Applied insights for using Generative Artificial Intelligence in Faculty Development in Health Professions Education.

IntroductionGenerative AI (GenAI) tools are transforming health professions education, offering opportunities to enhance faculty development (FD). Faculty developers are uniquely positioned to integrate GenAI into practice to address resource constraints, improve accessibility, and foster equity across diverse educational contexts. This Applied Insights article offers a perspective on how GenAI can be leveraged as a co-developer in FD by drawing on emerging literature and discussion points from a workshop at the8th International Faculty Development Conference in the Health Professions.Applied insightsThe applied insights are structured around key phases of FD: planning, content creation, delivery, and evaluation. They include actionable strategies for using GenAI in needs assessment, multilingual and culturally relevant resource creation, personalized learning plans, and when providing feedback and mentorship. Each insight is rooted in pedagogical rationale, evidence, and strategies to address ethical and practical challenges, with an emphasis on human oversight, contextual relevance, and continuous evaluation of GenAI’s impact.ConclusionsBy considering these insights, faculty developers can harness GenAI to co-design educational materials, extend their reach through innovative formats, and maintain ethical and equity-driven educational practices. This article highlights the transformative potential of GenAI in FD when thoughtfully integrated. GenAI can empower faculty developers to enhance the quality and inclusivity of HPE while safeguarding educational standards.

Artificial Intelligence has become a focal point of interest across various sectors due to its ability to generate creative and realistic outputs. A specific subset, generative artificial intelligence, has seen significant growth, particularly in late 2022. Tools like ChatGPT, Dall-E, or Midjourney have democratized access to Large Language Models, enabling the creation of human-like content. However, the concept 'Generative Artificial Intelligence lacks a universally accepted definition, leading to potential misunderstandings. While a model that produces any output can be technically seen as generative, the Artificial Intelligent research community often reserves the term for complex models that generate high-quality, human-like material. This paper presents a literature mapping of AI-driven content generation, analyzing 631 solutions published over the last five years to better understand and characterize the Generative Artificial Intelligence landscape. Our findings suggest a dichotomy in the understanding and application of the term "Generative AI". While the broader public often interprets "Generative AI" as AI-driven creation of tangible content, the AI research community mainly discusses generative implementations with an emphasis on the models in use, without explicitly categorizing their work under the term "Generative AI".

