For centuries, an apprenticeship has involved more than acquiring information. It means learning from a respected mentor, making mistakes, having those mistakes explained, being challenged and gradually developing the judgment to work independently.
But what happens when the person explaining your mistakes is no longer a person?
Across FE, artificial intelligence is increasingly marking assignments, writing feedback, answering questions and generating teaching materials. It promises instant feedback and relief from repetitive administration – an attractive prospect for financially stretched providers.
Apprentices, meanwhile, are discovering that the same technology can help them produce the work their providers are assessing.
The result is a potentially profound change in the relationship between learner, tutor and assessor.
If one machine helps produce an assignment and another judges it, how do we know the apprentice has actually learned anything?

Five-minute marking
AI-assisted assessment has moved rapidly from experimentation into everyday apprenticeship delivery.
Aptem’s Enhance platform offers AI marking assistance, automated knowledge checks, a virtual learner assistant and AI-generated progress-review summaries.
Its marking tool can propose grades and feedback for tutors to approve, with the company claiming it can reduce marking time by up to 50 per cent.
At Hawk Training, which began using Aptem’s marking assistance in November 2025, a Functional Skills reading mock paper that previously took 30 minutes to mark could be completed in five, according to an Aptem case study. Marking a writing paper fell from more than an hour to 25 minutes.
Aptem estimates that a provider with 1,000 learners could save £216,000 annually by automating routine learner queries, with another £73,500 saved through reduced marking time.
Bud Systems makes similar claims for Bud Mark, which evaluates submissions against assessment criteria and proposes feedback for tutors to approve. A 2025 trial with Skills4Group reportedly halved marking time.
OneFile has also partnered with Sainaptic to offer AI-powered marking. Sainaptic claims its AI model leads to a 7 per cent improvement in pass rate and a 97 per cent reduction in resubmissions.
Multiverse, one of the country’s largest apprenticeship providers, has gone further by allowing learners to receive formative project assessments directly from AI.
Its Instant Feedback system, launched in February 2026 for non-degree apprentices, assesses submitted projects against specified knowledge, skills and behaviours and normally responds within minutes.
Apprentices can challenge decisions and request a human review, although this can take up to 10 working days. During that period, the project remains locked against editing or resubmission.
A Multiverse spokesperson said that before the tool’s rollout, learners might wait weeks for feedback, whereas “today, apprentices can get fast feedback and learn in the moment, with a human still in the loop to review where required”.
Learners’ final portfolios are reviewed by a human coach before submission for end-point assessment (EPA).
Steve Kirk, internal quality assurer and EPA assessor
But Steve Kirk, an internal quality assurer and EPA assessor who recently founded the British Institute of Assessment Professionals, questions whether automated feedback can recognise the nuances that experienced assessors identify.
AI, he says, can pick out the main points but does not necessarily “tease out bits that are a little bit thin, like a human assessor would”.
“So AI is not great for helping develop your learners.”
The Office of Qualifications and Examinations Regulation (Ofqual) requires human oversight of AI marking for regulated qualifications. But that oversight could mean carefully challenging an AI-generated judgment – or simply clicking approve.
Tom Moule, senior AI specialist at Jisc, warned that oversight “has to go beyond simply rubber stamping an AI-generated outcome”.
“While AI can be enormously helpful in mapping student work to rubric criteria and in expressing educator viewpoints at pace, the consensus was that AI should not substitute for, nor unduly steer, the vital judgments made by the educators themselves.”
Education secretary Lucy Powell told the education select committee this week that Ofqual and ministers were clear that the awarding and marking of qualifications “really should be done by the human eye”, saying AI could otherwise “exacerbate prejudice” and other problems in awarding.
She argued that rigorous teacher assessment was essential to maintaining the credibility and standing of technical qualifications.
Yet the growing use of AI for formative assessment raises a related question: even when a human makes the final awarding decision, what happens if the feedback that prepared the learner for it was wrong?
