Digitalization of Multistep Chemistry Exercises with Automated Formative Feedback

This paper presents a system for digitalizing open-ended chemistry exercises at the university level with automated error-specific formative feedback, evaluating its impact on 238 first-year chemistry students' performance. The study focuses on post-secondary education and does not address K-12 settings or AI-specific concerns like cognitive offloading.

For various reasons, students receive less formative feedback at post-secondary institutions compared to secondary school. Considering feedback as one of the most important influencing factors on learning processes, formative feedback is a promising approach to improving students’ performances. In this context, new technologies, such as learning management systems (LMS) or intelligent tutoring systems (ITS), can make a valuable contribution to improving higher education teaching by providing aut

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