Culturally Responsive Pedagogy in the Age of AI: Reimagining L2 Writing Instruction
Dissertation Proposal Title: Culturally Responsive Pedagogy in the Age of AI: Reimagining L2 Writing Instruction
Dissertation Committee: Dr. Christine Tardy, Dr. Betul Czerkawski, Dr. Beatrice Dupuy, Dr. M'Balia Thomas
Abstract: Culturally Responsive Pedagogies (CRP) is a set of approaches that emphasize the importance of students’ cultural and linguistic identities in learning and challenging dominant norms (Gay, 2000; Ladson-Billing, 1995). Despite the anticipated increase in global English learners, there exists a significant gap in research concerning the effects of culturally responsive teaching on writing instruction for EFL students. They would greatly benefit from learning about standard academic practices while leveraging their cultural resources. This study aims to close this gap by providing L2 writing instructors with a new pedagogical framework, combining CRP principles and inclusive AI literacy — practice valuing diverse voices while critically examining exclusions embedded in AI tools. As generative AI tools become increasingly common in classrooms, concerns arise regarding their reinforcement of standardized English norms and marginalization of diverse rhetorical practices (Vetter et al., 2024). This study addresses: (1) How do instructors interpret CRP and inclusive AI literacy? (2) What challenges and opportunities arise when integrating AI into culturally affirming writing instruction?
Employing a mixed-methods approach, this dissertation investigates how university writing instructors interpret and apply the proposed pedagogical framework through a semester-long professional development (PD) model. The PD is based on the Critical Friends Group model and includes discussion circles, teacher reflective journaling, and adaptation of instructional artifacts (e.g., slides, handouts, exercises reflecting CRP, AI, or both). Surveys provide descriptive statistics tracking shifts in understanding and practice (mean and value); qualitative data are analyzed via reflective thematic analysis. The findings may provide valuable guidance for writing instructors, teacher educators, and curriculum developers on how to navigate AI-mediated writing while also honoring students’ linguistic and cultural repertoires.
