Work

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

Year: 2025

Type: conference-paper

Abstract: 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 nonself-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.

Source: Extended Abstracts of the CHI Conference on Human Factors in Computing Systems (CHI EA)

DOI: 10.1145/3706599.3719684

Authors: Minyoung Park, Bogyeom Park, Kyoungwon Seo

Institution: Seoul National University of Science and Technology

Language: English

Topic: Digital Mental Health Interventions

Subfield: Applied Psychology

Field: Psychology

Domain: Social Sciences

SDG: Quality education

Open Access status: bronze

Funders: Ministry of Science and ICT, South Korea, Institute for Information and Communications Technology Promotion