Chatgpt and Similar Generative Ai Tools in Higher Education:academic Integrity, Aigiarism, and Institutional Policy Responses —a Systematic Literature Review and Implementation Framework
Year: 2026
Type: review
Study design: Systematic Review
Abstract: Background: The rapid adoption of ChatGPT and similar large language model (LLM)-based tools has introduced a novel category of academic misconduct — AIgiarism — defined as the uncredited submission of AI-generated content as original student work. Unlike traditional text-based plagiarism, AIgiarism largely evades conventional detection systems, posing systemic challenges to the integrity frameworks of higher education institutions (HEIs) worldwide. Methods: A systematic literature review (SLR) was conducted following PRISMA guidelines (Page et al., 2021). Scopus, Web of Science, ERIC, ScienceDirect, and Google Scholar databases were searched for peer-reviewed publications from November 2022 to June 2025, yielding 78 studies meeting the inclusion criteria. Thematic synthesis and quality appraisal (MMAT; AMSTAR-2) were applied across three domains: usage typology, detection technology, and institutional policy. Results: Evidence indicates that 56–92% of students use AI tools for academic work (BestColleges, 2023; HEPI, 2025), while self-reported AIgiarism stands at ~8% (Blahopoulou & Ortiz-Bonnin, 2025). AI detection tools achieve 80–90% accuracy but generate false positive rates of 3–8%, disproportionately affecting non-native English speakers (Jiang et al., 2024; Giray, 2024). Five institutional policy models are identified and evaluated. Attitude towards traditional plagiarism significantly predicts AIgiarism attitude (β = .569, p < .001; Khalaf, 2025). Conclusions: No single intervention suffices. Effective responses combine assessment redesign, AI literacy education, transparent disclosure norms, and calibrated detection. A five-stage implementation framework is proposed for Uzbekistan's multilingual higher education system, aligned with the National AI Strategy 2030 (Decree PQ-358, 2024) and UNESCO Guidance (2023).
Language: English
Subfield: Education
Field: Social Sciences
Domain: Social Sciences
SDG: Quality education
Open Access status: diamond
OpenAlex ID: W7219925271