Background & further reading

TL; DR:
Learning analytics (LA) has long aimed to deliver personalised feedback at scale, but the rise of generative AI (GenAI) now pushes this further—requiring careful grounding in theory and evidence to avoid undermining effective feedback practice. Three main research gaps emerge:

1. Impact on learning: While LA feedback has shown positive effects on self-regulated learning, GenAI’s impact on student learning is mixed, with some studies showing negative effects.

2. Student feedback literacy: Understanding how students engage with AI-augmented feedback—and how their feedback literacy shapes this engagement—is crucial for effective design.

3. Teacher feedback literacy: Teachers play a key role in facilitating AI-supported feedback but their skills and practices remain underexplored, particularly in “open” automated feedback systems requiring human oversight.

Across these issues, it remains essential to identify where GenAI adds genuine value in the LA feedback process (e.g., data interpretation, feedback generation, dialogue) while ensuring accuracy, trustworthiness, and pedagogical soundness.

Extended Background:

With the advancement in learning analytics (LA), we have seen a number of LA feedback solutions for personalising feedback at scale (see Banihashem et al., 2022, for a review). Now, since the meteoric rise of generative AI technologies, there has been a subsequent increase in LA research to further augment LA feedback (e.g., Jin et al., 2024). The rapid adoption of GenAI in feedback processes creates a unique opportunity to rethink and extend established concepts of feedback. To ensure that new tools and approaches enhance rather than dilute effective practice, it is vital to ground these developments in robust theory and evidence.

Indeed, as interest continues to grow in the use of LA, and AI for personalised feedback, important questions emerge along with this. Earlier research in LA called for evidence of impact (Dawson et al., 2019). In response, many studies started to document LA feedback impact particularly in terms of self-regulated learning (e.g., Lim et al., 2021). Presently, with the emergence of GenAI, research is starting to show a mixed impact on student learning; concerningly, evidence shows possibly negative effects of the use of GenAI tools on student learning (e.g., Fan et al., 2025). Therefore, the first question that arises is: what is the impact of personalised feedback on students’ learning? 

Secondly, the contemporary view of feedback as process positions student responses as critical to effectiveness—the essence of feedback literacy (Carless & Boud, 2018). The importance of feedback literacy in LA feedback has been highlighted (e.g., Jin et al., 2024) and empirically supported recently (Weidlich et al., 2025a; Weidlich et al., 2025b). Currently, research on LA feedback augmented with GenAI is just emerging to document how students feel about such feedback (e.g., Weidlich et al., 2025a). However, more work is needed to understand: how are students engaging with LA feedback, especially when leveraging newer technologies? How may feedback literacy influence their engagement with this feedback, and how should the design of AI-mediated feedback processes take student feedback literacy into consideration? 

Thirdly, while a large proportion of LA feedback systems - namely, dashboards and recommender systems which comprise much of the work in LA - is fully automated, there also exist LA feedback tools which require humans in the loop, referred to as “open automated feedback (AF) tools” (Buckingham Shum et al., 2023). In adopting open AF tools, teachers require essential skills including teacher feedback literacy and automated feedback literacy (Buckingham Shum et al., 2023). However, in comparison to the research on learners’ perceptions and adoption of LA feedback, there is less research documenting teachers’ skills and use of LA feedback systems. This is a significant research gap, as teachers have much agency over the learning environment which can influence students’ learning and engagement. It is thus important to ask: how do teachers facilitate LA feedback? Especially when leveraging newer technologies, how does teacher feedback literacy contribute to LA feedback effectiveness? How might teacher feedback literacy look like in an AI-mediated feedback process?

Across these gaps, a cross-cutting issue is clarifying where in the LA feedback process GenAI can add the most value, for instance, in data interpretation, feedback text generation, or dialogic support (Yan et al., 2024). At the same time, questions of accuracy and trustworthiness remain fundamental, as the quality of AI-mediated feedback underpins its pedagogical effectiveness (see e.g. Seßler et al., 2025). 

References

Banihashem, S. K., Noroozi, O., van Ginkel, S., Macfadyen, L. P., & Biemans, H. J. A. (2022). A systematic review of the role of learning analytics in enhancing feedback practices in higher education. Educational Research Review, 37, 100489. 

Buckingham Shum, S., Lim, L.-A., Boud, D., Bearman, M., & Dawson, P. (2023). A comparative analysis of the skilled use of automated feedback tools through the lens of teacher feedback literacy. International Journal of Educational Technology in Higher Education, 20(1), 40. 

Carless, D., & Winstone, N. (2023). Teacher feedback literacy and its interplay with student feedback literacy. Teaching in Higher Education, 28(1), 150-163. 

Carless, D., & Boud, D. (2018). The development of student feedback literacy: enabling uptake of feedback. Assessment & Evaluation in Higher Education, 43(8), 1315-1325. 

Dawson, S., Joksimovic, S., Poquet, O., & Siemens, G. (2019). Increasing the Impact of Learning Analytics. In Proceedings of the 9th International Conference on Learning Analytics & Knowledge, Tempe, AZ, USA. 

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). 

Jin, F., Maheshi, B., Martinez-Maldonado, R., Gašević, D., & Tsai, Y.-S. (2024). Scaffolding Feedback Literacy: Designing a Feedback Analytics Tool with Students. Journal of Learning Analytics, 1-15. 

Lim, L.-A., Gasevic, D., Matcha, W., Ahmad Uzir, N. A., & Dawson, S. (2021). Impact of learning analytics feedback on self-regulated learning: Triangulating behavioural logs with students’ recall. In LAK21: 11th International Learning Analytics and Knowledge Conference (pp. 364-374). ACM. 

Seßler, K., Bewersdorff, A., Nerdel, C., & Kasneci, E. (2025). Towards adaptive feedback with ai: Comparing the feedback quality of llms and teachers on experimentation protocols. arXiv preprint arXiv:2502.12842.

Weidlich, J., Fink, A., Frey, A., Jivet, I., Gombert, S., Menzel, L., Giorgashvili, T., Yau, J., & Drachsler, H. (2025a). Highly informative feedback using learning analytics: how feedback literacy moderates student perceptions of feedback. International Journal of Educational Technology in Higher Education, 22(1), 43. 

Weidlich, J., Gotsch, F., Schudel, K., Marusic-Würscher, C., Mazzarella, J., Bolten, H., Bütler, D., Luger, S., Wohlfender, B., & Maag Merki (2025b). Teacher, Peer, or AI? Comparing Effects of Feedback Sources in Higher Education. PsyArxiv. 

Yan, L., Martinez-Maldonado, R., & Gasevic, D. (2024, March). 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).