Reconfiguring the Classroom in the Face of Artificial Intelligence: A Proposed Didactic Adaptation Strategy for Teachers Without Advanced Technological Training

Abstract

The vertiginous emergence of generative Artificial Intelligence (AI) has caused a methodological disruption in higher education, weakening traditional evaluation systems based on written products completed outside the classroom and generating uncertainty among teaching staff. The purpose of this article is to propose a methodological framework oriented to the reconfiguration of the face-to-face classroom space as a viable didactic adaptation strategy for teachers without advanced technological training. Methodologically, the study corresponds to qualitative research of a descriptive-documentary typology, based on the hermeneutic method and content analysis, examining an intentional sample of scientific and institutional documents published between 1986 and 2026, with particular emphasis on recent literature on generative AI published between 2023 and 2026. The main results materialize in the design of a proposal structured in three operational dimensions: the inversion of the evaluative process within the classroom, the diversification of real-time performance evidence, and the co-design of challenges based on local-context problems. According to the documentary review conducted, it is inferred that pedagogical resilience in the face of technological change would not require specialized programming skills, but rather a transformation of face-to-face teaching; the proposal suggests that shifting the weight of formative assessment from the final automated product toward the synchronous, face-to-face process could allow the teacher to regain greater control of educational mediation and potentially favor more effective scaffolding of critical thinking, an assumption that should be corroborated through subsequent empirical studies.