Detection of artificial intelligence-generated essays for academic assessment integrity using large language models
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AI-assisted assessments may create an unfair academic playing field by benefiting students with better access to AI tools and technology while disadvantaging those from underprivileged backgrounds.
“Additionally, AI-assisted assessments may create an unfair academic playing field by benefiting students with better access to AI tools and technology while disadvantag -ing those from underprivileged backgrounds [ 62, 64, 97].”
From Redefining student assessment in AI-infused learning environments: a systematic review of challenges and strategies for academic integrity (2026) · cites this paper as [97]
Institutions have shifted toward alternative assessment methods, such as oral exams (viva voce), real-world reflective tasks, scaffolded assignments, reflective journals, Bloom's Taxonomy higher-order thinking questions, and interactive problem-solving tasks, which require direct student engagement and cannot be easily outsourced to AI.
“To achieve this, institutions have shifted toward alternative assessment methods, such as oral exams (viva voce), real-world reflective tasks, scaffolded assignments, reflective journals, use of Bloom's Taxonomy higher-order thinking questions, and interactive problemsolving tasks, which require direct student engagement and cannot be easily outsourced to AI [ 56, 65, 67, 70, 73, 79, 89, 94, 97-99”
From Redefining student assessment in AI-infused learning environments: a systematic review of challenges and strategies for academic integrity (2026) · cites this paper as [97]
Reliance on AI for tasks requiring cognitive engagement results in declining independent reasoning, writing abilities, critical thinking, and analytical skills, making students less prepared for real-world problem-solving.
“The reliance on AI for tasks requiring cognitive engagement results in declining independent reasoning, writing abilities, critical thinking, and analytical skills, making students less prepared for realworld problem-solving scenarios [ 60, 72, 93, 97, 98].”
From Redefining student assessment in AI-infused learning environments: a systematic review of challenges and strategies for academic integrity (2026) · cites this paper as [97]
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“The reliance on AI for tasks requiring cognitive engagement results in declining independent reasoning, writing abilities, critical thinking, and analytical skills, making students less prepared for real-world problem-solving scenarios [ 60, 72, 93, 97, 98].”
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“…for the Dutch language and for academics with some promising results, the tools still perform worse than tools for English, and some tools with the best results use an LLM for detection, making the detection process opaque (e.g. Fivez et al., 2024; Qorich & El Ouazzani, 2025; Wang et al., 2024).”
Füzesi, I., Lengyel, P., & Pancsira, J. (2026) · International Review of Applied Sciences and Engineering · doi:10.1556/1848.2026.01535
- Authors
- Qorich, M., & El Ouazzani, R.
- Affiliations
- Université Moulay Ismail de Meknes (MA)
- Corresponding author
- Qorich, Mohammed
- Venue
- Expert Systems with Applications
- Publisher
- Elsevier BV
- Published
- 2025-10
- Volume
- 291
- Pages
- 128405
- ISSN
- 0957-4174 (ISSN-L)
- OpenAlex
- W4410910662
- Cited by
- 6
- References
- 86
- Citations by year
- 2026: 4 · 2025: 2
- FWCI
- 6.27
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Expert Systems with Applications (publishedVersion)
- Topics
- Social Sciences › Safety Research › Academic integrity and plagiarism
- Physical Sciences › Computer Science › Artificial Intelligence › Authorship Attribution and Profiling
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Keywords
- AI-generated text detection, academic integrity, generative AI in education, plagiarism detection, large language models, BART, CNN classifier, hyperparameter optimization, ChatGPT, GPT-2, RoBERTa
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