How Self-Disclosing Chatbots Influence Student Engagement, Assessment Accuracy, and Self-Reflection in Academic Stress Assessment

Academic stress poses substantial risks to students’ well-being and academic performance, emphasizing the need for assessment tools that encourage deeper self-reflection while ensuring accuracy. This study explores how self-disclosure in chatbots can enhance student engagement, assessment accuracy, and self-reflection in academic stress assessments. Two chatbots were developed: a non-self-disclosing (NSD) chatbot and a self-disclosing (SD) chatbot, both integrating the SISCO Inventory of Academic Stress (SISCO-AS) questionnaire. Chatbot interaction logs and interview responses from 40 university students were analyzed to measure student engagement, assessment accuracy, and the depth of self-reflection. The findings demonstrate that the SD chatbot significantly increased engagement, showed improvements in assessment accuracy, and facilitated deeper self-reflection compared to the NSD chatbot. This study highlights the pivotal role of self-disclosure in improving the quality of chatbot-based stress assessments and provides insights for designing tools that support students in recognizing and managing academic stress.