AI evidence extraction

TRANSFORMATION OF ACADEMIC INTEGRITY PRINCIPLES IN THE ERA OF GENERATIVE ARTIFICIAL INTELLIGENCE

Mykola TYSHCHENKO · Humanities science current issues · 2026

AI-generated evidence extraction, verified across multiple analytical personas. Not a substitute for the peer-reviewed original.

9/10

Relevance

2/4

Quality (LMQS)

Evidence

Citations

0.00

FWCI

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.24919/2308-4863/99-2-61

Methodology & findings

Study design

Qualitative comparative analysis grounded in systematic document analysis and evidence synthesis.

Primary method

Qualitative coding process; structured analytical matrix; triangulation strategy for validation across diverse materials; no quantitative statistical testing reported

Main result

The study found that "the integration of Generative AI into higher education has initiated a fundamental conceptual shift, moving the focus of academic integrity from the final product to the integrity of the creative and analytical process." The comparative analysis across three regions reveals distinct regional models: "the United States serves as a model for agile but fragmented institutional experimentation," Europe demonstrates "a trajectory toward stronger regulatory alignment, driven by the ethical imperatives of the EU AI Act," and Ukraine faces "uniquely complex" implementation "while simultaneously managing the implementation asymmetries caused by the exceptional constraints of wartime disruption."

Reports effect sizes.

Research paradigm

Qualitative comparative analysis; interpretivist/constructivist

Author conclusions

The authors conclude that "the fundamental principles of academic integrity are currently undergoing a profound structural transformation in their practical implementation, rather than being replaced or diminished in their core substance." They further state that "the strategic objective for higher education institutions—and particularly for technical universities like Igor Sikorsky Kyiv Polytechnic Institute—is not the futile pursuit of technological prohibition, but rather the intentional redesign of educational processes." The key recommendation is that "by integrating AI literacy as a core competency and shifting the focus from the final linguistic product to the integrity of the underlying intellectual journey, universities can secure the long-term value of academic qualifications and sustain the necessary trust between the institution, the student, and the global professional market."

Risk of bias

Language bias - predominantly English-language sources from US and Europe; Geographic representation bias - uneven data distribution across regions; Temporal bias - rapid field evolution may render earlier documents less relevant; Selection bias in document filtering based on four-tier eligibility framework; Language bias: Reliance on English-language datasets may underrepresent non-English academic discourse; Geographic bias: Uneven distribution of empirical data across target regions; Selection bias: Documents filtered based on four-tier eligibility framework focusing on English-language sources; Temporal bias: Rapid, non-linear development of GAI may not be fully captured by document analysis through 2026; Contextual bias: Ukrainian data shaped by wartime conditions, potentially non-representative of normal implementation; Language bias: English-language datasets from US and Europe provide more established evidence base; Ukrainian data is described as 'emergent'; Geographic representation bias: Uneven distribution of empirical data across regions; Publication selection bias: Document analysis may be skewed toward published/accessible materials in English; Temporal bias: Dataset spans 2017-2026, but 'hyper-acceleration' period may not be evenly represented in source materials; Context-sensitivity: Authors note findings are 'context-sensitive rather than directly equivalent' across regions, limiting generalizability

Limitations

The study acknowledges that "the methodology does not aim for statistical generalization but rather for a reliable analytical interpretation of how core academic values are being re-enacted through new regulatory and instructional instruments." Additionally, while "English-language datasets from the United States and Europe provide a more established evidence base, the emergent data from Ukraine offer a unique perspective on integrity implementation under conditions of extreme external pressure and digital transformation." The analysis is constrained by "uneven distribution of empirical data across the target regions."

Open questions raised

Need for clearer, more harmonized definitions of acceptable AI use that align with new regulatory standards
Understanding of how to implement integrity policies effectively under wartime constraints and digital infrastructure inequalities (Ukraine context)
Faculty development and systemic institutional investment in supporting consistent enforcement standards
Integration of AI literacy as a foundational integrity competency in higher education curricula
Need for clearer, more harmonized definitions of acceptable AI use that align with new regulatory standards, particularly addressing the 'norm-interpretation gap' in Europe
Requirement for development of entirely new pedagogical instruments and regulatory mechanisms to preserve academic integrity values in practice

Extracted from: pdfAgreement 61%

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