Date
Publisher
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.
Study at a glance
- Question: How do students interact with an AI assistant that explains feedback on their learning dashboards?
- Setting and data: Dialogues between higher education students and an AI assistant integrated into a learning dashboard over a 10-week semester.
- Method: Analysis of student-AI chat dialogues and student reflections.
Why it matters
Students, especially those with lower self-regulated learning competence, often struggle to engage with and interpret learning analytics feedback. Conversational generative AI assistants offer potential to scaffold this process through real-time, personalized, dialogue-based support.
Key findings
- During a 10-week college semester, lower self-regulating students asked an AI assistant for clarification and reassurance, while higher self-regulating peers requested technical, personalized strategies.
- The AI assistant provided clear and reliable explanations but had limited ability to personalize responses, handle emotional queries, or combine multiple data points.
- The AI tool supported lower self-regulating students in engaging with analytics feedback, helping narrow gaps with their higher self-regulating peers.
Summary drafted with AI from the paper’s abstract and may contain errors. See the full paper for details.
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