Rise of the Machines? The Evolving Role of AI Technologies in High-Stakes Assessment
Open accessAbstract
Our world has been transformed by technologies incorporating artificial intelligence (AI) within mass communication, employment, entertainment and many other aspects of our daily lives. However, within the domain of education, it seems that our ways of working and, particularly, assessing have hardly changed at all. We continue to prize examinations and summative testing as the most reliable way to assess educational achievements, and we continue to rely on paper-based test delivery as our modus operandi. Inertia, tradition and aversion to perceived risk have resulted in a lack of innovation (James, 2006), particularly so in the area of high-stakes assessment. The summer of 2020 brought this deficit into very sharp focus with the A-level debacle in England, where grades were awarded, challenged, rescinded and reset. These events are potentially catastrophic in terms of how we trust national examinations, and the problems arise from using just one way to define academic success and one way to operationalize that approach to assessment. While sophisticated digital learning platforms, multimedia technologies and wireless communication are transforming what, when and how learning can take place, transformation in national and international assessment thinking and practice trails behind. In this article, we present some of the current research and advances in AI and how these can be applied to the context of high-stakes assessment. Our discussion focuses not on the question of whether we should be using technologies, but on how we can use them effectively to better support practice. An example from one testing agency in England using a globally popular test of English that assesses oral, aural, reading and written skills is described to explain and propose just how well new technologies can augment assessment theory and practice.
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The emergence of GenAI in higher education has raised concerns about academic integrity, authenticity of student work, and potential reduction in students' development of essential cognitive and creative skills.
“The disruptive nature of GenAI as a technology and its emergence in higher education has raised a range of concerns around academic integrity, the authenticity of student work, and the potential reduction in students’ development of essential cognitive and creative skills [3, 4].”
From Generative AI in Higher Education: Balancing Innovation and Integrity (2025) · cites this paper as [3]
The Introduction raises concern about whether automated systems can facilitate authentic assessment of student learning. That supports a general concern about AI and assessment authenticity, but it does not address GenAI-produced student work, academic integrity, or reduced development of cognitive and creative skills. (LLM, conf=0.78)
Experimental — accuracy not yet measuredRichardson and Clesham emphasise the importance of training programs that equip users with the necessary skills to utilise GenAI tools effectively and responsibly.
“Richardson and Clesham [3] emphasise the importance of training programs that equip users with the necessary skills to utilise GenAI tools effectively and responsibly.”
From Generative AI in Higher Education: Balancing Innovation and Integrity (2025) · cites this paper as Richardson and Clesham [3]
The Conclusion says stakeholders in AI-assisted language assessment need improved literacy in the new testing landscape. It does not call for training programs or discuss skills for using generative AI tools effectively and responsibly; those specifics go beyond the passage. (LLM, conf=0.86)
Experimental — accuracy not yet measuredDifficulties in detecting AI-generated submissions can undermine the authenticity of student work.
“Richardson and Clesham [3] highlighted the difficulties in detecting AI-generated submissions, which can undermine the authenticity of student work.”
From Generative AI in Higher Education: Balancing Innovation and Integrity (2025) · cites this paper as [3]
The Introduction discusses whether AI can assess student answers authentically, and the Automated scoring section describes AI marking submitted essays. Neither addresses the difficulty of detecting AI-generated submissions or whether such submissions undermine the authenticity of student work (searched for detection, AI-written answers, plagiarism, and academic integrity). (LLM, conf=0.76)
Experimental — accuracy not yet measured
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- Authors
- Richardson, M., & Clesham, R.
- Affiliations
- University College London (GB)
- Pearson (United Kingdom) (GB)
- Corresponding author
- Richardson, Mary
- ORCID
- Richardson, Mary
- Venue
- London Review of Education
- Publisher
- UCL Press
- Published
- 2021
- Volume
- 19
- Issue
- 1
- ISSN
- 1474-8460 (ISSN-L)
- 1474-8479
- OpenAlex
- W3133711925
- Cited by
- 48
- References
- 40
- Citations by year
- 2026: 6 · 2025: 15 · 2024: 15 · 2023: 8 · 2022: 4
- FWCI
- 6.10
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- London Review of Education (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Information Systems › Educational Technology and Assessment
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Keywords
- AI-assisted assessment, high-stakes testing, automated assessment, national examinations, digital learning platforms, English language proficiency testing, assessment innovation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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