Abstract
As generative artificial intelligence (GenAI) becomes increasingly embedded in instructional planning, teacher education programs must move beyond efficiency-oriented adoption toward equity-centered, pedagogically grounded integration. This mixed-methods longitudinal study examines a year-long, scaffolded approach to integrating GenAI within an early childhood teacher residency program, with a specific focus on culturally responsive teaching (CRT) instructional planning. Guided by the AI/I-TPACK and CRT frameworks, fourteen bilingual teacher candidates (TCs) participated in this study. Data sources included pre- and post-surveys of CRT self-efficacy, lesson plans and instructional artifacts, reflective coursework, and focus group interviews. Quantitative analyses revealed statistically significant gains in TCs’ CRT self-efficacy across all CRTL standards, particularly in areas related to instructional enactment, differentiation, and student-centered planning. Qualitative findings indicate that while GenAI tools supported efficiency, multilingual scaffolding, and differentiation, they frequently lacked cultural specificity and reproduced dominant representations, requiring TCs to engage in critical evaluation, revision, and contextualization. Findings suggest that scaffolded, critical engagement with GenAI and CRT, within this context, coincided with stronger culturally responsive instructional planning. The study contributes a replicable design model for equity-oriented GenAI integration in teacher education and offers implications for preparing early childhood educators to use GenAI as a reflective pedagogical tool rather than a substitute for professional judgment.
Recommended Citation
Ko, Dr. Eun Kyung; Chen, Xiaoning; Thamotharan, Vishodana; and Sidarous, Julie. (). Scaffolded Generative AI in Instructional Planning: Supporting Teacher Candidates’ Culturally Responsive Teaching. i.e.: inquiry in education: Vol. 18: Iss. 1, Article 5.Retrieved from: https://digitalcommons.nl.edu/ie/vol18/iss1/5