Abstract
Intelligent tutoring systems (ITSs) have long held the gold standard for learning in digital learning environments. However, ITSs have historically required substantial authoring effort, limiting scaling. Computer assisted instruction has continued to be more widely available to students, and generative AI now enables richer student-computer interactions. One key feature of ITSs is the delivery of personalized feedback on student responses. In this paper, we discuss the nature of successful personalized feedback, the development of personalized feedback using generative AI as part of an automatic question generation system, and initial analysis of this feedback performance using data from students in a traditional university course.
