Background: With increased use of artificial intelligence in the classroom, there is now a need to better understand the complementarity of intelligent learning technology and teachers to produce effective instruction.Objective: The paper reviews the current research on intelligent learning technology designed to make models of student learning and instruction transparent to teachers, an area we call model‐based learning analytics. We intended to gain an insight into the coupling between the knowledge models that underpin the intelligent system and the knowledge used by teachers in their classroom decision making.Methods: Using a systematic literature review methodology, we first identified 42 papers, mainly from the domain of intelligent tutoring systems and learning analytics dashboards that conformed to our selection criteria. We then qualitatively analysed the context in which the systems were applied, models they used and benefits reported for teachers and learners.Results and Conclusions: A majority of papers used either domain or learner models, suggesting that instructional decisions are mostly left to teachers. Compared to previous reviews, our set of papers appeared to have a stronger focus on providing teachers with theory‐driven insights and instructional decisions. This suggests that model‐based learning analytics can address some of the shortcomings of the field, like meaningfulness and actionability of learning analytics tools. However, impact in the classroom still needs further research, as in half of the cases the reported benefits were not backed with evidence. Future research should focus on the dynamic interaction between teachers and technology and how learning analytics has an impact on learning and decision making by teachers and students. We offer a taxonomy of knowledge models that can serve as a starting point for designing such interaction.

Given the rising prominence of Artificial Intelligence (AI) in education, understanding its impact on teacher practices is essential. This paper presents an ABAB reversal design study conducted during a teacher training, where an AI-driven feedback system helped 19 teachers to create learning designs. It investigates the impact that the AI-driven feedback had on the quality of designs and assesses pre- and post-training shifts in teachers’ perceptions of the technology. We observed statistical differences between designs crafted without (in phase A1) and with AI (B1). Notably, a small positive influence persisted even after AI withdrawal (A2). This hints that specialized AI algorithms for learning design can assist teachers in effectively achieving their design objectives. Despite noticeable shifts in teachers’ perceived understanding and usefulness of AI, their trust and intention to use remained unchanged. For a successful teacher-AI partnership, future research should explore the long-term impact that AI usage can have on learning design practices and strategies to nurture trust.