An Innovative Approach to Medical Education: Leveraging Generative Artificial Intelligence to Promote Inclusion and Support for Indigenous Students

Abstract

Indigenous students remain significantly underrepresented in medical education, contributing to persistent health inequities in their communities. Systemic barriers, including cultural isolation, inadequate resources, and biased curricula, hinder their success. But what if generative artificial intelligence (GAI) could be the game-changer? This scoping review explores the potential of generative artificial intelligence (GAI) in making medical education more inclusive and supportive for Indigenous students through a comprehensive analysis of existing literature. From AI-powered engagement platforms to personalised learning systems and immersive simulations, GAI can be harnessed to bridge the gap. While GAI holds promise, challenges like biased datasets and limited access to technology must be addressed. To unlock GAI’s potential, we recommend faculty development, expansion of digital infrastructure, and Indigenous-led AI design. By carefully harnessing GAI, medical schools can take a crucial step towards creating a more diverse and equitable healthcare workforce, ultimately improving health outcomes for Indigenous communities.

1. Introduction

Inclusive medical education aims to provide equitable learning opportunities for all students, regardless of their background, ability, or circumstances. Despite ongoing efforts to enhance inclusivity, medical education continues to fall short in addressing the diverse learning needs of the student population. This has resulted in persistent disparities, particularly among students with disabilities, those from underrepresented backgrounds, and learners from low-resource settings [1,2]. One group that continues to face significant underrepresentation is Indigenous students. According to the Australian Institute of Health and Welfare (AIHW), only 0.5% of doctors identify as Aboriginal and/or Torres Strait Islander [3]. This stark disparity has far-reaching consequences, contributing to poorer health outcomes and reduced life expectancy among Indigenous communities when compared to non-Indigenous counterparts [4]. Bridging this gap requires targeted and sustained support for Indigenous students in medical education [5,6]. Supporting the success of Indigenous medical students is essential for individual academic achievement and fostering equity and social justice within healthcare. Equitable access to educational opportunities enables Indigenous students to build clinical competence, influence health policy, and amplify Indigenous perspectives in decision-making processes [5]. However, despite diversity and inclusion initiatives, Indigenous students continue to face barriers, such as lower academic performance, slower progression, higher attrition rates, and lower graduation outcomes [7,8,9].

These outcomes are often the result of systemic barriers, including limited access to culturally appropriate academic resources, insufficient institutional support, and culturally unsafe learning environments. Additionally, the shortage of Indigenous mentors and personalised tutors [9], systemic bias, and insufficient curriculum content relevant to Indigenous health create a hostile learning environment for Indigenous students and further exacerbate the challenges they face [5,10,11]. In response to these issues, several medical accreditation bodies, including Australia and Aotearoa/New Zealand, now require medical schools to implement culturally responsive curricula and initiatives supporting Indigenous learners. Medical education is thus at a transformative crossroads, shaped by two intersecting imperatives: the need for inclusivity and the rapid advancement of educational technologies. In a landscape marked by increasing diversity among both students and patients, medical education must become more accessible, equitable, and adaptive to a broad range of learner needs, including those shaped by disability, socioeconomic status, geographic isolation, and varied learning styles [12].

Advancements in educational technology present unprecedented opportunities to overcome traditional barriers, personalise learning experiences, and enhance clinical training in ways that were previously unimaginable [13]. Particularly, the potential of generative artificial intelligence (GAI) to enhance inclusion and enrich learning experiences has been suggested [14,15,16,17]. For Indigenous medical students, GAI presents unprecedented opportunities to overcome longstanding barriers by facilitating access to culturally relevant, personalised educational content, especially in remote regions where resources may be limited [18]. Notably, GAI can be utilised to create customised lesson materials, write essays, produce images and graphics, create personalised tutorials, provide tailored feedback, and assess student assignments [19,20]. In addition, GAI has the potential to promote equity and support for Indigenous medical students by creating customised learning experiences tailored to individual learners’ needs, abilities, and styles [21]. AI-powered adaptive systems adjust course difficulty, offer real-time feedback, and provide personalised recommendations in multiple languages, enhancing learning experiences, academic outcomes, and career prospects [22]. Ultimately, this enriches the students’ learning experience and contributes to a more representative and culturally competent healthcare workforce.

