Traditional interventions for academic procrastination often overlook individual-specific factors. This study explores the potential of Large Language Models (LLMs) to personalize procrastination interventions. Through interviews and focus groups with 15 university students and 6 experts, using a technology probe (SPARK), the research highlights the need for LLMs to provide structured, deadline-oriented steps, enhanced user support, and adaptive questioning. Findings emphasize the importance of structured planning coupled with real-world examples while cautioning against using LLMs for therapeutic guidance.
Publisher
International Conference on Human Factors in Computing Systems
Published On
Dec 21, 2023
Authors
Ananya Bhattacharjee, Yuchen Zeng, Sarah Yi Xu, Dana Kulzhabayeva, Minyi Ma, Rachel Kornfield, Syed Ishtiaque Ahmed, Alex Mariakakis, Mary P Czerwinski, Anastasia Kuzminykh, Michael Liut, Joseph Jay Williams
