Addressing Situated Teaching Needs: A Multi-Agent Framework for Automated Slide Adaptation

TL;DR

This paper presents a multi-agent framework that automates slide adaptation to meet instructors' pedagogical and contextual needs, reducing manual effort and improving alignment with teaching goals.

Contribution

The paper introduces a novel multi-agent system for automated slide adaptation based on instructor specifications, validated through real-world course requests.

Findings

High intent alignment and content coherence scores

F1 score of 0.89 indicating high agreement with experts

Comparable visual clarity to baseline methods

Abstract

The adaptation of teaching slides to instructors' situated teaching needs, including pedagogical styles and their students' context, is a critical yet time-consuming task for educators. Through a series of educator interviews, we first identify and systematically categorize the key friction points that impede this adaptation process. Grounded in these findings, we introduce a novel multi-agent framework designed to automate slide adaptation based on high-level instructor specifications. An evaluation involving 16 modification requests across 8 real-world courses validates our approach. The framework's output consistently achieved high scores in intent alignment, content coherence and factual accuracy, and performed on par with baseline methods regarding visual clarity, while also demonstrating appropriate timeliness and a high operational agreement with human experts, achieving an F1 score of 0.89. This work heralds a new paradigm where AI agents handle the logistical burdens of instructional design, liberating educators to focus on the creative and strategic aspects of teaching.