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Large Language Models (LLMs), such as ChatGPT, are quickly advancing AI tothe frontiers of practical consumer use and leading industries to re-evaluatehow they allocate resources for content production. Authoring of openeducational resources and hint content within adaptive tutoring systems islabor intensive. Should LLMs like ChatGPT produce educational content on parwith human-authored content, the implications would be significant for furtherscaling of computer tutoring system approaches. In this paper, we conduct thefirst learning gain evaluation of ChatGPT by comparing the efficacy of itshints with hints authored by human tutors with 77 participants across twoalgebra topic areas, Elementary Algebra and Intermediate Algebra. We find that70% of hints produced by ChatGPT passed our manual quality checks and that bothhuman and ChatGPT conditions produced positive learning gains. However, gainswere only statistically significant for human tutor created hints. Learninggains from human-created hints were substantially and statisticallysignificantly higher than ChatGPT hints in both topic areas, though ChatGPTparticipants in the Intermediate Algebra experiment were near ceiling and noteven with the control at pre-test. We discuss the limitations of our study andsuggest several future directions for the field. Problem and hint content usedin the experiment is provided for replicability.

Learning gain differences between ChatGPT and human tutor generated algebra hints