Mapping generative AI use in the human brain: divergent neural, academic, and mental health profiles of functional versus socio emotional AI use

arXiv cs.AI / 4/13/2026

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Key Points

The study uses surveys and high-resolution structural MRI (n=222) to relate different patterns of conversational generative AI use in young university students to brain structure, academics, and mental health.
Higher general and functional AICA use is associated with better GPA and brain signatures including larger dorsolateral prefrontal and calcarine gray matter volumes, along with hippocampal network clustering and local efficiency.
In contrast, more frequent socio-emotional AICA use correlates with worse mental health outcomes such as depression and social anxiety, and with reduced volume in superior temporal and amygdalar regions involved in social and affective processing.
The authors argue that generative AI can have distinct effects depending on usage motivation—potentially supporting cognitive systems for learning while tracking distress-linked usage through socio-emotional networks.
The findings are positioned as guidance for designing AI-influenced learning environments that maximize educational benefits while reducing mental-health risks.

Abstract

The widespread adoption of generative artificial intelligence conversational agents (AICAs) among university students constitutes a novel cognitive social environment whose impact on the maturing brain remains elusive. Combining surveys with high resolution structural MRI, we examined patterns of general, functional, and socio emotional AICA use, academic performance, mental health, and brain structural signatures in a comparatively large sample of 222 young individuals. Across computational anatomy, meta analytic network level, and behavioral decoding analyses, we observed use specific associations. Higher general and functional AICA use frequencies were linked to better academic outcomes (GPA), larger dorsolateral prefrontal and calcarine gray matter volume, and enhanced hippocampal network clustering and local efficiency. In contrast, more frequent socio emotional AICA use was associated with poorer mental health (depression, social anxiety) and lower volume of superior temporal and amygdalar regions central to social and affective processing. These findings indicate that the same class of AI tools exerts distinct effects depending on usage patterns and motivations, engaging prefrontal hippocampal systems that support cognition versus socio emotional systems that may track distress linked usage. These heterogeneities are crucial for designing environments that harness the educational benefits of AI while mitigating mental health risks.