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

arXiv: https://arxiv.org/abs/2312.13581 · HTML: https://mlsanity.com/paper/2312.13581/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Dec 2023.

## Built on by (most cited first, 12 of 12)

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- [Generating Privacy-Preserving Personalized Advice with Zero-Knowledge Proofs and LLMs](https://mlsanity.com/paper/2502.06425/) (2025)
- [Supporting Productivity Skill Development in College Students through Social Robot Coaching: A Proof-of-Concept](https://mlsanity.com/paper/2512.01105/) (2025)
- [Psi-Bench: Evaluating Persona-Sensitive Influencing in Persuasive Dialogues](https://mlsanity.com/paper/2606.02754/) (2026)
- [On the Prospects of Dynamic LLM Conversations in Software Development](https://mlsanity.com/paper/2608.30756/) (2026)
- [From Momentary Emotion Inference to Sustained Emotion Support: Evaluating a Companion Agent in a Longitudinal Study](https://mlsanity.com/paper/2609.16344/) (2026)

## Abstract (arXiv)

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.