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

Psychology

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

A. Bhattacharjee, Y. Zeng, et al.

Explore how personalized advice generated by large language models can tackle academic procrastination. This innovative study, conducted by a team from the University of Toronto and Northwestern University, reveals the importance of tailored support and structured steps to help students manage their time effectively.

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Playback language: English
Abstract
Traditional interventions for academic procrastination often fail due to their inability to address individual-specific factors. Large language models (LLMs) offer potential for personalized interventions, but user expectations and LLM limitations are underexplored. This study used interviews and focus groups with university students and experts to evaluate a technology probe generating personalized procrastination advice. Results highlight the need for structured, deadline-oriented steps, enhanced user support, and adaptive questioning based on factors like busyness. Findings offer design implications for LLM-based procrastination management tools while cautioning against using LLMs for therapeutic guidance.
Publisher
This is a hypothetical example and does not represent an actual publication.
Published On
Nov 22, 2024
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
Tags
academic procrastination
personalized interventions
large language models
user expectations
adaptive questioning
deadline-oriented steps
user support
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