Lightweight Prompt Engineering for Cognitive Alignment in Educational AI: A OneClickQuiz Case Study
Learning, Data and Robotics (LDR), esieaLab ESIEA（ESIEA 学习、数据与机器人实验室（LDR））；Faculty of Science, Lebanese University（黎巴嫩大学理学院）
Antoun Yaacoub, Zainab Assaghir, Jérôme Da-Rugna
备注：
Published in the 36th Central European Conference on Information and Intelligent Systems(CECIIS)at: Varaždin, Croatia. September 17-19/2025. ISSN 1847-2001 (Print). ISSN 1848-2295 (Online)
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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