The Efficacy of Conversational Artificial Intelligence in Rectifying the Theory of Mind and Autonomy Biases. Data & Protocol

Published: 10 July 2024| Version 1 | DOI: 10.17632/h2xn2bxz5r.1


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. 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.



Uniwersytet Medyczny imienia Karola Marcinkowskiego w Poznaniu, Heidelberg University, Uniwersytet Warszawski, Uniwersytet Marii Curie-Sklodowskiej


Mental Health, Human-Computer Interaction, Conversational Agent


Alexander von Humboldt-Stiftung

Narodowym Centrum Nauki