ArticleProceedings of the SIGCHI conference on human factors in computing systems. CHI Conference2024
Understanding the Role of Large Language Models in Personalizing and Scaffolding Strategies to Combat Academic Procrastination.
Article in Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Investigating the Role of Situational Disruptors in Engagement with Digital Mental Health Tools.CSCW : proceedings of the Conference on Computer-Supported Cooperative Work. Conference on Computer-Supported Cooperative Work · 2025Article
- Article
- Behavior Change Support Systems for Self-Treating Procrastination: Systematic Search in App Stores and Analysis of Motivational Design Archetypes.Journal of medical Internet research · 2025Article
- MindScape Study: Integrating LLM and Behavioral Sensing for Personalized AI-Driven Journaling Experiences.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2024Article
- Contextual AI Journaling: Integrating LLM and Time Series Behavioral Sensing Technology to Promote Self-Reflection and Well-being using the MindScape App.Extended abstracts on Human factors in computing systems. CHI Conference · 2024Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.