Generative Artificial Intelligence or Generative AI is an innovation with a terrible name.First, generative means something that originates, produces, or even reproduces.Next, artificial Intelligence is defined as intelligence exhibited by machines (machine intelligence).So, if the two are put together the result is a machine that originates intelligence—in other words, a thinking machine. Hmmm!Generative artificial intelligence is typically defined as a technology capable of generating text, images, videos, or other data, usually in response to prompts.At best the name generative AI is confusing, and to many it is scary.Everett Rogers in his classic book Diffusion of Innovations (2003) states that “the name given to an innovation often affects its perceived compatibility, and therefore its rate of adoption (p. 250).”Certainly, Generative AI is an innovation—it is new and is probably the innovation of this decade.Everett Rogers defined Innovation as “an idea, practice or object that is perceived as new by an individual or other unit of adoption (p. 12)”.What does all this mean to distance educators?Maybe first we should learn more about Generative AI. So, just for the fun of it, the written sections above were entered into Gemini AI, a generative AI tool, and a competitor to ChatGPT.Here is what Gemini produced:You’re right, “Generative AI” while descriptive, and can be misleading.Here’s a breakdown of why it might not be the best term and some suggestions for alternatives:The Problems with “Generative AI”The best term will depend on the context. “Machine Learning for Content Creation” might be more suitable for a technical audience, while “Creative AI” could be better for a general audience.This revision process took about 5 seconds, and by any measure the results were impressive—the Generative AI analysis seemed insightful and accurate.Rogers recommends that potential innovation users should learn about the innovation by studying its attributes—there are five—relative advantage, compatibility, complexity, trialability, and observability.First is the idea of relative advantage—defined as the degree to which an innovation is perceived as being better than the idea it replaces. Relative advantage is often expressed as the innovation’s economic profitability or its ability to convey social positions.The next characteristic of an innovation is its compatibility, which is the degree to which the innovation is consistent with existing values, past experiences, or user needs. Innovations can be either compatible or incompatible.Next is complexity, explained as whether the innovation is perceived as difficult or easy to use.A complex innovation, as perceived by potential adopters, can significantly hinder adoption.Trialability is the ability to experiment with an innovation before adoption. If an innovation can be easily tried it will be likely to have a rapid rate of adoption or rejection.Observability is the visibility of applying the innovation. Observability means seeing the results of using the innovation. High observability promotes faster decisions about adoption.In other words, does Generative AI allow us to do things better? Next, are the results of use compatible with what the user needs or wants? Third, is Generative AI easy or difficult to use, and can we quickly try it out? Finally, can we see the results when Generative AI is used?Computer scientists argue that Generative AI is better, compatible, easier, not complex, and results are clear—maybe this is true, but the name is still horrible.Generative artificial intelligence sounds intimidating and threatening. Perhaps ‘creative artificial intelligence’ would be more inviting—perhaps not!Distance educators should understand, study, and evaluate Generative Artificial intelligence and write about it—perhaps someone from the U.S. Distance Learning Association could “coin” a new name.(Distance Learning would love to publish manuscripts on this and other related topics.)And finally, Galen said “The chief merit of language is clearness, and we know that nothing detracts so much from this as do unfamiliar terms.”NOTE: Rewrites of the second half of this column using personal intelligence (PI) took 5 tries and 4 hours—a relative advantage?

While industry practices evolve rapidly, marketing education in Australia and New Zealand faces challenges in keeping pace, particularly regarding the adoption of current marketing technologies (Harrigan et al., 2022). Generative AI, exemplified by systems like ChatGPT and DALL·E, has demonstrated benefits for learning (Baidoo-Anu & Ansah, 2023). However, despite its potential, there remains a dearth of practical guidance on effectively incorporating these technologies into marketing courses. This gap persists even as general frameworks for responsible and ethical AI use, such as the Australian Framework for Generative AI in Schools (2023), emerge. As the demand for graduates with generative AI skills grows in the job market, educators must explore innovative pedagogical approaches to bridge this gap. This academic poster presents an innovative application of generative artificial intelligence (GenAI) in the context of teaching digital marketing at the postgraduate level. Its purpose is to bridge the gap between academic theory and industry practice by encouraging educators to integrate AI tools into their curriculum through experiential learning pedagogy (Kolb, 2014), characterized by a learning process whereby knowledge is created through hands-on experiences. The poster exemplifies how various types of GenAI technologies — specifically text-based, image-based, and video-based — can enhance teaching content, tutorial exercises, and assessments within the digital marketing course. The poster showcases examples of how these GenAI tools are integrated in the course content, to guide students in generating innovative ideas for using AI in marketing to gain a competitive edge: Text-based GenAI: Tools like ChatGPT and Gemini can automatically generate search keywords for search engine marketing. By integrating text-based GenAI tools with established marketing technology (MarTech) tools such as Google Ads and Google Ads Keyword Planner, students engage in practical exercises that combine AI-generated initial ideas (e.g., search keywords) with further analysis (e.g., search volume, click-through rates, and bidding costs) using established MarTech tools. This hands-on approach enhances their learning experience and prepares them for real-world applications. Image-based GenAI: Platforms such as DALL·E, Midjourney, and Stable Diffusion enable the creation of custom images for display advertising, enhancing visual communication in marketing materials. Through experiential learning activities, students can explore ideas, seek unusual combinations, and inspire creativity faster with image-based GenAI tools, resulting in a greater variety of display ad materials. Video-based GenAI: Applications like Sora and Synthesia facilitate the production of short video clips suitable for social media marketing (e.g., YouTube Shorts, TikTok). By engaging in dynamic content creation exercises, students learn to streamline content creation, reduce manual work, and save both time and budget, thereby gaining practical skills in social media marketing. By incorporating these GenAI technologies through experiential learning pedagogy, educators can enrich the learning experience, foster critical thinking, and prepare students for the evolving landscape of digital marketing. Future research can study the use of GenAI in marketing education using theoretical frameworks such as the Unified Theory of Acceptance and Use of Technology (Venkatesh et al., 2016).

