JMIR Publications

ABSTRACT

The study evaluates the efficacy of Conversational Artificial Intelligence (CAI) in rectifying cognitive biases and recognizing affect in human-AI interactions, which is crucial for digital mental health interventions. Cognitive biases—systematic deviations from normative thinking—affect mental health, intensifying conditions like depression and anxiety. Therapeutic chatbots can make cognitive-behavioral therapy (CBT) more accessible and affordable, offering scalable and immediate support. The research employs a structured methodology with clinical-based virtual case scenarios simulating typical user-bot interactions. Performance and affect recognition were assessed across two categories of cognitive biases: theory of mind biases (anthropomorphization of AI, overtrust in AI, attribution to AI) and autonomy biases (illusion of control, fundamental attribution error, just-world hypothesis). A qualitative feedback mechanism was used with an ordinal scale to quantify responses based on accuracy, therapeutic quality, and adherence to CBT principles. Therapeutic bots (Wysa, Youper) and general-use LLMs (GTP 3.5, GTP 4, Gemini Pro) were evaluated through scripted interactions, double-reviewed by cognitive scientists and a clinical psychologist. Statistical analysis showed therapeutic bots were consistently outperformed by non-therapeutic bots in bias rectification and in 4 out of 6 biases in affect recognition. The data suggests that non-therapeutic chatbots are more effective in addressing some cognitive biases.