轻量提示工程能提升AI出题的认知匹配度
Lightweight Prompt Engineering for Cognitive Alignment in Educational AI: A OneClickQuiz Case Study
- 对比三种提示策略，详尽提示最准
- 简单和角色提示常偏离预期认知层级
- 适合教育AI内容生成与评估优化
人工智能在教育技术中的快速融合有望革新内容创作与测评。然而，AI生成内容的质量与教学目标的契合度仍是关键挑战。本文研究轻量级提示工程对OneClickQuiz（基于Moodle插件的生成式AI工具）中问题认知匹配度的影响。评估了三种提示变体：详细基线、简化版本和角色化方法，在布卢姆分类学的知识、应用、分析三个层级上进行测试。通过自动化分类模型与人工评审发现，明确详尽的提示对精准认知匹配至关重要。简化和角色化提示虽能生成清晰相关的问题，但常偏离目标认知层级，导致内容过难或背离预期教学目标。研究强调战略提示工程对构建教学有效的AI教育解决方案的重要性，并为学习分析与智能学习环境中的高质量内容生成提供优化建议。
原文摘要 · Abstract (English)
The rapid integration of Artificial Intelligence (AI) into educational technology promises to revolutionize content creation and assessment. However, the quality and pedagogical alignment of AI-generated content remain critical challenges. This paper investigates the impact of lightweight prompt engineering strategies on the cognitive alignment of AI-generated questions within OneClickQuiz, a Moodle plugin leveraging generative AI. We evaluate three prompt variants-a detailed baseline, a simpler version, and a persona-based approach-across Knowledge, Application, and Analysis levels of Bloom's Taxonomy. Utilizing an automated classification model (from prior work) and human review, our findings demonstrate that explicit, detailed prompts are crucial for precise cognitive alignment. While simpler and persona-based prompts yield clear and relevant questions, they frequently misalign with intended Bloom's levels, generating outputs that are either too complex or deviate from the desired cognitive objective. This study underscores the importance of strategic prompt engineering in fostering pedagogically sound AI-driven educational solutions and advises on optimizing AI for quality content generation in learning analytics and smart learning environments.
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