Personalized Feedback in University Contexts: Exploring the Potential of AI-Based Techniques

Many recent studies highlighted the importance of feedback on the quality of learning. It empowers students to take ownership of their learning, fosters engagement and motivation, and enables personalized learning experiences. However, the use of feedback processes in real-world everyday teaching often becomes unsustainable, due to the number of students and the timing of the courses, especially in university contexts. The present study aimed to address this challenge in real university classes, laying the groundwork for the future development of an automated intelligent system that can support university teachers in delivering personalized feedback to a large group of students in real-world environments. Specifically, it focused on the prospect of gathering quality data from an actual academic course, intending to appropriately fuel AI-based techniques. These techniques could enhance the comprehension of students’ learning evidence and offer valuable insights to teachers. This paper presents an experimental work, carried out in the academic year 2020/21, which involved 220 students attending the first year of the Master’s Degree course in Primary Education. The components of teachers’ professional vision were explored using a rubric developed by the research team. Preliminary results suggested that the rubric can be effective in capturing sequential information about students’ development of professional vision. Thus, it can be further exploited to collect data from the process and feed AI-based algorithms. Further developments include exploring other machine learning techniques to reduce observers’ bias and support teachers in providing more effective personalized feedback.