Mapping generative AI use in the human brain: divergent neural, academic, and mental health profiles of functional versus socio emotional AI use
For educators and mental health professionals, this work reveals that the impact of AI tools on young adults diverges sharply by usage type, highlighting the need for tailored interventions.
This study examined how different patterns of generative AI conversational agent use (general, functional, socio-emotional) relate to brain structure, academic performance, and mental health in 222 university students. Functional use was associated with better grades and larger prefrontal/hippocampal volumes, while socio-emotional use correlated with poorer mental health and smaller social-affective brain regions.
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.