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Academic integrity in the context of educational digitalization: new challenges and assurance mechanisms

Aug 2026 · Academic Visions · 0 citations · 17 references

Abstract

The article explores the issue of ensuring academic integrity under the conditions of the digital transformation of education, driven by the rapid proliferation of generative artificial intelligence and the need to rethink traditional approaches to organizing the educational process and assessing learning outcomes. The aim of the study is to provide a theoretical substantiation of a conceptual approach to ensuring academic integrity in the context of educational digital transformation, identify contemporary challenges associated with the use of generative artificial intelligence, and outline pedagogical and institutional mechanisms for fostering a culture of responsible digital technology adoption in higher education institutions. The methodological framework of the study comprises general scientific and specialized methods: analysis, synthesis, systematization, generalization, comparative, systems, and content analysis, as well as conceptual analysis of scientific sources, legal frameworks, and international guidelines. As a result of the research, the main challenges to ensuring academic integrity during educational digital transformation have been systematized. A conceptual approach to maintaining academic integrity has been substantiated, integrating legal, methodical-didactic, technological-expert, and cultural-educational components. The scientific novelty lies in the theoretical substantiation of a conceptual framework for ensuring academic integrity in the era of educational digital transformation, which combines legal, pedagogical, technological, and cultural-educational mechanisms into a unified system supporting academic culture and the responsible use of generative artificial intelligence. The practical significance of the findings stems from their applicability to enhancing internal quality assurance systems, regulating local policies on generative AI usage, updating assessment methods for learning outcomes, and advancing the professional development of academic staff.

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