2025/06/19 by Chen, Angxuan
#FOS: Computer and information sciences #Human-Computer Interaction (cs.HC)
paper · doi:10.48550/arxiv.2506.16312
This study investigated the impact of a theory-driven, explainable Learning Analytics Dashboard (LAD) on university students' human-AI collaborative academic abstract writing task. Grounded in Self-Regulated Learning (SRL) theory and incorporating Explainable AI (XAI) principles, our LAD featured a three-layered design (Visual, Explainable, Interactive). In an experimental study, participants were randomly assigned to either an experimental group (using the full explainable LAD) or a control group (using a visual-only LAD) to collaboratively write an academic abstract with a Generative AI. While quantitative analysis revealed no significant difference in the quality of co-authored abstracts between the two groups, a significant and noteworthy difference emerged in conceptual understanding: students in the explainable LAD group demonstrated a superior grasp of abstract writing principles, as evidenced by their higher scores on a knowledge test (p= .026). These findings highlight that while basic AI-generated feedback may suffice for immediate task completion, the provision of explainable feedback is crucial for fostering deeper learning, enhancing conceptual understanding, and developing transferable skills fundamental to self-regulated learning in academic writing contexts.