Understanding the Role of Large Language Models in Personalizing and Scaffolding Strategies to Combat Academic Procrastination
CoRR(2023)
摘要
Traditional interventions for academic procrastination often fail to capture
the nuanced, individual-specific factors that underlie them. Large language
models (LLMs) hold immense potential for addressing this gap by permitting
open-ended inputs, including the ability to customize interventions to
individuals' unique needs. However, user expectations and potential limitations
of LLMs in this context remain underexplored. To address this, we conducted
interviews and focus group discussions with 15 university students and 6
experts, during which a technology probe for generating personalized advice for
managing procrastination was presented. Our results highlight the necessity for
LLMs to provide structured, deadline-oriented steps and enhanced user support
mechanisms. Additionally, our results surface the need for an adaptive approach
to questioning based on factors like busyness. These findings offer crucial
design implications for the development of LLM-based tools for managing
procrastination while cautioning the use of LLMs for therapeutic guidance.
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