This review contributes to the growing body of knowledge on the intersection of educational technology and inclusivity in medical education by synthesising the current literature on the role of generative AI in promoting equity, inclusion, and support for Indigenous medical students. The review explores existing applications, assesses potential benefits and limitations, and considers how GAI can be leveraged to address systemic challenges, foster inclusion, and support the success of Indigenous learners in medical education.

2. Methods

The methodology for this scoping review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) extension for Scoping Reviews (PRISMA-ScR) guidelines, providing a structured framework for study selection, data extraction, and reporting.

2.1. Search Strategy

The search terms used in this scoping review were carefully selected to capture relevant studies. The search terms included a combination of keywords related to generative artificial intelligence, inclusivity, and Indigenous students in education. Specifically, the search terms used were (“Inclusive Education” OR “Generative Artificial Intelligence in Education” OR “AI-powered Inclusive Education”) AND (“Indigenous Students” OR “Indigenous Education” OR “Culturally Responsive Education” OR “First Nations Education”) AND (“Equity” OR “Support” OR “Inclusion” OR “Diversity”) AND (“Generative AI” OR “AI-powered mentoring” OR “AI-driven support”). These search terms were used to search the titles, abstracts, and keywords of studies in the databases.

2.2. Information Sources

This scoping review synthesised findings from different databases—PubMed, ERIC, Scopus, Google Scholar, Medline, and Embase—to comprehensively map the existing literature on the impact of generative artificial intelligence on inclusivity and support for Indigenous students in medical education. The research team developed and refined tailored search strategies for each database to ensure that relevant studies were captured. The search was focused on studies conducted in countries with Indigenous populations, including Canada, Australia, New Zealand, and the USA.

2.3. Inclusion Criteria

The inclusion criteria for this scoping review were carefully defined to ensure that relevant studies were captured. Studies were included if they were peer-reviewed journal articles published within the last 20 years, written in English, and relevant to the study’s objectives. Specifically, studies were included if they examined the impact of generative artificial intelligence on inclusivity and support for Indigenous students in education. A range of study designs was considered, including quantitative, qualitative, and mixed-methods studies and case reports.

2.4. Exclusion Criteria

The exclusion criteria were also clearly defined to exclude studies irrelevant to the research question. Studies were excluded if they were non-peer-reviewed publications, such as conference abstracts, editorials, and opinion pieces. Additionally, studies were excluded if they were unrelated or inapplicable to Indigenous students or education, unavailable as full texts, or not written in English.

2.5. Selection of the Sources of Evidence

Our initial search yielded 7620 articles. Following a preliminary screening, 7287 articles were excluded due to ineligibility, irrelevance, methodological flaws, duplication, or publication in non-peer-reviewed sources. This left 333 articles for further evaluation. However, 114 articles were inaccessible due to subscription restrictions, 93 were unrelated to AI in the context of indigenous students, and 18 lacked full-text availability. Ultimately, 108 articles were included in the final review. The search and screening process is outlined in Figure 1.

2.6. Data Extraction

Data extraction was conducted by two independent reviewers who systematically extracted data from relevant studies. The data extraction form was piloted and refined to ensure that relevant data were captured. The reviewers extracted authorship and publication year data, study design and methodology, and key findings related to generative artificial intelligence, inclusivity, and support for Indigenous students in education. Any discrepancies in data extraction were resolved through discussion and consensus.

2.7. Study Quality Assessment

During the screening process, the quality of each article was carefully evaluated. Although a formal appraisal tool was not used, reviewers assessed each study’s methodological rigour and relevance. A broad inclusion criterion was applied, encompassing various study designs and sample sizes, to capture multiple perspectives and experiences on leveraging generative artificial intelligence (GAI) to promote inclusion and support for Indigenous students. This approach enabled the identification of patterns and emerging themes. The reviewers acknowledged the standard limitation of small sample sizes in Indigenous population research due to recruitment and retention challenges. Despite this limitation, the review synthesised available evidence to provide insights into innovative approaches for harnessing GAI’s potential to support Indigenous students.

