用AI自动分析学生汇报幻灯片，实时给出评分与改进建议。
AISSA: Implementation and Deployment of an AI-based Student Slides Analysis tool for Academic Presentations
- 结合大模型与学习分析仪表盘，按教师设定的评分标准分析幻灯片
- 46名本科生测试显示系统可靠且经济可行，学生认可其改进价值
- 适合需要大规模反馈的高校课程，尤其适用于大班教学场景
在高等教育中，尤其在大班授课环境下，教师难以在学生演讲前提供及时且详尽的形成性反馈。本文提出AISSA（基于人工智能的学生幻灯片分析工具），一个基于Web的系统，融合大型语言模型（LLMs）与学习分析仪表盘，实现可扩展的、基于评分量表的幻灯片反馈。该系统允许学生在正式演讲前上传幻灯片，自动获得基于教师定义评价标准的量化评分和定性反馈。系统分析幻灯片层面特征与内容，通过大语言模型（ChatGPT 5.2）生成结构化反馈，并以交互式仪表盘呈现给学生与教师。我们在真实教学环境中对46名本科生进行了试点部署。结果表明，AISSA技术上可靠、经济上可行，且学生认为其有助于迭代优化幻灯片。研究提示，将基于大模型的分析与学习分析仪表盘结合，是实现大规模、高质量幻灯片形成性反馈的有前景路径。
原文摘要 · Abstract (English)
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.
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