The Interplay of Learning Analytics and Artificial Intelligence

Abstract

The widespread use of digital systems and tools in education has opened up opportunities for collecting, measuring, and analysing data about user (learner, teacher) interactions with a variety of learning resources and activities, with the ultimate objective of better understanding learning and advancing both learning outcomes and the overall learning experience. This promise motivated the development of Learning Analytics (LA) as a research and practical field and the use of insights derived from learning trace data for evidence-based decision making in a variety of educational settings. While LA has made a significant contribution to better understanding of learning and the environments in which it takes place, many open questions and challenges remain. Furthermore, new opportunities and challenges continue to emerge with the ever-changing modalities of teaching and learning, the latest of which are associated with the rapid development and accessi-bility of Artificial Intelligence (AI). Taking the cyclical model of LA as its exploration framework, this paper examines how key components of the LA model -- namely data, methods, and actions -- relate to and may benefit from the latest develop-ments in AI, and especially Generative AI. Aiming for evi-dence-based analysis and discussion of the interplay between LA and AI, the paper relies on the latest empirical research in LA and the related research fields of AI in Education and Educational Data Mining.