As AI reshapes industries, its presence in education has expanded, transforming teaching and learning. Institutions increasingly implement AI alongside evolving curricula supported by educational technology (EdTech). While AI introduces possibilities, it also disrupts learning in ways both promising and problematic. The U.S. Department of Education’s Office of Educational Technology highlights AI’s applications, challenges, and the urgent need for ethical oversight [1]. A key concern is “personalization”—powerful yet still limited for learner diversity, particularly neurodivergent students.

Neurodivergence includes gifted learners—those with exceptional intellectual abilities—who, despite strengths, are often underserved due to assumptions of self-sufficiency, stigma, peer misunderstanding, and insufficient challenge in mainstream classrooms [2].

Large Language Models (LLMs) support advanced natural language tasks (generation, summarization, translation, Q&A) by processing vast text corpora [3][4][5]. AI could reduce understimulation and improve personalization for gifted learners; however, its potential remains underexamined. This article investigates how gifted adolescents perceive and interact with LLMs to identify opportunities and challenges for classroom integration that enhance intellectual stimulation without compromising human interaction or equity.

Research Questions:

• What perspectives do gifted learners hold regarding the use of LLMs for academic discussions?
• What precautions should educators consider when incorporating AI in classrooms for gifted learners?

A. Gifted Learners
Gifted learners exhibit advanced reasoning, complex interests, strong memory, quick learning, and heightened sensitivity [6][7][8], yet face misidentification and inadequate differentiation. AI can offer adaptive learning to sustain challenge and engagement.

B. Identification
Traditional identification often misses diverse gifted profiles. Machine learning can analyze broad datasets (behavior, performance, demographics) to detect giftedness beyond conventional metrics and broaden access [9].

C. Content and Curriculum Development
Conventional curricula frequently lack depth and flexibility for gifted learners who thrive with accelerated, inquiry-based, and interdisciplinary approaches [10]. AI can tailor curricula to prior knowledge, styles, and interests, fostering deeper mastery [11].

D. Boredom and Amotivation
Repetitive or simplistic content provokes boredom and amotivation [12][13]. Intelligent Tutoring Systems can adapt difficulty, pacing, and format with instant feedback to maintain interest and autonomy [14].

E. Socio-emotional Challenges
Gifted learners may face isolation, perfectionism, and emotional intensity, compounded with coexisting conditions. AI tools (sentiment analysis, emotion recognition, conversational agents) can support targeted interventions and early alerts [15].

F. Are We There Yet?
Despite rapid progress, limitations persist: bias, lack of social adaptivity, inadequate support for neurodivergent needs, and insufficient challenge for critical thinking [1][16].

A. Research Design
Qualitative, semi-structured interviews examined gifted learners’ perceptions of LLMs, enabling depth and flexibility.

B. Participants
Sixteen gifted learners (11–17 years; mean 14) identified by their schools participated via purposive sampling with parental consent and IRB approval.

C. Procedure
AI Interaction Task: Each participant engaged 30–45 minutes with an LLM (primarily ChatGPT) on an academic interest area.
Semi-Structured Interviews: Conducted within two days (20–45 minutes), audio-recorded for transcription and analysis.

Core themes included: (1) initial impressions, (2) perceived benefits/limitations, (3) comparison with traditional learning, (4) potential applications, (5) concerns, and (6) improvement suggestions.

D. Data Collection and Ethical Considerations
Procedures followed strict privacy/ethical guidelines for minors (consent, confidentiality, secure handling). Pseudonyms were used; identifiable data excluded.

Thematic analysis (Braun & Clarke, 2006) with dual coding revealed three themes: content personalization, scaffolding/compatibility, and challenges. Learners valued AI’s adaptation to specialized interests and advanced pacing, yet noted modality limits, information overload, and unpredictable responses tied to prompt quality. They emphasized that AI lacks non-verbal richness present in human discussions.

Table I. Themes for “What perspectives do gifted learners hold regarding the use of LLMs for academic discussions?”

Theme	Interview Response Summary (Support and Criticisms)
Content Personalization	AI adapts to preferred topics and interests, personalizing learning; may become too narrow/rigid.
Scaffolding & Compatibility	Provides novel, challenging topics at learner pace/level; text-only modality may limit responsiveness for rapid thinkers.
Challenges	Information overload requires critical evaluation; responses depend on prompt quality and sources; limited tactile/visual/auditory affordances; communication confined to text.

When considering classroom integration, learners identified four precautionary themes: content personalization, practical learning & accessibility, social factor & human facilitation, content diversification.

Table II. Themes for “What precautions should educators consider when incorporating AI in classrooms for gifted learners?”

Theme	Interview Response Summary (Support and Criticisms)
Content Personalization	AI enables insights within student interests; risk of echo-chambers and confirmation bias versus broader exploration in class.
Practical Learning & Accessibility	In-depth information, instant response, flexible pacing; classroom discussions build social/communication skills and support practical activities (e.g., music/art stimuli).
Social Factor & Human Facilitation	AI may reduce participation anxiety; lacks dynamic social tension and nuanced, real-time feedback that foster persuasion and idea exchange.
Content Diversification	AI aggregates global sources; classrooms contribute culturally rich, peer-diverse perspectives.

AI can bridge gaps in curriculum differentiation, personalize pacing/complexity, and curate interdisciplinary content, including connections to global mentors/peers—valuable where resources are scarce. However, it cannot replicate social-emotional depth essential for interpersonal growth [18]. Rigid frameworks may dampen creativity and intrinsic motivation. Gifted learners’ critical awareness of bias underscores the need for transparent, inclusive training data and clear communication of AI limitations. Overall attitudes are cautiously positive, with persistent concerns over access and over-standardization; AI should supplement, not replace, human-led learning.

Integrating AI into gifted education can address identification, amotivation, and socio-emotional challenges. A balanced human-AI approach should prioritize privacy, transparency, and bias mitigation while safeguarding emotional development and equitable access. Priorities include teacher training, AI tools tailored to gifted contexts, interdisciplinary collaboration, longitudinal research, and systematic inclusion of student feedback. Thoughtful design and continuous evaluation can help AI enhance educators’ ability to meet gifted learners’ complex needs.

We thank the participating schools and gifted learners. Appreciation is extended to El Naggar and Gaad for data collection support, and to El Naggar and Inocencio for contributions to analysis and manuscript preparation. We are grateful to our families for their patience during multiple revisions.