Generative AI and Learning Analytics

The rapid adoption of generative AI (GenAI) in education has raised critical questions about its implications for learning and teaching. While GenAI tools offer new avenues for personalized learning, enhanced feedback, and increased efficiency, they also present challenges related to cognitive engagement, student agency, and ethical considerations. Learning analytics (LA) provides a crucial lens to examine how GenAI affects learning behaviours and outcomes by offering data-informed insights into GenAI’s impact on students, educators, and educational ecosystems. Thus, obtained insights allow for evidence-based decision-making aimed at balancing GenAI’s benefits with the need to foster deep learning, creativity, and self-regulation of learning. This special issue of the Journal of Learning Analytics presents 10 research papers that explore the intersection of GenAI and LA, offering diverse perspectives that benefit students, teachers, and researchers. To structure these contributions, we adopt Clow’s generic framework of the LA cycle, categorizing the papers into four key areas: (1) understanding learning and learner contexts, (2) leveraging AI-generated data for learning insights, (3) applying LA methods to generate meaningful insights, and (4) designing interventions that optimize learning outcomes. By bringing together these perspectives, this special issue advances research-informed educational practices that ensure that GenAI’s potential is harnessed responsibly, reinforcing educational goals while safeguarding learners’ autonomy and cognitive development. Collectively, these contributions illustrate the reciprocal relationship between GenAI and LA, demonstrating how each can inform and refine the other. We reflect on the broader implications for LA, including the need to re-examine the boundaries of LA in the presence of GenAI, while preserving key principles from human-centred design and maintaining ethical and privacy standards that are foundational to LA.