# What Students Ask, How a Generative AI Assistant Responds: Exploring Higher Education Students' Dialogues on Learning Analytics Feedback

arXiv: https://arxiv.org/abs/2601.04919 · HTML: https://mlsanity.com/paper/2601.04919/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 40 references that are in the map.
- First posted on arXiv 8 Jan 2026.

## Builds on (most cited first, 5 of 5)

- [Generative AI in Higher Education: A Global Perspective of Institutional Adoption Policies and Guidelines](https://mlsanity.com/paper/2405.11800/) (2024)
- [The Interplay of Learning, Analytics, and Artificial Intelligence in Education: A Vision for Hybrid Intelligence](https://mlsanity.com/paper/2403.16081/) (2024)
- [Chatting with a Learning Analytics Dashboard: The Role of Generative AI Literacy on Learner Interaction with Conventional and Scaffolding Chatbots](https://mlsanity.com/paper/2411.15597/) (2024)
- [Understanding Learner-LLM Chatbot Interactions and the Impact of Prompting Guidelines](https://mlsanity.com/paper/2504.07840/) (2025)
- [Have Learning Analytics Dashboards Lived Up to the Hype? A Systematic Review of Impact on Students' Achievement, Motivation, Participation and Attitude](https://mlsanity.com/paper/2312.15042/) (2023)

## Built on by (most cited first, 1 of 1)

- [REFINE: Real-world Exploration of Interactive Feedback and Student Behaviour](https://mlsanity.com/paper/2603.29142/) (2026)

## Abstract (arXiv)

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.