As part of his ‘AI Reflections’ blog series, our Executive Director of Education Services and Innovation, Liam Sammon, reflects on one of the biggest questions facing education today – whether AI should be embraced or restrained. Drawing on his experience across post-16 education and his leadership of assessor services at The Skills Network, Liam shares a thoughtful perspective on the realities of AI in assessment, the challenges around authenticity, and how the sector can move forward responsibly.
Is AI that big a thing?
Over 15 years ago, when electronic white boards were the big technology thing I remember visiting a college and they proudly showed me all their classrooms with new white boards. However, none of them were on.
AI is definitely on; it’s the most transformative piece of technology that I’ve experienced in my 25 plus years in post-16 education. There is very little in education AI can’t assist with; teaching/coaching, teaching and learning resources, assessment, learner/learning management systems, learner support, admissions and enrolments…the list goes on.
However, this blog can’t go on and on, so I’m going to focus on just one topic: AI in online assessment. This is important to The Skills Network, given we assess circa 250,000 learner submissions every year for ourselves and our partners. And important to me personally, as I’m responsible for assessor services (circa 200 assessors) and EQUAL Assess (a new and enhanced version of EQUAL Sigma); our AI powered assessment tool.
To embrace or restrain AI in assessment?
When it comes to AI, the education sector appears to be divided between those who want to restrain AI (detection) and those who want to embrace it (adaptation). Though as with most important topics in education, the debate is more nuanced than this.
For assessment, in the detection camp are the regulators: Ofqual(Office of Qualifications and Examinations Regulation) in England and the JCQ’s (Joint Council for Qualifications) interpretation of Ofqual’s condition A8, which becomes guidance for Centres.
JCQ’s AI Use in Assessments guidance (Revision 2, April 2025) states: “If AI misuse is detected or suspected by the Centre and the declaration of authentication has been signed by the student, the case must be reported to the relevant awarding organisation.“ AI misuse that is judged as malpractice is subject to the same sanctions as ‘making a false declaration of authenticity’ and ‘plagiarism’, which can include being barred from taking qualifications for several years.
No-one would dispute the importance of valid assessment and authenticity of work; but let me present two scenarios, one analogue and one AI.
The first would be considered valid (subject to plagiarism checks re: the textbook). The second, potential malpractice, that will at least warrant further investigation, coming down to a) how much the learner has changed the LLM response and used “their own words” and b) how much they are taking credit for AI work and are they properly referencing the use of AI and not “misused” AI.
But is the AI Scenario ‘bad’ learning and not as valid a reflection of the learner’s understanding of the topic under assessment as the Analogue Scenario? Critically it all comes down to intent. Let’s assume in the second case the learner’s intent was to improve their learning (as in the first); but you can easily see how this can err into malpractice, and we can’t see into the hearts and minds of learners.
The Skills Network, like many others, use online tools to detect AI. But again, it all comes down to intentions, and no detection technology can reliably look into a learner’s intent, let alone 100% manage the issue of false positives and negatives. There is evolving assessment practice supplementing online AI detection tools such as identifying a lack of personal voice (see later) or over-simplified and balanced arguments in learner’s work; but LLMs are getting smarter, or rather learners are getting smarter, at avoiding these detection methods and again these practices don’t get at the heart of the matter. That is learner intent.
This is where practice needs to evolve and where online learning can play its part beyond AI detection tools and this is something I expand on later with the developments we’re planning for our LMS, EQUAL, and how we develop our online courses.
So how could assessment practice adapt to AI?
In the embrace AI camp there are those educators, who believe assessment needs to adjust to the use of AI and not just focus on detection.
One of the arguments put forward by the embrace AI camp is to change assessment strategies and move up the Bloom’s taxonomy. This is mainly being led by the Higher Education (HE) sector where the use of AI is recognised and seen as an aid to developing skills, particularly critical thinking skills and critical thinking skills applied in the use of AI as a ‘thought partner’. This is fine for HE; but what about Level 3 and below, which covers most vocational qualifications? Moving up the Bloom’s taxonomy would make the qualifications more demanding, meeting neither learner or employer needs and ultimately moving the qualification up the Level scale.
Another approach is to build AI skills into the qualification specification and part of the assessment objectives. This is more fitting for vocational qualifications, giving the focus on preparing for work and the increasing use of AI in the workplace; but there are challenges in adding in this content. Do you replace content in the qualification with AI specific skills content; but then what do you take out? Or just add it in, making the qualification more demanding? Building AI literacy skills into the curriculum is something we are planning for given the Department for Education’s (DfE) ambitions for digital/AI literacy to be built into the curriculum by 2028, as published in “Every Child Achieving and Thriving” and I cover this later.
Another approach, which is part-detection and part-assessment strategy, is to build personal and work experience into assessment, which loosely comes under the term ‘authentic assessment’. The most AI-proof assessment strategy is direct observation (it is near impossible for a learner to use AI to fake this and in an online context this can be done via real-time video). However, one of the principles of good assessment is manageability and at The Skills Network we pride ourselves in delivering asynchronistic learning to those who face barriers to traditional learning methods: those who work shifts, have care responsibilities, difficulty with transport, Learners with Learning Difficulties and/or Disabilities (LLDD) etc. Adding to the assessment process burden would just exclude these groups further.
We need ‘authentic assessment’ methods that don’t exclude these groups. In research by NCFE and The Open University on how resilient different types of assessment questions are to AI (Developing Robust Assessment in the Light of Generative AI Developments, 2024) which covered Level 3 qualifications, researchers found that reflection on work practice was a fairly robust assessment strategy against AI and this can be applied within an online learning context.
What is The Skills Network doing about AI in assessment?
For us compliance is non-negotiable – fact – and we’re compliant now, as per the numerous AO EQA visits we’ve had; but this is an evolving area and we want to stay a step ahead, whilst maintaining the balance between good use and misuse of AI. Below are some of the new things we are doing/planning to do with regards to AI in assessment.
AI is transforming education and if you want to contribute to the debate, comment on this blog or speak to Liam directly, contact him via LinkedIn.
