With the global mental health care crisis (WHO, 2025), artificial intelligence (AI) tools are being explored as viable options for support. Their support for the mental health of students with gifts and talents (SWGT) is examined here. AI tools may be purpose-built for mental health support or companionship or designed for general assistance. The foundational large language model (LLM) for each of these is trained on different materials, which may not contain up-to-date information relevant to SWGT. SWGT should be educated on issues related to privacy and security, the possibility of false or misinformation from the AI tool, the prioritization of user engagement over helpful responding, and the dangers of overinvolvement. Talent development may be affected by the use of AI tools not trained to encourage psychosocial skills associated with success in the student’s domain of gifts or talents. Some controls are recommended to support SWGT’s positive use of AI.
Generative artificial intelligence complicates a central problem in identifying musical giftedness: deciding which features of an outcome can reasonably be attributed to the student who presents it. A piece may display coherent formal design, an elaborate harmonic plan, idiomatic instrumentation, or convincing timbral balance even when those decisions were neither made nor understood by the learner. This conceptual article examines how music co-created with AI can be interpreted when the finished product no longer reveals the student’s contribution on its own. Drawing on research on musical talent development, AI-assisted creation, and assessment validity, it considers three sources of information: compositional problem framing, analytical listening and revision, and the traceability of musical decisions. Transfer across tasks provides a further check. These sources are not diagnostic criteria. They are intended to support more cautious interpretations of musical evidence in AI-mediated creative work and to complement broader developmental evidence used in identification processes.
Stereotypes of the gifted are nothing new. Lewis Terman hoped to combat them with his longitudinal research showing that gifted students are not just identified as gifted in school and then go on to fade. But his study, and others like it, failed adequately to take into account the nonindependence of predictors and criteria—that is, the children who were assessed and given the label of “gifted” would receive opportunities that other, unassessed young people would not have. Those not labeled by society as “gifted” then may turn against those who are so labeled and see the success of the labeled not as a reason to uphold the system of labeling, but rather to overturn it. An alternative is to develop a more constructive, universally beneficial definition of giftedness–one that is transformational in nature and directs people toward a common good, not just their own good and the good of others like them.
Generative artificial intelligence (AI) has entered gifted education faster than the field has decided what it changes. This conceptual article argues that the pedagogical question is not whether AI helps. It is which cognitive work stays with the learner. Two claims follow. First, hybrid human–AI intelligence is bilateral. The learner shapes the AI partner as the partner shapes the learner. Learners using identical products do not have identical partners. The resulting advantage accumulates in the partnership rather than the person, invisibly. Second, the same question asked of moral development divides ethical functioning into two clusters. High ability confers one and not the other. AI supplies fluent performance of the first and nothing to the second, and schools assess the first. Both claims converge. What matters has stopped being visible in the work a student hands in. We specify five capacities worth teaching, three testable studies, and one recommendation for practice.
This present study aims to examined differences in peer bullying, empathy, and social anxiety between gifted and typically developing students. A correlational survey design was employed within a quantitative research framework. The sample consisted of 161 children and 406 adolescent students (162 gifted, 405 typically developing) enrolled in Grades 4 - 7 in public schools in Turkey. The findings indicate that gifted students report significantly higher levels of peer bullying compared to their typically developing peers, while also demonstrating higher levels of empathy. No significant differences were observed between the groups in terms of social anxiety. These results suggest that, despite their elevated empathic capacities, gifted adolescents may experience increased vulnerability within peer interactions, highlighting the complex interplay between cognitive and socio-emotional characteristics in bullying dynamics. The present study contributes to the literature by providing a comparative and integrative perspective on the socio-emotional profiles of gifted students and their positioning within peer bullying processes.