Education secretary Lucy Powell
When AI gets it wrong
One Multiverse apprentice complained that an AI marker had approved a project as meeting all distinction criteria, only for a human assessor to fail that element of their final portfolio over incorrect notation the AI had missed.
“Had their AI bot flagged it in the first place, or they had a human review as a failsafe, I wouldn’t have had the inconvenience,” said the apprentice, who corrected the error and eventually passed with distinction.
Elsewhere, a Learndirect learner complained about repeated plagiarism accusations and inadequate human feedback, while a former associate assessor alleged that the company’s automated marking had coincided with freelance payment rates falling to £1 per unit.
Learndirect confirmed introducing Supported Assistive Marking (SAM), but disputed that it constituted AI marking.
Human marking is hardly flawless either. One software developer building an AI marking tool for essay subjects pointed out that consistency between human markers was “a long way from perfect”.
“’Is it accurate enough’ cannot be answered without asking ‘compared to what’? I wonder what providers are benchmarking against.”
Ofqual research from 2018 found a marker’s chance of matching a senior examiner’s GCSE, AS or A Level grade ranged from about 96 per cent in maths to 52 per cent in English language and literature, though the chance of the same or an adjacent grade exceeded 95 per cent.

The next frontier: video assessment
Even video assessment may not escape automation.
Dr Rajeshwari Iyer, co-founder of AI marking company sAInaptic, said awarding organisations and assessment centres were increasingly turning to video evidence because of concerns about learners using AI to complete written assessments.
Her company has been exploring whether AI could be used to mark a video of, for example, an engineering apprentice filming themselves putting on personal protective equipment, or a hospitality apprentice checking a guest out of a hotel.
Apprentice SImon Phipp
Apprentices outsourcing their own learning
While providers use AI to evaluate apprentices’ work, apprentices increasingly use it to help produce their portfolios.
Awarding organisations are also grappling with AI appearing during EPAs themselves.
NCFE, an educational charity and leader in vocational and technical learning, has warned of cases in which applications such as Grammarly, ChatGPT and automated meeting notetakers appeared to be running in the background during assessments. It advised training partners to ensure assessment devices did not contain tools or retrievable information that could give apprentices an unfair advantage.
Emma (not her real name), a level five operations management apprentice, discovered the dangers of relying on chatbots in a more ordinary setting.
Having routinely used AI to draft emails, she found herself stalling when required to write one independently.
“That cognitive offloading sneaks up on you without realising,” she said.
Yet Emma also uses AI when feedback from her tutors lacks sufficient depth, entering her EPA guidance into a chatbot to obtain further insights.
Another apprentice, Simon Phipp, uses AI to check assignments before submitting them on his chartered manager degree apprenticeship, hoping to establish whether he has included the right information.
Such practices are increasingly difficult to distinguish from ordinary professional behaviour in the apprentice’s workplace.
Phipp points out that employees might use AI to improve reports, analyse information or draft correspondence. He finds the inconsistency with his IT management job, which actively encourages AI use, “difficult”.
“Why should apprentices be prohibited from using the same tools?”
Kirk believes higher-level apprenticeships may be particularly vulnerable to AI cheating because they frequently require learners to compare theories, evaluate evidence and critique ideas.
“AI can give you the strengths and weaknesses of a theory in 30 seconds that you could otherwise have spent days researching,” he said.
“That’s where it gets very dangerous.”
Since AI began entering widespread use in late 2022, the number of higher-level apprenticeship achievements recorded has risen by around 80 per cent. But the rise has been driven predominantly by better retention rather than pass rates. It could reflect AI helping providers identify struggling learners and giving tutors more time to intervene. But the figures do not establish that AI caused the improvement.
They also cannot tell us whether apprentices are developing a deeper understanding of their subjects, or whether providers are simply becoming more efficient at moving them through programmes.
In evidence to the education select committee, the Department for Education warned that AI can allow students to “bypass the cognitive effort entirely”, while excessive personalisation could undermine curriculum coherence.
