Artificial intelligence is transforming how students learn, participate and demonstrate their capabilities. Yet many assurance conversations still focus on surveillance and single high-stakes checkpoints.
We suggest that the strongest integrity foundation is the curriculum itself.
Why design is the strongest integrity backbone
Episode 3 of the second season of OES’s The Thought Bubble podcast positions pedagogical assurance as the backbone that makes relational and technological assurance meaningful and proportionate.
Rather than treating assurance as a final gate at the end of a subject, the episode asks what happens when institutions treat curriculum design itself as integrity infrastructure.
Traditional approaches have leaned heavily on isolated checkpoints: a final exam, a capstone assignment, a single high-stakes decision point that is expected to “prove” that learning has happened.
In an AI-enabled environment, this looks increasingly fragile. The episode instead focuses on whether programs create coherent, cumulative evidence of capability over time through outcome mapping, scaffolded assessment, progression in complexity and multiple, triangulated forms of evidence.
Learn more about the series here:
The Thought Bubble, Season Two – ‘Assurance of Learning in the Age of AI: A Connected Approach’
Curriculum as integrity infrastructure
The episode features a conversation between OES Academic Director and curriculum and assessment leader, Dr Lucy Elliott, and learning design and program expert, Jesse Keenan. Together, they unpack how program-level architecture – not just individual tasks – underpins assurance of learning.
Lucy notes that universities are increasingly focused on demonstrating assurance through richer evidence of student capability over time, and that strong assessment design provides the integrity infrastructure needed to ensure practices are consistent, credible and scalable.
While Jesse explains the role of learning designers as pedagogical specialists who work in partnership with academics: they do not determine disciplinary content, but help translate subject expertise into structured, aligned learning experiences and assessment pathways that support meaningful engagement and repeated demonstration of capability.
A central theme is moving from isolated tasks to mapped, cumulative pathways. Starting from course learning outcomes, Lucy and Jesse describe outcome mapping, capability progression and multiple opportunities for students to demonstrate learning at increasing levels of complexity, often in applied and real-world contexts.
Flexibility and student ownership are presented as strengths, not threats, when they are intentionally designed – supporting diverse learners, different entry pathways and authentic demonstrations of learning that are harder for AI to fake.
Designing for AI-enabled learning, not around it
Lucy and Jesse are clear that AI has changed the assessment landscape and that universities need to embrace it thoughtfully rather than simply focus on detection or prohibition. They argue that assessment should encourage critical and ethical use of AI in ways that align with disciplinary expectations, and that students still need to demonstrate their own learning and judgement.
Jesse emphasises the idea of the ‘backward design’ approach to program development; starting with what students should know and be able to do, then deciding how that learning will be evidenced, including through dialogue, process-oriented tasks and opportunities to critique AI-generated outputs.
Examples of this include students building or configuring AI tools, reflecting on their use, and applying them in meaningful ways that develop both disciplinary knowledge and future-ready skills. This reframes AI as a catalyst for deeper learning design work, not just a risk factor to be policed.
Rigour with humanity: David Robertson on defensible evidence pathways
To widen the lens, episode 3 also features David Robertson, Director of Learning Design at OES. David describes curriculum design as part of the assurance mechanism itself: every course, assessment and progression point is deliberately mapped so that capability development is visible across a program.
Coherence, visibility and progression are framed as the backbone of credible, defensible assurance – particularly in a capability-based economy.
David outlines practical shifts that reduce integrity risk: moving away from one-off, high-stakes exams towards connected bodies of work, portfolios and progressive projects that create a narrative of learning and embed integrity into the structure.
He highlights “assessment for learning” over “assessment of learning”, choice and flexibility for different learning styles, and proportionate assessment loads that focus on tasks which genuinely demonstrate capability.
Crucially, he distinguishes between “compliance design” and “assurance design”. Compliance design focuses on ticking boxes for accreditation; assurance design is about the journey between those points, creating a transparent, credible story of how learning builds over time.
Empathy and a deep understanding of learner experience are presented as essential to this work, especially as AI and technology evolve – reinforcing the connection between the pedagogical and relational pillars of the Connected Assurance Framework.
Continue the journey with Episode 3 of The Thought Bubble podcast
Episode 3 shows that the strongest integrity foundation is the curriculum itself: scaffolded, cumulative assessment generates richer evidence than isolated exams, and design is the backbone that enables relational and technological assurance to be meaningful and proportionate. It sets up the next episode, which turns to relational assurance, and how trust, belonging, feedback and human connection shape the evidence we see in student learning.
If you are working on program design, AI-aware assessment, academic integrity, quality assurance or re-registration readiness, this episode offers concrete, design-led ways to strengthen assurance without narrowing access.
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