Across the education and skills sector, conversations about artificial intelligence have been dominated by one concern: fraud. Can students write essays using AI? How do we detect work generated by AI? And what does this mean for the credibility of the qualification?
These are important questions. But they can also be the wrong starting point.
AI is not just a challenge to assessment integrity. It also reveals something deeper about how we currently assess learning. In many ways, the rapid advances in AI are exposing the weaknesses of existing assessment systems, with slow feedback cycles, heavy marking workloads, and assessment formats that often prioritize recall over true understanding.
Rather than viewing AI purely as a threat, the field has an opportunity to rethink its assessment more fundamentally.
This moment may be a turning point.
Distortions hidden in the evaluation system
For many years, assessments have quietly created a significant operational burden across the skills sector.
Teachers, trainers, and evaluators spend countless hours grading written responses, evaluating portfolios, and providing feedback. In many contexts, particularly in vocational education and apprenticeships, assessment often runs alongside full teaching loads and administrative responsibilities.
The result is a system in which feedback, perhaps the most important part of evaluation, is often delayed. Learners may wait days or weeks to gain insight into their work, long after the learning moment has passed.
In theory, assessment is designed to support learning. In reality, the process may be primarily focused on grading and compliance.
This is where the emergence of AI begins to change the conversation.
AI and the transition from scoring to feedback
When we talk about AI in assessment, the assumption is often that technology will replace human markers. While this concern is understandable, it can lead to you missing out on more meaningful opportunities.
AI has the potential to shift the focus of assessment from scoring as an administrative task to feedback as a learning tool.
When used responsibly, AI systems can quickly analyze learner responses and generate structured insights that help educators understand where learners are struggling, where there are misconceptions, and where additional support is needed.
Instead of waiting for the end of the assignment cycle, feedback can be faster, more consistent, and more actionable.
Importantly, this does not eliminate the role of the evaluator. Instead, you will be able to direct human expertise where it matters most, interpreting complex responses, guiding learners, and exercising professional judgment.
In this model, AI supports the evaluation process rather than replacing it.
What regulators are signaling
Regulators are also beginning to decide how to use AI within their rating systems. Recent research by Ofqual exploring the use of artificial intelligence in marking highlights both the opportunities and challenges of the technology. Regulators have stressed that any use of AI must be consistent with the core principles of fairness, transparency and trust, and that AI should not replace human judgment in high-stakes assessment decisions.
There is a growing consensus across the UK and internationally that AI cannot be used as the sole decision maker in high-stakes assessments. Transparency, fairness and accountability remain fundamental principles.
However, there is also growing recognition that AI can play a role in supporting assessment processes, particularly in areas such as generating feedback, quality assurance, and analyzing learner responses.
In other words, the new regulatory model is one of careful integration, not prohibition.
Although human oversight remains central, technology can help improve the efficiency and consistency of rating systems.
Reconsider what to evaluate
Beyond the scoring process itself, AI is forcing a rethink of assessment design in this area as well.
If learners can easily write essays using generative AI, the question is: What skills are you actually trying to measure?
This challenge has already led educators to explore more authentic forms of assessment. Scenario-based tasks, applied problem solving, professional discussions, and portfolio-based evidence are likely to become increasingly important in assessing real-world competency.
For the skills sector, this change could be particularly impactful. Vocational qualifications are inherently designed to measure applied knowledge and practical abilities, and these qualities are difficult to replicate with AI-generated responses alone.
Therefore, AI could act as a catalyst, accelerating the movement toward assessments that better reflect real-world capabilities.
Tipping point for the skills sector
Conversations about AI in education often focus on the disruptive. But disruption can also create opportunity.
The skills sector has long been characterized by innovation, employer involvement and an emphasis on practical competencies. These strengths place the company in a unique position to lead the conversation about how valuations will evolve in the age of AI.
If approached carefully, AI can help reduce administrative burden on educators, provide faster and more meaningful feedback to learners, and support more robust and scalable assessment systems.
None of this will happen overnight. It will require collaboration between regulators, awarding bodies, training providers and technology developers.
But the direction is becoming clear.
AI isn’t just challenging rating systems, it’s forcing departments to redesign them.
And for the skills sector, its redesign may represent one of the most important opportunities in a generation.
Kavitha Ravindran, Co-Founder and Chief Growth Officer, sAInaptic
