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AI Marking – Why Keeping Humans in the Loop Matters

Exploring the symbiotic relationship between AI and human expertise in educational assessment

John Quy

John Quy

March 01, 20257 min read

AI Marking – Why Keeping Humans in the Loop Matters

AI Marking: A Tool, Not a Replacement

What happens when we put AI in charge of marking exams? Will our trusted human markers become obsolete, replaced by cold, unfeeling algorithms? Or could AI, far from being the feared "reaper of assessment," actually enhance the marking process without sidelining human expertise? At Excelas.ai, we believe the answer lies in a symbiotic relationship where AI and humans work together for fairer, more consistent results.

The rise of AI in education presents dazzling possibilities. But it's worth asking: should this powerful technology be left to run wild or carefully guided? In essence, AI marking is not about erasing the human role but amplifying it. Imagine AI as a masterful assistant rather than a lone arbiter—speeding up marking, reducing bias, but always with human oversight.

Our research at Excelas.ai aligns with (and extends) findings from various sector pioneers: AI marking matches human accuracy—sometimes even surpassing it in consistency—and delivers feedback at breakneck speed.

AI can nail the marking of essays and open-ended responses with remarkable reliability

But how does it contend with human nuances or accommodate diverse learner profiles?

Here's a telling fact: When AI marked English proficiency on a large scale, its scores deviated by only 0.22 marks from human averages, comfortably within the natural variation of human markers themselves (~0.55 marks). This is no small feat. It means AI can uphold marking standards while easing the human workload.

Moreover, AI can offer continuous formative feedback, enabling learners to tune their skills long before the dreaded exam day. And by automating repetitive marking tasks, teachers enjoy a reprieve from the marking treadmill—freeing them to focus on purposeful teaching and learner engagement.

Ethical AI Marking: It's Not Just About The Marks

If you think AI is just about crunching numbers, think again. There's an ethical dimension that demands attention.

Early AI models sometimes penalised errors—like misspellings—with undue harshness, inadvertently disadvantaging learners with dyslexia or dyspraxia. At Excelas.ai, we've refined our algorithms to focus on understanding rather than mere correctness. After all, what good is a marked script if it fails to appreciate the essence of a learner's response?

"AI must be additive, not a replacement," we assert. Transparency is essential—we must understand and explain the reasoning behind each assigned mark, particularly for summative assessments where every fraction counts.

Keeping the Human in the Loop: A Necessity, Not a Nicety

The idea of humans and AI collaborating in marking is not a pipe dream but a practical necessity. As Lynsey Meakin from the University of Derby noted during a recent panel discussion,

AI in assessment is much like the calculator's debut—once feared, now indispensable.

Experts across the board agree: AI excels in supporting human markers by handling the heavy lifting of routine scoring and content generation. Specialists like Richard Eckersley (Institute for Chartered Accountants in England and Wales) and Paul Houghton (NEBOSH) advocate AI's role in streamlining marking workflows—human eyes remain critical for context, nuance, and judgement.

Sara Pierson of Oxford University Press highlights AI's value in formative assessments, supporting learning progression rather than high-stakes gatekeeping. Dr Matthew Glanville from the International Baccalaureate voices similar sentiments: AI should be harnessed in low-stakes environments to liberate teachers' time for actual teaching.

You are never going to lose the human in the loop. Never

As Dr Gráinne Watson aptly puts it. AI provides a consistent baseline, but the ultimate decision—and accountability—rests with the human marker.

AI and Human Collaboration

AI and Human Collaboration

AI and humans working collaboratively—optimising assessment outcomes.

What Does This Mean For Your Organisation?

AI marking isn't the distant future—it's here, ready to address longstanding challenges in education assessment. For awarding bodies, schools, and educational technology providers alike:

  • Expect faster, fairer, and more transparent marking processes
  • Gain deeper insights into learner performance through rich AI-generated feedback
  • Empower educators with tools that reduce administrative burdens and enrich teaching time
  • Open pathways for smaller organisations to adopt AI without needing vast datasets—thanks to synthetic data and state-of-the-art Large Language Models (LLMs)

Curious how AI marking could fit your specific context? Excelas.ai invites you to join our hands-on Proof of Concept group. Whether you're ready to pilot or just want to explore possibilities, we're keen to collaborate.

Final Thoughts: The Future of Fair Assessment

AI marking marks a pivotal shift in educational assessment. But the future belongs not to AI or humans alone, but to their partnership—a partnership grounded in ethics, transparency, and mutual respect. The goal? Fairer outcomes and richer learning support for every student.

So, will AI replace human markers? No. Will it transform how marking happens? Absolutely.

By embracing this dynamic duo, education can finally realise its twin aspirations: accuracy and humanity.

References

  1. Dicheva, D., Dichev, C., Agre, G., & Angelova, G. (2015). Gamification in education: A systematic mapping study. Journal of Educational Technology & Society, 18(3), 75–88.
  2. Epstein, J. L. (2011). School, family, and community partnerships: Preparing educators and improving schools. Routledge.
  3. Shute, V. J., Leighton, J. P., Jang, E. E., & Chu, M. W. (2016). Advances in the science of assessment. Educational Assessment, 21(1), 34–59.
  4. Vaswani, A., Shazeer, N., Parmar, N., et al. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.
  5. Willingham, D. T. (2009). Why don't students like school? John Wiley & Sons.

Want to know more? Visit Excelas.ai for deeper insights into AI in education.

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