Russian Federation
UDC 614.2
UDC 004.8
Introduction. Generative artificial intelligence (Generative Artificial Intelligence, GenAI), including ChatGPT and other large language models, is becoming an important factor in the digital transformation of pharmacy education. Its use expands opportunities for personalised learning, modelling of clinical and pharmaceutical situations, development of educational materials, academic writing support, formation of drug information skills, and enhancement of students’ critical thinking. At the same time, the implementation of such tools is associated with a range of methodological, ethical, organisational, and managerial risks, including the generation of inaccurate information, artificial intelligence hallucinations, fabricated references, algorithmic bias, violations of academic integrity, and insufficient artificial intelligence literacy among learners. Aim. The purpose of the review: to systematize the international experience of using ChatGPT and other tools of generative artificial intelligence in pharmaceutical education, to determine the educational effects, risks and methodological deficiencies of the available evidence base, as well as to substantiate the network scientific and methodological cluster model of multicenter research in Russian educational organizations implementing secondary vocational and higher pharmaceutical education programs in an enlarged group of specialties and areas of study 33.00.00 «Pharmacy». The article discusses the main scenarios for using ChatGPT, Gemini, Claude, Copilot, DeepSeek, GigaChat, Alice AI, and Cloud.ru in pharmacy education. These scenarios include explaining complex learning topics, generating educational tasks and prompts, preparing clinical and pharmaceutical cases, supporting students’ independent work, developing expert information verification skills, and forming a digital educational pathway. The international evidence base on this issue is shown to consist mainly of cross-sectional surveys, pilot educational interventions, narrative reviews, and scoping reviews. Such methodological heterogeneity limits the direct extrapolation of international findings to the Russian system of pharmacy education. Particular attention should be paid to assessing the validity of responses generated by large language models, analysing correlations between learners’ digital competence and the effectiveness of generative artificial intelligence use, and developing institutional regulations for its safe and ethically justified application. Conclusion. ChatGPT and other generative artificial intelligence tools should be considered not as autonomous sources of professional decisions, but as supervised educational technologies requiring pedagogical support, expert evaluation, regulatory guidance, and the development of artificial intelligence literacy. Conducting a multicentre study in Russia will make it possible to obtain comparable empirical data on the prevalence, scenarios, barriers, risks, and educational effects of these technologies in pharmacy education.
ChatGPT, Gemini, Claude, Copilot, DeepSeek, GigaChat, Alice AI, Cloud.ru, generative artificial intelligence, prompt, large language models, hallucinations of artificial intelligence
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