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 this short blog we will explain how upgrades to and within Licence Centres work.
Billionaires are building bunkers. From luxury compounds in the depths of New Zealand to secretive estates far from prying eyes, some of the world’s wealthiest are investing heavily in “doomsday” preparations, hoping to insulate themselves from whatever crisis may come next. In a world fixated on uncertainty—whether from technological disruption, planetary challenges, or the unpredictable rise of AI—future-proofing is on everyone’s agenda.
The Department of Art and Design at Utah State University’s Caine College is a dynamic community dedicated to excellence in visual art, design, and scholarship. Recognised for its nationally accredited programs, the department combines rigorous foundational study, creative innovation, and professional skill development within a supportive environment.
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.
The foundations of modern assessment are shifting. Not long ago, RM Compare introduced “ranks to rulers”: a pioneering process that transformed human judgments—ranking student work by quality—into reliable, calibrated measuring scales that educators and students could trust. Now, new AI validation methods are taking these ideas further, making instant, trustworthy assessment a reality for everyone.
As AI steps into the heart of education, we celebrate the speed and efficiency of machine-marked assessments. But a deeper question shadows every advance: If an AI can now judge student work, who—if anyone—is judging the AI? Could an RM Compare AI Validation Layer be the answer?
In this short blog we will explain how upgrades to and within Licence Centres work.