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This literature scoping review aims to demystify the ways in which those who teach can partner students by exploring initiatives such as involving them as self or peer assessors, as co-creators of assessment activities and marking criteria, and the use of collaborative opportunities to co-own the assessment process.
The purpose of this scoping review is to explore what the literature says regarding students as partners in the process of assessment in higher education. In recent years, there has been an increasing emphasis on students as partners in teaching, learning and assessment. Indeed, some educators would argue this is part of a larger paradigm shift in the latter part of last century towards student-centred learning. Conscious of this paradigm shift and the ever-present competing assessment discourses, the review investigates the growing interest in student partnership in assessment specifically, distinct from wider arenas of student partnership.
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This resource was created to share the learning from teaching adults with intellectual disabilities (ID) in IADT college as part of a national suite of pilot projects.
This case study outlines a first-year intervention at SETU Waterford using a timetabled weekly session to tackle common causes of academic failure such as time management, assessment planning. and study skills. It is intended for programme teams seeking practical, low-resource approaches to improving student progression and retention.
This OER guides students through human-in-the-loop software development, demonstrating how AI tools can be effectively supervised, refined, and integrated across the Software Lifecycle. Designed for computing educators and learners, it combines agile practice, teamwork, DISC awareness, testing, and critical reflection on human–AI collaboration.
This OER introduces students to designing and developing AI-powered assistants for agile software development using Flowise (no code). Learners as a team explore Retrieval Augmented Generation (RAG) and agent-based systems, applying AI to real-world agile practices while considering technical design, evaluation, and cost-aware decision-making
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