From insight to intervention: learning analytics and generative AI support for learning design and educational decision-making

Abstract

Recent advances in learning design tools, learning analytics, and generative artificial intelligence (GenAI) have created new possibilities for educators to design, monitor, and adapt learning experiences. Yet evidence that these possibilities routinely translate into student-centred, inclusive, personalised, and effective practice remains limited. This editorial synthesises four contributions to the special issue on learning analytics and AI support for learning design and educational decision-making. Across student, teacher, disciplinary, and institutional levels, the studies show that technology does not become educationally valuable through technical capability alone. Its value is mediated by learning design, community and interaction, digital literacy, educator and learner agency, ethical fitness, organisational strategy, resources, and wide stakeholder engagement. At the same time, the evidence base remains dominated by cross-sectional, self-report, discourse-based, and expert-judgement studies. We argue that the field must now move from demonstrating associations and proposing frameworks towards intervention research that tests causal mechanisms, implementation conditions, longer-term outcomes, and distributional effects. A future agenda should connect learning analytics and GenAI tightly to educational visions, pedagogical intentions, preserve human and epistemic agency, and build trustworthy socio-technical infrastructures that enable educators and learners to act on evidence.