The impact of generative AI on the teaching work of high school students
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The review positions the rise of generative AI in high school as an ambivalent pedagogical change and aims to analyze its impact on core teaching tasks. It uses an exploratory literature review design (2023–2025) with searches in Scopus, Web of Science, ERIC, and Education Source; it applies inclusion criteria for K-12/high school, peer screening, quality assessment, and thematic synthesis. The findings show: (i) greater efficiency in planning and design, with personalization by level and a shift in the teaching role toward curation and verification, but with a risk of curricular misalignment; (ii) tensions of academic integrity (plagiarism and detector limitations), which drive authentic assessment and traceability; (iii) sociocultural biases in model outputs that require critical mediation; and (iv) privacy concerns that demand institutional governance. It concludes that educational value depends on curricular guardrails, validation protocols, data policies, and professional development; it proposes a strategic tripod (teacher training, governance, process evaluation) and a future agenda on effects, alignment, and traceability.
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