Artificial Intelligence and the Transformation of Academic Integrity in Higher Education: A Systematic Review

This study examines how Artificial Intelligence (AI) is transforming academic integrity in higher education, altering both learning opportunities and the risks associated with misconduct. As creative AI tools become embedded in everyday academic work, they provide valuable support for writing, research assistance, and skills development. Still, they also challenge long-held assumptions about authorship, originality, and assessment. Emerging evidence suggests that students are using AI in a variety of ways, from supporting legitimate learning to producing fully automated assignments. However, AI-driven integrity technologies, such as plagiarism detectors and authorship checking models, are becoming more effective but continue to face issues of bias, false positives, and limited transparency. This rapid shift has created a gap between technological change and academic readiness, highlighting the need for institutions to rethink assessment design, improve integrity frameworks, and foster a culture of responsible AI use, rather than relying solely on surveillance and sanctions. This review compiles the latest studies published between 2020 and 2025 to map current practices, risks, and policy responses. The findings suggest that academic integrity in the age of artificial intelligence (AI) cannot be focused solely on preventing fraud. But this needs to expand to support ethical digital literacy, redesign learning tasks that require human reasoning, and ensure fairness in automated decision-making systems. The study concludes with recommendations for educators, researchers, and policymakers to balance innovation with responsibility to ensure that AI becomes a tool for transforming learning, rather than a threat to academic values.

@article{Phornprasert2026,
  title     = {Artificial Intelligence and the Transformation of Academic Integrity in Higher Education: A Systematic Review},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {6},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Wannakorn Phornprasert and Wongpanya S. Nuankaew and Pratya Nuankaew},
  doi       = {10.14569/IJACSA.2026.0170649},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170649}
}