In this series, we’ve been arguing that sovereignty is the only sustainable path for professional judgment in an agentic world. We started by looking at the infrastructure we’ll need for sovereign intelligence, then at the difference between sovereign systems and AI wrappers, and most recently at dark patterns that steer people into feeding someone else’s models.
In our series on digital sovereignty, we have explored how "Harvesting Models" use dark patterns to bypass consent and how "AI Wrappers" quietly extract value from your institutional judgment data. But there is one final, definitive test of a vendor’s intent: What happens when you want to leave?
In earlier posts we argued that sovereignty is the only sustainable path for professional judgment, and that organisations need infrastructure which lets them own and operate their own intelligence. There’s another layer to this: even if the architecture is centralised, the interface can quietly decide who wins. The way AI is presented in a product can make the difference between a conscious choice and a gentle shove.
In the last two posts, we argued that sovereignty is the only sustainable path for professional judgment, and that the real work now is building the infrastructure that lets organisations own and operate their own intelligence.
Most RM Compare users open a standard report, see a reliability value and a set of graphs, and then have to decide - often very quickly - whether their session “worked.” A single number is doing far too much heavy lifting in that moment. This first post reframes how to think about your reports. Instead of asking “Is my reliability high enough?”, the more useful question is “How healthy is this session, and what is it telling me to do next?”
If you have ever opened an RM Compare standard report, seen a lot of graphs and a reliability number, and thought “Is this good? What do I do now?”, this series is for you. Many users invest time and energy in running sessions, only to feel deflated when reliability looks lower than they hoped or when misfit graphs appear to suggest something has gone wrong. The aim of this series is to turn those moments of frustration into confident, informed decisions about how to run and improve your sessions.
The Chancellor demands productivity. NFER warns up to three million jobs may disappear by 2035. As FAB 2025 opens, it’s time for the sector to do more than just “cope” - it’s time to lead.
In our first two blog posts, we documented the crisis: groundbreaking research from Princeton and Dartmouth showing how generative AI has destroyed the signalling value of written applications , and explored why this threatens educational meritocracy and demands new assessment approaches . The evidence is overwhelming. The consequences are severe. Traditional written signals no longer work.
The rise of generative AI has disrupted far more than how we write - it has fundamentally broken a mechanism that labour markets, universities, and employers have relied on for decades to identify talent. Groundbreaking new research from Princeton and Dartmouth reveals just how profound this disruption is, and why solutions like RM Compare's Adaptive Comparative Judgement are now essential.
The rise of generative AI in recruitment able to craft convincing CVs, cover letters, and even interview responses has triggered profound changes in how talent is identified and selected. With traditional signals of merit increasingly at risk of manipulation or obsolescence, forward-thinking organizations are searching for dependable, evidence-driven alternatives to ensure fairness and real quality in assessment.
What if the most important thing we assess in schools isn't what students know individually, but how well they can think, learn, and solve problems together? In October 2025, UNESCO published groundbreaking research by Dr Imogen Casebourne and Professor Rupert Wegerif on "AI and Education for collective intelligence: A futures perspective".
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.
True educational transformation is only possible when every learner, teacher, and leader is supported by assessment systems that are accessible, fair, and adaptable to all needs. Traditional approaches too often leave out those who learn, communicate, or create in different ways. The future must be truly inclusive, meeting people where they are and how they learn.