Pitts, Rani & Mildort (2026, AIED) show with 432 undergraduates that higher trust in an AI assistant is associated with lower appropriate reliance: students who trusted the assistant more were worse at discriminating correct from misleading AI suggestions during Python problem-solving. The relationship is non-linear and moderated by AI literacy and need for cognition — trust is not a safe proxy for appropriate use.
The experiment
432 undergraduates solved Python output-prediction problems with recommendations + explanations from an AI chatbot that included accurate and intentionally misleading suggestionsAppropriate reliance measured behaviourally: accepting correct suggestions, rejecting incorrect onesSurveys captured trust, AI literacy, need for cognition, programming self-efficacy, programming literacyFindings
Non-linear trust→reliance relationship: higher trust → lower appropriate reliance (weaker discrimination between correct/incorrect recommendations)Moderators: AI literacy and need for cognition significantly shaped how trust translated into reliance behaviourImplication: interventions should target calibration — instructional and system supports that encourage reflective evaluation of AI assistance during problem-solvingConnected Concepts
AI LiteracyMetacognitionAgentic AILLMRAGConnected Articles
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Pitts, G., Rani, N., & Mildort, W. (2026). Trust and Reliance on AI in Education: AI Literacy and Need for Cognition as Moderators. arXiv:2604.01114. AIED 2026.