2.8. Data Analysis and Synthesis

The extracted data was charted and synthesised using a narrative approach, identifying common themes and patterns across studies. This analysis aimed to comprehensively understand the potential of GAI to revolutionise medical education, making it more inclusive and supportive for Indigenous students. The synthesis highlighted practical approaches, research gaps, and areas for future investigation, contributing to a deeper understanding of GAI’s potential to transform medical education for Indigenous students.

2.9. Limitations of the Study

This study’s findings should be interpreted considering its potential limitations. Firstly, the restriction to English language publications may have resulted in relevant studies that were not in the English language being missed. Secondly, the search strategy may not have captured all relevant studies, despite the use of comprehensive search terms and databases. Additionally, the focus on studies conducted in specific countries may limit the generalizability of the findings to other contexts. Despite these limitations, our methodology provides a robust foundation for examining the impact of generative artificial intelligence on inclusivity and support for Indigenous students in medical education.

3. Results

3.1. Contemporary Landscape of Indigenous Students’ Experiences in Medical Education

3.1.1. Systemic and Structural Barriers to Indigenous Student Success

The contemporary landscape of Indigenous student experiences in medical education is complex and multifaceted, shaped by historical, sociocultural, and systemic factors [23]. Despite efforts by medical schools to promote equity and inclusion, Indigenous students continue to encounter significant challenges, including systemic inequities, cultural disconnection, and experiences of racism [23,24,25]. In addition, the inadequacy of culturally relevant support systems further exacerbates the situation, ultimately leading to lower participation and completion rates compared to their non-Indigenous peers [6,26]. One significant challenge is the historical legacy of colonialism, which has created persistent barriers for Indigenous students. Traditional educational environments often require Indigenous students to conform to Western norms rather than adapting the educational system to support their unique cultural identities and learning styles [27]. Consequently, Indigenous students often experience a disconnect between their cultural identity and the prevalent Western culture in medical education.

3.1.2. Academic, Social, and Economic Challenges

Indigenous students are underrepresented in medical education, making up only a minute percentage of the student population [25,28,29]. This lack of representation further fuels feelings of isolation and exclusion, making it difficult for Indigenous students to connect with their peers and lecturers. Language and literacy barriers can also be a significant obstacle [30,31], particularly for those with limited proficiency in the dominant language of instruction [32,33]. As a result, Indigenous students may face stereotyping and bias from lecturers and peers, perpetuating negative attitudes and low expectations. This can lead to a hostile learning environment, making it difficult for Indigenous students to succeed.

In addition to these challenges, Indigenous students also face significant financial barriers owing to limited access to scholarships, grants, and other forms of financial aid, ultimately resulting in financial stress and decreased academic performance [23]. These barriers can contribute to difficulties completing coursework, accessing educational resources, and participating in online learning environments.

3.1.3. Emerging Strengths and Transformative Opportunities

Despite these challenges, several trends highlight the resilience and determination of Indigenous students in medical education. Indigenous student enrolment in medical education is increasing, although at a slower rate than other student groups [29]. There is also a growing demand for an Indigenous-centred medical curriculum, incorporating courses and learning activities that prioritise Indigenous perspectives and experiences [34,35]. Indigenous students are taking on leadership roles in medical education, advocating for Indigenous rights and interests. Medical schools are also developing targeted support services, including academic advising, counselling, and cultural support, to foster a more inclusive environment for Indigenous students. Notably, Kereopa-Yorke et al.’s research highlights practical efforts to promote inclusion and support for Indigenous medical students. Their qualitative, multi-method approach, which included in-depth case studies of the Indigenous Protocol and AI Working Group, led to the development of a framework for Indigenous AI governance. This framework is grounded in principles of sovereignty, reciprocity, and collective benefit, aligning with the principles of “He Waka Eke Noa”, which emphasises unity and collective responsibility [36]. By codesigning AI systems with Indigenous communities, developers can create culturally responsive learning experiences and personalised support tailored to the unique needs of Indigenous students, ultimately promoting more inclusive and supportive learning environments. Effectively addressing these challenges requires a comprehensive approach that involves exploring innovative strategies to bridge the gap in equity and inclusion [6]. The emergence of generative artificial intelligence (GAI) technology presents a promising solution as GAI can be leveraged to develop culturally responsive learning materials, provide personalised learning support, and create online learning environments that are inclusive and supportive of Indigenous students’ unique needs and experiences.

