Evidence map›Paper›PMID 41984910›Full record

ArticlePloS one2026

Auto-assessment of assessment: A human-in-the-loop AI framework addressing policy gaps in academic assessment.

Wasiq Khan, Luke Topham, Nathan Jones, Peter Atherton, Raghad Al-Shabandar, Hoshang Kolivand, Iftikhar Khan, Abbas Alatrany, Abir Hussain

Abstract read
In one paragraph

Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Wasiq KhanLiverpool John Moores University, School of Computer Science and Mathematics, Liverpool, United Kingdom.ORCID https://orcid.org/0000-0002-7511-3873
Luke TophamLiverpool John Moores University, School of Computer Science and Mathematics, Liverpool, United Kingdom.
Nathan JonesLiverpool John Moores University, School of Computer Science and Mathematics, Liverpool, United Kingdom.
Peter AthertonLiverpool John Moores University, School of Computer Science and Mathematics, Liverpool, United Kingdom.
Raghad Al-ShabandarAdvisory Office, Office of the Prime Minister, Baghdad, Iraq.
Hoshang KolivandLiverpool John Moores University, School of Computer Science and Mathematics, Liverpool, United Kingdom.ORCID https://orcid.org/0000-0001-5460-5679
Iftikhar KhanLiverpool John Moores University, School of Computer Science and Mathematics, Liverpool, United Kingdom.
Abbas AlatranyUniversity of Leicester, School of Computer Science, Leicester, United Kingdom.
Abir HussainUniversityof Sharjah, College of Engineering, Sharjah, United Arab Emirates.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Education is being transformed by rapid advances in Artificial Intelligence (AI), including emerging Generative Artificial Intelligence (GAI). Such technology can significantly support academics and students by automating monotonous tasks and making personalised suggestions. However, despite the potential of the technology, there are significant concerns regarding AI misuse, particularly by students in assessments. There are two schools of thought: one advocates for a complete ban on it, while the other views it as a valuable educational tool, provided it is governed by a robust usage policy. This contradiction clearly indicates a major policy gap in academic practices, and new policies are required to uphold academic standards while enabling staff and students to benefit from technological advancements. We surveyed 117 academics from three countries (the UK, the UAE, and Iraq) and identified that most academics retain positive opinions regarding AI in education. For example, the majority of experienced academics do not favour complete bans, and they see potential benefits of AI for students, teaching staff, and academic institutions. Importantly, academics specifically identified the particular benefits of AI for autonomous assessment (71.79% of respondents agreed). Therefore, for the first time, we introduce a novel AI framework for evaluating students' work (e.g., reports, coursework, etc.) within a human-in-the-loop assessment process, in which automated grade suggestions are generated and subsequently reviewed by qualified instructors based on students' knowledge and in-depth understanding of the submitted content. The survey results and evaluation outcomes further highlight a significant lack of awareness of modern AI-based tools among experienced academics, a gap that must be addressed to uphold educational standards.

Indexed as

Artificial IntelligenceEducational MeasurementGenerative Artificial IntelligenceHumansStudentsSurveys and Questionnaires

Identifiers

PMID41984910
PMCPMC13082603

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.