Providing timely and actionable feedback on oral presentation slides is challenging in higher education, particularly in large classes where teachers cannot realistically deliver detailed formative feedback before students present. This paper introduces AISSA (AI-based Student Slides Analysis tool), a web-based system that combines large language models (LLMs) and Learning Analytics dashboards to support scalable, rubric-based feedback on presentation slides. AISSA allows students to upload their slide decks prior to an oral presentation and automatically receive quantitative scores and qualitative feedback based on teacher-defined evaluation rubrics. The system analyzes both slide-level features and slide content, generates structured feedback through an LLM (ChatGPT 5.2), and presents the results through interactive dashboards for students and teachers. We tested AISSA on a pilot deployment with 46 undergraduate students in a real academic setting. The results indicate that AISSA is technically reliable, economically feasible, and perceived by students as useful for iterative slide improvement. These findings suggest that combining LLM-based analysis with Learning Analytics dashboards is a promising approach for supporting formative feedback on presentation slides at scale.

翻译：在高等教育中，为口头演示幻灯片提供及时且可操作的反馈颇具挑战，尤其是在大规模课堂中，教师无法在学生演示前提供详细的形成性反馈。本文介绍了AISSA（基于人工智能的学生幻灯片分析工具），一个结合大型语言模型（LLMs）与学习分析仪表盘的网络系统，旨在支持可扩展的、基于量规的幻灯片反馈。AISSA允许学生在口头演示前上传其幻灯片文件，并根据教师定义的评估量规自动获得定量评分与定性反馈。该系统分析幻灯片层面特征及内容，通过LLM（ChatGPT 5.2）生成结构化反馈，并通过交互式仪表盘向学生和教师展示结果。我们在真实学术场景中对46名本科生进行了试点部署测试。结果表明，AISSA在技术上可靠、经济上可行，且学生认为其对幻灯片的迭代改进具有实用价值。这些发现表明，将基于LLM的分析与学习分析仪表盘相结合，是规模化支持幻灯片形成性反馈的一种有前景的方法。