NEW! ⚡NOW LbE Reporting
The new ⚡|NOW reporting allows a user to view work from the standard that is of a similar standard to their own, as well as work that is of a quality that is a little higher and a little lower.
The new ⚡|NOW reporting allows a user to view work from the standard that is of a similar standard to their own, as well as work that is of a quality that is a little higher and a little lower.
Despite being only a few weeks since our last Newsletter, we have plenty to share in this update, the highlight being the extension of RM Compare | ⚡NOW onto non-mobile devices.
When you build a mobile experience for comparative judgement, the temptation is to start with the desktop application and ask: how can we fit everything onto a smaller screen? With RM Compare |⚡NOW, we asked a different question: what does someone need to make a useful comparison, right now?
Hot on the heels of the 'mobile first' release of RM Compare | ⚡NOW. we are pleased to make it accessible on any* device.
Shared Standards connects assessment evidence across RM Compare. Studio creates comparative ranks, Hub securely governs mark-derived and ACJ evidence, and Live applies approved rulers to new work. Together, they support consistent standardisation, moderation and on-demand comparison - helping organisations make professional judgement transparent, reusable and reliable.
This post explains how awarding organisations can maintain shared standards even when assessment questions change each year. Rather than relying on a fixed set of historic examples, a living master ruler uses approved anchor items and bridge calibration to test how new task-specific work relates to an established standard. This allows organisations to link, limit or separate new evidence responsibly—maintaining continuity without forcing false equivalence.
Awarding organisations already hold valuable standards evidence in marked candidate work, mark schemes, annotations and standardisation materials. This post explains how those assets can become governed reference sets in RM Compare—and, where the assessment construct aligns, be strengthened through ACJ into stable, reusable rulers. The result is a dual-evidence standard that preserves the authority of original marking while supporting more consistent standardisation, moderation and live comparison.
This post explores why awarding organisations should move beyond static item repositories and build standards repositories: governed, reusable evidence of what quality looks like. It shows how marked work, annotations, mark schemes and examiner judgement can become enduring assets, and how ACJ can strengthen suitable reference sets into stable, reusable rulers for standardisation, moderation and live assessment.
Moving beyond the transactional illusion of AI-era education. A student can now sit alone, prompt a machine and produce a polished essay, working code or a plausible solution to a complex problem in minutes. For education, that is more than an administrative headache. It challenges a familiar assumption: that a finished piece of work, submitted out of sight, is reliable evidence of what a student knows, understands or can do.
Most digital products in education, awarding, and professional development are hooked on the exact same commercial metric: Attention. Daily Active Users (DAU), session length, streaks, push notifications, and time-in-app sit at the absolute center of traditional EdTech strategy. What if that entire metric is fundamentally broken? What if the true measure of a product's success is that individual usage falls over time?
Across school systems and education ministries, assessment is increasingly focused on complex, open-ended outcomes. But consistently evaluating skills, knowledge and understanding in this context can be difficult. Traditional rubric-based marking can lead to variation between evaluators, an emphasis on surface-level features rather than the quality of learning, and substantial moderation workload. This study examined whether that shared standard could help create reliable assessment scales with fewer judgements over time.
Whether you are leading assessment strategy across a school trust, maintaining standards for an awarding body, or driving digital transformation in an enterprise, the core operational challenge remains the same: how to capture subjective human judgment and turn it into a consistent, scalable asset.
As artificial intelligence and Large Language Models (LLMs) become deeply embedded in global education, a central question looms over the industry: Can we truly trust AI to act as an autonomous judge of quality?