Psychology

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

A. Bhattacharjee, Y. Zeng, et al.

Discover how Large Language Models could transform procrastination interventions tailored to individual needs! In groundbreaking research by 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, and Joseph Jay Williams, this study reveals the necessity of structured planning and real-world examples while cautioning against using LLMs for therapeutic guidance.

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Abstract

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

DOI

https://doi.org/10.1145/3613904.3642081

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