Teacher-AI complementarity: From design to implementation and reflection

Citation (APA 7)

Chejara, P., Tammets, K., Laanpere, M., Volt, A., Kasepalu, R., Sarmiento-Márquez, E. M., & Sillat, L. H. (2024). Teacher-AI complementarity: From design to implementation and reflection. Joint Proceedings of LAK 2025 Workshops, co-located with the 15th International Conference on Learning Analytics and Knowledge (LAK 2025), Dublin, Ireland, March 03–07, 2025.. https://doi.org/10.1111/bjet.13402

Abstract

Traditionally Artificial Intelligence (AI) was primarily focused on building adaptive tutoring systems that mimicked the role of teachers to deliver personalized instruction. Over time, AI applications have expanded to other domains, such as drop-out prediction and performance analytics, with a central goal of understanding and enhancing learning. This expansion has driven the growth of research fields like Educational data mining, Learning Analytics, and AI in Education. These fields have illustrated the potential of AI harnessing data from learning platforms and even from physical classroom spaces. Thus, AI can help teachers to efficiently observe and understand what is happening in their classrooms, augmenting the teacher’s ability to maximize positive impact on learning. One emerging approach to achieving this synergy between humans and AI is hybrid intelligence, which emphasizes the collaboration and co-evolution of humans and AI. In this paper, we present our ongoing research efforts to design and develop educational technologies with an ability to evolve and adapt from their interactions with teachers and students, and align with human values and norms.