Since the widespread release of generative artificial intelligence (GenAI) tools in recent years, there has been a dramatic impact on health professions education, particularly in the context of learner assessment. As GenAI continues to evolve, traditional paradigms in health professions education are changing, requiring students, administrators, and educators to navigate ongoing disruption to their practice. We examine how GenAI will specifically impact assessment practices, offering three key postulates to guide future teaching and learning: (1) educators must re-examine assessment constructs to align with GenAI-enhanced learning environments, (2) GenAI can outperform median learner performance with appropriate prompting, and (3) GenAI will become a required tool for health professions assessment. Ultimately, we believe that rather than viewing the advent of GenAI as a threat, educators should harness its potential to empower students and augment learning.

This systematic review investigates the ethical challenges and strategic responses surrounding the use of Generative AI (GenAI) and related tools in academic writing within global higher education. Following the PRISMA 2020 framework, a rigorous search and screening process across academic databases identified 18 peer-reviewed articles published between 2020 and 2025, which were subjected to in-depth thematic analysis. The findings reveal four major ethical concerns: threats to academic integrity through plagiarism, authorship misrepresentation, and diminished originality; issues of bias and fairness arising from algorithmic limitations and unequal access to technology; limited transparency due to nondisclosure of AI use and the absence of clear citation standards; and risks to data privacy linked to the use of student and proprietary information. In response, the literature highlights strategies that include the development of institutional ethical guidelines and policies, enhanced digital literacy and training for faculty and students, improved design and regulation of AI tools with embedded ethical safeguards, and the promotion of transparent human–AI collaboration guided by human oversight. This review demonstrates the significance of adopting a comprehensive, multi-layered approach rather than relying on isolated interventions. For educators, it underscores the need to cultivate critical digital literacy skills; for policymakers, it emphasizes the importance of enforceable and context-sensitive frameworks; and for researchers, it points to future inquiry on the ethical–technological nexus. Collectively, the findings provide actionable insights to ensure that GenAI’s integration into academic writing supports integrity, fairness, and trust in higher education.

Generative artificial intelligence, the new buzzword in technology, is the next step in the evolution of traditional artificial intelligence. Unlike traditional AI that excels in data analyzing and automating processes, generative AI (GenAI) is a pioneer in creating new and original content. GenAI is very close to human intelligence, capable of logical thinking, imitating human behavior and armed with decision making capabilities. Generative AI creates new texts, images, music, 3D designs and codes, thus strongly influencing the activities, strategies, and consumer interactions of various industries. Key industries most affected by GenAI are banking and finance, retail and consumer goods, medicine and pharmaceuticals, education, media and marketing. In marketing, generative AI is significant in the process of personalization, content creation, audience engagement and interactions, performing the STP strategy (segmentation, targeting, positioning), market research, etc. Although it has great advantages, GenAI also has significant limitations, such as unresolved ethical issues, the spread of outdated or imprecise data, lack of legal regulation and control, etc. This paper, with the aid of secondary research, is aimed at exploring the possibilities of GenAI and its impact on marketing, especially advertising.

