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This paper introduces AISSA, a web-based tool leveraging LLMs (ChatGPT 3.5) and learning analytics dashboards to provide automated, rubric-based feedback on student presentation slides. AISSA analyzes slide content and features, generates structured feedback, and presents results via interactive dashboards. A pilot study with 46 undergraduates demonstrated AISSA's technical reliability, economic feasibility, and perceived usefulness for iterative slide improvement.
Automating rubric-based feedback on presentation slides is now feasible and perceived as useful, thanks to LLMs and learning analytics dashboards.
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.