3.2. Leveraging AI to Enhance Indigenous Students’ Inclusion in Medical Education

3.2.1. Revolutionising Learning Spaces Through Technology

The extant classroom predominantly comprises conventional hybrid face-to-face and online learning [37]. However, the foremost drawback of this system is the dearth of proper engagement, interaction, and socialisation between those present physically in the classroom and those online [38]. Several teachers have also reported the negative impact necessitated by the extra work involved in managing a hybrid classroom, thereby overtasking the teachers [39]. Consequently, there is a compelling need to minimise the burden on teachers and bridge the physical distance while humanising the experience of physical separation through optimum socialisation. Future e-learning technology will provide personalised learning experiences tailored to individual needs, increasing accessibility and engagement for Indigenous students and students from diverse backgrounds. Advanced features like real-time language translation and AI-powered adaptive learning will also help bridge learning gaps and foster a more inclusive environment [40].

Virtual learning environments can bridge geographic divides for Indigenous students, providing access to coursework, mentorship, and peer networks from anywhere. At the same time, online platforms offer flexible learning opportunities that allow students to learn at their own pace and stay connected to their communities. Additionally, virtual reality and simulation-based training can create immersive and interactive learning experiences tailored to diverse learning styles and abilities [41]. A classic example is the adoption of virtual classrooms that utilise mixed reality technologies like holography, which generates the illusion of three-dimensional imagery, creating an immersive experience that simulates human interaction with the digital world. By leveraging holography, virtual classrooms can feature holographic professors and students, revolutionising how we approach learning and teaching [42,43]. This technology will enable Indigenous students to participate in classroom learning from the comfort of their own homes while still experiencing high levels of engagement, interaction, and socialisation. Beyond providing a lifelike experience for students and lecturers, the advent of telepresence technology will significantly enhance socialisation, interaction, and teamwork among students while eliminating the costs, time, and stress associated with commuting to campus. This innovative technology has the potential to revolutionise electronic teaching methods. However, the main obstacles to its widespread adoption are the high setup costs, which may be unaffordable for many students, and the lack of reliable internet connectivity in rural or suburban areas.

3.2.2. AI-Powered Collaboration and Engagement

Establishing engagement platforms for Indigenous medical students presents a unique opportunity to enhance their connection with their communities while fostering academic success. Studies have shown that initiatives that promote collaborative learning activities can significantly improve student outcomes and foster a sense of belonging [44,45,46]. GAI holds significant promise in facilitating these initiatives by providing tools for community members to contribute to the development of educational content [47], share resources, and participate in mentorship [48,49,50,51].

The integration of GAI into medical education can facilitate collaborative learning experiences that cater to the diverse needs of students, promoting engagement and retention [19,22,52]. This participatory approach enriches the learning experience and strengthens the ties between students and their communities. Studies have also shown that positive relationships among Indigenous students, peers, and instructors significantly contribute to academic success [53]. By leveraging GAI technologies, platforms can be designed to break down social and cultural barriers [54,55], creating a more inclusive and supportive learning environment [56,57,58]. For instance, AI can analyse student interactions and preferences to suggest relevant community events, peer connections, and Indigenous mentors that align with their cultural backgrounds and academic interests, ultimately enriching their learning experience [15,59]. The collaborative nature of AI-powered platforms can also promote community engagement and the co-creation of learning materials [60], which is essential for a culturally relevant curriculum that enriches the student’s learning experience and empowers Indigenous communities by recognising their expertise and contributions to medical education [35]. This is supported by Kennedy et al., who highlighted the importance of relational learning with Indigenous communities and advocated for partnerships that are respectful and mutually beneficial [61].

