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• Who Owns Student Work When AI Is in the Loop? (2/4)

  When people talk about AI in assessment, the conversation usually goes straight to marking accuracy, bias or workload. Underneath all of that sits a quieter question that is just as important: who actually owns the work being processed – and what does that ownership mean once AI is involved?

• If AI Is Serious About Learning Outcomes, ‘Ground Truth’ Has to Mean More Than Last Year’s Exam Scores (Part 2/2)

  If AI Is Serious About Learning Outcomes, ‘Ground Truth’ Has to Mean More Than Last Year’s Exam Scores (Part 2/2)

• Who Gets to Define “Learning” in an AI World? (Part 1/2)

  OpenAI’s new “Learning Outcomes Measurement Suite” is more than a product announcement; it is a bid to define how AI‑mediated learning will be measured – and, by implication, what will count as learning in the years ahead. Their recent education push wraps research, product and policy language into a single, compelling story about how students learn with AI.

• Newsletter March 2026

  For this edition we have produced a couple of short series focusing on two critical considerations in the world of assessment, and education more generally. The 'AI World' is moving fast and finding time for necessary thinking and consideration has never been more important.

• Defending Epistemic Integrity in the Age of AI Assessment

  The educational assessment sector is currently gripped by the promises of Generative AI. The prospect of instant, practically free grading at infinite scale is undeniably seductive to overburdened systems. However, in the rush toward algorithmic efficiency, we are in danger of sacrificing something fundamental: Epistemic Integrity.

• The Skills Imperative 2035: Why the Future of Assessment Can’t Be a Tick-Box Exercise

  If you have been following the conversation around the future of education, you know the refrain: the world is changing, and schools need to adapt. But the latest report from the National Foundation for Educational Research (NFER), The Skills Imperative 2035, moves beyond generalities and puts hard data behind the challenge.

• Playing Nicely with Rubrics: How RM Compare Uses Rubrics and Comparative Judgment to Improve Assessment in the AI Era

  Learn how RM Compare combines rubrics with adaptive comparative judgment to make assessment more reliable, transparent, and fair in an age of generative AI

• Item Misfit: Listening to What the Work Is Telling You

  If judge misfit shows where people see things differently, item misfit shows where the work itself is provoking disagreement. In an RM Compare report, item misfit does not mean “bad work” or “faulty items.” It means, very specifically, “here are the pieces of work that your judging pool did not see in the same way.” Those items are often where your assessment task, your construct, and your judges’ thinking come most sharply into focus.

• Digital Equity and Mobile-First Assessment: Why RM Compare is Redesigning for Real Access

  Assessment technology should work for every learner, not just those in affluent schools with modern devices and reliable broadband. Yet, as RM Compare moves toward a mobile-first user experience, new evidence reveals just how critical this shift is to educational equity. For millions of students in the UK and billions worldwide, smartphones are not merely one device choice, they are the only digital device available.

• How to enjoy fries on the beach, undisturbed by seagulls: Surprising Truths from a recent ACJ Study

  A recent study (2025) from Jeffrey Buckley (Technological University of the Shannon) and Caiwei Zhu (Delft University of Technology) set out to answer a critical question for anyone seeking fairness and efficiency in educational assessment: How feasible is Adaptive Comparative Judgement (ACJ) when deployed in real classrooms?

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