Reassessing Academic Integrity in the Age of AI: A Systematic Literature Review on AI and Academic Integrity

Synthesis: Balalle and Pannilage (2025) present a PRISMA-based systematic literature review (25 studies from 1,443 records across Scopus, PubMed, DOAJ, and BASE) examining the impact of artificial intelligence on academic integrity in higher education. The review finds a genuine research gap — AI and academic integrity sit in the same keyword cluster but few studies cover both — and documents that AI functions as both a threat to integrity (AI-generated writing, paraphrasing tools, contract cheating) and a tool for detection (Turnitin AI/similarity scoring). Its central call is for institutions to build a culture of academic integrity through clear policy, assessment redesign, and ethics training, rather than relying on detection software alone.

Methods

The review used a PICO-framed research question ("What is the role of AI in influencing academic integrity, and how can educational institutions ensure ethical AI usage?") and the PRISMA 2020 flow for study selection:

1,443 records identified (PubMed 62, DOAJ 1,136, Scopus 235, BASE 7, plus 3 expert recommendations); 32 duplicates removed → 1,408 screened → 78 full-text retrieved → 25 included in the final analysis.
Risk of bias was assessed with the Cochrane ROBINS-I tool via Nested Knowledge's semi-automated platform. The most significant bias was in participant selection; 9 studies showed selection concerns, and several others showed bias due to confounding, missing data, or selective reporting.
A VOSviewer keyword co-occurrence network (4 clusters) surfaced the field's structure: cluster 1 (academic integrity, academic misconduct, AI, student character), cluster 2 (academic writing, generative AI, large language model), cluster 3 (ChatGPT, higher education, quality assurance), cluster 4 (plagiarism).
The most-cited works were Cotton et al. (2024) "Chatting and cheating" (755 citations), Sullivan (2023) "ChatGPT in higher education" (221), and Crawford et al. (2023) "Leadership is needed for ethical ChatGPT" (188).

Key findings

AI as both threat and detection tool

The review documents AI's dual role:

As a threat: Students use generative AI (ChatGPT, Google Bard, Writesonic, Jasper, Wordtune, automated paraphrasing tools) to complete assignments, reducing originality and risking the credibility of qualifications. Non-native English speakers show a high tendency to breach integrity when struggling to write in English.
As a detection tool: Most institutions use Turnitin (similarity + AI content rate) integrated into learning management systems; online proctoring systems and cameras are used for exam monitoring. However, the review cautions that a low AI content rate may be a false positive, and that plagiarism-detection tools are unreliable for AI-generated work — institutions should not rely on them alone.

Detection is an industry, not a solution

Academic cheating and detection have become "thriving industries" — companies sell both AI-writing/humanizing software and detection software. The review argues institutions can develop their own manual detection procedures and train lecture panels, and that multiple assessment methods (oral exams, traditional writing tests, essays) should be used to detect AI misconduct rather than software alone (citing Bozkurt 2024).

Culture of integrity over policing

Across the included studies, the dominant recommendation is preventive and cultural rather than purely technological:

Clearly define what constitutes academic misconduct (plagiarism, AI writing, cheating) and establish explicit policies and expectations.
Develop an honour code agreed by students and faculty, and a jointly-developed campus AI-use policy, formally documented and distributed.
Build a culture of academic integrity from orientation onward, with regular workshops and training on citation practices, academic ethics, plagiarism, and AI detection tools.
Redefine academic integrity for the technology-based era — the old definition no longer fits.
Balance detection with assessment redesign (some institutions have returned to pen-and-paper exams), while recognizing that banning AI is difficult to enforce (ChatGPT is hard to prevent, per Sharples 2022).

AI also has legitimate value

The review is balanced: AI can enhance writing efficiency, improve non-native English writing, act as a virtual tutor students ask questions to without hesitation, and improve learning abilities (Darvishi et al. 2024; Maphoto et al. 2024; Milano et al. 2023). The task is to harness these benefits while upholding ethical standards, not to ban the tools.

Implications for AI in Education

Detection is necessary but insufficient. The review reinforces the wiki's Plagiarism Detection coverage — AI detection tools (including Turnitin AI scores) are unreliable, and detection must be paired with assessment redesign and culture-building (see Reducing AI Misuse).
Assessment redesign matters more than policing. Returning to oral exams, process artifacts, and multiple assessment methods echoes the wiki's Authentic Assessment and Assessment literature.
Policy must be clear and co-developed. Institutions need explicit, shared AI-use policies — a finding consistent with Educational Policy AI and Academic Integrity research showing policy lag behind actual use.
A balanced, cultural approach. The review's call to "create a culture of academic integrity" connects to Framing AI Use For Students (how expectations are communicated) and to the ethics dimension of AI Misuse Learning Harm.

Connected Concepts

Connected Articles

Coauthorship Integrity Reconceptualising Assessment Validity For The Age Of Gene — Coauthorship integrity and assessment validity
GenAI Chinese Higher Education Integrity 2026 — Balancing Gen-AI in Chinese higher education
Detecting LLM Generated Text Latent Prompt — Detecting LLM-generated text
GenAI Declaration Frameworks Higher Education — GenAI declaration frameworks
AI Assessment Scale Reform — The AI Assessment Scale and assessment reform

Citation

Balalle, H., & Pannilage, S. (2025). Reassessing academic integrity in the age of AI: A systematic literature review on AI and academic integrity. Social Sciences & Humanities Open, 11, 101299. https://doi.org/10.1016/j.ssaho.2025.101299