The growing integration of artificial intelligence (AI) into education has significantly influenced how knowledge is produced, interpreted, and mobilized within teaching and learning processes (Luckin et al., 2016; Holmes, Bialik, & Fadel, 2019). In the field of teacher education, AI is predominantly discussed in relation to digital competence, instructional efficiency, and personalized learning environments, often emphasizing teachers’ preparedness to operate within increasingly data-driven and automated educational systems (Redecker, 2017; Williamson, Eynon, & Potter, 2020). While this body of research highlights the transformative potential of AI, it largely conceptualizes knowledge as a technical and instrumental resource, frequently detached from ethical, cultural, and social justice considerations (Selwyn, 2019; Akgun & Greenhow, 2021). As a result, the relationship between knowing AI and acting in culturally responsive and equitable ways remains under-theorized and insufficiently examined in teacher education research (Kizilcec & Lee, 2020).

Critical scholarship has increasingly problematized the assumption that AI technologies are neutral or value-free. Studies demonstrate that algorithms and data-driven systems frequently reproduce existing power relations, cultural hierarchies, and structural inequalities embedded in their design and training data (Benjamin, 2019; Noble, 2018). Despite these concerns, teacher education research tends to prioritize AI literacy and technological proficiency, with limited attention to how teachers are prepared to engage critically with AI as a cultural and ethical phenomenon. This creates a significant gap between knowledge production in AI-related teacher education research and the pedagogical actions required to promote equity and inclusion in diverse educational contexts.

Culturally responsive pedagogy offers a powerful framework for interrogating this gap. Grounded in the recognition of learners’ cultural identities and the moral responsibilities of teaching, culturally responsive pedagogy positions educational practice as inherently value-laden and action-oriented (Gay, 2018; Ladson-Billings, 1995). However, existing literature suggests that even when issues of diversity and equity are acknowledged, they are rarely integrated into discussions of emerging educational technologies such as AI. Consequently, the literature reflects a persistent knowing–acting divide: while research increasingly recognizes the risks and biases associated with AI, it offers limited guidance on how such knowledge can be translated into culturally responsive pedagogical action.

The ECER 2026 theme, *“Knowing and Acting: The changing conditions and potentials of education research,”* provides a timely lens for examining this disconnect. In the context of AI and teacher education, this theme invites critical reflection on how educational research frames knowledge, whose knowledge is privileged, and how research contributes—or fails to contribute—to transformative educational practice. Yet, to date, no comprehensive review has systematically examined how AI-related teacher education literature conceptualizes the relationship between knowing and acting from a culturally responsive and ethical perspective.

Addressing this gap is essential for advancing both theory and practice. A critical synthesis of literature can illuminate dominant epistemological assumptions, identify silences and omissions, and foreground the conditions under which AI-related knowledge may support culturally responsive and socially just educational action. Such an analysis is crucial not only for teacher education research but also for broader debates about responsibility, equity, and agency in the age of artificial intelligence.

This study employs a critical thematic literature review design to examine how research on artificial intelligence in teacher education conceptualizes the relationship between knowledge (knowing) and pedagogical action (acting) in relation to cultural responsiveness, equity, and ethics (Braun & Clarke, 2006; Grant & Booth, 2009). Rather than aiming to quantify trends or evaluate effectiveness, the review seeks to critically synthesize and interpret existing scholarship to reveal dominant themes, underlying assumptions, and conceptual gaps, an approach particularly suited to examining emerging and interdisciplinary fields such as AI in education (Booth, Sutton, & Papaioannou, 2016; Snyder, 2019). By adopting such a critical stance, the review moves beyond descriptive mapping to interrogate how knowledge is framed, whose perspectives are privileged, and how research contributes to—or constrains—transformative pedagogical action (Gough, Oliver, & Thomas, 2017). The literature search was conducted across major academic databases commonly used in educational research, including Scopus, Web of Science, and ERIC. Key search terms included combinations of *artificial intelligence*, *teacher education*, *preservice teachers*, *AI literacy*, *culturally responsive pedagogy*, *equity*, *ethics*, and *teacher agency*. Peer-reviewed journal articles published in English were considered, with a focus on studies situated within teacher education contexts. Inclusion criteria were defined to capture studies that explicitly addressed AI in relation to teacher education or preservice teacher preparation. Studies focusing solely on technical system development or student outcomes without pedagogical or educational implications were excluded. Following the screening process, the selected studies were subjected to an iterative thematic analysis. The analysis proceeded in three stages. First, studies were examined to identify how AI-related knowledge was conceptualized (e.g., technical, pedagogical, ethical). Second, attention was given to how—or whether—issues of cultural responsiveness, equity, and social justice were addressed. Third, the review analyzed how literature framed the relationship between AI-related knowledge and pedagogical action, including notions of responsibility and teacher agency. Throughout the process, a critical stance was adopted to interrogate whose knowledge is foregrounded, which perspectives are marginalized, and how research contributes to or constrains transformative educational practice.

This review is expected to demonstrate that majority of AI-related teacher education literature privileges technical and instrumental forms of knowledge, while giving limited attention to culturally responsive, ethical, and action-oriented dimensions of teaching. Although concerns about bias, fairness, and accountability are increasingly acknowledged, these issues are often discussed abstractly and remain weakly connected to pedagogical practice and teacher agency. By synthesizing the literature through a knowing–acting lens, the study is expected to identify a persistent conceptual gap between AI literacy and culturally responsive pedagogical action. The review will highlight how cultural responsiveness is frequently treated as an add-on rather than a foundational principle in AI-related teacher education research. Theoretically, the study contributes to teacher education and AI scholarship by integrating culturally responsive pedagogy and teacher agency into the analysis of AI-related knowledge production. Conceptually, it offers a critical framework for understanding how AI knowledge can be reoriented toward ethical and socially just educational action. Practically, the findings provide guidance for teacher ED researchers and curriculum designers seeking to move beyond technical competence toward preparing teachers as culturally responsive and ethically responsible agents in AI-mediated educational contexts. By doing so, the study responds directly to the core concerns of ECER 2026 and underscores the transformative potential of educational research.

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