Rethinking teaching with GenAI: Theoretical models and operational tools
Abstract
The rapid evolution of generative Artificial Intelligence (AI) has raised new questions regarding the effective integration of digital tools into the educational process, necessitating theoretical and methodological approaches capable of fully harnessing its potential. This paper examines the ESLAI framework (Situated Learning Episodes with AI) (Panciroli et al., 2023) and the TPAIK model (Technological, Pedagogical, Artificial Intelligence Knowledge) (Pratschke & Islam, 2023) to highlight how the informed use of generative AI must be supported by disciplinary, pedagogical, and computational competencies. Additionally, the S.P.Ai.C.E. model (Synergy between People and Artificial Intelligence for Collaborative Education) is presented as a framework designed to guide educators in the selection and validation of AI tools in contexts characterized by rapid technological obsolescence. The analysis of these three models demonstrates how a training design based on situated learning can leverage AI affordances to make teaching practices more dynamic, personalized, and context sensitive. The findings from the implementation of these models (Adamoli et al., 2024) suggest that by combining a solid theoretical foundation with operational procedures for testing and evaluation, it is possible to foster an ethical and sustainable use of AI in education. This approach promotes co-creative knowledge processes while developing meta-reflective competencies.
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Copyright (c) 2025 Salvatore Messina, Chiara Panciroli
Questo lavoro è fornito con la licenza Creative Commons Attribuzione - Non commerciale - Non opere derivate 4.0 Internazionale.
