Fostering Human Agency in Age of AI

A Learning Analytics Perspective

Authors

DOI:

https://doi.org/10.18608/jla.2025.9485

Keywords:

learning analytics, human agency, meaningful control, generative AI, learners, teachers, institutions, editorial

Abstract

As learning analytics (LA) and artificial intelligence (AI) increasingly shape how learning processes are monitored and supported, human agency has emerged as a critical concern. With generative AI rapidly transforming learning practices and influencing pedagogical decision-making, safeguarding the agency of both learners and educators is becoming essential. This editorial discusses key dilemmas in designing and evaluating AI-mediated learning systems that maintain meaningful human control, foster critical engagement, and enable ethical and effective integration into learning settings. We conclude by outlining future research opportunities for the learning analytics community and reflecting on the journal’s development over the past year.

References

Ahn, J., Campos, F., Hays, M., & DiGiacomo, D. (2019). Designing in context: Reaching beyond usability in learning analytics dashboard design. Journal of Learning Analytics, 6(2), 70–85. https://doi.org/10.18608/jla.2019.62.5a

Bandura, A. (2006). Toward a psychology of human agency. Perspectives on Psychological Science, 1(2), 164–180. https://doi.org/10.1111/j.1745-6916.2006.00011.x

Celik, I., Kontkanen, S., Laru, J., & Dalyanci, A. A. (2025). Co-constructing adaptive lesson plans with GenAI: Pre-service teachers’ Intelligent-TPACK and prompt engineering strategies. Computers & Education, 241, 105485. https://doi.org/10.1016/j.compedu.2025.105485

Cheng, Y., Fan, Y., Li, X., Chen, G., Gašević, D., & Swiecki, Z. (2025). Asking generative artificial intelligence the right questions improves writing performance. Computers and Education: Artificial Intelligence, 8, 100374. https://doi.org/10.1016/j.caeai.2025.100374

Cheng, A., Pei, B., Liu, C. (2025). Balancing Act: Early, Fair, and Accurate Identification of At-Risk Students. Journal of Learning Analytics, 12(3), 47–65. https://doi.org/10.18608/jla.2025.8761

Code, J. (2020). Agency for learning: Intention, motivation, self-efficacy and self-regulation. Frontiers in Education, 5, Article 19. https://doi.org/10.3389/feduc.2020.00019

Dai, W., Lin, J., Ji-Yoon, F., Tsai, Y., Srivastav, N., LeBodic, P., Gašević, D., & Chen, G. (2025). Learning Analytics for Early Identification of At-Risk Students and Feedback Intervention. Journal of Learning Analytics, 12(3), 102–125. https://doi.org/10.18608/jla.2025.8735

European Data Protection Supervisor. (2025, September 23). TechDispatch #2/2025 — Human oversight of automated decision-making: Active involvement and meaningful intervention. https://www.edps.europa.eu/data-protection/our-work/publications/techdispatch/2025-09-23-techdispatch-22025-human-oversight-automated-making_en

EU. (2024). Regulation (EU) 2024/1689 on artificial intelligence (Artificial Intelligence Act). Official Journal of the European Union. European Parliament and Council. https://op.europa.eu/en/publication-detail/-/publication/d79f3e5d-41bc-11f0-b9e2-01aa75ed71a1/language-en

Fan, Y., Tang, L., Le, H., Shen, K., Tan, S., Zhao, Y., Shen, Y., Li, X., & Gašević, D. (2025). Beware of metacognitive laziness: Effects of generative artificial intelligence on learning motivation, processes, and performance. British Journal of Educational Technology, 56(2), 489–530. https://doi.org/10.1111/bjet.13544

Giannakos, M., Azevedo, R., Brusilovsky, P., Cukurova, M., Dimitriadis, Y., Hernandez-Leo, D., … Rienties, B. (2024). The promise and challenges of generative AI in education. Behaviour & Information Technology, 44(11), 2518–2544. https://doi.org/10.1080/0144929X.2024.2394886

Handa, K., Bent, D., Tamkin, A., McCain, M., Durmus, E., Stern, M., Schiraldi, M., Huang, S., Ritchie, S., Syverud, S., Jagadish, K., Vo, M., Bell, M., & Ganguli, D. (2025). Anthropic education report: How university students use Claude. Anthropic. https://www.anthropic.com/news/anthropic-education-report-how-university-students-use-claude

Jia, W., & Pun, J. (2025). Exploring EMI teachers’ agency in addressing language-related challenges with ChatGPT: A multiple case study. Computer Assisted Language Learning, 1–29. https://doi.org/10.1080/09588221.2025.2511061

Kahn, P., Carrigan, M., Smith, P., Murtagh, L., Liu, R., & Song, F. (2025). Teacher agency and generative artificial intelligence: Teaching in higher education as a responsive, cultural activity. Learning, Media and Technology, 1–12. https://doi.org/10.1080/17439884.2025.2575993

Li, T., Nath, D., Cheng, Y., Fan, Y., Li, X., Raković, M., Khosravi, H., Swiecki, Z., Tsai, Y.-S., & Gašević, D. (2025). Turning real-time analytics into adaptive scaffolds for self-regulated learning using generative artificial intelligence. In Proceedings of the 15th International Learning Analytics and Knowledge Conference (LAK ’25) (pp. 667–679). ACM. https://doi.org/10.1145/3706468.3706559

