Artificial Intelligence in Education: Misinterpretation Dynamics and the Rejection of ‘AI Psychosis’ as a Clinical Construct

The accelerated integration of Artificial Intelligence (AI) within education systems has generated widespread discourse regarding its cognitive and psychological implications for learners. Among these discussions, the term “AI psychosis” has emerged in public and media narratives to describe perceived adverse mental effects linked to AI use. However, this term is not recognized within established psychiatric diagnostic frameworks, including the Diagnostic and Statistical Manual of Mental Disorders, Text Revision (DSM-5-TR) and the International Classification of Diseases (ICD-11) (American Psychiatric Association, 2022; World Health Organization, 2019). This paper critically examines the origins and misuse of this label, arguing that it reflects a misinterpretation of human-AI interaction rather than a clinically valid condition.Grounded in cognitive psychology and educational theory, this study introduces the AI Misinterpretation Model (AIMM), a structured framework designed to explain how students and learners interpret, engage with, and internalize AI outputs in educational contexts. The model consists of three interrelated layers: perception, interaction, and integration, each contributing to the formation of cognitive distortions when AI is misapplied. Key mechanisms such as anthropomorphism, authority bias, and maladaptive cognitive offloading are identified as central to these misinterpretations, particularly in environments where AI literacy in education remains underdeveloped.Within educational settings, these misinterpretations may influence learning behaviors, epistemic judgment, and student autonomy, especially when AI systems are treated as authoritative rather than assistive tools. This paper emphasizes the necessity of embedding AI literacy frameworks in education, promoting critical thinking, cognitive boundary awareness, and responsible AI engagement among learners.By rejecting “AI psychosis” as a legitimate diagnostic construct and reframing it as a function of misinterpretation within educational and cognitive systems, this study contributes a novel interdisciplinary perspective at the intersection of AI, psychology, and education. The findings highlight the urgent need for educational policies and pedagogical strategies that align AI integration with scientifically grounded understandings of human cognition.