Generative AI (GenAI) is increasingly deployed in health professions education, particularly for simulated patients and instructional imagery. However, concerns have emerged regarding demographic bias in AI-generated outputs, with potential consequences for equity, realism, and global applicability. This study presents a multi-method analysis of demographic representation across simulated ‘patient’ cohorts (GPT-3.5, GPT-4-mini) and AI-generated ‘clinical’ images (DALL·E 3, Midjourney). Quantitative comparisons against national census and survey benchmarks revealed significant overrepresentation of lighter skin tones, males, and middle-aged adults, alongside the near-complete absence of certain ethnic and age groups. However, prompt-based interventions incorporating demographic data achieved marked improvements in representativeness. These findings raise important questions about the readiness of current GenAI models for use in inclusive medical training environments. Inaccurate or stereotyped representations may undermine educational authenticity, reinforce existing disparities, and skew students’ expectations about the patient populations they will encounter in practice. Building on this analysis, we propose a framework for systematically auditing AI tools in medical education. Central to this is the development of an “AI report card” to evaluate models on key dimensions of demographic safety, regional appropriateness, and educational validity. The report card is designed to support educators and institutions in selecting GenAI tools that align with their curricular and equity goals. This work contributes to ongoing international efforts to ensure that the globalisation of health professions education is underpinned by principles of fairness, inclusivity, and contextual relevance. Future work will validate the framework across diverse educational settings and explore model fine-tuning and prompt engineering strategies to ensure safer, more representative AI-assisted simulation.

Teaser: An experiential learning intervention to train medical educators to effectively engage generative AI for instructional design is described.Theory-informed and evidence-based educational offerings promote student learning and equity but are time-consuming and require health professions educators to have content expertise in inclusive instructional design. While -generative AI (GAI) offers the potential to overcome these barriers, educators must learn to effectively leverage GAI tools for evidence-based instructional design. In this work, the authors piloted and evaluated a 2-part experiential learning activity to equip educators to effectively engage with GAI for instructional design purposes. The authors implemented the GAI innovation in the graduate-level "Teaching 100" course (enrollment n = 27) at Harvard Medical School September-November 2023. Educators used GAI to annotate their lesson plans to identify application of, and opportunities to incorporate, evidence-based principles of teaching and learning. The 2-part assignment provided scaffolded instruction on prompt engineering and engaged learners in metacognitive reflection on AI-generated content. The authors evaluated the effectiveness of the GAI innovation according to the Kirkpatrick Model: descriptive analysis of self--reflections evaluated educators' subjective experience (Level 1) and planned behavioral changes (Level 3), while quantification of prompt quality pre-/post-instruction measured educators' learning (Level 2). Among educators who completed the 2-part assignment (n = 17/27, 62% completion rate), the quality of -educator-generated AI prompts improved following instruction in prompt engineering: pre-instruction 1.4 (1.2) (mean [SD]) vs post-instruction 4.0 (0.8). The difference in means (2.6 points) was statistically significant (P < .0001, 95% CI [1.9, 3.3]). Metacognitive reflections revealed specific actions educators planned to pursue to implement GAI feedback to improve their instructional design. Educators reported that AI-based assignments enhanced their learning. The authors are developing a stand-alone, interactive GAI tool to be broadly deployed as a faculty development instructional design resource. This future work will yield a scalable solution to the challenge of developing AI literacy among health professions educators to leverage GAI for theory-informed and evidence-based instructional design.

Generative AI (GenAI) use is increasing across society in many different industries. Despite widespread adoption in workplaces, there is little consensus on the scope of its benefits and challenges at the level of most industries. Universities are being called upon to equip graduates with important knowledge and skills using GenAI for their professional contexts. Higher education, however, faces issues in effectively and sustainability embedding a use of GenAI in the student experience, which requires adjustments to learning and teaching activities, assessment, and learning outcomes and in access to useful GenAI platforms relevant to the various disciplines. As pedagogical models, ethical debates, and technologies continue to develop in this space, university teachers’ experiences of teaching with GenAI have yet to be explored in detail. Adopting a phenomenographic perspective, this study examines university teachers’ conceptions, perceptions, and approaches to using GenAI in teaching. Leveraging semi-structured interviews with 30 teaching academics, variations of teaching using GenAI were identified. Quantitative analysis was also conducted to capture the associations between these variations. By exploring the qualitative differences between these variations, a nuanced and important contribution to the GenAI discussion from the understanding of university teachers is uncovered. The results show that some ways of understanding and teaching with GenAI are more likely to help students develop effective knowledge and skills for the workplace than others. The findings also offer education leaders evidence to help design effective support for teachers using GenAI to innovate in the student experience. Through investigating the university teacher experience of GenAI, this research adds to the growing debate on the GenAI enabled benefits and challenges that are set to shape the higher education sector.

