Adaptive Teaching, Learning, and Support (ATLaS)

Deutsch

The junior research group "ATLaS" is part of the research center CATALPA.

The ATLaSjunior research group investigates how teaching and learning can be measured, diagnosed, and supported through process-oriented, theory-driven approaches and adaptive support with advanced and emerging technologies.

Goals and research questions

We focus on understanding and supporting learning as a dynamic, regulated process in technology-enhanced learning environments. By integrating perspectives from the learning sciences and educational psychology with statistical, learning analytic, and AI-based methods, we diagnose and model learning processes and translate these insights into adaptive, learner-centered support that fosters meaningful human-technology partnerships.

Two interconnected research pillars drive our work:

1. Adaptive and personalized process-oriented support

We advance a process-oriented approach to supporting learning adaptively that moves beyond static snapshots of learning by measuring learning and regulation processes as they unfold. Building on theory-and-data-driven methods, we develop and test evidence-based adaptive scaffolds that support learners’ needs in a timely and personalized way. Our aim is to develop theoretically grounded and empirically validated approaches that inform adaptive and personalized support, using AI, including generative AI, not as a standalone solution but as a means to enhance learner support and teaching processes meaningfully.

2. Facilitating productive and meaningful human-technologypartnerships

Although educational research has benefited from technological advancements in learning support, learners do not always engage with support tools in meaningful and high-quality ways. We therefore investigate how learners can be better supported in using these tools productively. We examine the quality of learner-tool interactions using multimodal process data, identify patterns of high- and low-quality interactions, and develop targeted interventions that promote productive use of learning technologies, thereby strengthening human-technology partnerships.

  • Dr. Lyn Lim

  • We are currently building the ATLaS team. If you are interested in joining or collaborating, please get in touch.

    Find current vacancy here

    • 2026

    • Journals

    • Lim, L., & Bannert, M. (2026). How do students regulate their learning with a genAI chatbot? Learning Letters. Advance online publication. https://doi.org/10.20851/ll.v8.61

    • 2025

    • Journals

    • Alnashiri, H., Rakovic, M., Nawaz, S., Li, X., Lamsa, J., Lim, L., Bannert, M., Jarvela, S., & Gašević, D. (2025). Using Trace Data of Secondary Students to Understand Metacognitive Processes in Writing From Multiple Sources. Journal of Computer Assisted Learning, 41(5), Article e70114. https://doi.org/10.1111/jcal.70114
    • Li, X., Fan, Y., Li, T., Raković, M., Singh, S., van der Graaf, J., Lim, L., Moore, J., Molenaar, I., Bannert, M., & Gašević, D. (2025). FLoRA Engine. Journal of Learning Analytics, 12(1), 391–413. https://doi.org/10.18608/jla.2025.8349
    • Mooij, S. de, Lämsä, J., Lim, L., Aksela, O., Athavale, S., Bistolfi, I., Jin, F., Li, T., Azevedo, R., Bannert, M., Gašević, D., Järvelä, S., & Molenaar, I. (2025). A Systematic Review of Self-Regulated Learning through Integration of Multimodal Data and Artificial Intelligence. Educational Psychology Review, 37(2). https://doi.org/10.1007/s10648-025-10028-0

    • Talks and Poster Presentations

    • Athavale, S., Lim, L., & Bannert, M. (08/25-08/29, 2025). Training secondary school students’ SRL before learning with an AI-based online learning environment. In S. de Mooij & J. Lämsä (Chairs), Beyond learning traces: Advancing self-regulated learning with AI – From measurement to support. 21st Biennial Conference of the European Association for Research on Learning and Instruction (EARLI 2025), Graz, Austria.
    • Bannert, M., & Lim, L. (08/25-08/29, 2025). Promoting SRL through AI-based learning environments. In Azevedo, R. &Bannert, M. (Organizers & Chairs), Leveraging Human-AI Collaboration Research to Revolutionize Metacognition and Self-Regulation. 21st Biennial Conference of the European Association for Research on Learning and Instruction (EARLI 2025), Graz, Austria.
    • Lim, L., & Bannert, M. (08/25-08/29, 2025). Towards Learner-Driven SRL Tools: How Students Regulate and Interact with a genAI Chatbot. In Hirt, C.N., & Lim, L. (Organizers & Chairs), Potentials and Challenges on the Road to Rome: Promoting Self-Regulated Learning with Digital Tools. 21st Biennial Conference of the European Association for Research on Learning and Instruction (EARLI 2025), Graz, Austria.
    • Lim, L., Saint, J., van der Graaf, J., Fan, Y., Singh, S., Rakovic, M., Molenaar, I., Gašević, D., & Bannert, M. (08/25-08/29, 2025). Understanding Scaffold Interactions' Impact on Learning: A Fine-Grained Analytical Approach. 21st Biennial Conference of the European Association for Research on Learning and Instruction (EARLI 2025), Graz, Austria.
    • Mooij, S. de, Lämsä, J., Lim, L., Aksela, O., Athavale, S., Bistolfi, I., Jin, F., Li, T., Azevedo, R., Bannert, M., Gašević, D., Järvelä, S., & Molenaar, I. (08/25-08/29, 2025). A systematic review of SRL: Integrating multimodal data and artificial intelligence. In S. de Mooij & J. Lämsä (Chairs), Beyond learning traces: Advancing self-regulated learning with AI – From measurement to support. 21st Biennial Conference of the European Association for Research on Learning and Instruction (EARLI 2025), Graz, Austria.
Research Center CATALPA | 20.03.2026