AI-powered platforms can also facilitate the development of essential skills, such as leadership, communication, and problem-solving, through AI-facilitated group projects and simulations [47]. Using these platforms, Indigenous medical students can access inclusive online learning materials that reflect their cultural perspectives and experiences, promoting cultural connection and relevance, ultimately leading to increased engagement, motivation, and academic success. Additionally, AI-powered platforms can address mental health and well-being among Indigenous students by providing accessible resources through AI chatbots, which can offer timely assistance while maintaining cultural identity and connection to their communities [62]. Furthermore, AI-powered platforms can help bridge the digital divide that Indigenous students may face due to limited access to reliable internet connectivity or digital technologies. This can hinder their participation in online learning environments, exacerbating existing disparities in medical education. GAI can address this issue by providing Indigenous students with access to digital technologies, internet connectivity, and mobile-friendly and offline access to learning materials. This allows students to engage with online learning resources effortlessly, regardless of location or technological capabilities [63].

3.2.3. Personalised Learning Through AI-Powered Adaptive Platforms

Integrating AI-powered adaptive learning platforms in medical education presents a promising avenue for enhancing the learning experiences of Indigenous medical students. These platforms can identify learning gaps and provide tailored educational resources for tailored learning experiences that cater to unique learning styles and cultural contexts, thereby addressing some of the systemic barriers faced in medical education [54,64]. Indeed, AI-powered adaptive learning platforms can use data analytics to identify areas where Indigenous students need additional support and tailor the learning experience accordingly [65]. Intelligent tutoring systems can provide customised learning and real-time feedback through interactive and immersive learning environments, helping Indigenous students stay on track and address knowledge gaps [66,67].

Natural Language Processing (NLP) support is another essential aspect of AI-powered adaptive learning platforms, facilitating comprehension and communication. AI-powered language tools can assist Indigenous students with language barriers, providing real-time translation and interpretation services and helping them navigate complex medical terminologies and concepts [68,69,70]. Flexible learning pathways can also accommodate different learning styles and abilities, ensuring Indigenous students can learn at their own pace [71,72]. Notable examples of generative AI (GenAI) supporting Indigenous education include Groten et al.’s initiative, which integrates ChatGPT to generate Cree star stories into a science curriculum. This approach aligns with the Truth and Reconciliation Commission (TRC) of Canada’s educational calls to action, addressing curriculum gaps and promoting engagement with cultural narratives that resonate with Indigenous students’ identities [73]. Another promising example is Santiago-Benito et al.’s novel method for collecting and translating Mixtec texts into Spanish using neural networks. This methodology has potential applications for other Indigenous languages, such as Amuzgo, Tlapaneco, and Zoque, enriching the learning experience of Indigenous medical students.

Through personalising learning experiences, enhancing cultural competence, and fostering community engagement and a supportive learning environment, these AI-driven platforms can address the unique challenges faced by Indigenous medical students, ultimately improving their academic performance [74,75]. As medical schools strive to create more inclusive and equitable learning environments, integrating AI technologies will be instrumental in achieving these goals (Figure 2).

3.2.4. Mobile Learning: AI-Powered Educational Software Applications and Personal Digital Assistants

In recent times, acquaintance with Educational Software Applications (ESAs) and Personal Digital Assistants (PDAs) is increasingly becoming an essential skill for medical students, enabling them to access and manage vast amounts of medical information efficiently [76,77]. The need for inclusive, portable, and up-to-date access to educational resources, diagnostic tools, and clinical guidelines drives this trend. Several mobile medical education applications are available for Android and iPhone devices. For instance, Stanford University offers a range of applications that can be downloaded from the Apple App Store and accessed through their “Student Apps” portal. Additionally, a wide variety of educational applications can be utilised by students on both tablets and smartphones, paving the way for enhanced learning experiences in the future.

