研究解析中国大学生在不同学术任务中的生成式 AI 提示词策略
Generative artificial intelligence prompting strategies among Chinese university students across academic tasks
AI 导读
一项发表于 arXiv 的研究通过问卷调查了 234 名中国大学生对生成式 AI 提示词能力的感知与策略使用。学生普遍对提示词能力有信心，尤其在提供相关背景信息和根据初始输出迭代修改方面；但策略对任务类型敏感度有限，仅“指定输出格式”在创意工作坊场景中比研究型作业更被认可。固定提示词模板认可度最低，认知感知与提示词能力关联最强，开放回答则显示提示词与意图对齐是常见挑战。
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