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.

翻译：针对学业拖延的传统干预措施往往难以捕捉其背后细微、个体化的因素。大语言模型（LLM）通过允许开放式输入（包括根据个体独特需求定制干预措施的能力），在解决这一缺口方面展现出巨大潜力。然而，用户对LLM在此情境下的期望及其潜在局限性尚未得到充分探索。为此，我们通过访谈和焦点小组讨论，对15名大学生和6名专家进行了调查，并在过程中展示了一种用于生成拖延管理个性化建议的技术原型。研究结果凸显了LLM需提供结构化、基于截止日期的步骤及增强用户支持机制的必要性。此外，我们的结论还揭示了基于忙碌程度等因素采用适应性提问策略的需求。这些发现为开发基于LLM的拖延管理工具提供了关键设计启示，同时提醒避免将LLM用于治疗指导。