The Impact of Artificial Intelligence on Research Integrity: Evidence from Canada, China, and Kazakhstan

Synopsys: This contribution presents an international comparative study of how generative AI is reshaping research integrity policy and practice in Canada, China, and Kazakhstan. Using document analysis and expert interviews, it identifies shared uncertainties around acceptable AI use—especially disclosure, authorship, and accountability—alongside context-specific regulatory responses. It concludes with evidence-based recommendations for embedding AI-focused integrity education and governance into higher education, including clearer standards, AI literacy, and case-based training to prevent AI-assisted misconduct.

Abstract: The rapid expansion of artificial intelligence (AI), and particularly generative AI tools, is reshaping research and higher education across the globe. Although AI offers significant advantages, such as increased productivity, support for multilingual academic writing, and faster data processing, it also poses substantial challenges for research integrity (cf. Denisova-Schmidt, 2025; Eaton, 2023; Lindebaum et al., 2025). Universities and research organizations are increasingly being confronted with questions related to AI-assisted authorship, transparency, plagiarism, fabrication, and the responsible governance of automated research practices. As AI becomes more deeply integrated into scholarly work, traditional academic integrity frameworks are being tested and institutions are struggling to distinguish between responsible innovation and emerging forms of misconduct. Despite the growing urgency of these discussions, empirical evidence remains limited regarding how different national contexts regulate AI-related misconduct and prepare academic communities for ethical AI use.

This presentation reports the findings of an international comparative study examining how AI is transforming research integrity policies and practices across three strategically selected countries: Canada, China, and Kazakhstan. These cases were chosen to reflect diverse academic environments in terms of technological development, research capacity, governance structures, ethical regulations, linguistic contexts, and political traditions. Canada represents a highly developed research ecosystem with strong institutional integrity infrastructures and a growing emphasis on transparency and responsible innovation. China provides insight into a technologically advanced system characterized by distinctive state and university-level supervisory approaches to academic integrity. Kazakhstan offers the perspective of an emerging research context, where regulatory frameworks are still evolving and institutional experience with AI-related misconduct remains comparatively limited.

The study employed a mixed-methods design combining document analysis and expert interviews conducted between late 2025 and early 2026. First, we systematically analyzed national and institutional policies, journal guidelines, ethics regulations, and funding agency documents to assess how AI-related risks are formally recognized and governed. Second, we conducted semi-structured interviews with key stakeholders, including faculty members, doctoral supervisors, research administrators, journal editors, and representatives of academic and funding bodies. Interview data explored the perceived benefits and risks of AI in research, as well as common integrity dilemmas, institutional responses to AI-assisted misconduct, and expectations for future governance.

The findings show both common challenges and important national differences in how AI is understood and governed in academic research. Across all three countries, respondents reported growing uncertainty about what counts as acceptable AI use, uneven institutional guidance, and a pressing need for clearer rules on disclosure, authorship, and accountability. At the same time, the study highlights how these issues play out differently across contexts, allowing participants to compare where systems are aligned, where gaps remain, and which approaches appear most promising. For conference attendees, this session offers practical value by translating these cross-country insights into concrete recommendations for academic integrity education and policy. Participants will leave with a clearer understanding of emerging risks, examples of effective responses, and actionable ideas for their own institutions, including how to strengthen AI literacy, develop clearer authorship and transparency guidelines, use case-based training to address AI-related misconduct, update ethics review processes, and build institutional capacity among supervisors and administrators. By combining comparative findings with practical guidance, the session will help attendees consider how adaptable training frameworks and governance strategies can support the responsible use of AI in research.

Keywords: Canada, China, generative artificial intelligence (GenAI), higher education, Kazakhstan, research integrity

References:
Denisova-Schmidt, E. (2025). A critical introduction to academic corruption. In E. Denisova-Schmidt, P. Altbach, & H. de Wit (Eds.). Handbook on corruption in higher education, Edward Elgar Publishing, 2−11.

Eaton, S. E. (2023). Postplagiarism: Transdisciplinary ethics and integrity in the age of artificial intelligence and neurotechnology. International Journal for Educational Integrity, 19(1), 1–10. https://doi.org/10.1007/s40979-023-00144-1

Lindebaum, D., Nolan, E., Ashraff, M., Islam, G., & Ramirez, M. F. (2025). The transformation of epistemic agency and governance in higher education through large language models—Toward a future of organized immaturity. Organization Studies. https://doi.org/10.1177/01708406251392002