Can Multimodal LLMs See Science Instruction? Benchmarking Pedagogical Reasoning in K-12 Classroom Videos

TL;DR

This paper introduces SciIBI, a novel video benchmark for analyzing K-12 science classroom discourse, revealing current multimodal LLMs' limitations in understanding pedagogical reasoning and emphasizing the need for human-AI collaboration.

Contribution

The paper presents SciIBI, the first video benchmark for science classroom discourse, and evaluates state-of-the-art LLMs, highlighting their struggles with pedagogical reasoning and the inconsistent impact of video input.

Findings

Models often rely on surface shortcuts rather than true understanding.

Adding video input yields inconsistent improvements across architectures.

Current models cannot reliably distinguish pedagogically similar practices.

Abstract

K-12 science classrooms are rich sites of inquiry where students coordinate phenomena, evidence, and explanatory models through discourse; yet, the multimodal complexity of these interactions has made automated analysis elusive. Existing benchmarks for classroom discourse focus primarily on mathematics and rely solely on transcripts, overlooking the visual artifacts and model-based reasoning emphasized by the Next Generation Science Standards (NGSS). We address this gap with SciIBI, the first video benchmark for analyzing science classroom discourse, featuring 113 NGSS-aligned clips annotated with Core Instructional Practices (CIP) and sophistication levels. By evaluating eight state-of-the-art LLMs and Multimodal LLMs, we reveal fundamental limitations: current models struggle to distinguish pedagogically similar practices, suggesting that CIP coding requires instructional reasoning beyond surface pattern matching. Furthermore, adding video input yields inconsistent gains across architectures. Crucially, our evidence-based evaluation reveals that models often succeed through surface shortcuts rather than genuine pedagogical understanding. These findings establish science classroom discourse as a challenging frontier for multimodal AI and point toward human-AI collaboration, where models retrieve evidence to accelerate expert review rather than replace it.