Impact of generative artificial intelligence on healthcare education: A systematic review of learning outcomes and academic integrity

Abstract

The recent development of Generative Artificial Intelligence (GenAI) in healthcare education has brought both great opportunities to improve the learning outcomes and pose serious concerns about the academic integrity, authenticity of the assessment, and ethical application. The purpose of conducting this systematic review was to assess the effect of GenAI technologies, such as ChatGPT and large language models, on healthcare education, in particular, learning outcomes, student engagement, clinical reasoning, and academic misconduct. The PRISMA framework was used to identify, screen, and synthesize relevant studies published in large academic databases to study the new trends in medical education, nursing education, pharmacy education, dental education, and allied health education. The results show that GenAI has affected positively personalized learning, adaptive learning, self-directed learning, and simulation-based learning offering instant feedback, intelligent tutoring systems, virtual patients, and increased support of problem-solving skills and critical thinking. Students have said that they have improved in clinical reasoning, digital literacy, AI literacy, and confidence in evidence-based decision-making. Automated content generation and formative assessment support were also helpful to faculty members in reducing the administrative workload and enhancing curriculum innovation. Nonetheless, the review also found serious issues with plagiarism, academic misconduct, excessive dependence on AI-generated content, biases, data privacy, transparency, and lower authenticity of evaluation. Conventional summative assessment practices were discovered to be more susceptible to unethical AI applications, and assessment redesign, reflective learning, oral examination, and competency-based education were demanded.

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