Understanding the Role of Large Language Models in Personalizing and Scaffolding Strategies to Combat Academic Procrastination

Traditional interventions for academic procrastination often fail to capture nuanced, individual-specific factors. Large language models (LLMs) may address this gap by accepting open-ended inputs and customizing interventions to unique needs, yet user expectations and limitations remain underexplored. The authors conducted interviews and focus group discussions with 15 university students and 6 experts, using a technology probe that generated personalized advice for procrastination management. Findings highlight the need for structured, deadline-oriented steps, enhanced user support mechanisms, and adaptive questioning based on factors like busyness. The study provides design implications for LLM-based tools to manage procrastination while cautioning against their use for therapeutic guidance.