Integrating AI and machine learning in STEM education: Challenges and opportunities
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This study investigates the integration of Artificial Intelligence (AI) and Machine Learning (ML) in STEM education, emphasizing the transformative potential and inherent challenges of these technologies. The purpose of this research is to provide a thorough understanding of how AI and ML can enhance educational outcomes, personalize learning experiences and address critical issues within the STEM fields. Utilizing a comprehensive review of current literature, this study examines the state of AI and ML integration in STEM education, identifies key ethical considerations and explores future trends and research directions. Key findings reveal that AI and ML significantly contribute to personalized learning, adaptive teaching strategies, and increased student engagement. However, challenges such as data privacy concerns, ethical dilemmas and the necessity for extensive educator training and infrastructure investment are prominent. The study underscores the importance of developing ethical frameworks and guidelines to ensure responsible use, mitigate biases and promote transparency. The conclusions drawn from this research highlight the critical need for collaboration among educators, technology developers, policymakers and researchers to fully leverage the potential of AI and ML. Recommendations include investing in professional development for educators, ensuring equitable access to AI tools and fostering international cooperation to share best practices and innovative solutions. Further, ongoing research into the ethical and practical implications of these technologies is essential for their successful integration into STEM education. This study elucidates the profound opportunities AI and ML present in transforming STEM education and calls for a strategic, ethical, and collaborative approach to overcome existing challenges and enhance educational practices. Keywords: Artificial Intelligence, Machine Learning, STEM Education, Personalized Learning, Ethical AI, Educational Technology.
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Domain-specific applications contribute meaningfully but remain subject-specific and therefore necessitate tailored guidelines.
“Although domain-specific applications contribute meaningfully, they remain subject-specific and therefore necessitate tailored guidelines (Deo, 2015;Joseph and Uzondu, 2024).”
From An integrated AI literacy framework for higher‑education teachers: a critical review and conceptual synthesis (2026) · cites this paper as Joseph and Uzondu, 2024
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- Authors
- Joseph, O. B., & Uzondu, N. C.
- Venue
- Computer Science & IT Research Journal
- Publisher
- Fair East Publishers
- Published
- 2024-08-03
- Volume
- 5
- Issue
- 8
- Pages
- 1732-1750
- ISSN
- 2709-0043 (ISSN-L)
- 2709-0051
- OpenAlex
- W4401288918
- Cited by
- 64
- References
- 50
- Citations by year
- 2026: 33 · 2025: 27 · 2024: 4
- FWCI
- 23.96
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Computer Science & IT Research Journal (publishedVersion, cc-by-nc)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- artificial intelligence in education, machine learning in education, STEM education, personalized learning, adaptive learning, student engagement, data privacy, ethical AI, algorithmic bias, transparency, teacher professional development, educational technology, AI ethics framework
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