Integrating Generative AI in Dental Education: A Scoping Review of Current Practices and Recommendations.

Generative AI (GenAI) tools like ChatGPT are increasingly relevant in dental education, offering potential enhancements in personalised learning and clinical reasoning. However, specific guidance from dental institutions remains unexplored. To identify, analyse and summarise existing guidelines from universities and organisations on using GenAI in dental education, focusing on recommendations for academic staff. A scoping review (10.17605/OSF.IO/3XMP7) searched for GenAI guidance on university websites, search engines (Google Search, Scholar, Perplexity and PubMed) and through contacting relevant academics (January 2022 to June 2024). Two reviewers independently screened and extracted data, including implementation details, AI tools and permitted/prohibited uses. Thematic analysis revealed common applications, benefits, challenges and recommendations. Thirty-one unique documents were included from 21 universities in 15 countries and three international organisations. Thematic analysis identified common applications, benefits, challenges and recommendations for integrating GenAI, including facilitating teaching and learning, personalised learning, efficient content creation and encouraging critical thinking. However, challenges such as academic integrity, ethical use, bias and privacy issues were also identified. No dental education-specific guidelines were found. This review identified and summarised existing GenAI guidelines from universities and organisations relevant to dental education. The guidelines emphasise ethical use, transparency, academic integrity, secure environments and AI misuse detection tools. However, the absence of dental specific guidance presents an opportunity to fill this gap, providing recommendations for academic staff to integrate GenAI effectively while promoting critical thinking and responsible AI use.

Uribe SE ，Maldupa I ，Schwendicke F 《-》

Development of Evidence-Based Guidelines for the Integration of Generative AI in University Education Through a Multidisciplinary, Consensus-Based Approach.

The introduction highlights the transformative impact of generative artificial intelligence (GenAI) on higher education (HE), emphasising its potential to enhance student learning and instructor efficiency while also addressing significant challenges such as accuracy, privacy, and ethical concerns. By exploring the benefits and risks of AI integration, the introduction underscores the urgent need for evidence-based, inclusive, and adaptable frameworks to guide universities in leveraging GenAI responsibly and effectively in academic environments. This paper presents a comprehensive process for developing cross-disciplinary and consensus-based guidelines, based on the latest evidence for the integration of GenAI at European University Cyprus (EUC). In response to the rapid adoption of AI tools such as LLMs in HE, a task group at EUC created a structured framework to guide the ethical and effective use of GenAI in academia, one that was intended to be flexible enough to incorporate new developments and not infringe on instructors' academic freedoms, while also addressing ethical and practical concerns. The framework development was informed by extensive literature reviews and consultations. Key pillars of the framework include: addressing the risks and opportunities presented by GenAI; promoting transparent communication; ensuring responsible use by students and educators; safeguarding academic integrity. The guidelines emphasise the balance between, on the one hand, leveraging AI to enhance educational experiences, and, on the other maintaining critical thinking and originality. The framework also includes practical recommendations for AI usage, classroom integration, and policy formulation, ensuring that AI augments rather than replaces human judgement in educational settings. The iterative development process, including the use of GenAI tools for refining the guidelines, illustrates a hands-on approach to AI adoption in HE, and the resulting guidelines may serve as a model for other higher education institutions (HEIs) aiming to integrate AI tools while upholding educational quality and ethical standards.

Symeou L ，Louca L ，Kavadella A ，Mackay J ，Danidou Y ，Raffay V ... -  《-》

Generative artificial intelligence (Gen-AI) in pharmacy education: Utilization and implications for academic integrity: A scoping review.

Generative artificial intelligence (Gen-AI), exemplified by the widely adopted ChatGPT, has garnered significant attention in recent years. Its application spans various health education domains, including pharmacy, where its potential benefits and drawbacks have become increasingly apparent. Despite the growing adoption of Gen-AIsuch as ChatGPT in pharmacy education, there remains a critical need to assess and mitigate associated risks. This review exploresthe literature and potential strategies for mitigating risks associated with the integration of Gen-AI in pharmacy education. To conduct a scoping review to identify implications of Gen-AI in pharmacy education, identify its use and emerging evidence, with a particular focus on strategies which mitigate potential risks to academic integrity. A scoping review strategy was employed in accordance with the PRISMA-ScR guidelines. Databases searched includedPubMed, ERIC [Education Resources Information Center], Scopus and ProQuestfrom August 2023 to 20 February 2024 and included all relevant records from 1 January 2000 to 20 February 2024 relating specifically to LLM use within pharmacy education. A grey literature search was also conducted due to the emerging nature of this topic. Policies, procedures, and documents from institutions such as universities and colleges, including standards, guidelines, and policy documents, were hand searched and reviewed in their most updated form. These documents were not published in the scientific literature or indexed in academic search engines. Articles (n = 12) were derived from the scientific data bases and Records (n = 9) derived from the grey literature. Potential use and benefits of Gen-AI within pharmacy education were identified in all included published articles however there was a paucity of published articles related the degree of consideration to the potential risks to academic integrity. Grey literature recordsheld the largest proportion of risk mitigation strategies largely focusing on increased academic and student education and training relating to the ethical use of Gen-AI as well considerations for redesigning of current assessments likely to be a risk for Gen-AI use to academic integrity. Drawing upon existing literature, this review highlights the importance of evidence-based approaches to address the challenges posed by Gen-AI such as ChatGPT in pharmacy education settings. Additionally, whilst mitigation strategies are suggested, primarily drawn from the grey literature, there is a paucity of traditionally published scientific literature outlining strategies for the practical and ethical implementation of Gen-AI within pharmacy education. Further research related to the responsible and ethical use of Gen-AIin pharmacy curricula; and studies related to strategies adopted to mitigate risks to academic integrity would be beneficial.

