The bibliometric analysis identified a marked increase in scholarly output after 2022. It also identified three dominant areas of inquiry: detection and prevention strategies, pedagogical adaptation, and ethical governance. In the qualitative component, the sample included 27 students and 21 instructors from higher-education institutions in Cebu. Students commonly used GenAI to support learning, productivity, and academic tasks while negotiating unclear expectations about acceptable use.

Instructors responded by increasingly redesigning assessments, placing greater emphasis on process-oriented evaluation, authentic learning tasks, and performance-based approaches rather than relying exclusively on AI-detection technologies. According to the article abstract, the combined results indicate that, in the studied contexts, GenAI is associated with evolving approaches to academic integrity and assessment that extend beyond concerns about academic misconduct alone. The findings also support the importance of context-sensitive institutional policies, AI-literacy initiatives, and assessment practices that promote transparency, accountability, and meaningful demonstrations of student learning in AI-enabled higher education.

The reported findings are bounded by the methods and settings analyzed. The bibliometric evidence comes from the Scopus corpus selected for the review, while the qualitative evidence comes from 48 participants in higher-education institutions in the Cebu region of the Philippines. The abstract does not provide the full survey of interview findings or the detailed analysis of the publication corpus, so the reported results should not be generalized beyond those scopes. The authors state that the findings may inform research and practice in comparable higher-education environments, while emphasizing that interpretation should remain within the analyzed corpus and the Cebu-based qualitative setting.

Subject of Research: Generative AI use and academic integrity in higher-education assessment

Article Title: From diffusion to negotiation: generative AI and academic integrity in higher education assessment

Article References: Mangubat, F. M., & Mangubat, F. N., Jr (2026). From diffusion to negotiation: generative AI and academic integrity in higher education assessment. SN Social Sciences, 6(9), Article 374. https://doi.org/10.1007/s43545-026-01667-3

Image Credits: AI Generated

DOI: 10.1007/s43545-026-01667-3

Keywords: generative artificial intelligence, academic integrity, higher education, assessment, mixed-methods research, bibliometric analysis, student learning, AI detection, pedagogical adaptation, ethical governance, diffusion, negotiation