Integration of AI in STEM Education, Addressing Ethical Challenges in K-12 Settings

Related Topic Relevance: 7/10 1 cited 2025 paper

This paper provides a critical conceptual review of ethical challenges in integrating AI into K-12 STEM education, proposing a three-phased implementation roadmap and professional development model that emphasizes equity-centered design, bias audits, and AI literacy for both teachers and students.

The rapid integration of Artificial Intelligence (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

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2.1 Pedagogical knowledge 5 Ethics and bias

Tool Types

AI Tutors 1-to-1 conversational tutoring systems.

Personalised Adaptive Learning Systems that adapt content and difficulty to individual learners.

Teacher Support Tools Tools that assist teachers — lesson planning, content generation, grading, analytics.

Integration of AI in STEM Education, Addressing Ethical Challenges in K-12 Settings

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