Guher Gorgun

I work at the intersection of psychometrics, learning sciences, and artificial intelligence, driven by a fundamental question: how do we build learning and assessment systems that truly work for everyone? Through this work, I explain complex learner behavior and develop adaptive systems that serve the needs of diverse learners. I am particularly interested in ensuring these systems can be implemented in real-world settings, where scalability, fairness, validity, and reliability are essential for achieving meaningful impact. Having worked across both industry and academia in international environments, I have seen firsthand why bridging theory with practice matters—and why the systems we build must be deployable, not just theoretically sound. I am passionate about sharing these experiences and designing next-generation assessment methods that are both AI-powered and human-centered.

My research focuses on these two core questions: (1) how we can better model learners using computational methods and (2) how we can create adaptive assessment systems tailored to individual learners. I address these questions through the analysis of process data and digital assessments, developing novel methodological approaches that integrate psychometrics with data mining, machine learning, and natural language processing.

My work has been published in prestigious journals, including Educational and Psychological Measurement and Education and Information Technologies, as well as in international conference proceedings, such as Artificial Intelligence in Education (AIED) and the Association for the Advancement of Artificial Intelligence (AAAI).