IPN colloquium on AI-supported tutoring systems: How is Generative AI changing learning with digital systems?

On July 28, Prof. Dr. Zachary A. Pardos from the University of California, Berkeley will be a guest at the IPN. In his presentation "AI-Enhanced Intelligent Tutoring Systems: Bridging Authoring, Feedback, and Student Pathways", the educational researcher and computer scientist will provide exciting insights into current developments in AI-supported learning systems and their potential for university teaching.

Prof. Pardos teaches at the School of Information and the Graduate School of Education and the College of Computing, Data Science, and Society at UC Berkeley and conducts research in the field of learning analytics, adaptive learning and AI in educational settings. The central topic of his lecture is the question of how generative AI (GenAI) can be used to automatically generate central elements of intelligent tutoring systems (ITS) such as task setting, feedback or competence assignment - and what opportunities and challenges this presents for research, teaching and the design of individual learning paths.

International cooperation with the IPN

The lecture also takes place against the backdrop of a new, transatlantic collaboration: IPN doctoral student Marlene Steinbach was a guest of Prof. Pardos in Berkeley for research purposes in the spring. What began as an exchange at a conference developed into a joint study idea with initial pilot data - the basis for further cooperation projects.

The colloquium is aimed at all interested parties within and outside the IPN.

The most important information in brief:

• Date: July 28, 2025, 13:00-14:30
• Venue: IPN lecture hall, Olshausenstraße 62 or via Zoom

Find out more: In a short interview, we talk to Prof. Pardos about his current research on GenAI and tutoring systems, about the opportunities of open source approaches in education and about his collaboration with the IPN.

IPN: You are working on intelligent tutoring systems supported by generative AI. What makes this approach particularly exciting for education today?

Prof. Pardos: It's a theoretically grounded use of GenAI that we aren't seeing so much of in chatbots deployments and other popular approaches in education. It's taking a new technology (i.e., GenAI) and seeing how it can solve an existing problem in education (i.e., the scalability of ITS). For many who have been around in education, that is more exciting than promises of new technology revolutions. 

IPN: Why did you choose to develop OATutor as an open-source platform, and how does that approach benefit research and collaboration in the education field?

Prof. Pardos: There are more engineers, learning scientists, and education psychologists interested in technology now than ever before. If everyone wanting to work on adaptive tutoring has to start from scratch, re-producing all the tutor components, then we are going to have a lot of wasted funding and productivity. Open-sourcing the system means getting to the novel extensions, re-designs, and experimentation faster. It also enables greater inclusivity in tutor research, allowing research to be conducted by PIs and teams that don't have the resources to develop a tutor from the ground up, but do have enough funds to do recruitment and hire a student. For educators, it means getting an effective mastery-based tool, refined by the empirical findings of the field, with no licensing fee that can inhibit adoption. Because it is open license and relatively simple to maintain (i.e., there's no backend database), adopters can run the system on their own - thus eliminating dependency on tech providers to continue use of the system.

IPN: You recently started to collaborate with Marlene Steinbach during her visit at Berkeley. How did that collaboration come about, and what role did your collaboration with Marlene Steinbach play in advancing your current research or inspiring new directions in your work?

Prof. Pardos: The collaboration with Marlene has been great. There's a popularly espoused concern about the harms GenAI can have on learners because of hallucinations. If GenAI is 80% accurate in a domain, is that enough for it to be used in tutoring a student? A new publication from our collaboration, recently presented at Learning @ Scale in Palermo, suggests that hallucinations from GenAI don't affect learners in obvious ways and that erroneous feedback can be more helpful than fully correct feedback, though more time consuming. Why this may be the case is the subject of future work.

IPN: Looking to the future: What current developments in AI and education do you find most promising or important?

Prof. Pardos: I'm most interested in conversations around theory building with AI and personalization and studying its role and effects on agency, motivation, and engagement. These are of course constructs from education psychology and I think that collaborations between those on the computational and psychological sides of education are most promising when it comes to advancing AI in education.  Colleagues Jennifer Meyer and Olga Viberg recently facilitated discussions around a subset of these topics that brought scholars from different disciplines together. More of this is needed.