Mortlock R ，Lucas C 《-》

The Perceived Concerns of Nurse Educators' Use of GenAI in Nursing Education: Protocol for a Scoping Review.

Since the emergence of generative AI (GenAI) in fall 2022, its impact on higher education has been significant yet under-researched, leading to mixed reactions among nurse educators, ranging from enthusiasm to skepticism. A preliminary search of seven databases found no scoping reviews specifically that addressed nurse educators' concerns about using GenAI. Therefore, this study aims to map the existing literature on nurse educators' concerns regarding the use of GenAI in nurse education. Included are any types of sources (peer-reviewed and nonpeer-reviewed) in English and from any country and were authored by an academic nurse educator that reported on "academic nurse educators," and "artificial intelligence" (such as GenAI, Generative AI, ChatGPT, large language models) in nursing education. Articles that did not report "nurse educator concerns," or were focused on clinical practice were excluded. This protocol (see PRISMA-P in Appendix 1) establishes the study parameters for the planned scoping review, which will be conducted from April to July 2024. We will follow Joanna Briggs Institute, a comprehensive methodology, to ensure a rigorous approach. The final review will include relevant literature from eight academic databases published from Fall 2022 through April 2024. Data will be reported using the PRISMA-ScR checklist and flow diagram (2020) along with other visual diagrams to add validity to our findings. An inductive analysis approach will be used to code the evolving data, identify recurring themes, and pinpoint potential gaps in the literature. The final scoping review will present the search results, the study inclusion process, and the data analysis. Our planned review will potentially provide crucial insights into nurse educators' concerns on using GenAI, pinpointing gaps within the literature, and providing direction for future research. This protocol was registered on May 8, 2024, on Open Science Framework (OSF). The registry number is OSF.IO/SZ8WR. This registration ensures the transparency and credibility of our research process, as it provides a public record of our study design and methods.

Gehring DR ，Titus SK ，George R 《-》

Generative Artificial Intelligence in Medical Education-Policies and Training at US Osteopathic Medical Schools: Descriptive Cross-Sectional Survey.

Interest has recently increased in generative artificial intelligence (GenAI), a subset of artificial intelligence that can create new content. Although the publicly available GenAI tools are not specifically trained in the medical domain, they have demonstrated proficiency in a wide range of medical assessments. The future integration of GenAI in medicine remains unknown. However, the rapid availability of GenAI with a chat interface and the potential risks and benefits are the focus of great interest. As with any significant medical advancement or change, medical schools must adapt their curricula to equip students with the skills necessary to become successful physicians. Furthermore, medical schools must ensure that faculty members have the skills to harness these new opportunities to increase their effectiveness as educators. How medical schools currently fulfill their responsibilities is unclear. Colleges of Osteopathic Medicine (COMs) in the United States currently train a significant proportion of the total number of medical students. These COMs are in academic settings ranging from large public research universities to small private institutions. Therefore, studying COMs will offer a representative sample of the current GenAI integration in medical education. This study aims to describe the policies and training regarding the specific aspect of GenAI in US COMs, targeting students, faculty, and administrators. Web-based surveys were sent to deans and Student Government Association (SGA) presidents of the main campuses of fully accredited US COMs. The dean survey included questions regarding current and planned policies and training related to GenAI for students, faculty, and administrators. The SGA president survey included only those questions related to current student policies and training. Responses were received from 81% (26/32) of COMs surveyed. This included 47% (15/32) of the deans and 50% (16/32) of the SGA presidents (with 5 COMs represented by both the deans and the SGA presidents). Most COMs did not have a policy on the student use of GenAI, as reported by the dean (14/15, 93%) and the SGA president (14/16, 88%). Of the COMs with no policy, 79% (11/14) had no formal plans for policy development. Only 1 COM had training for students, which focused entirely on the ethics of using GenAI. Most COMs had no formal plans to provide mandatory (11/14, 79%) or elective (11/15, 73%) training. No COM had GenAI policies for faculty or administrators. Eighty percent had no formal plans for policy development. Furthermore, 33.3% (5/15) of COMs had faculty or administrator GenAI training. Except for examination question development, there was no training to increase faculty or administrator capabilities and efficiency or to decrease their workload. The survey revealed that most COMs lack GenAI policies and training for students, faculty, and administrators. The few institutions with policies or training were extremely limited in scope. Most institutions without current training or policies had no formal plans for development. The lack of current policies and training initiatives suggests inadequate preparedness for integrating GenAI into the medical school environment, therefore, relegating the responsibility for ethical guidance and training to the individual COM member.

Ichikawa T ，Olsen E ，Vinod A ，Glenn N ，Hanna K ，Lund GC ，Pierce-Talsma S ... -  《-》