Development and Evaluation of an AI-Powered Intelligent Reminder System to Support Academic Time Management: A Mixed-Methods Study of Cohort 50 and Cohort 51 Students at Can Tho University of Medicine and Pharmacy

Background: Poor time management and procrastination are prevalent among health-science students facing high academic workloads, yet existing digital tools lack intelligent personalisation. Objectives: To develop an AI-powered intelligent reminder system tailored to the needs of Cohort 50 (K50) and Cohort 51 (K51) students at Can Tho University of Medicine and Pharmacy (CTUMP), and to assess its potential effectiveness in improving academic time management and reducing procrastination. Materials and Methods: A mixed-methods design integrating a cross-sectional online survey and prototype development was employed.
A total of 470 students completed a validated 32-item questionnaire covering time-management behaviour, procrastination frequency, AI readiness, and feature preferences. Convenience sampling was used; limitations of this approach are addressed through robustness checks. Data were analysed using IBM SPSS Statistics version 26.0. Descriptive statistics (frequencies and valid percentages) were computed for all items, with chi-square tests of independence and Spearman rank-order correlations applied to examine relationships between AI usage frequency, AI adoption readiness, procrastination severity, and time-management self-rating. Results: Among 470 respondents (59.8% female; 84.7% K51), 38.1% rarely or never created a study plan; 50.2% self-rated their time management as 'Average' and 18.7% as 'Poor' or 'Very Poor'. Frequent procrastination (Often + Very Often) was reported by 32.1%, while 63.2% were frequently distracted by social media and 50.2% reported frequent academic stress.
A statistically significant positive correlation was observed between procrastination frequency and self-reported time-management deficits (Spearman's ρ = 0.47, p < 0.001). Although 71.1% already used AI tools regularly or daily, only 33.4% expressed readiness to adopt an AI reminder system. Top-requested features were social-media usage alerts (91.1%), intelligent schedule analysis (71.9%), and habit analytics (47.9%). The developed prototype integrated a natural language processing engine via the GPT-4o API, graduated push notifications, and Google Calendar synchronisation. Conclusions: CTUMP students exhibit significant time-management deficits alongside high demand for an intelligent AI reminder system. The findings provide an evidence base for a Phase 2 randomised controlled trial to evaluate system efficacy.

artificial intelligence; time management; procrastination; medical education; reminder system

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