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.
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?
Recent research indicates that we can aim higher on reliability while asking judges to do less work. For RM Compare users, that is good news: it supports designing sessions that are both more efficient and more defensible.
If a single grade can change a student’s lifetime earnings, then we can’t afford a grading system that behaves like a lottery. Recent analysis shows that moving just one grade band at A‑level can translate into thousands of pounds more – or less – in annual income over a career.
Edgar Morin died in Paris on 29 May 2026 at the age of 104, bringing to a close one of the most wide-ranging intellectual lives of the last century. Across that long life he returned, again and again, to one central idea: reality is not made of isolated parts that can be fully understood by breaking them down, but of interwoven systems in which order, disorder, uncertainty, part and whole are inseparable.
As educational ministries and professional bodies globally modernise their frameworks, learning objectives have fundamentally shifted. Traditional education models assessed knowledge recall through closed, easily quantifiable test formats.