Artificial intelligence is rapidly changing how students learn, engage and demonstrate what they know.
Much of the conversation about technology and assurance of learning is still anchored in a narrow question: can we detect when students are using AI?
Episode 5 of The Thought Bubble’s new season explores why signals that simply show whether or not students have used AI to complete an assessment could distract educators from using data and interactions to identify when students need support.
Listen to episode 5 now:
Why detection is not assurance
Hosted by Amanda Ford, OES’s Associate Director of Generative AI, Episode 5 reframes the role of technology in assurance of learning.
A central idea in the episode is the distinction between detection and assurance.
As OES learning design expert Liam Ford explains, identifying whether a student has used AI does not demonstrate that learning has taken place.
Detection can surface behaviour, but it cannot provide evidence of capability, particularly when assessment design is weak.
This shifts the conversation from “how do we catch misuse?” to “how do we build confidence that learning has occurred?”
Learn more about the series here:
The Thought Bubble, Season Two – ‘Assurance of Learning in the Age of AI: A Connected Approach’
AI as an exposure, not just a disruption
The episode challenges the idea that AI has created entirely new problems.
Instead, it argues that AI has exposed existing weaknesses in how learning and assessment have been structured. Where evidence of learning was already thin or overly reliant on single tasks, AI makes those limitations more visible.
This reframes technology from being the source of risk to being a catalyst for better design.
Understanding signals, not proof
Data and system-generated signals are a key focus of the discussion.
Engagement patterns, progression data and interaction with learning materials provide useful insight into how students are learning over time. These signals help educators understand whether students are building capability consistently or engaging only at isolated points.
However, the episode makes a clear distinction: these signals are not proof of learning on their own.
They become meaningful when interpreted alongside curriculum design and relational evidence, contributing to a broader, more credible picture that supports academic judgement.
Technology across the learning journey
Episode 5 expands the role of technology beyond assessment controls.
Rather than focusing only on identity verification or secure environments, the discussion explores how technology supports the entire learning journey. This includes identifying disengagement, enabling timely intervention and supporting students as they encounter challenges.
Examples such as virtual tutoring tools demonstrate how technology can actively support learning, not just monitor it. These tools help remove barriers and create more opportunities for meaningful engagement.
The episode also highlights risks such as cognitive offloading, where over-reliance on AI too early can limit the development of deep understanding. This reinforces the need to design learning experiences that build capability over time, rather than bypass it.
Balancing control, equity and trust
A recurring theme is the importance of proportionate controls.
Highly restrictive approaches may create a sense of security, but can limit access and disproportionately impact students who rely on flexible study. The episode emphasises that maintaining standards does not require eliminating flexibility.
Instead, technology should be aligned with pedagogy and academic judgement, supporting visibility and trust without narrowing participation.
Across the discussion, a consistent message emerges: technology is not the solution to assurance of learning, but it is a critical enabler when used as part of a connected system.
Continue the journey with Episode 5 of The Thought Bubble podcast
Episode 5 shows how systems, data and tools can strengthen assurance when they support design and judgement, rather than replace them.
It sets up the final episode, which turns to the system level, exploring how assurance decisions shape trust, participation and the future of higher education.
If you are working on learning platforms, assessment systems, AI policy or institutional quality frameworks, this episode offers a practical perspective on how to use technology to strengthen assurance without reducing it to surveillance.
Listen to The Thought Bubble podcast now >
Explore OES’s connected approach to assurance of learning >