Integrating Educational Software Applications (ESAs) and Mobile Devices in medical education can harness how Indigenous students learn and interact. One of the most significant benefits of this technology is its ability to foster inclusivity, providing equal access to education regardless of ability, location, or socioeconomic background. ESA and Mobile Devices offer personalised learning experiences, accommodating diverse learning styles and needs, such as interactive simulations, virtual labs, and multimedia resources [78].

Using ESAs and Mobile Devices in medical education also facilitates accessibility for students with disabilities. For instance, text-to-speech functionality, font size adjustment, and high-contrast modes enable students with visual impairments to engage with educational content. Moreover, Mobile Devices can allow indigenous students to access educational resources anywhere, anytime, reducing barriers for those with mobility or transportation issues. Additionally, ESAs and Mobile Devices provide real-time feedback, assessment, and tracking, enabling instructors to identify knowledge gaps and offer targeted support, ensuring no student is left behind.

The inclusivity potential of ESAs and Mobile Devices extends beyond accessibility as they also facilitate cultural inclusivity by offering diverse patient scenarios, case studies, and virtual interactions, preparing students to address the unique needs of diverse patient populations [78]. Furthermore, ESAs and Mobile Devices enable collaboration and communication among students from various backgrounds, fostering empathy, understanding, and teamwork. As medical education evolves, strategically integrating ESAs and PDAs can harness inclusivity, creating a supportive learning environment that enriches Indigenous students’ experience and boosts performance.

3.2.5. Game-Based Learning and AI Educational Games

Digital Educational Games (DEGs) offer a pioneering approach to harnessing medical education by providing students with an inclusive and engaging learning experience [79]. DEGs can be customised to fit different learning styles and needs, making medical education more accessible and effective [80,81]. By leveraging game-based learning (GBL) design elements, DEGs can foster community and collaboration among Indigenous students, promoting social inclusivity and reducing feelings of isolation. Moreover, DEGs can be easily updated to reflect the latest medical advances and guidelines, ensuring students receive current and relevant information [82]. DEGs, such as “Serious Games (SGs)”, are being increasingly used to enhance learning, offering engaging and challenging simulations that improve reaction times and hand-eye coordination in surgical training [83]. A classic example is ElderQuest, a role-playing game that geriatric clerkship students at Florida State University College of Medicine play to seek the Gray Sage, a powerful wizard who is ill and whom each participant must restore to health [84].

DEGs can also address the needs of students with disabilities, such as visual or hearing impairments [85], by incorporating advanced accessibility features like text-to-speech functionality, font size adjustment, and closed captions [86]. Additionally, DEGs can provide personalised learning experiences, allowing students to learn at their own pace and focus on areas where they need improvement. This personalised approach can help reduce anxiety and stress, creating a more inclusive learning environment. By providing a safe and supportive space for Indigenous students to practice and apply their knowledge, DEGs can help build their confidence and competence [79].

Using DEGs and simulations in medical education can also promote cultural inclusivity by incorporating diverse patient scenarios, case studies, and virtual interactions [87]. This can help prepare students to address the unique needs of diverse patient populations, reducing healthcare disparities and improving patient outcomes. Furthermore, DEGs can facilitate collaboration and communication among students from various backgrounds, fostering empathy, understanding, and teamwork. By leveraging DEGs, medical educators can optimise the potential of DEGs to create a more inclusive and supportive learning environment, ultimately leading to better health outcomes and improved patient care.

3.2.6. AI-Enabled Career Guidance and Mentorship

AI-driven career guidance and mentoring systems have the potential to significantly enhance the career development of Indigenous medical students by addressing the unique challenges they face in navigating their academic and professional journeys. The integration of AI into career guidance can provide tailored support that is particularly beneficial for Indigenous students, who often encounter systemic barriers, inequities in accessing medical careers, cultural disconnection, and a lack of role models [9,88,89].

Indeed, studies have shown that Indigenous students often lack access to career advisors and guidance counsellors and may receive inadequate or misinformed support, diverting them away from health careers [89]. This highlights the need for personalised career guidance platforms that leverage AI to provide customised insights and resources for each student. For instance, AI can analyse individual students’ interests, skills, and strengths, offering tailored career recommendations and pathways that align with their unique backgrounds and aspirations [90,91,92,93,94]. AI-driven career coaching models can also enhance medical students’ career exploration and decision-making processes by incorporating data from various sources, including academic performance, personal interests, and career trends [90,93]. This approach can aid in specialty selection, reduce dropout rates, and provide ongoing support throughout their educational journey.

