# Turning 500+ Students into Teachers: A Semester-Long Study of an AI Teachable Agent in an Undergraduate Algorithms Course

Chenyang Wang, Christopher Petrie, Miltiadis Stouras, Nicolas Ettlin, Amaury George, Paola Mejia-Domenzain, Vinitra Swamy, Tanja Käser, Ola Svensson
2026 · ACM Conference on Learning @ Scale (L@S '26)

## Abstract

Large language model (LLM) tools can provide students with rapid solutions but may reduce opportunities for productive struggle and explanation generation that support conceptual learning. Learning-by-teaching (LBT) offers an alternative solution by positioning students as tutors; however, evidence for LLM-based teachable agents remains limited, particularly for longitudinal deployments and large-scale evaluations that connect LBT interactions to conceptual understanding in authentic courses. We present Explique, a platform that integrates an AI teachable agent, Algorithm Apprentice, into an undergraduate algorithms course to operationalise LBT at scale. We report an 11-week field deployment in a real course with 546 students, analysing 3,809 student-agent LBT dialogues alongside quiz and survey data. Students engaged consistently in multi-turn teaching interactions over the semester, although the depth and authenticity of these interactions varied, including instances of direct reuse of externally sourced content. Using generalised linear mixed-effects models, we find that explanation-oriented dialogue behaviours (e.g., elaboration and showing reasoning) are associated with fewer quiz attempts (i.e., fewer incorrect submissions), whereas external-content reuse is associated with slightly more repeated attempts. Compared to a baseline reading activity, the LBT condition corresponds to a modest reduction in expected quiz attempts, although this comparison is confounded by substantial differences in time-on-task. Overall, these results provide longitudinal, large-scale evidence on LLM-based teachable agents in an authentic computer science course and inform the design and practice of systems that aim to support sustained, effortful and scalable LBT interactions.

## Links
- DOI: https://doi.org/10.1145/3774398.3811623
- HTML: https://paola-md.github.io/papers/turning-500-students-into-teachers-a-semester-long-study-of-an-ai-teacha.html
- Cite: https://paola-md.github.io/publications.bib

Part of the publication record of Paola Mejia-Domenzain, https://paola-md.github.io/