Development of a Mobile Intervention for Procrastination Augmented With a Semigenerative Chatbot for University Students: Pilot Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Procrastination negatively affects university students' academics and mental health. Traditional time management apps lack therapeutic strategies like cognitive behavioral therapy to address procrastination's psychological aspects. Therefore, we developed and integrated a semigenerative chatbot named Moa into a to-do app. OBJECTIVE: We intended to determine the benefits of the Moa-integrated to-do app over the app without Moa by verifying behavioral and cognitive changes, analyzing the influence of engagement patterns on the changes, and exploring the user experience. METHODS: The developed chatbot Moa guided users over 30 days in terms of self-observation, strategy establishment, and reflection. The architecture comprised response-generating and procrastination factor-detection algorithms. A pilot randomized controlled trial was conducted with 85 participants (n=37, 44% female; n=48, 56% male) from a university in South Korea. The control group used a to-do app without Moa, whereas the treatment group used a fully automated Moa-integrated app. The Irrational Procrastination Scale, Pure Procrastination Scale, Time Management Behavior Scale, and the Perceived Stress Scale were examined using linear mixed models with repeated measurements obtained before (T0) and after (T1) 1-month use and after 2-month use (T2) to assess the changes in irrational procrastination, pure procrastination, time management and behavior, academic self-regulation, and stress. Intervention engagement, divided into "high," "middle" and "low" clusters, was quantified using app access and use of the to-do list and grouped using k-means clustering. In addition, changes in the psychological scale scores between the control and treatment groups were analyzed within each cluster. User experience was quantified based on the usability, feasibility, and acceptability of and satisfaction with the app, whereas thematic analysis explored the users' subjective responses to app use. RESULTS: In total, 75 participants completed the study. The interaction of time × procrastination was significant during the required use period (P=.01). The post hoc test indicated a significant improvement from T0 to T1 in the Time Management Behavior Scale and Perceived Stress Scale scores only in the treatment group (P<.001 and P=.009). The changes in Pure Procrastination Scale score after the required use period were significant in all clusters except for the low cluster of the control group. The high cluster in the treatment group exhibited a significant change in the Irrational Procrastination Scale after Bonferroni correction (P=.046). Usability was determined to be good in the treatment group (mean score 72.8, SD 16.0), and acceptability was higher than in the control group (P=.03). Evaluation of user experience indicated that only the participants in the treatment group achieved self-reflection and experienced an alliance with the app. CONCLUSIONS: The chatbot-integrated app demonstrated greater efficacy in influencing user behavior providing psychological support. It will serve as a valuable tool for managing procrastination and stress together. TRIAL REGISTRATION: Clinical Research Information Service (CRIS) KCT0009056; https://tinyurl.com/yc84tedk.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".