AI in STEM Education: Assessment, Integrity, and Teacher Readiness

Artificial Intelligence is transforming STEM education, but its fastest-growing impact area - assessment, feedback, and academic integrity - remains under-researched compared to broader “AI in the classroom” studies. This Research Topic focuses specifically on how AI reshapes evaluation and trust in STEM learning, and on whether teachers and institutions are prepared for that shift.

Generative AI, automated feedback systems, and learning analytics are already changing how STEM assessments are designed, delivered, and policed. Tools that once supported instruction now also complicate it: AI can generate solutions to problem sets, blur authorship in lab reports and coding assignments, and produce feedback whose reliability is not yet well understood. At the same time, adaptive and AI-assisted assessment offers real opportunities to measure inquiry, computational thinking, and problem-solving in ways traditional testing cannot. Between these opposing pressures sits the teacher, expected to design fair assessments, uphold academic integrity, and interpret AI-generated feedback with little formal training or institutional guidance.

This Research Topic invites empirical, theoretical, and practice-oriented contributions examining the specific intersection of AI, assessment, and integrity in STEM education, across primary, secondary, higher education, and teacher education settings. We welcome studies on:

• AI-generated or AI-assisted feedback and its validity, reliability, and impact on learning.

• Academic integrity policy and detection practices for AI-assisted work in STEM coursework.

• Adaptive assessment platforms and learning analytics used to evaluate inquiry-based or computational skills.

• Teacher and institutional readiness to design, interpret, and defend AI-informed assessment.

• Equity implications of AI-assisted assessment for students with unequal access to AI tools.

• Case studies of assessment redesign in response to generative AI.

We welcome original empirical studies, systematic and scoping reviews, theoretical and conceptual papers, and case studies. Contributions grounded in real classroom, lab, or institutional data are especially encouraged.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Conceptual Analysis
• Curriculum, Instruction, and Pedagogy
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Conceptual Analysis
• Curriculum, Instruction, and Pedagogy
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy Brief
• Review
• Systematic Review

Keywords: Artificial Intelligence in Education, Academic Integrity, AI-Assisted Assessment, STEM Education, Teacher Readiness

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.