Adopting AI in Health Sciences Education: A Scoping Review of Effectiveness, Limitations, and Equitable Access

Objective: The objective of this scoping review is to map and synthesize evidence regarding the effectiveness, limitations, and specifically the equitable access barriers of artificial intelligence (AI) tools used in teaching across diverse health sciences disciplines. Introduction: Artificial intelligence (AI) is rapidly evolving in health sciences education, offering potential benefits for clinical reasoning, skill acquisition, and personalized learning. However, current evidence is often limited to specific discipline areas such as nursing education or lacks consistent reporting on safety, bias, and equitable access. Given the rapid evolution of AI technologies —particularly the emergence of large language models (e.g., ChatGPT, Claude) from 2020 until present — this review focuses on contemporary literature from 2020 to 2026 to ensure relevance to current educational practice. A comprehensive synthesis is needed to guide evidence-based and inclusive implementation across diverse health sciences programs. Inclusion criteria: This review will consider studies involving health sciences students (prelicensure and graduate) across health science disciplines. The core concept is AI-enabled educational tools used for instruction, assessment, simulation, or support. The context includes any health sciences learning environment (didactic, lab, simulation, online). Methods: The databases to be searched will include MEDLINE, CINAHL, APA PsycINFO, Education Research Complete, Web of Science, and Scopus. The search will be limited to studies published in English from 2020 to the present. Two independent reviewers will screen titles/abstracts and full-text articles. Data will be extracted using a standardized tool and results will be presented using evidence mapping and a narrative summary focusing on effectiveness, limitations, and equity factors. Keywords: Artificial intelligence; Health Professions; Education; Students; Educational Technology; Resource Allocation