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Objectives and Scope
Objectives and Scope
Smart Learning Environments (SLEs) are technology-enhanced learning systems designed to provide personalized and context-aware support to learners in hybrid learning scenarios, as well as to assist teachers in the process of designing effective learning experiences. This workshop aims to explore how Learning Analytics (LA) can advance Generative AI (GenAI)-enhanced human-centered SLEs. We will discuss how LA can support personalization, adaptive feedback, orchestration, and data-driven decision-making within SLEs while ensuring transparency, fairness, and human agency.
Call for Papers - Topics of interest
We invite researchers to contribute to the Learning Analytics for Human-Centered Generative AI-Enhanced Smart Learning Environments (LA4GenAISLE) workshop, which will take place in person on April 28th, 2026, as part of the LAK26 conference. We welcome submissions on the following topics, but not limited to:
LA in agentic architectures for GenAI-enhanced SLEs.
Incorporating contextual data into GenAI-based LA.
LA and GenAI for reducing teacher orchestration load in SLEs.
LA fostering agency in GenAI-enhanced SLEs.
GenAI-based student models.
Connecting Learning Design information with AI-generated LA indicators.
Using LA and pedagogical intentions to drive GenAI behaviour.
Leveraging past learning data to evaluate GenAI-based Interventions in LA.
Using LA to advance ethical and responsible AI practices in SLEs.
Human-centered research and educational technology design methods.
Submission guidelines
Submission guidelines
The organisers welcome two types of contributions: short empirical papers to showcase work in progress, and short discussion papers provoking the audience to think about key issues and problems.
The submissions need to be between 5 and 7 pages long formatted using the 1-column style CEUR-ART template. All manuscripts must be carefully anonymized for review before submitting them using the EasyChair system.
Each of the submitted manuscripts will undergo a double-blinded peer-review process by members of the organizing team and by fellow authors.
Contributions are to be submitted through the EasyChair platform: https://easychair.org/my2/conference?conf=la4genaisle2026
Accepted papers will appear in the LA4GenAISLE 2025 Workshop Proceedings. Proceedings shall be submitted to CEUR-WS.org for online publication.
Contact e-mail: la4genaisle2026@easychair.org
Important dates
Important dates
Submission opens: October 21st, 2025
Deadline for manuscript submissions: D̶e̶c̶e̶m̶b̶e̶r̶ ̶4̶t̶h̶,̶ ̶2̶0̶2̶5̶ --> December 18th, 2025
Notification of acceptance: D̶e̶c̶e̶m̶b̶e̶r̶ ̶1̶9̶t̶h̶,̶ ̶2̶0̶2̶5̶ --> January 15th, 2026
Workshop date: April 28th, 2026
Preliminary program
Preliminary program
Overview and introduction (15 min)
Participants’ presentations (75 min)
Collaborative discussion (75 min)
Wrap-up and next steps (15 min)
Workshop organizers
Workshop organizers
Pedro Manuel Moreno-Marcos, Universidad Carlos III de Madrid, Spain
Miguel L. Bote-Lorenzo, Universidad de Valladolid, Spain
Patricia Santos, Universitat Pompeu Fabra, Spain
Peter Brusilovsky, University of Pittsburgh, USA
Mutlu Cukurova, University College London, United Kingdom
Dragan Gašević, Monash University, Australia
Nancy Law, University of Hong Kong, Hong Kong
Marcus Specht, FernUniversität, Germany
Daniel Spikol, University of Copenhagen, Denmark
Katrien Verbert, Katholieke Universiteit Leuven, Belgium
Carlos Delgado Kloos, Universidad Carlos III de Madrid, Spain
Juan I. Asensio-Pérez, Universidad de Valladolid, Spain
Davinia Hernández-Leo, Universitat Pompeu Fabra, Spain
References
References
Cohen, A., Nørgård, R. T., & Mor, Y. (2020). Hybrid learning spaces––Design, data, didactics. British Journal of Educational Technology, 51(4). https://www.doi.org/10.1111/bjet.12964
Delgado-Kloos, C., Asensio-Pérez, J. I., Hernández-Leo, D., Moreno-Marcos, P. M., Bote-Lorenzo, M. L., Santos, P., ... & Tabuenca, B (2025). GENIE Learn: Human-Centered Generative AI-Enhanced Smart Learning Environments. In Proceedings of the 17th International Conference on Computer Supported Education, CSEDU 2025, 15-26, https://www.doi.org/10.5220/0013076000003932
Hernández-Leo, D., Muñoz-Merino, P. J., Bote-Lorenzo, M. L., Gašević, D., & Järvelä, S. (2023). Editorial Technologies for Data-Driven Interventions in Smart Learning Environments. IEEE Transactions on Learning Technologies, 16(3), 378-381. https://doi.org/10.1109/TLT.2023.3275728
Khosravi, H., Shibani, A., Jovanovic, J., Pardos, Z. A., & Yan, L. (2025). Generative AI and Learning Analytics: Pushing Boundaries, Preserving Principles. Journal of Learning Analytics, 12(1), 1-11. https://doi.org/10.18608/jla.2025.8591
Misiejuk, K., López-Pernas, S., Kaliisa, R., & Saqr, M. (2025). Mapping the landscape of generative artificial intelligence in learning analytics: A systematic literature review. Journal of Learning Analytics, 12(1), 12-31. https://doi.org/10.18608/jla.2025.8591
Papamitsiou, Z., & Economides, A. A. (2016). Learning Analytics for Smart Learning Environments: A meta-analysis of empirical research results from 2009 to 2015. In Learning, design, and technology (pp. 3533-3555). Springer, Cham. https://www.doi.org/10.1007/978-3-319-17727-4_15-1
Prieto, L. P., Viberg, O., Yip, J. C., & Topali, P. (2025). Aligning human values and educational technologies with value‐sensitive design. British Journal of Educational Technology, 56(4), 1299-1310. https://doi.org/10.1111/bjet.13602
Sofianos, K. C., Stefanidakis, M., Kaponis, A., & Bukauskas, L. (2024). Assist of AI in a smart learning environment. In IFIP International Conference on Artificial Intelligence Applications and Innovations (pp. 263-275). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-63223-5_20
Tabuenca, B., Serrano-Iglesias, S., Martín, Carruana, A., Villa-Torrano, C., Dimitriadis, Y., Asensio-Pérez, J. I., ... & Kloos, C. D. (2021). Affordances and core functions of Smart Learning Environments: A systematic literature review. IEEE Transactions on Learning Technologies, 14(2), 129-145. https://doi.org/10.1109/TLT.2021.3067946
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