Generative AI and Learning Analytics

Pushing Boundaries, Preserving Principles

Authors

DOI:

https://doi.org/10.18608/jla.2025.8961

Keywords:

generative AI, learning analytics, editorial

Abstract

The rapid adoption of generative AI (GenAI) in education has raised critical questions about its implications for learning and teaching. While GenAI tools offer new avenues for personalized learning, enhanced feedback, and increased efficiency, they also present challenges related to cognitive engagement, student agency, and ethical considerations. Learning analytics (LA) provides a crucial lens to examine how GenAI affects learning behaviours and outcomes by offering data-informed insights into GenAI’s impact on students, educators, and educational ecosystems. Thus, obtained insights allow for evidence-based decision-making aimed at balancing GenAI’s benefits with the need to foster deep learning, creativity, and self-regulation of learning. This special issue of the Journal of Learning Analytics presents 10 research papers that explore the intersection of GenAI and LA, offering diverse perspectives that benefit students, teachers, and researchers. To structure these contributions, we adopt Clow’s generic framework of the LA cycle, categorizing the papers into four key areas: (1) understanding learning and learner contexts, (2) leveraging AI-generated data for learning insights, (3) applying LA methods to generate meaningful insights, and (4) designing interventions that optimize learning outcomes. By bringing together these perspectives, this special issue advances research-informed educational practices that ensure that GenAI’s potential is harnessed responsibly, reinforcing educational goals while safeguarding learners’ autonomy and cognitive development. Collectively, these contributions illustrate the reciprocal relationship between GenAI and LA, demonstrating how each can inform and refine the other. We reflect on the broader implications for LA, including the need to re-examine the boundaries of LA in the presence of GenAI, while preserving key principles from human-centred design and maintaining ethical and privacy standards that are foundational to LA.

References

Abbas, M., Jam, F. A., & Khan, T. I. (2024). Is it harmful or helpful? Examining the causes and consequences of generative AI usage among university students. International Journal of Educational Technology in Higher Education, 21. https://doi.org/10.1186/s41239-024-00444-7

Baker, R. S., Gašević, D., & Karumbaiah, S. (2021). Four paradigms in learning analytics: Why paradigm convergence matters. Computers and Education: Artificial Intelligence, 2, 100021. https://doi.org/10.1016/j.caeai.2021.100021

Bergner, Y. (2017). Measurement and its uses in learning analytics. In C. Lang, G. Siemens, A. Wise, & D. Gašević (Eds.), Handbook of learning analytics (1st edition, pp. 35–48). SoLAR. https://doi.org/10.18608/hla17.003

Buckingham Shum, S., Ferguson, R., & Martinez-Maldonado, R. (2019). Human-centred learning analytics. Journal of Learning Analytics, 6(2), 1–9. https://doi.org/10.18608/jla.2019.62.1

Clow, D. (2012). The learning analytics cycle: Closing the loop effectively. In Proceedings of the Second International Conference on Learning Analytics and Knowledge (LAK 2012), 29 April–2 May 2012, Vancouver, British Columbia, Canada (pp. 134–138). ACM. https://doi.org/10.1145/2330601.2330636

Cukurova, M. (2024). The interplay of learning, analytics and artificial intelligence in education: A vision for hybrid intelligence. British Journal of Educational Technology, 56(2), 469–488. https://doi.org/10.1111/bjet.13514

Darvishi, A., Khosravi, H., Sadiq, S., Gašević, D., & Siemens, G. (2024). Impact of AI assistance on student agency. Computers & Education, 210, 104967. https://doi.org/10.1016/j.compedu.2023.104967

Deng, R., Jiang, M., Yu, X., Lu, Y., & Liu, S. (2025). Does ChatGPT enhance student learning? A systematic review and meta-analysis of experimental studies. Computers & Education, 227, 105224. https://doi.org/10.1016/j.compedu.2024.105224

Dimitriadis, Y., Martínez-Maldonado, R., & Wiley, K. (2021). Human-centered design principles for actionable learning analytics. In T. Tsiatsos, S. Demetriadis, A. Mikropoulos, & V. Dagdilelis (Eds.), Research on E-learning and ICT in education: Technological, pedagogical and instructional perspectives (pp. 277–296). Springer. https://doi.org/10.1007/978-3-030-64363-8_15

