College of Education and Human Development

Department of Educational Psychology

People

Stephen Hutt

  • Pronouns: he/they

  • Assistant professor

Stephen Hutt develops theory-informed learning analytics and AI that connect research on metacognition and self-regulation to the design of equitable, adaptive learning technologies.

    DEGREES

    Ph.D. Computer Science, University of Colorado-Boulder

    MEng, Computer Science and Artificial Intelligence, University of York, UK

      AREAS OF INTEREST

      My research explores how theory-driven analytics and AI can advance equitable learning by connecting cognitive science with real-world educational practice.

      • Learning Analytics & AI in Education - Developing adaptive, human-centered technologies to support diverse learners.
      • Metacognition & Self-Regulation -  Modeling how learners monitor, control, and reflect on their own learning processes.
      • Engagement & Motivation - Using multimodal data (e.g., eye-tracking, webcams, physiological sensors) to understand how students sustain attention and persistence.
      • Equity & Inclusion in Educational Technology - Designing AI systems that address bias and support neurodiverse and underrepresented students.
      • Theory-to-Practice Loop - Translating insights from learning sciences into practical interventions that improve teaching and learning outcomes.
        BIOGRAPHY

        Stephen Hutt is an assistant professor of educational psychology at the University of Minnesota and Co-Director of the Learning Informatics Lab. His research sits at the intersection of artificial intelligence, learning sciences, and cognitive science, with a focus on how AI can support equitable and adaptive learning. He leads projects on multimodal sensing (e.g., eye-tracking, webcams, physiological data) to better understand student engagement, self-regulation, and neurodiverse learning needs, with the goal of designing AI-driven technologies that are inclusive, ethical, and effective. His work has been supported by the National Science Foundation, AERDF, and other partners, and he regularly collaborates across disciplines to advance both theory and practice in human-centered AI for education.

          AWARDS

          Marilyn Sime for Educational Excellence Faculty Award

            PUBLICATIONS

            G. D. Jaiyeola, A. Y. Wong, R. L. Bryck, C. Mills, and S. Hutt, “One size does not fit all: Considerations when using webcam-based eye tracking to models of neurodivergent learners’ attention and comprehension,” in Proceedings of the 15th International Learning Analytics and Knowledge Conference, LAK ’25, Association for Computing Machinery, 2025, pp. 24–35, ISBN: 9798400707018. DOI: 10.1145/3706468.3706472 (AR=30%)

            J. Ocumpaugh, R. D. Roscoe, R. S. Baker, S. Hutt, and S. J. Aguilar, “Toward Asset-based Instruction and Assessment in Artificial Intelligence in Education,” International Journal of Artificial Intelligence in Education, Jan. 2024, ISSN: 1560-4306. DOI: 10.1007/s40593-023-00382-x

            S. Hutt, A. Wong, A. Papoutsaki, R. S. Baker, J. I. Gold, and C. Mills, “Webcam-based eye tracking to detect mind wandering and comprehension errors,” Behavior Research Methods, Jan. 2023, ISSN: 1554-3528. DOI: 10 . 3758/s13428-022-02040-x (IF = 7.2)

            S. Hutt and G. Hieb, “Scaling up mastery learning with generative ai: Exploring how generative ai can assist in the generation and evaluation of mastery quiz questions,” in Proceedings of the Eleventh ACM Conference on Learning @ Scale, L@S ’24, Atlanta, GA, USA: Association for Computing Machinery, 2024, pp. 310–314, ISBN: 9798400706332. DOI: 10 . 1145 / 3657604 . 3664699

            S. Hutt, A. DePiro, J. Wang, S. Rhodes, R. S. Baker, G. Hieb, S. Sethuraman, J. Ocumpaugh, and C. Mills, “Feedback on feedback: Comparing classic natural language processing and generative ai to evaluate peer feedback,” in Proceedings of the 14th Learning Analytics and Knowledge Conference, LAK ’24, Kyoto, Japan, Association for Computing Machinery, 2024, pp. 55–65, ISBN: 9798400716188. DOI: 10.1145/3636555.3636850

            S. Hutt, S. Das, and R. S. Baker, “The right to be forgotten and educational data mining: Challenges and paths forward,” in Proceedings of the 16th International Conference on Educational Data Mining, 2023

            S. Hutt, J. Ocumpaugh, J. M. A. L. Andres, N. Bosch, L. Paquette, G. Biswas, and R. S. Baker, “Sharpest tool in the shed: Investigating smart models of self-regulation and their impact on learning,” in proceedings of the International Conference on Educational Data Mining, 2021

            S. Hutt, K. Krasich, J. R. Brockmole, and S. K. D’Mello, “Breaking out of the lab: Mitigating mind wandering with gaze-based attention-aware technology in classrooms,” CHI ’21, Yokohama, Japan: Association for Computing Machinery, 2021, ISBN: 9781450380966. DOI: 10.1145/3411764.3445269

            Stephen Hutt