AI in Education
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Machine learning (ML) in science and STEM education: a systematic review

AI in Education Staff•••Updated June 30, 2026•1 min read•Read source
Machine learning (ML) in science and STEM education: a systematic review
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Key Takeaways

  • •This systematic review highlights the increasing imperative for educators to integrate Machine Learning into science and STEM curricula, preparing students for an AI-driven workforce and research landscape.
  • •This reflects a broader educational trend where computational thinking and data literacy are becoming foundational skills across all disciplines, moving beyond traditional theoretical understanding.
  • •Consequently, institutions must prioritize professional development for faculty and invest in resources that enable hands-on ML application, ensuring students gain practical, future-ready competencies.

Machine learning (ML) in science and STEM education: a systematic review  Frontiers

Our Take

This systematic review highlights the increasing imperative for educators to integrate Machine Learning into science and STEM curricula, preparing students for an AI-driven workforce and research landscape. This reflects a broader educational trend where computational thinking and data literacy are becoming foundational skills across all disciplines, moving beyond traditional theoretical understanding. Consequently, institutions must prioritize professional development for faculty and invest in resources that enable hands-on ML application, ensuring students gain practical, future-ready competencies.

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Analysis & Perspectives

Rethinking Assessment and Academic Integrity to Cultivate AI Literacy and Critical Thinking Skills

This article examines the transformative impact of artificial intelligence on educational practices, particularly concerning assessment design and academic integrity. It advocates for a strategic rethinking of these areas to proactively cultivate students' AI literacy and critical thinking skills. The piece outlines new pedagogical approaches to prepare learners for an AI-driven future while upholding educational values.

Strategizing Teacher Professional Development for AI-Enhanced Pedagogies and Curriculum Design

This article explores strategic approaches to teacher professional development, focusing on preparing educators for the integration of artificial intelligence into classroom pedagogies. It outlines how to design professional learning that equips teachers to develop and implement AI-enhanced curricula effectively, ensuring readiness for future educational landscapes.

People Also Ask

What role does education play in the development of AI?▾
Education shapes the next generation of AI researchers, ethicists, and practitioners. Universities produce the talent that builds AI systems, while K-12 education increasingly incorporates computational thinking and data literacy to prepare all students — not just future engineers — to participate meaningfully in an AI-shaped society.
How is AI changing the way students learn?▾
AI is personalizing learning at scale through adaptive platforms that adjust difficulty and pacing to each student. It is also automating administrative tasks for teachers, enabling new forms of assessment like real-time comprehension checks, and making expert tutoring more accessible through AI-powered tools like Khan Academy's Khanmigo.
What skills do students need to thrive in an AI-driven world?▾
Students need a blend of technical literacy (understanding how AI works), critical thinking (evaluating AI outputs), creativity (doing what AI cannot), and ethical reasoning (understanding impacts on society). The OECD and UNESCO both highlight adaptability and human-centered skills as the most future-proof investments for learners.
Is AI replacing teachers?▾
AI is not replacing teachers — it is automating repetitive tasks like grading multiple-choice assessments and generating first drafts of lesson plans. The irreplaceable aspects of teaching — mentorship, social-emotional support, classroom management, and moral guidance — remain fundamentally human and are increasingly valued as AI handles more mechanical tasks.

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