Integration of AI in STEM Education, Addressing Ethical Challenges in K-12 Settings (2510.19196v1)

Abstract: The rapid integration of AI into K-12 STEM education presents transformative opportunities alongside significant ethical challenges. While AI-powered tools such as Intelligent Tutoring Systems (ITS), automated assessments, and predictive analytics enhance personalized learning and operational efficiency, they also risk perpetuating algorithmic bias, eroding student privacy, and exacerbating educational inequities. This paper examines the dual-edged impact of AI in STEM classrooms, analyzing its benefits (e.g., adaptive learning, real-time feedback) and drawbacks (e.g., surveillance risks, pedagogical limitations) through an ethical lens. We identify critical gaps in current AI education research, particularly the lack of subject-specific frameworks for responsible integration and propose a three-phased implementation roadmap paired with a tiered professional development model for educators. Our framework emphasizes equity-centered design, combining technical AI literacy with ethical reasoning to foster critical engagement among students. Key recommendations include mandatory bias audits, low-resource adaptation strategies, and policy alignment to ensure AI serves as a tool for inclusive, human-centered STEM education. By bridging theory and practice, this work advances a research-backed approach to AI integration that prioritizes pedagogical integrity, equity, and student agency in an increasingly algorithmic world. Keywords: Artificial Intelligence, STEM education, algorithmic bias, ethical AI, K-12 pedagogy, equity in education