The danger is that a machine produces an assignment, another machine assesses it, and neither human necessarily develops a deeper understanding.
Obafemi Rufai apprentice
Who makes the rules?
Some apprentices say their providers have done little to clarify what AI use is acceptable.
Emma regularly used AI to make her writing more “vocational” in style. She and fellow apprentices were told of what she describes as a “zero tolerance policy” towards AI-generated content in portfolios only 11 months into the programme.
She knows of an apprentice who failed an EPA after AI-generated content was identified in a written assignment.
Emma now faces the task of reviewing her portfolio to ensure it does not contain material that could cause problems. She “would have appreciated stronger controls over what is allowed made clear from the start”.
Phipp describes a similar experience.
He was not told until six months into his programme that AI use was prohibited, having previously used Grammarly to rewrite material in a more academic style.
Phipp says learners on his programme who received a “high AI score” were asked to resubmit their work without AI, rather than automatically failing.
“Whether [those learners then] use AI to rewrite it, I don’t know, but there are always ways around things,” he said.
GLP Training apprentice Obafemi Refai likewise said he received no guidance, but was subsequently told to rewrite work flagged as containing 10 to 15 per cent AI-generated material.
The problem is compounded by doubts about the reliability of AI detection software.
One provider employee acknowledged to FE Week that the AI checkers it used were “not always accurate”.
In a case summary published in July 2025, involving an MBA student who suspected their dissertation feedback was AI-generated, the Office of the Independent Adjudicator said it was reasonable for the provider to place less weight on the results of an AI detector, noting the tools “can be unreliable”. The complaint was not upheld.
Rather than relying on detection scores, Kirk recommends assessors triangulate evidence, discuss work with learners and examine whether they can demonstrate their understanding.
Some providers are developing more nuanced policies.
Remit Training teaches apprentices to distinguish between “red flags”, such as copying AI-generated work without demonstrating understanding, and “green flags”, including using AI for research, planning and improving their own writing.
But the approach requires a clear understanding of what an assessment is intended to measure.
If an apprentice uses AI to produce a workplace report, is the assessment testing their ability to write the report unaided, or their ability to use the tools available in their occupation?
Scott Hayden, BCoT’s head of teaching, learning and innovation
What cannot be automated
At Basingstoke College of Technology, teachers have developed Fixter, an AI tool that carries out initial marking and helps draft feedback.
But before the feedback is completed, the system requires the teacher to contribute a personal observation about the learner – something only someone who knows them could provide.
Scott Hayden, the college’s head of teaching, learning and innovation, describes it as an “AI assessment sparring partner built by teachers for teachers”.
“This isn’t a race to efficiency,” he said. “The question now is not whether to use AI, but how to design assessment that cannot simply be outsourced to it… using AI to hold a first draft steady while you do the real work of thinking.”
He invokes science-fiction writer Ted Chiang’s comparison of using AI to complete assignments with bringing a forklift into a weight room.
“You move the weight, but you never build strength yourself.”
Edtech researcher Thomas Arnett believes AI may soon surpass teachers at explaining concepts, identifying misconceptions and providing instant, personalised feedback.
But teaching is about more than transferring information.
“A better AI tutor can explain fractions more clearly. It cannot make a student care about fractions. And caring – being embedded in a community and a set of relationships that make academic effort feel meaningful – turns out to be most of the problem.”
For Emma, the value of her skills coach at BMS Progress lies in what a machine cannot replicate.
“He can pick up on the subtlest of details, and he understands the wider picture of what it’s like working with me as a person.”
Phipp likewise says the most valuable parts of his apprenticeship involved discussions with peers about how they applied their learning at work.
Apprentices will encounter AI in their workplaces, and providers have a responsibility to help them use it critically, confidently and safely.
The challenge is to establish which tasks AI can perform without diminishing the skills an apprenticeship is supposed to develop.