Despite concerns about students’ use of generative AI (GenAI) in assessment, the technology has become embedded into students’ everyday assessment practices. It is unclear how students are making judgements about their ways of working with GenAI and what impact this has upon their learning. This qualitative multimodal study examines students exercising judgement as they work with GenAI to complete assessment tasks. Twenty-six interviews were conducted with Australian university students, primarily using a scroll-back approach, which revisits traces of students’ historical interactions with GenAI in the interviews. Employing a holistic definition of judgement and a narrative approach to analysis, we interpreted six distinct categories of judgement events. These are: 1) making judgements about knowledge when working with GenAI; 2) learning to judge GenAI through its limitations; 3) relying on GenAI for things they could not otherwise do; 4) adopting ideas with low levels of criticality; 5) misjudging GenAI contributions as their own; and 6) submitting GenAI content in an assignment without judging it. This study suggests GenAI use strongly shapes student learning in complex ways when undertaking assessment tasks, and that making judgements about GenAI entails a student making judgements about their own knowledge, deficits, and quality of contributions.

This article explores the sudden shift in higher education that was brought about by the generative artificial intelligence (GAI), a technology that is reshaping pedagogical, evaluating, research and administrative spheres. While GAI holds great promise with the benefits of personalized instruction, improved operational efficiency, expanded accessibility, and innovation, at the same time it creates deep ethical concerns that include issues related to academic integrity, systemic bias, lack of transparency, privacy breaches, accountability, and loss of trust and equity. Contemporary universities are therefore under increasing pressure to establish by far rigorous, principled governance systems that prudent directions for the responsible use and deployment of GAI technologies: Based on current scholarship, the forth study provides a critical examination of some of the key ethical dilemmas, institutional responses, and issues of governance paradigms that have transpired in the intersection of GAI integration ostracism in academic settings. Using the literature review methodology in the form of narrative, analysis can synthesize an existing scholarly discourse and identify key governors dimension(s) necessary for responsible implementation. The results suggest that ethical governance needs to move beyond ad hoc rules focused on academic malpractices and instead adopt a broad institutional model based on transparency, accountability, fairness, privacy, human oversight and constant refining of policies. In response to this, a pragmatic ethical governance structure organized around institutional leadership, policy formulation, conscientious procurement, stakeholder engagement, assessment reform, capacity development, monitoring mechanism, and iterative evaluation is proposed in the article. It concludes that the practice of proactive governance is essential in ensuring the alignment of technological innovativeness with the fundamental values of education as well as human dignity ensuring that GAI can help enhance the educational quality and at the same time protect the trust and inclusion while maintaining its integrity and institutional credibility.

