Incorporating feedback into the learning process is crucial for learners’ success. Recent advancements in Learning Analytics (LA) and Artificial Intelligence (AI) have introduced a range of feedback generation and delivery opportunities to improve teaching and learning processes, yet questions remain about how learners engage with such tools and their impact. In this study, we investigated postgraduate students’ engagement with analytics feedback in relation to their level of self-regulated learning (SRL) competence and performance in a semester-long (ten-week) course. We specifically focused on the Interactive-Constructive-Active–Passive (ICAP) framework of cognitive engagement. Initially, students were asked to participate in an established SRL questionnaire, based on Zimmerman’s theory of self-regulation to evaluate their SRL competence (N = 39). Throughout the semester, their online behaviour data from Moodle and Google Docs was collected and analyzed to form personalised analytics feedback for each student. We examined how students with different SRL competencies engage with analytics feedback and the impact of this engagement on students’ course performance. Results indicated that students with high SRL competence actively engage with analytics feedback more than students with low SRL competence. However, students' analytics feedback engagement did not significantly affect their course performance. Additionally, we analyzed students’ reflections on the feedback provided to investigate how they perceived it in relation to their learning experiences and performance. Students argued in their reflections that analytics feedback was beneficial in identifying and regulating their online behaviours and providing motivation through objective insights. They also noted limitations in accurately reflecting their behaviours and learning quality, the need for more personalised recommendations and timely feedback, and suggested design improvements to ensure clarity, foster interaction and incorporate tailored, in-depth insights. We conclude the paper with a discussion on future design and research suggestions for ways of monitoring and supporting students’ cognitive engagement with analytics feedback interventions.

Learning analytics dashboards (LADs) aim to support students' regulation of learning by translating complex data into feedback. Yet students, especially those with lower self-regulated learning (SRL) competence, often struggle to engage with and interpret analytics feedback. Conversational generative artificial intelligence (GenAI) assistants have shown potential to scaffold this process through real-time, personalised, dialogue-based support. Further advancing this potential, we explored authentic dialogues between students and GenAI assistant integrated into LAD during a 10-week semester. The analysis focused on questions students with different SRL levels posed, the relevance and quality of the assistant's answers, and how students perceived the assistant's role in their learning. Findings revealed distinct query patterns. While low SRL students sought clarification and reassurance, high SRL students queried technical aspects and requested personalised strategies. The assistant provided clear and reliable explanations but limited in personalisation, handling emotionally charged queries, and integrating multiple data points for tailored responses. Findings further extend that GenAI interventions can be especially valuable for low SRL students, offering scaffolding that supports engagement with feedback and narrows gaps with their higher SRL peers. At the same time, students' reflections underscored the importance of trust, need for greater adaptivity, context-awareness, and technical refinement in future systems.