AISSA Demo: AI-based Student Slides Analysis Tool for Automated Grading and Feedback

2026-10-05

AI Summary

This study addresses the inefficiency and lack of personalized feedback in evaluating oral presentations within higher education by developing an automated assessment web system integrating large language models (LLMs) with a rubric-based rule engine. The proposed system automatically parses slide content to generate quantitative scores and qualitative feedback, while supporting student reflection and instructor monitoring through an interactive learning analytics dashboard. Its core innovation lies in deeply integrating LLM-driven automated scoring with visual learning analytics to establish a closed loop of immediate, context-aware formative feedback. Empirical outcomes demonstrate that the system significantly enhances assessment efficiency and students' self-regulated learning capabilities, effectively optimizing instructors' pedagogical evaluation workflows.

Abstract

We present an AI-based web-oriented tool designed to support formative feedback for oral presentation slides in higher education: AISSA. It allows students to upload their slides before presentation and automatically receive rubric-based quantitative scores and qualitative feedback generated by large language models (LLMs). The tool analyses slide-level features and content using a teacher-defined rubric and delivers feedback through interactive Learning Analytics dashboards. These dashboards enable students to visualize performance indicators, inspect feedback in context, and reflect on strengths and areas for improvement, while teachers can review automated assessments, provide their own evaluations, and monitor student engagement with feedback.

Problem

• Automated Grading
• Formative Feedback
• Oral Presentation Slides
• Higher Education

Innovation

• Large Language Models
• Automated Grading
• Formative Feedback
• Learning Analytics
• Slide Analysis