The audit was deliberately designed to measure what universities show the world, not what they may do behind closed doors. Abdulrahman and Ahmed argue that public visibility is itself a governance issue: students, lecturers, parents and policymakers rely on official websites to understand what counts as acceptable behaviour, and guidance that is hidden, outdated or written only in technical English cannot shape everyday academic practice. Between 12 and 20 June 2026, the researchers applied an identical five-step search protocol to each of the 30 institutions, drawn purposively from the Kurdistan Regional Government’s public list of 38 higher-education entities. They searched for terms ranging from plagiarism and academic integrity to ChatGPT, generative AI, privacy policy and data protection, checking Kurdish and Arabic sections of websites where relevant. Crucially, the team treated absence of evidence carefully: a score of zero meant only that nothing was publicly visible during the search period, not that a university had no internal policy at all.

Each university was assessed across 11 dimensions, spanning plagiarism policy, academic integrity guidance, generative AI guidance, acceptable and unacceptable AI use, AI-use disclosure, assessment redesign, student AI literacy, staff guidance, privacy and data protection, institutional governance, and Kurdish or local-language accessibility. Every dimension was scored from 0 to 3, giving a maximum institutional score of 33. The researchers specified the framework before scoring, refined it through review by six independent experts in academic integrity, artificial intelligence and higher-education policy, and then tested it for consistency. A subset of 10 universities was independently double-coded, covering 110 coding decisions: the two coders agreed exactly on 105 of them, a 95.5 percent agreement rate, with a quadratic weighted kappa of 0.975, an exceptionally high figure. A sensitivity analysis shifting the classification thresholds by one or two points confirmed that the overall pattern was robust.

The headline finding is the distribution of institutions across a proposed four-stage readiness model that moves from plagiarism control, through academic integrity and AI awareness, to full AI governance. Twenty-one of the 30 universities, 70 percent, sat at Stage 1, indicating either low public visibility or a narrow orientation toward plagiarism control. Five institutions, 16.7 percent, reached Stage 2, reflecting broader academic-integrity provisions; three, 10 percent, showed genuine AI awareness at Stage 3; and only one university, 3.3 percent, demonstrated a more developed public-facing AI governance position at Stage 4. Public and private institutions performed almost identically, with mean scores of 4.63 and 4.43 out of 33 respectively, though the single Stage 4 case was a private university. The researchers are careful to note that the sample was purposive rather than probabilistic, so no claims about sector-wide population effects are made.

Direct evidence of generative-AI-specific policy was strikingly rare. Only two universities, 6.7 percent of the sample, showed direct public evidence of GenAI policy or guidance, four more showed partial evidence, and the remaining 24, fully 80 percent, had no clear public GenAI policy visible during the search window. This is perhaps the study’s most consequential finding: while students and lecturers are already using AI tools daily for translation, grammar support, coding assistance and summarisation, the overwhelming majority of institutions offer no visible guidance on what is permitted, what must be disclosed, or what data should never be pasted into a public chatbot. The strongest performers were Tishk International University, followed by the American University of Iraq–Sulaimani, the University of Kurdistan Hewlêr, the American University of Kurdistan, Koya University and the University of Duhok, yet even these combined institutions did not show complete evidence across all 11 dimensions.

The pattern of strongest and weakest dimensions tells a coherent story about where institutional attention has been directed. Plagiarism policy scored highest at a mean of 0.73 out of 3, followed by academic integrity at 0.67 and general AI awareness at 0.60. At the bottom of the table sat the dimensions that matter most for responsible AI use: AI-use disclosure averaged just 0.13, acceptable-use guidance 0.17, privacy and data protection 0.20, and AI-aware assessment redesign 0.23. The authors argue these gaps are not minor administrative details but the core of meaningful governance. Without disclosure rules, students cannot know how to report AI assistance; without acceptable-use examples, they may assume all AI use is either forbidden or freely allowed, both of which corrode fairness; without privacy guidance, sensitive student data and unpublished research can flow into commercial AI systems unchecked; and without assessment redesign, universities default to unreliable detection software and punishment.

