Dual human-AI thematic analysis of a qualitative interview following cognitive rehabilitation in chronic severe traumatic brain injury

Abstract

Qualitative research offers important insight into lived experiences of individuals with traumatic brain injury (TBI), particularly in the chronic phase where standardized measures may not fully capture subjective adaptation. This methodological case report examines the feasibility and interpretive value of combining human and artificial intelligence (AI)-assisted thematic analysis to explore subjective experience following cognitive rehabilitation in chronic severe TBI. A semi-structured qualitative interview was conducted with a 30-year-old male, 52 months post severe TBI, following 40 hours of cognitive rehabilitation. The de-identified transcript was analyzed using human-coded thematic analysis and exploratory AI-assisted thematic analysis (Grok 3, xAI). AI output was treated as preliminary and interpreted under full human oversight. Standardized psychosocial measures (BRIEF-A, NeuroQOL) were reported descriptively for contextual background. Human-coded analysis identified affective metacognition, perceived readiness for educational re-engagement, and persisting challenges. AI-assisted analysis showed substantial thematic convergence while highlighting broader interpretive patterns related to self-concept and existential reflection. Standardized self-report demonstrated partial convergence with qualitative findings and divergence in social domains. Dual human-AI thematic analysis is feasible for qualitative neurorehabilitation research and may extend thematic exploration when under careful human interpretation. Qualitative interviews captured aspects of subjective experience not fully reflected in standardized measures.

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