Psychological Mechanisms and Mental Health Considerations in AI Education: Addressing Schizophrenia Spectrum Disorders and Payment Conversion Barriers
Lihua ZhangAbstract
Objective
This study investigates the psychological mechanisms through which perceived risks inhibit payment conversion for AI education adaptive agents, with particular focus on populations with schizophrenia spectrum disorders and other mental health conditions. The research develops evidence-based intervention strategies that account for unique psychological needs in educational technology adoption.
Subjects and Methods
A mixed-methods approach was employed with 1,200 potential users of AI education services, including subgroups with diagnosed mental health conditions. The study combined experimental scenarios measuring risk perception, payment decision tasks, and comprehensive psychological assessments using the Technology Acceptance Model, Risk Perception Scale, and Mental Health Screening Inventory. Structural equation modeling analyzed risk perception patterns, while a randomized controlled trial tested specialized intervention strategies for users with mental health considerations.
Results
Perceived risks demonstrated significant inhibitory effects (privacy concerns β = -0.68, p < 0.01; effectiveness doubts β = -0.59, p < 0.01). The intervention strategies increased conversion rates by 35% and reduced decision anxiety by 42%. Notably, users with schizophrenia spectrum disorders showed 28% greater sensitivity to privacy concerns but responded well to tailored transparency interventions. Mental health-aware approaches improved accessibility and reduced psychological barriers to technology adoption.
Conclusions
Psychological interventions effectively mitigate perceived risks while addressing mental health needs in AI education adoption. The findings underscore the importance of developing inclusive, mental health-conscious strategies for educational technology implementation, particularly for vulnerable populations with schizophrenia spectrum disorders.
Corresponding Author
Lihua Zhang, Tongji University, Shanghai, 200092, China.