
This annual publication by EADTU shares cutting-edge developments in digital teaching and learning across Europe, with the aim of empowering universities to innovate in response to technological, societal, and pedagogical change.This year's report reflects the accelerating impact of generative AI on higher education, not only as a tool for automation but as a catalyst for transformation. Universities are rethinking pedagogical strategies, assessment methods, and student support services in ways that were unimaginable just a few years ago. At the same time, institutions continue to prioritise student engagement and flexible learning pathways, ensuring that innovation remains grounded in inclusion and educational purpose. The report is structured around three core themes: Generative AI’s Influence on Pedagogy and Learning examines how institutions are adapting to the opportunities and challenges of AI-enhanced education, with a special focus on hybrid learning and the redefinition of assessment practices. Applying Generative AI in Practice showcases emerging use cases, from intelligent tutoring systems and immersive simulations to AI-driven support in STEM labs and inclusive language education. Student Engagement through Innovation and Flexible Learning presents diverse approaches to engaging learners — including renewable assessment, community-based language education, hybrid tutoring models, and the implementation of microcredentials as a flexible learning response to evolving learner needs. Each contribution offers hands-on insights, reflective commentary, and forward-looking strategies that can guide institutions through this time of rapid transition. As always, the report emphasises quality, inclusion, and learner-centred design — pillars that remain essential in a digital-first higher education landscape.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
