What can a dating app teach us about assessment? Quite a lot, as it turns out. The dramatic difference in decision time across platforms, highlighted in the chart below, is at the heart of RM Compare’s mobile-first transformation. It’s also a bridge—built by research—that connects traditional marking assumptions to the new realities of digital assessment.
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?
Innovation isn’t a straight line - it’s a dynamic cycle. Nobody grasped this better than John Boyd, the US Air Force Colonel and visionary strategist behind the OODA loop. Boyd’s model—Observe, Orient, Decide, Act—was developed to help fighter pilots out-think and out-manoeuvre their opponents. Over time, it became a blueprint for learning organisations everywhere: success comes from cycling through these stages faster and smarter
At the heart of every debate around assessment, whether about student work, creative output, or performance reviews, lies a deceptively simple truth that all judgements are comparative. This insight isn’t just a philosophical curiosity, it is the key to understanding why trust and validity in marking are so challenging in an era of artificial intelligence.
What can a dating app teach us about assessment? Quite a lot, as it turns out. The dramatic difference in decision time across platforms, highlighted in the chart below, is at the heart of RM Compare’s mobile-first transformation. It’s also a bridge—built by research—that connects traditional marking assumptions to the new realities of digital assessment.
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?
Innovation isn’t a straight line - it’s a dynamic cycle. Nobody grasped this better than John Boyd, the US Air Force Colonel and visionary strategist behind the OODA loop. Boyd’s model—Observe, Orient, Decide, Act—was developed to help fighter pilots out-think and out-manoeuvre their opponents. Over time, it became a blueprint for learning organisations everywhere: success comes from cycling through these stages faster and smarter
As AI-powered tools increasingly participate in educational assessment, a critical challenge comes into sharp focus: AI judges very differently from humans. This difference is more than technical; it threatens the fairness, trust, and validity of assessment systems if left unaddressed.
At the heart of every debate around assessment, whether about student work, creative output, or performance reviews, lies a deceptively simple truth that all judgements are comparative. This insight isn’t just a philosophical curiosity, it is the key to understanding why trust and validity in marking are so challenging in an era of artificial intelligence.
In 2025, the educational assessment sector experienced a step change in the evidence base supporting Comparative Judgement (CJ) as a validation layer—bolstering the RM Compare approach described throughout this paper. Major independent studies, regulatory pilots, and industry-led deployments have converged on the effectiveness, reliability, and transparency that CJ-powered systems provide for AI calibration and human moderation alike.
Artificial intelligence is becoming deeply embedded in how we teach, learn, and assess. But as LLMs and automated marking tools step into spaces once reserved for humans, a fundamental question emerges: can AI truly understand what we mean by quality and value?
Can LLM's understand concepts of 'value' and 'quality' in the same way as humans do? If not what does this mean for AI assessment? We completed a short study to explore more, and to think about some of the implications.
In today’s rapidly changing educational landscape, the key challenge isn’t just whether AI can mark student work, but how to ensure every mark is reliably fair. With that mission in mind, our latest proof of concept was designed to demonstrate why RM Compare is uniquely positioned as the foundation for trustworthy, scalable automated assessment.