The illusion of teaching competence in AI-mediated education
TL;DR
The concept of the “illusion of teaching competence” is introduced as a distinct theoretical construct, referring to the overestimation of pedagogical expertise derived from technological fluency and instrumental performance, and differentiating it from related constructs such as self-efficacy and competence overestimation.
Abstract
The accelerated integration of artificial intelligence (AI) in education has intensified debates on the reconfiguration of teaching competence and the professional status of the teaching profession. Although recent research has increasingly examined teachers’ digital competence, AI literacy, and attitudes toward technological tools, the pedagogical and professional dimensions of teaching competence remain under-theorized. This study presents a critical integrative review incorporating systematic elements of international literature published between 2018 and 2025, aiming to examine how teaching competence is conceptualized and redefined in the context of AI use in education. The final corpus comprises 15 studies selected through explicit inclusion and exclusion criteria and analyzed using thematic and interpretive synthesis. The findings indicate a recurring tendency to operationalize teaching competence in terms of technological proficiency, AI tool use, perceived usefulness, and instructional efficiency, while the situated, relational, reflective, and decision-making dimensions of teaching are less systematically addressed. Across the reviewed literature, a discrepancy emerges between high levels of self-efficacy in AI use and limited evidence of pedagogically grounded justification, suggesting a gap between perceived competence and pedagogically grounded professional expertise. Building on this synthesis, this study introduces the concept of the “illusion of teaching competence” as a distinct theoretical construct, referring to the overestimation of pedagogical expertise derived from technological fluency and instrumental performance, and differentiating it from related constructs such as self-efficacy and competence overestimation. By articulating this concept, the study provides a theoretical lens for understanding how evaluative criteria centered on technological performance may obscure the pedagogical foundations of teaching competence in AI-mediated contexts. The findings suggest that AI contributes to the professionalization of teaching only when integrated within pedagogically grounded frameworks that support reflective practice, pedagogical judgment, and context-sensitive decision-making.