Although artificial intelligence is fundamentally reshaping the ecology of music learning, existing research has disproportionately emphasized performance outcomes while underexamining psychological mechanisms, leaving the tension between technological empowerment and cognitive dependence theoretically underarticulated. Following PRISMA 2020, we systematically searched four databases and included 21 empirical studies to examine how three AI tool types-assessment-oriented AI, generative AI, and Comprehensive/adaptive AI-differentially shape learners' self-beliefs and cognitive agency in music education. The evidence base remains geographically and developmentally concentrated: most studies were conducted in China and in higher education, while early childhood settings were absent. Using thematic analysis, we conducted cross-type comparisons and synthesized psychological pathways. Assessment-oriented AI most consistently strengthened ability beliefs via objectified, visualized feedback and positioned cognitive agency around self-monitoring, self-reactiveness, and self-reflectiveness. Generative AI tended to enhance value-attitude beliefs and intentionality by lowering technical barriers and reconfiguring learners' creative roles toward aesthetic decision-making and output curation. Comprehensive/Adaptive AI more often supported forethought and sustained engagement by dynamically maintaining alignment between task challenge and learner capability. Across studies, psychological empowerment manifested as increased perceived competence and control, heightened motivation and engagement, and visible self-regulated learning behaviors. Cognitive dependence, however, emerged through outsourcing evaluative authority, score-driven goal distortion, algorithm-accommodating self-censorship, and attributional shifts that tether confidence to technological support. Developmental differences were also observed regarding dependence mechanisms: primary learners tended to perceive AI as a restrictive "scoring referee," whereas higher education students demonstrated strategic agency in orchestrating AI assistance. Specifically, a critical construct-tool mismatch was identified: while assessment AI consistently supports self-reflectiveness, generative AI currently lacks sufficient evidence for fostering learners' forethought. In light of the identified construct-tool mismatch, future research should prioritize addressing the paucity of evidence on how generative and adaptive AI foster forethought and intentionality, thereby clarifying whether such technologies ultimately reconstruct or erode learners' cognitive agency.

The integration of Generative Artificial Intelligence (GenAI) into health professions education (HPE) is rapidly transforming learning environments, raising questions about its impact on teaching and learning. This mixed methods study explores clinical educators’ and undergraduate students’ perceptions and attitudes about using GenAI tools in HPE at a tertiary hospital in Singapore. Using the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT) as theoretical frameworks, we designed and administered a survey and conducted interviews to assess participants’ perceived usefulness, ease of use, and concerns related to GenAI adoption. Quantitative survey data were analyzed for frequencies and percentages, while qualitative responses underwent thematic analysis. Results showed that students demonstrated higher GenAI adoption rates (68.7%) compared to educators (38.5%), with GenAI perceived as valuable for efficiency, research, and personalized learning. However, concerns included over-reliance on GenAI, diminished critical thinking, and ethical implications. Educators emphasized the need for institutional guidelines and training to support responsible GenAI integration. Our findings suggest that while GenAI holds great potential for enhancing education, structured institutional policies and ethical oversight are crucial for its effective use. These insights contribute to the ongoing discourse on GenAI adoption in HPE.

The significance of ongoing study and development of AI technology is emphasized as this chapter explores the part and utility of generative AI in medical education and training, examines the difficulties it encounters, and systems unborn development patterns in the medical field. We can have a better understanding of how generative AI is impacting medical education going forward and offering fresh styles for training healthcare workers by reading this thorough review. A branch of artificial intelligence called” generative AI” is concerned with creating systems that can produce original and cultural labors, including textbooks, music, plates, and more. These systems may produce content that mimics mortal-generated content on their own by exercising deep literacy ways, particularly generative models. The interesting field of generative artificial intelligence focuses on creating systems that can singly produce original, creative content. It makes it possible for machines to perform creative and imaginative tasks in addition to further conventional bones. By exercising generative models and deep literacy approaches, these systems may induce innovative labors that nearly mimic mortal-generated content, including literature, music, prints, and more. This system makes it possible to produce creative and original content, making it an effective tool for various uses. Generative models are central to the idea of generative AI. Generative AI enables machines to autonomously induce creative content, similar as images, music, textbooks, and more. This addresses the need for new and different content in colorful disciplines, including art, entertainment, design, and marketing. Generative AI opens new possibilities for creative expression and expands the boundaries of mortal imagination. The possibilities for creative expression are increased, and the limits of mortal imagination are pushed by generative AI. Medical training serves as a means of guaranteeing that the performance of the mortal pool is observed in a realistic and secure setting. Their use of generative AI to produce virtual cases to instruct medical scholars. These realistic clinical scenarios in the simulations were designed to help medical professionals and students make better diagnoses and treatment-related decisions.