Authentic assessment and academic integrity in the context of generative artificial intelligence in higher education
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Generative artificial intelligence has transformed the conditions under which knowledge is produced, reviewed, and assessed in higher education, creating tensions between the legitimate use of digital tools, responsible authorship, and academic integrity. This article aimed to critically analyze the relationship between authentic assessment and academic ethics in response to the incorporation of generative artificial intelligence into university learning processes. An exploratory literature review was conducted through a documentary, non-experimental, and cross-sectional design, based on recent scientific articles, reviews, institutional guidelines, and academic documents related to assessment, integrity, transparency, and the educational use of these technologies. The results show that authentic assessment cannot be limited to final products or contextualized tasks, as it requires the incorporation of process-based evidence, verifiable reasoning, disciplinary judgment, defense of decisions, and transparent disclosure of artificial intelligence use. Likewise, automatic detection tools were found to have limitations in verifying authorship and should not be considered the sole evidence of academic misconduct. It is concluded that generative artificial intelligence does not invalidate authentic assessment but requires more process-oriented, ethical, traceable, and intellectually responsible assessment models.
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