Drachsler, H., & Greller, W. (2016). Privacy and analytics: it’s a DELICATE issue a checklist for trusted learning analytics. In Proceedings of the Sixth International Conference on Learning Analytics and Knowledge (LAK 2016), 25–29 April 2016, Edinburgh, Scotland, UK (pp. 89–98). ACM. https://doi.org/10.1145/2883851.2883893

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/https://doi.org/10.1111/bjet.13544

Gašević, D., Dawson, S., & Siemens, G. (2015). Let’s not forget: Learning analytics are about learning. TechTrends, 59, 64–71. https://doi.org/10.1007/s11528-014-0822-x

Hadas, E., & Hershkovitz, A. (2025). Assessing creativity across multi-step intervention using generative AI models. Journal of Learning Analytics, 12(1), 91–109. https://doi.org/10.18608/jla.2025.8571

Henkel, O., Horne-Robinson, H., Dyshel, M., Abood, R., Thompson, G., Ch, N., & Moreau-Pernet, B. (2025). Learning to love LLMs for answer interpretation: Chain-of-thought prompting and the AMMORE dataset. Journal of Learning Analytics, 12(1), 50–64. https://doi.org/10.18608/jla.2025.8621

Jin, F. J.-Y., Maheshi, B., Lai, W., Li, Y., Gasevic, D., Chen, G., Charwat, N., Chan, P. W. K., Martinez-Maldonado, R., Gašević, D., & Tsai, Y.- S. (2025). Students’ perceptions of generative AI–powered learning analytics in the feedback process: A feedback literacy perspective. Journal of Learning Analytics, 12(1), 152–168. https://doi.org/10.18608/jla.2025.8609

Jin, Y., Yang, K., Yan, L., Echeverria, V., Zhao, L., Alfredo, R., Milesi, M., Fan, J., Li, X., Gasevic, D., & Martinez-Maldonado, R. (2025). Chatting with a learning analytics dashboard: The role of generative AI literacy on learner interaction with conventional and scaffolding chatbots. arXiv preprint arXiv:2411.15597. https://doi.org/10.48550/arXiv.2411.15597

Khosravi, H., Viberg, O., Kovanovic, V., & Ferguson, R. (2023). Generative AI and learning analytics. Journal of Learning Analytics, 10(3), 1–6. https://doi.org/10.18608/jla.2023.8333

Kitto, K., Poquet, O., Many, C., & Ferguson, R. (2025). What are the grand challenges of learning analytics? In Companion Proceedings of the 15th International Conference on Learning Analytics and Knowledge (LAK 2025), 3–7 March 2025, Dublin, Ireland (pp. 428–431). ACM.

Knight, S., & Buckingham Shum, S. (2017). Theory and learning analytics. In C. Lang, G. Siemens, A. Wise, & D. Gašević (Eds.), Handbook of learning analytics (1st edition, pp. 17–22). SoLAR. https://doi.org/10.18608/hla17.001

Knight, S., Buckingham Shum, S., & Littleton, K. (2014). Epistemology, assessment, pedagogy: Where learning meets analytics in the middle space. Journal of Learning Analytics, 1(2), 23–47. https://doi.org/10.18608/jla.2014.12.3

Knight, S., Gibson, A., & Shibani, A. (2020). Implementing learning analytics for learning impact: Taking tools to task. The Internet and Higher Education, 45, 100729. https://doi.org/10.1016/j.iheduc.2020.100729

Kovanovic, V., Viberg, O., Khosravi, H., & Ferguson, R. (2024). Skills and competencies for thriving in the age of generative AI: The role of learning analytics. Journal of Learning Analytics, 11(3), 1–5. https://doi.org/10.18608/jla.2024.8779

Lai, J. W., Qiu, W., Thway, M., Zhang, L., Jamil, N. B., Su, C. L., Ng, S. S. H., & Lim, F. S. (2025). Leveraging process-action epistemic network analysis to illuminate student self-regulated learning with a Socratic chatbot. Journal of Learning Analytics, 12(1), 32–49. https://doi.org/10.18608/jla.2025.8549

