Abstract
Artificial intelligence (AI) is transforming education through adaptive learning, predictive analytics, and automated assessments, reshaping how students learn and how teachers engage with instructional content. However, AI-driven technologies also introduce challenges such as algorithmic bias (the systematic reinforcement of inequities through AI decision-making), data privacy concerns, and the erosion of teacher agency (the ability of educators to make informed instructional decisions). These challenges reveal the limitations of Culturally Responsive Teaching (CRT), a pedagogical framework designed to incorporate students’ cultural backgrounds into curriculum and instruction but not explicitly developed to address the ethical complexities of AI integration. This paper introduces Culturally Responsive AI Pedagogy (CRAIP) as an expansion of CRT, integrating insights from Critical Algorithm Studies (CAS), which critiques how AI systems perpetuate biases, and Data Justice, which advocates for the fair and ethical use of data in education. CRAIP’s five core principles—Algorithmic Fairness, Culturally Responsive AI Curriculum, Teacher Agency, Student AI Literacy, and Ethical AI Governance—provide a structured approach to ensuring that AI-powered education remains equitable, transparent, and culturally inclusive. By analyzing real-world cases where CRT does not adequately address AI-driven challenges, this paper highlights the urgent need for CRAIP as an essential framework for ethical and inclusive AI integration in education.
Presenters
Benjamin BoisonDirector, Centre for Learning and Teaching Innovation, Aurora College, Canada
Details
Presentation Type
Theme
KEYWORDS
Culturally Responsive AI Pedagogy, Critical Algorithm Studies, AI Ethics
