Generative Artificial Intelligence in Gifted Education

Gifted education focuses on identifying and nurturing learners who demonstrate exceptional intellectual ability, creativity, or task commitment, often requiring differentiated instruction beyond standard curricula. Traditionally, gifted programs have focused on acceleration and enrichment. However, many of these programs still face challenges in personalizing learning, ensuring equitable identification, and addressing diverse manifestations of giftedness.

Recent advances in artificial intelligence (AI), particularly generative AI (GenAI) and other AI models, offer transformative potential to reimagine this landscape. These tools, alongside adaptive learning systems and learning analytics, can enhance the identification of gifted learners and enable differentiated instruction and responsive feedback aligned with their cognitive, creative, and socio-emotional profiles. In addition, Socratic chatbots and dialogic AI tutors have introduced new pathways for inquiry-based, metacognitive, and self-directed learning. As education systems shift toward data-informed personalization, integrating GenAI into gifted education invites a new paradigm for cultivating excellence while promoting inclusivity and innovation.

Despite global advances in educational technology, the integration of GenAI into gifted education remains fragmented and conceptually underdeveloped. Existing approaches often treat GenAI models as tools for acceleration or efficiency, rather than as a transformative agent for cognitive, creative, and affective development. This Research Topic aims to address this gap by advancing a multidimensional understanding of how GenAI and other AI models can be designed, implemented, and evaluated to enrich gifted education. Specifically, it seeks to explore how GenAI can support equitable identification of giftedness, personalize learning trajectories, and cultivate higher-order thinking and creativity. The goal is to unite interdisciplinary perspectives (from psychology, learning sciences, and computer science) to build theoretical, empirical, and practical foundations for an AI-enhanced gifted education ecosystem that promotes both excellence and inclusion. Through contributions drawn from varied disciplines and contexts, this Research Topic seeks to uncover how GenAI can transform the pedagogical, ethical, and policy dimensions of gifted education in ways that are both innovative and inclusive.

This Research Topic welcomes empirical, conceptual, methodological, and review papers that explore the intersection of GenAI and gifted education. Submissions may address, but are not limited to:

- GenAI-based identification and assessment of giftedness
- Pedagogical impact of GenAI on gifted learners
- Adaptive and personalized learning environments
- Creative cognition and problem-solving supported by GenAI
- Socio-emotional development and well-being of gifted learners in GenAI-mediated contexts
- Ethical, cultural, and policy implications of GenAI integration
- Measurement and analytics innovations, including explainable AI and formative assessment dashboards
- Design and implementation studies of GenAI-infused gifted programs
- Ethics, equity, and policy for responsible GenAI in gifted education

Contributions employing qualitative, quantitative, or mixed-method designs are encouraged, as well as design-based and theoretical explorations. The Topic particularly invites interdisciplinary perspectives that bridge education, psychology, neuroscience, and computer science to advance a shared understanding of how AI can foster innovation, inclusion, and excellence in gifted education.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Review
• Systematic Review
• Technology and Code

Keywords: GenAI, Gifted education, Personalized learning, ChatGPT

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.