ARTIFICIAL INTELLIGENCE AND SCHOOL ASSESSMENT:FORMATIVE POTENTIAL, TEACHER MEDIATION, AND ETHICALCHALLENGES IN CONTEMPORARY EVALUATIVE PRACTICES

Abstract

The incorporation of artificial intelligence (AI) into educational systems has expanded the possibilities for monitoring learning, producing feedback, and personalizing instruction. In school assessment, however, the adoption of these technologies requires more precise conceptual distinctions between different AI paradigms and a critical analysis of their pedagogical and ethical effects. This study aims to analyze the impacts of AI on school assessment processes by examining its formative potential, epistemological limits, and the ethical challenges involved in its use. Methodologically, this is a qualitative bibliographic study with an analytical and interpretive orientation. Rather than presenting itself as an exhaustive systematic review, the study explicitly adopts the format of an analytical bibliographic review, organized around academic literature and institutional documents relevant to the topic. The
analysis is guided by the articulation between formative assessment theory and a critical sociotechnical reading of educational datafication. The study shows that the effects of AI on assessment are not homogeneous: rule-based systems tend to operate better in structured tasks; models supported by learning analytics and educational data mining expand monitoring and diagnostic capacity; and generative systems open new possibilities for open-ended tasks, but still show instability, opacity, and a persistent need for human oversight. The article concludes that AI can contribute to more continuous, responsive, and formative assessment practices, provided that its use remains subordinated to teachers' pedagogical judgment, data protection, algorithmic transparency, and principles of equity.