Liu, Z., Xing, W., Jiao, X., & Li, C. (2025). Exploring Fairness and Explainability in LLM-Generated Support for Online Learning Discussion Forums. Journal of Learning Analytics, 12(3), 8–33. https://doi.org/10.18608/jla.2025.8885

Mavrikis, M., Cukurova, M., Di Mitri, D., Schneider, J., & Drachsler, H. (2021). A short history, emerging challenges and co-operation structures for artificial intelligence in education. Bildung und Erziehung, 74(3), 249–263. https://doi.org/10.13109/buer.2021.74.3.249

Michos, K., Schmit, M., & Petko, D. (2025) Learning Analytics in Schools: Is Digital Data Use Influenced by Teacher-Level or School-Level Factors? Journal of Learning Analytics, 12(3), 87–101. https://doi.org/10.18608/jla.2025.8567

Molenaar, I. (2022). Towards hybrid human–AI learning technologies. European Journal of Education, 57(4), 542–555. https://doi.org/10.1111/ejed.12527

Mouta, A., Pinto-Llorente, A. M., & Torrecilla-Sánchez, E. M. (2025). “Where is agency moving to?”: Exploring the interplay between ai technologies in education and human agency. Digital Society, 4, 49. https://doi.org/10.1007/s44206-025-00203-9

Pallant, J. L., Blijlevens, J., Campbell, A., & Jopp, R. (2025). Mastering knowledge: The impact of generative AI on student learning outcomes. Studies in Higher Education, 1–22. https://doi.org/10.1080/03075079.2025.2487570

Paulsen, L., & Lindsay, E. (2024). Learning analytics dashboards are increasingly becoming about learning and not just analytics: A systematic review. Education and Information Technologies, 29(11), 14279–14308. https://doi.org/10.1007/s10639-023-12401-4

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), 1295–1310. https://doi.org/10.1111/bjet.13602

Schneiderman, B. (2020). Human-centered artificial intelligence: Reliable, safe & trustworthy. arXiv. https://arxiv.org/abs/2002.04087

Sepp, S. (2025). Towards More Transparency in Learning Analytics: Sharing Information with University Students Increases their Awareness of Data Collection Practices. Journal of Learning Analytics, 12(3), 34–46. https://doi.org/10.18608/jla.2025.8713

Singh, M. Bangay, S., & Sajjanhar, A. (2025) Augmented Reality Enhanced Analytics for Education: a Systematic Review. Journal of Learning Analytics, 12(3), 126–155. https://doi.org/10.18608/jla.2025.8447

Stadler, M., Bannert, M., & Sailer, M. (2024). Cognitive ease at a cost: LLMs reduce mental effort but compromise depth in student scientific inquiry. Computers in Human Behavior, 160, 108386. https://doi.org/10.1016/j.chb.2024.108386

Sterz, S., Baum, K., Biewer, S., Hermanns, H., Lauber-Rönsberg, A., Meinel, P., & Langer, M. (2024). On the quest for effectiveness in human oversight: Interdisciplinary perspectives. In FAccT ’24: Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency (pp. 2495–2507). ACM. https://doi.org/10.1145/3630106.3659051

Tan, X., Cheng, G., & Ling, M. H. (2025). Artificial intelligence in teaching and teacher professional development: A systematic review. Computers and Education: Artificial Intelligence, 8, 100355. https://doi.org/10.1016/j.caeai.2024.100355

Viberg, O., Jivet, I., & Scheffel, M. (2023). Designing culturally aware learning analytics: A value-sensitive perspective. In Practicable learning analytics (pp. 177–192). Springer International Publishing. https://doi.org/10.48550/arXiv.2212.09645

Xu, X., Qiao, L., Cheng, N., Liu, H., & Zhao, W. (2025). Enhancing self-regulated learning and learning experience in generative AI environments: The critical role of metacognitive support. British Journal of Educational Technology, 56(5), 1842–1863. https://doi.org/10.1111/bjet.13599

Yan, L., Martinez-Maldonado, R., & Gašević, D. (2024). Generative artificial intelligence in learning analytics: Contextualising opportunities and challenges through the learning analytics cycle. In Proceedings of the 14th Learning Analytics and Knowledge Conference (pp. 101–111). ACM. https://doi.org/10.1145/3636555.363685

Yildiz Durak, H., Eğin, F., & Onan, A. (2025). A comparison of human-written versus AI-generated text in discussions at educational settings: Investigating features for ChatGPT, Gemini and BingAI. European Journal of Education, 60(1). https://doi.org/10.1111/ejed.70014

Yu, L., Ha lpin, P. F., Bernacki, M. L., Ren, S., Plumley, R. D., & Greene, J. A. (2025) Interpreting Predictive Learning Sequences in a College Math Course through a Self-Regulated Learning Framework. Journal of Learning Analytics, 12(3), 66–86. https://doi.org/10.18608/jla.2025.8865

Downloads

Published

2025-12-15

How to Cite

Viberg, O., Poquet , O., Kovanovic, V., & Khosravi, H. (2025). Fostering Human Agency in Age of AI: A Learning Analytics Perspective. Journal of Learning Analytics, 12(3), 1-7. https://doi.org/10.18608/jla.2025.9485

Most read articles by the same author(s)

1 2 > >>