023). Giftedness in childhood. Cambridge University Press.
Stringer, P. (2018). Dynamic assessment in educational settings: Is potential ever realised? Educational Review, 70(1), 18-30.
Suazo-Galdames, I. C., & Chaple-Gil, A. M. (2025). AI-Driven Assessment Systems in Higher Education: Effectiveness for Enhancing Critical Thinking and Creativity. Ingénierie des Systèmes d'Information, 30(6).
Wang, Y. C., Kuo, C. C., & Wu, S. M. (2019). Creative and problem-solving thinking of gifted and talented young children observed through classroom dialogues. Universal Journal of Educational Research, 7(12), 2677-2692.
Warne, R. T. (2019). An evaluation (and vindication?) of Lewis Terman: What the father of gifted education can teach the 21st century. Gifted Child Quarterly, 63(1), 3-21.
Wiggins, C., & Jones, M. L. (2023). How data happened: A history from the age of reason to the age of algorithms. WW Norton & Company.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 DAAR EL-IDARAH: Journal of Islamic Education Management
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
