As AI reshapes assessment, final products are becoming harder to interpret on their own.
Episode 4 of The Thought Bubble podcast’s new season argues that the student–educator relationship is itself a core assurance mechanism, not a soft add-on.
Why relational assurance matters in an AI-enabled environment
Hosted by OES’s Associate Director of Gen AI, Amanda Ford, Episode 4 explores what educators can know about their students’ learning outcomes that assessment integrity detection tools simply can’t show on their own.
In an AI-enabled environment, institutions need more than one checkpoint; the quality of evidence is shaped by the environment students are in, and by the relationships that influence honesty, engagement, confidence and willingness to ask questions.
Student voices featured in this episode underscore this point – one student admits not feeling “seen” by university and relying on peers to stay on track; another talks about feeling less supported and more disconnected in online study unless people actively check in. These experiences highlight that if connection becomes optional, so does visibility of learning.
Learn more about the series here:
The Thought Bubble, Season Two – ‘Assurance of Learning in the Age of AI: A Connected Approach’
From artefacts to patterns of interaction
OES Academic Program Directors Andrew McLean and Sally Trudgen are experts in best practice for teaching and instruction delivery. They join the episode to unpack how relational assurance works in practice; particularly how the student-learner connection can be strengthened in online and asynchronous contexts.
Andrew points out that universities are entering a period where demonstrating learning outcomes and the quality of educational experiences is increasingly important, especially in online and hybrid delivery models. And that generative AI is accelerating the need to rethink teaching models while preserving what already works.
They describe key strengths of the OES model: high-quality learning design, clear structure, asynchronous accessibility, and the central role of online learning advisers who guide students and support progress.
Combined with data-informed early intervention, these elements create a system oriented towards proactive care rather than reactive rescue. In this context, educator-student interactions over time reveal patterns of engagement, questions and feedback that single assessments cannot.
Listen to episode 4 now:
Making learning processes visible
A major theme in the episode is moving beyond the final product to make learning processes visible. Andrew and Sally explain that in an AI-enabled environment, a polished submission no longer gives the same level of confidence it once did. Instead, they emphasise early conversations, check-ins and structured touchpoints that create reference points for growth.
For example, in placement-based units, introducing an early one-on-one conversation between the student and educator helps surface the student’s starting point, concerns and goals, providing a baseline against which later development can be interpreted. In research-based units, pre-assessment coaching conversations and small-group discussions allow students to refine questions and test ideas aloud, leading to stronger alignment and more interpretable evidence in final outputs.
Sally notes that students are not asking for constant contact; they want targeted, meaningful engagement at moments of uncertainty. This includes pre-submission reassurance that they are on the right track while there is still time to adjust, rather than after results are released.
Relational assurance as equity and trust work
Episode 4 also frames relational assurance as an equity issue. For flexible and equity-cohort learners balancing work, caring responsibilities and complex lives, consistent educator presence and proactive support can make the difference between persistence and withdrawal, and between guarded, minimal artefacts and richer, more authentic evidence of learning.
Andrew warns that a sector-wide pivot towards surveillance and detection in response to AI would not build trust. Instead, he and Sally argue for designing learning so that it is easy to see, support and validate through structured, purposeful interactions – from online learning adviser outreach to small-group conversations and pre-placement coaching. Their pilots show students becoming more engaged, confident and successful when given these opportunities to connect and reflect.
Relational assurance, they suggest, is about choosing meaningful moments where interaction makes learning more visible and trustworthy, not adding meetings for their own sake. Curriculum creates pathways; relationships help students move through them and help educators interpret what they see. Technology can support visibility, but cannot substitute for trust, dialogue or academic judgement.
Continue the journey with Episode 4 of The Thought Bubble podcast
Episode 4 shows how relationships shape the evidence students produce and how consistent educator presence strengthens assurance for online, flexible and equity cohorts. It sets up the next episode, which turns to the technological pillar where we explore how platforms, data and AI can support assurance by improving traceability, visibility and review, without becoming narrow surveillance infrastructure.
If you are working on online teaching models, student support, academic integrity, AI policy or quality assurance, this episode offers practical insights into relational assurance as a core part of credible, humane assurance of learning.
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