Chris Teplovs

My teaching and research explore how AI can make assessment continuous, embedded in the learning process, and useful to learners, rather than a high-stakes event at the end of it.

Most assessment is still summative, measuring students under pressure, at a single moment, against a single standard. That approach often misreads what learners know, offers little feedback they can act on, and tends to widen existing inequities. AI has not yet solved this, and hyper-personalized tutors introduce a problem of their own: although they promise to meet each student where they are, they turn learning into a private exchange between one student and a machine, leaving out the collaborative, social processes through which people build knowledge together.

I design systems that work differently, tuning assessment to what students are trying to understand and achieve, including goals that traditional tests never capture. In my own data science courses, for example, students demonstrate understanding by explaining and defending their analytical reasoning, rather than only by submitting code that runs.

My work draws on the learning sciences, data science, and social learning, with an emphasis on design-based implementation, in which I build educational technologies, deploy them in real classrooms, and refine them based on what happens there.

Areas of interest

AI in educational assessment; data science curriculum design; AI agents; building AI apps

Education

I completed my B.Sc in Zoology, M.A. in Education, and Ph.D. in Curriculum, Teaching and Learning at the University of Toronto, Canada