Lekan, K., & Pardos, Z. A. (2025). AI-augmented advising: A comparative study of GPT-4 and advisor-based major recommendations. Journal of Learning Analytics, 12(1), 110–128. https://doi.org/10.18608/jla.2025.8593

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 Conference on Learning Analytics and Knowledge (LAK 2025), 3–7 March 2025, Dublin, Ireland (pp. 667–679). ACM. https://doi.org/10.1145/3706468.3706559

Liu, X., Zambrano, A. F., Baker, R. S., Barany, A., Ocumpaugh, J., Zhang, J., Pankiewicz, M., Nasiar, N., & Wei, Z. (2025). Qualitative coding with GPT-4: Where it works better. Journal of Learning Analytics, 12(1), 169–185. https://doi.org/10.18608/jla.2025.8575

Macfadyen, L. P., Lockyer, L., & Rienties, B. (2020). Learning design and learning analytics: Snapshot 2020. Journal of Learning Analytics, 7(3), 6–12. https://doi.org/10.18608/jla.2020.73.2

McKeon, R. (1966). Philosophic semantics and philosophic inquiry. In Z. K. McKeon (Ed.), Freedom and history and other essays: An introduction to the thought of Richard McKeon (pp. 242–256). The University of Chicago Press.

Mills, C., Martinez-Maldonado, R., Karumbaiah, S., Brooks, C., Kitto, K., & Kovanovic, V. (2022). Defining the scope of LAK [Interactive panel]. https://www.solaresearch.org/events/lak/lak22/schedule/

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

Ochoa, X., Ferguson, R., Siemens, G., & Azevedo, R. (2022). Where is learning analytics going?: Examining values, goals, practices and perceptions of the field [Interactive panel]. https://www.youtube.com/watch?v=tbpwToTTsi8&ab_channel=SocietyforLearningAnalyticsResearch

Ochoa, X., Huang, X., & Shao, Y. (2025). Exploring the potential of generative AI to support non-experts in learning analytics practice. Journal of Learning Analytics, 12(1), 65–90. https://doi.org/10.18608/jla.2025.8615

Pardo, A., & Siemens, G. (2014). Ethical and privacy principles for learning analytics. British Journal of Educational Technology, 45(3), 438–450. https://doi.org/10.1111/bjet.12152

Pardos, Z. A., & Bhandari, S. (2024). ChatGPT-generated help produces learning gains equivalent to human tutor-authored help on mathematics skills. PLOS One, 19(5), e0304013. https://doi.org/10.1371/journal.pone.0304013

Pozdniakov, S., Brazil, J., Mohammadi, M., Dollinger, M., Sadiq, S., & Khosravi, H. (2025). AI-assisted co-creation: Bridging skill gaps in student-generated content. Journal of Learning Analytics, 12(1), 129–151. https://doi.org/10.18608/jla.2025.8601

Reza, M., Anastasopoulos, I., Bhandari, S., & Pardos, Z. A. (2025). PromptHive: Bringing subject matter experts back to the forefront with collaborative prompt engineering for educational content creation. In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems (CHI 2025), 26 April–1 May 2025, Yokohama, Japan.

Saqr, M., Vogelsmeier, L. V., & López-Pernas, S. (2024). Capturing where the learning process takes place: A person-specific and person-centered primer. Learning and Individual Differences, 113, 102492. https://doi.org/10.1016/j.lindif.2024.102492

Shibani, A., Knight, S., & Buckingham Shum, S. (2019). Contextualizable learning analytics design: A generic model and writing analytics evaluations. In Proceedings of the Ninth International Conference on Learning Analytics and Knowledge (LAK 2019), 4–8 March 2019, Tempe, Arizona, USA (pp. 210–219). ACM. https://doi.org/10.1145/3303772.3303785

Shibani, A., Knight, S., Kitto, K., Karunanayake, A., & Buckingham Shum, S. (2024). Untangling critical interaction with AI in students’ written assessment. In F. F. Mueller, P. Kyburz, J. R. Williamson, & C. Sas (Eds.), Extended Abstracts of the 2024 CHI Conference on Human Factors in Computing Systems (CHI EA 2024), 11–16 May 2024, Honolulu, Hawaii, USA (357:1–357:6). ACM. https://doi.org/10.1145/3613905.3651083

Shibani, A., Raj, V., & Buckingham Shum, S. (2025). Towards analytics for self-regulated human-AI collaboration in writing. In Companion Proceedings of the 15th International Conference on Learning Analytics and Knowledge (LAK 2025), 3–7 March 2025, Dublin, Ireland (pp. xx–xx). ACM.

