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While open-ended self-explanations have been shown to promote robust learningin multiple studies, they pose significant challenges to automated grading andfeedback in technology-enhanced learning, due to the unconstrained nature ofthe students' input. Our work investigates whether recent advances in LargeLanguage Models, and in particular ChatGPT, can address this issue. Usingdecimal exercises and student data from a prior study of the learning gameDecimal Point, with more than 5,000 open-ended self-explanation responses, weinvestigate ChatGPT's capability in (1) solving the in-game exercises, (2)determining the correctness of students' answers, and (3) providing meaningfulfeedback to incorrect answers. Our results showed that ChatGPT can respond wellto conceptual questions, but struggled with decimal place values and numberline problems. In addition, it was able to accurately assess the correctness of75% of the students' answers and generated generally high-quality feedback,similar to human instructors. We conclude with a discussion of ChatGPT'sstrengths and weaknesses and suggest several venues for extending its use casesin digital teaching and learning.

Evaluating ChatGPT's Decimal Skills and Feedback Generation in a Digital Learning Game