Contemporary Research Analysis Journal

The rapid expansion of generative artificial intelligence (GenAI) is changing the conditions under which teaching, learning evidence, and scientific research are produced and assessed in higher education. This study examines these transformations through a qualitative-quantitative documentary analysis of 686 publications published between 2022 and 2026. Each document was coded using 44 binary thematic indicators grouped into six domains: assessment of teaching, assessment of learning, research supervision, assessment by academic juries, GenAI-related risks and limitations, and assessment transformations or good practices. Descriptive thematic frequencies were complemented by Multiple Correspondence Analysis (MCA), Benzécri and Greenacre corrections of inertia, and pairwise Phi coefficients. Publications from 2025 and 2026 accounted for 72.0% of the corpus, confirming the very recent and rapidly expanding character of the field. After Benzécri correction, the first three MCA dimensions explained 70.20% of corrected inertia, compared with 21.74% of raw inertia; the more conservative Greenacre correction yielded 47.86%. Phi analysis showed a strong association between authentic assessment and oral/defence-based assessment (φ = .694; χ² = 330.43; p < .001), while process traceability was positively associated with human validation (φ = .177; χ² = 21.41; p < .001). Across the corpus, integrity, transparency, explicit task-level policies, AI literacy, authentic assessment, traceability, oral justification, and human validation emerged as recurrent responses. The findings indicate a shift from assessment centred on the final product toward an evidence-based ecosystem combining product, process, traceability, explicit justification, dialogue, and accountable human judgment. On this basis, the study proposes the P-P-T-E-V model—Product, Process, Traceability, Explicitation, and Human Validation—as an integrative framework for assessing teaching-related evidence and scientific research in the GenAI era.

generative artificial intelligence, higher education assessment, scientific research, research supervision, multiple correspondence analysis, academic integrity

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