This paper explores how the adoption of Team-Based Learning (TBL) is responding to sector-wide challenges around student engagement, belonging, critical thinking, academic integrity and the accelerating impact of artificial intelligence. TBL is a structured, student-centred pedagogical approach that focuses on readiness checks, team accountability and applied, real-world problem-solving. It supports a deeper approach to learning and helps students strengthen transversal skills such as communication, critical thinking, and teamwork, which are becoming increasingly significant given the rapid developments in Artificial Intelligence (AI) and evolving graduate attributes.

Key initiatives to support TBL integration include adopting a dedicated Learning Activity Management System (LAMS) that enables seamless integration of TBL and other teaching strategies to support active and collaborative learning across face-to-face, online, and hybrid environments. An overview of LAMS will be presented, including features such as focused tracking, AI-enabled lesson development, real-time analysis of student work, instant feedback to the lecturer, and peer review. In addition, to support professional development, TUS has developed a Certificate in Team-Based Learning (15 ECTS at level 9). This is supplemented by the creation of open-access resources and a TBL101 workshop.

National Forum Pathfinder funding has led to a partnership approach between the Technological University of the Shannon (TUS) and South East Technological University (SETU), laying the groundwork for a scalable, future-focused ecosystem of Team-Based Learning (TBL) in Irish higher education. The project team has conducted a cross-institutional scoping process to assess staff readiness for large-scale pedagogical change. A pivotal moment involved bringing academic leaders from TUS and SETU to the National TBL Hub at the University of Manchester, where they observed a mature, institution-wide TBL ecosystem in action. Introductory TBL workshops and the integration of LAMS software have enabled staff across both universities to experience how TBL can be delivered consistently and seamlessly.

Meaningful pedagogical transformation requires relational, sustained, ecosystem-level academic development rather than one-off interventions. This paper concludes by outlining a shared ambition to collaboratively develop national support structures that enable active learning at scale and prepare graduates for a world where human collaboration and critical reasoning matter more than ever.

Project work or essay work is a vital way in which our students learn. Gen AI can now successfully complete most of this work with little assistance and potentially almost no learning by learners. Removing these assessments in favour of exams removes a vital component of learning. Interviewing students about their work does enable their understanding to be evaluated; however, it is extremely time consuming for the lecturers rendering it unviable in most courses.

We have piloted a novel approach to this problem:

1. A student submits their project work / essay work.

2. We develop a standard with a set of exemplars of what good questions look like to test the understanding of their individual work.

3. From each individual piece of work, we generate (with a local LLM) a set of basic and advanced versions of these questions (with answers).

4. In a locked down environment (other websites cannot be accessed), each student answers their personalised questions.

5. The lecturers can use a selection of the answers to help evaluate the understanding of the student and be used as a component of their project/essay grade (at its most extreme, where very limited understanding is demonstrated - indicating that little of the work is the student’s own work - a project which would otherwise have done well will fail).

We also explored using this for formative assessment - our approach is intended to support learners to use AI tools in ways that enhance learning and understanding rather than undermine it. We piloted this across two diverse student cohorts (computing and psychology) this semester. In this talk we show the details of the process and present the lessons we learned. We also surveyed learners and educators as the first steps towards codesign of the next iteration of this work.

Technology-enhanced formative assessments (TEFAs) are widely used by educators to support formative learning and feedback, and are frequently associated with positive student perceptions of engagement and learning. However, less attention has been paid to the relationship between students’ perceived value of such assessments and their actual patterns of use over time. This paper examines this relationship through an empirical study of student engagement with TEFAs within the author’s own higher education teaching practice.

Informed by formative assessment literature (Black and Wiliam, 1998; Nicol and Macfarlane‐Dick, 2006; Carless, 2007) and drawing on participatory action research traditions (Elliott, 1991; Coughlan and Coghlan, 2002), the study combined quantitative learning analytics data with qualitative student feedback to explore how students engaged with TEFAs across a semester. While students consistently reported that TEFAs were useful for understanding content, identifying knowledge gaps and supporting exam preparation, engagement data revealed uneven and highly strategic patterns of use.

Findings indicate that students’ engagement with TEFAs was strongly shaped by summative assessment structures, with activity peaking in the period immediately preceding final examinations. Sustained or regular engagement was limited, despite positive perceptions of value. This disconnect highlights the influence of assessment culture and learner priorities in shaping engagement with formative assessment technologies. This challenges assumptions that positive feedback alone leads to consistent formative use.

The paper argues that student engagement with TEFAs is best understood as situated and conditional, rather than as a straightforward indicator of effectiveness. By highlighting the tension between perceived value and actual engagement behaviour, the study contributes a state of the actual (Selwyn, 2008) account of TEFA use that complicates dominant narratives of engagement in EdTech research. The paper concludes by discussing implications for educators designing and evaluating TEFAs, emphasising the need to align formative assessment intentions with the realities of summative-driven learning environments.