Agentic AI for Reflective Conversational Journaling: A Context-Aware Human–AI System for Cognitive-Load Redistribution

Abstract

Journaling can support mental health and self-reflection, but traditional journaling requires users to act simultaneously as reflector, facilitator, and recorder, which may increase cognitive load and potentially hinder sustained practice or contribute to rumination. This study proposes the Reflective Conversational Journal (RCJ), an AI-based system in which AI supports facilitation and recording while users focus on reflection. Grounded in cognitive load theory, Rogers’ person-centered counseling principles, and Socratic questioning, RCJ was designed around three principles: contextual connectivity, structured recording, and empathy and questioning. A prototype integrating an AI agent, a template engine, and a client application was developed as a context-aware human–AI interaction system. Four experts in journaling and psychological counseling evaluated RCJ over one week and completed a post-use evaluation comprising Likert-scale items and open-ended questions. The mean score across the nine design-validity and implementation-fidelity items was 4.67/5 (SD = 0.48). Experts perceived contextual linking as useful for recognizing behavioral patterns and automatic structuring as helpful for reducing recording burden. However, limited depth in questions and interaction fatigue from frequent questioning were identified as areas for improvement. The findings provide preliminary evidence of design validity and implementation fidelity rather than objective evidence of cognitive-load reduction or clinical effectiveness. RCJ operationalizes a complementary human–AI role structure in which AI supports facilitation and recording while the user retains the reflector role.