Underlying the whole study is a conceptual argument: generative AI has broken the traditional definition of plagiarism. AI-assisted work is rarely simple copying from an identifiable source. A student may use a chatbot to improve grammar, translate between Kurdish and English, generate an outline, explain a concept, summarise readings or draft code. Some of these uses support learning; others may quietly replace the student’s own intellectual contribution. Plagiarism policies remain necessary, the authors contend, but they are no longer sufficient. The study points to international practice, including the Artificial Intelligence Assessment Scale, which helps educators communicate different permitted levels of AI use depending on the learning outcome, shifting the conversation from detection toward transparent assessment design. AI-aware assessment options such as oral defences, process logs, reflective writing, drafts with feedback histories and tasks requiring students to justify their reasoning offer a sturdier defence than similarity checkers, which are notoriously unreliable when AI-generated text has been human-edited.

The multilingual context of Kurdistan higher education adds a distinctive layer to the problem. Students and lecturers routinely work across Kurdish, Arabic and English, and AI tools are heavily used for translation and English academic-writing support. The study insists that AI governance in the region must be locally grounded: guidance should be available in Kurdish and, where relevant, Arabic and English, with examples drawn from regional realities such as multilingual writing, translation and local case studies. Policies written only in technical English, the authors warn, will not reach all students equally, making language accessibility a matter of equity rather than mere convenience. Recent research on Kurdish English-language teachers, showing that digital literacy, technophilia and technophobia all shape how AI is integrated into teaching, reinforces the case for institutional literacy programmes rather than purely punitive responses.

From the findings, the researchers derive a practical toolkit: the four-stage readiness model and an 11-component AI governance checklist that universities, quality-assurance units and policymakers can use to audit themselves. Their recommendations are concrete. Universities should update plagiarism and integrity policies with GenAI-specific examples, create a dedicated and easy-to-find public AI guidance page, provide disclosure templates and assignment-level rules, support lecturers in redesigning assessments, publish AI-literacy resources covering hallucination, fabricated references, bias and over-reliance, and specify what information must never be entered into public AI tools. Responsibility for AI governance should be assigned to a named body, whether a quality-assurance unit, digital-learning team or cross-institutional working group, and reviewed regularly, because the technology is changing far faster than policy cycles.

The authors acknowledge the limits of their method. A website audit measures visibility, not implementation; universities may have internal rules that never surfaced in the search. Websites are dynamic, and the team has published a supplementary evidence workbook and audit archive on Zenodo preserving row-level scores, exact search queries, dates and negative-search logs to make the study reproducible. Equal weighting of the 11 dimensions is a simplification, and document analysis cannot capture the depth of classroom practice. Yet the central conclusion stands with unusual clarity for a policy audit: plagiarism rules are necessary but no longer sufficient. Generative AI has transformed academic writing, authorship, assessment and digital responsibility, and universities that communicate nothing publicly about it leave students to guess where support ends and misconduct begins. The Kurdistan Region’s universities, this study suggests, now have a roadmap for the journey from plagiarism control to genuine, transparent and multilingual AI governance; the question is how quickly they will travel it.

Subject of Research: Public-facing generative AI governance readiness in Kurdistan Region higher education institutions

Article Title: Mapping generative AI governance readiness in Kurdistan Region universities

Article References: Abdulrahman, A. A., & Ahmed, S. K. (2026). Mapping generative AI governance readiness in Kurdistan Region universities. Discover Education, 5(1), Article 1132. https://doi.org/10.1007/s44217-026-02242-x

Image Credits: AI Generated

DOI: 10.1007/s44217-026-02242-x

Keywords: generative AI, higher education, academic integrity, plagiarism, AI governance, ChatGPT, assessment redesign, AI literacy, data privacy, Kurdistan Region, policy audit, digital learning