🤖 AI Summary
This study addresses the misalignment between large language models (LLMs) and human experts in child-oriented instructional tasks, where such discrepancies can impair learning outcomes. For the first time, it quantitatively evaluates the alignment of multiple mainstream foundation models in out-of-distribution educational settings, revealing shared biases across models and demonstrating that ensemble methods may exacerbate negative alignment with desired learning outcomes. Through cross-model behavioral correlation analysis, expert-weighted ensembling, benchmark comparisons, and robust alignment metrics, the research finds that 50% of misalignment errors are shared across models, pointing to pretraining data as a critical source of these systematic deviations. This work provides crucial insights into the limitations of current LLMs for educational AI applications, highlighting the need for targeted alignment strategies in pedagogical contexts.
📝 Abstract
LLMs increasingly excel on AI benchmarks, but doing so does not guarantee validity for downstream tasks. This study evaluates the performance of leading foundation models (FMs, i.e., generative pre-trained base LLMs) with out-of-distribution (OOD) tasks of the teaching and learning of schoolchildren. Across all FMs, inter-model behaviors on disparate tasks correlate higher than they do with expert human behaviors on target tasks. These biases shared across LLMs are poorly aligned with downstream measures of teaching quality and often \textit{negatively aligned with learning outcomes}. Further, we find multi-model ensembles, both unanimous model voting and expert-weighting by benchmark performance, further exacerbate misalignment with learning. We measure that 50\% of the variation in misalignment error is shared across foundation models, suggesting that common pretraining accounts for much of the misalignment in these tasks. We demonstrate methods for robustly measuring alignment of complex tasks and provide unique insights into both educational applications of foundation models and to understanding limitations of models.