  While open-ended self-explanations have been shown to promote robust learning
in multiple studies, they pose significant challenges to automated grading and
feedback in technology-enhanced learning, due to the unconstrained nature of
the students' input. Our work investigates whether recent advances in Large
Language Models, and in particular ChatGPT, can address this issue. Using
decimal exercises and student data from a prior study of the learning game
Decimal Point, with more than 5,000 open-ended self-explanation responses, we
investigate ChatGPT's capability in (1) solving the in-game exercises, (2)
determining the correctness of students' answers, and (3) providing meaningful
feedback to incorrect answers. Our results showed that ChatGPT can respond well
to conceptual questions, but struggled with decimal place values and number
line problems. In addition, it was able to accurately assess the correctness of
75% of the students' answers and generated generally high-quality feedback,
similar to human instructors. We conclude with a discussion of ChatGPT's
strengths and weaknesses and suggest several venues for extending its use cases
in digital teaching and learning.
