Artificial Intelligence-mediated Adaptive Learning in English as a Foreign Language instruction: A conceptual cartography
Keywords:
Artificial intelligence, Educational technology, Foreign language, Learning personalization, Learning processAbstract
Artificial Intelligence (AI)-mediated adaptive learning has gained relevance in English as a Foreign Language (EFL) teaching due to its capacity to personalize learning experiences and provide real-time feedback. However, conceptual ambiguities persist regarding whether this process operates as a mediated, driven, or enhanced approach, as well as concerning its theoretical foundations, characteristics, and implementation strategies in the educational field. The objective of this study was to define the concept of AI-mediated adaptive learning in EFL teaching and to identify its main characteristics, theoretical support, and methodological elements. A conceptual cartography was conducted based on eight analytical axes: notion, categorization, characterization, differentiation, classification, linkage, methodology, and exemplification. A total of 57 scientific articles retrieved from ScienceDirect, ERIC, SpringerLink, and Dialnet databases were analyzed. The findings demonstrate that this type of learning is characterized by immediate personalization, adaptability, interactivity, and pedagogical scaffolding that strengthens students' cognitive and affective experiences. A close relationship was identified with socioconstructivist mediation and connectivism, both of which articulate adaptive learning. Finally, a methodological process oriented toward the pedagogical implementation of AI-mediated adaptive systems is proposed, consisting of six phases: initial preparation by teachers and institutions, student diagnosis, intervention design, student training, implementation, and evaluation. This cartography provides a useful conceptual framework for researchers, educators, and institutions interested in integrating emerging technologies into foreign language teaching.
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