Peer Review Policy

A robust peer review policy is fundamental to advancing the credibility and quality of AI applications in education. Under such a policy, research or innovations—whether new algorithms, pedagogical models, or system evaluations—are subjected to scrutiny by independent experts in the field before publication or implementation. Reviewers assess the work for accuracy, originality, and relevance, ensuring that claims about AI’s impact on learning outcomes are supported by evidence and free from bias or exaggeration. The process typically involves blind or double-blind evaluations to maintain impartiality, with clear guidelines for reviewers to address ethical concerns, such as data privacy or equitable access. Authors may be required to revise their work based on feedback, enhancing its rigor and applicability. By fostering this collaborative and critical exchange, a peer review policy not only tackles challenges like unchecked hype but also shapes future perspectives, ensuring AI in education evolves responsibly and effectively.