Shibani, A., Rajalakshmi, R., Mattins, F., Selvaraj, S., & Knight, S. (2023). Visual representation of co-authorship with GPT-3: Studying human-machine interaction for effective writing. In M. Feng, T. Käser, & P. Talukdar (Eds.), Proceedings of the 16th International Conference on Educational Data Mining (EDM 2023), 11–14 July 2023, Bengaluru, India. International Educational Data Mining Society. https://educationaldatamining.org/EDM2023/proceedings/2023.EDM-long-papers.16/2023.EDM-long-papers.16.pdf

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

Swiecki, Z., Khosravi, H., Chen, G., Martinez-Maldonado, R., Lodge, J. M., Milligan, S., Selwyn, N., & Gašević, D. (2022). Assessment in the age of artificial intelligence. Computers and Education: Artificial Intelligence, 3, 100075. https://doi.org/10.1016/j.caeai.2022.100075

Uttamchandani, S., & Quick, J. (2022). An introduction to fairness, absence of bias, and equity in learning analytics. In C. Lang, G. Siemens, A. F. Wise, D. Gašević, & A. Merceron (Eds.), Handbook of learning analytics (2nd edition, pp. 205–212). SoLAR. https://doi.org/10.18608/hla22.020

Whitehead, R., Nguyen, A., & Järvelä, S. (2025). Utilizing multimodal large language models for video analysis of posture in studying collaborative learning: A case study. Journal of Learning Analytics, 12(1), 186–200. https://doi.org/10.18608/jla.2025.8595

Xie, Z., Wu, X., & Xie, Y. (2024). Can interaction with generative artificial intelligence enhance learning autonomy? A longitudinal study from comparative perspectives of virtual companionship and knowledge acquisition preferences. Journal of Computer Assisted Learning, 40(5), 2369–2384. https://doi.org/10.1111/jcal.13032

Yan, L., Greiff, S., Teuber, Z., & Gašević, D. (2024). Promises and challenges of generative artificial intelligence for human learning. Nature Human Behaviour, 8(10), 1839–1850. https://doi.org/10.1038/s41562-024-02004-5

Yan, L., Martinez-Maldonado, R., & Gasevic, D. (2024). Generative artificial intelligence in learning analytics: Contextualising opportunities and challenges through the learning analytics cycle. In Proceedings of the 14th International Conference on Learning Analytics and Knowledge (LAK 2024), 18–22 March 2024, Tokyo, Japan (pp. 101–111). ACM. https://doi.org/10.1145/3636555.3636856

Yang, K., Raković, M., Liang, Z., Yan, L., Zeng, Z., Fan, Y., Gašević, D., & Chen, G. (2025). Modifying AI, enhancing essays: How active engagement with generative AI boosts writing quality. In Proceedings of the 15th International Conference on Learning Analytics and Knowledge (LAK 2025), 3–7 March 2025, Dublin, Ireland (pp. 568–578). ACM. https://doi.org/10.1145/3706468.3706544

Zhai, C., Wibowo, S., & Li, L. D. (2024). The effects of over-reliance on AI dialogue systems on students’ cognitive abilities: A systematic review. Smart Learning Environments, 11. https://doi.org/10.1186/s40561-024-00316-7

Zhan, C., Joksimović, S., Ladjal, D., Rakotoarivelo, T., Marshall, R., & Pardo, A. (2024). Preserving both privacy and utility in learning analytics. IEEE Transactions on Learning Technologies, 17, 1615–1627. https://doi.org/10.1109/TLT.2024.3393766

Zipf, S., Wu, C., & Petricini, T. (2025). Using the information inequity framework to study GenAI equity: analysis of educational perspectives. Information Research: An International Electronic Journal, 30(iConf), 533–547. https://doi.org/10.47989/ir30iConf47284

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Published

2025-03-27

How to Cite

Khosravi, H., Shibani, A., Jovanovic, J., A. Pardos, Z. ., & 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.2025.8961

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