Unpacking Intersectional Knowledge in Engineered Artificial Intelligence: A Mode of Address Perspective

Potential Abstract:
Artificial intelligence (AI) technologies are increasingly being integrated into educational settings, promising to enhance learning experiences and outcomes for students. However, the deployment of AI in education raises critical questions about the intersectional knowledge embedded within these systems. This study examines how AI technologies, specially engineered for educational purposes, carry and perpetuate biases rooted in intersectional identities such as race, gender, and socio-economic status. Drawing on a mode of address framework, we explore how AI systems communicate with and shape the experiences of diverse students within educational contexts.

Through a critical analysis of AI algorithms and their outcomes, we uncover the ways in which intersectional knowledge is constructed and operationalized within these systems. Our findings highlight the importance of foregrounding intersectionality in the design and implementation of AI technologies to ensure equitable and inclusive educational practices. By centering a mode of address perspective, we illuminate the power dynamics embedded in AI interactions and uncover hidden biases that may perpetuate systemic inequalities.

This research contributes to the growing discourse on AI in education by offering a nuanced understanding of how intersectional knowledge is manifested in engineered systems and the implications for educational equity and social justice. By critically examining the ways in which AI technologies address and interact with diverse student populations, we advocate for a more conscious and deliberate approach to the development of AI tools in educational settings.

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