arXiv cs.AI· Lei Fan, Fangxue Liu·· 14 小时前AI 评分22
中国大学生在不同学术任务中的生成式 AI 提示词策略研究
Generative artificial intelligence prompting strategies among Chinese university students across academic tasks
AI 导读
一项基于 234 名中国大学生问卷的研究考察了其生成式 AI 提示词能力与策略，学生普遍对提示词能力有信心，尤其在提供相关上下文和根据初始输出优化提示词方面。提示词策略对任务类型不敏感：在创意工作坊准备场景中，指定输出格式比研究型作业更受认可，其他匹配策略无调整后差异。固定提示词模板的认可度低于其他策略，感知认知与提示词能力关联最明显，开放式回答则凸显提示词意图对齐这一常见挑战。
正文 · 原文
Abstract:Generative artificial intelligence (GenAI) is increasingly embedded in university learning, yet effective human-GenAI interaction depends not only on access to these tools but also on students' ability to formulate, evaluate, and refine prompts for academic tasks. This study examined university students' perceived GenAI prompting capability and reported prompting strategies. Questionnaire data from 234 students were analyzed using descriptive statistics, measurement modelling, scenario comparisons, structural associations, and qualitative coding of optional comments. Students reported using GenAI across diverse academic activities and generally expressed confidence in their prompting capabilities, particularly in providing relevant context and refining prompts following initial outputs. However, prompting strategies showed limited sensitivity to task types: specifying output format was more strongly endorsed in a creative workshop preparation scenario than in a research-oriented assignment, whereas other matched strategies showed no adjusted differences. Students also commonly endorsed both concise instructions and providing all relevant information at the outset, suggesting that effective brevity may depend more on selecting and organizing task-relevant information than on prompt length alone. By contrast, fixed prompt templates received lower endorsement than other prompting strategies. In the adopted factor model, perceived cognition showed the clearest unique association with perceived prompting capability, while open-ended responses highlighted prompt-intent alignment as a common challenge.
| Subjects: | Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.39571 [cs.HC] |
| (or arXiv:2610.39571v1 [cs.HC] for this version) | |
| https://doi.org/10.48550/arXiv.2610.39571 arXiv-issued DOI via DataCite |
Submission history
From: Fangxue Liu [view email]
[v1]
Thu, 29 Oct 2026 08:14:21 UTC (1,121 KB)
来源：arXiv cs.AI · arxiv.org