AISSA: Implementation and Deployment of an AI-based Student Slides Analysis tool for Academic Presentations

TL;DR

AISSA is a web-based tool that uses large language models and dashboards to provide scalable, rubric-based feedback on student presentation slides, tested successfully with undergraduates.

Contribution

This paper introduces AISSA, a novel system integrating LLMs and Learning Analytics for automated, formative feedback on presentation slides in higher education.

Findings

AISSA is technically reliable and economically feasible.

Students found AISSA useful for improving their slides.

The system effectively analyzes slide features and content for feedback.

Abstract

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.