The Learning Orchestration Framework: Supporting Student Agency and Credible Evidence of Learning in AI-Mediated Assessment
Dr James Wood (SFHEA), Assistant Professor, Durham University, UK
James Wood
Learning orchestration describes how students coordinate feedback and learning support from teachers, peers, artefacts such as rubrics, exemplars and disciplinary resources, and generative AI in ways that sustain rather than replace their judgement. As AI becomes routine in study practices, this coordination has grown more complex and more important. The challenge for educators is not to block AI, but to design assessments that reveal students’ cognitive, epistemic and ethical decision-making in distributed learning environments. This capability is now central to academic integrity and to contemporary knowledge work, where professionals must verify AI outputs, calibrate trust and justify decisions transparently.
This masterclass introduces the Learning Orchestration Framework, developed in recent publications (Wood and Pitt, 2025; Wood, in review), which conceptualises these practices as a higher-order capability through which students manage cognitive effort, calibrate trust across sources and make ethical choices about AI use. Drawing on empirical work, we will examine the calibration activities learners undertake, including verifying AI against disciplinary anchors, triangulating feedback and building trust hierarchies, and consider how assessment design can make this reasoning visible and creditable.
We will work with the dual choreography at the heart of Learning Orchestration, where educators design enabling conditions such as scaffolding, time and epistemic anchors while learners enact cognitive, epistemic and ethical calibration. Participants will analyse provided examples and may draw on their own assessment tasks to locate hidden reasoning, then develop orchestration-aware designs that scaffold learning and help make decision-making examinable through staged reflections, annotated drafts, process tracking and transparent task structures.
The session reframes academic integrity as transparent and justified reasoning across human and AI sources, and offers design principles and planning tools for assessments that develop, reveal and reward learning-orchestration capability.
Biography
Dr James Wood (SFHEA) is an Assistant Professor at Durham University, specialising in assessment transformation in AI-mediated higher education. He developed the Learning Orchestration Framework, which explains how students coordinate human and AI feedback to exercise evaluative judgement, sustain agency and produce credible evidence of learning in digitally rich environments.
James serves on the Assessment & Evaluation in Higher Education editorial board and the AHE executive committee, and is a frequent keynote speaker and workshop facilitator on assessment redesign, feedback literacy and ethical AI integration.
Before joining Durham, he taught at Bangor University, where he also held senior leadership roles directing assessment strategy and postgraduate and EdD programmes, he has also worked at Seoul National University, King’s College London and UCL. His work focuses on student and educator capability development for AI, particularly how generative technologies reshape judgement, feedback processes and contemporary assessment practice.
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