Addressing Situated Teaching Needs: A Multi-Agent Framework for Automated Slide Adaptation
This addresses the problem of reducing logistical burdens for educators in instructional design, potentially freeing them to focus on creative teaching aspects, though it appears incremental as it builds on existing automation concepts.
The paper tackled the time-consuming task of adapting teaching slides to instructors' needs by introducing a multi-agent framework that automates slide adaptation based on high-level specifications, achieving high scores in intent alignment, content coherence, and factual accuracy with an F1 score of 0.89 in evaluations.
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.