Enhancing academic stress assessment through self-disclosure chatbots: effects on engagement, accuracy, and self-reflection
Bibliographic record
Abstract
Abstract Academic stress significantly affects students’ well-being and academic performance, highlighting the need for more effective assessment methods to guide targeted interventions. This study investigates how self-disclosure chatbots—designed to share relevant experiences and thoughts—can enhance academic stress assessments by increasing student engagement, improving accuracy, and fostering deeper self-reflection. Two chatbot conditions were developed: a self-disclosure (SD) chatbot that used personal narratives to build empathy, and a non-self-disclosure (NSD) chatbot. In a randomized experiment with 50 university students, participants interacted with either the SD or NSD chatbot. Results showed that the SD chatbot elicited significantly higher engagement, as evidenced by longer session lengths (15.55 ± 5.92 min) and higher word counts (240 ± 114.02 words), compared to the NSD chatbot (11.31 ± 5.21 min; 162.38 ± 66.24 words). Assessment accuracy—evaluated by comparing results from the SISCO Inventory of Academic Stress with chatbot-generated evaluations—was slightly higher for the SD chatbot (0.936) than for the NSD chatbot (0.862), based on accuracy within a ± one-point deviation. Moreover, students who interacted with the SD chatbot reported deeper self-reflection and developed more actionable strategies for managing their stress. Overall, these findings illuminate the value of self-disclosure in chatbot-based assessments and highlight broader applications for addressing academic stress and mental health challenges in educational settings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".