Furthermore, AI-driven systems can facilitate mentorship opportunities by matching students with mentors who share similar backgrounds or interests and understand their unique challenges and aspirations. This fosters a supportive network that encourages retention, progression, and improved overall outcomes in medical education and practice [95,96].

Incorporating AI in career guidance aligns with the broader educational trend of integrating technology into medical curricula. Medical students increasingly recognise the importance of AI in their future careers, with many advocating for its inclusion in educational programs [97,98,99]. This growing acceptance can be leveraged to enhance the effectiveness of AI-driven career guidance systems, ensuring that Indigenous students are informed about their career options and equipped with the necessary skills to thrive in an evolving healthcare landscape. In summary, AI-driven career guidance systems hold significant promise for supporting Indigenous medical students’ career development. By providing personalised, data-driven insights and fostering mentorship opportunities, AI can help bridge the gap in Indigenous students’ representation in medical education, empowering them to pursue and succeed in their academic journeys.

3.2.7. AI-Enabled Immersive Simulation Platforms with Cultural Contexts

Experiential learning is a vital learning approach for medical trainees, and AI-powered platforms can enhance this by simulating real-world scenarios, incorporating Indigenous cultural perspectives [100]. This immersive approach fosters a deeper understanding of the cultural nuances and health inequities affecting Indigenous populations, promoting a holistic perspective on healthcare delivery [101]. The integration of immersive AI technologies into experiential learning frameworks can enhance indigenous student engagement, knowledge retention, and cultural understanding, as these platforms leverage advanced technologies like virtual reality (VR), augmented reality (AR), mixed reality (MR), and online virtual worlds to create customised engaging, interactive learning scenarios that can be beneficial for Indigenous medical students [102]. Studies have also demonstrated that immersive virtual reality (IVR) creates high-fidelity simulations, providing authentic learning experiences while ensuring safety and interactivity [103]. AI-driven platforms can enhance indigenous students’ learning experiences by dynamically adapting the learning content based on user interactions [104]. This adaptability is vital in Indigenous students’ contexts, where cultural sensitivity and relevance are paramount. AI-driven simulations can facilitate the incorporation of Indigenous narratives and practices that enable learners to practice clinical skills in scenarios reflecting real-world challenges faced by Indigenous populations, preparing them for future interactions in a culturally respectful manner. For instance, holographic simulations can mimic real patients, anatomic structures, or clinical tasks, mirroring real-life medical scenarios [105]. Similarly, AR platforms, like GigXR, provide immersive simulation experiences, enhancing spatial awareness and visualisation. These platforms recreate authentic medical scenarios, simulate realistic patient interactions, and offer interactive 3D models of anatomy and physiology. This enables Indigenous students to practice and learn in a secure, controlled environment, developing essential clinical skills [106]. This benefits indigenous students with diverse learning styles, allowing them to engage with complex medical concepts more interactively and effectively, regardless of their physical location [105]. In addition, virtual platforms, exemplified by “Second Life”, revolutionise medical education by providing engaging and interactive learning experiences. By leveraging AI technology, this platform empowers students to interact with complex data, personalised digital avatars, and realistic virtual settings. This immersive approach not only fosters active learning but also promotes inclusivity by bridging geographical and ability-related divides [107,108]. This is further corroborated by studies showing that programs immersing students in cultural communities foster greater cultural competence among healthcare providers [101,109].

3.2.8. Intelligent Feedback and Assessment Systems

The integration of AI in educational assessment and feedback has gained significant traction in recent years, leading to innovative approaches that enhance learning experiences [110]. AI-enabled systems, particularly Intelligent Tutoring Systems (ITSs), have become pivotal in personalising learning and delivering immediate feedback [66,67]. These systems use AI algorithms to analyse student performance, adapt content, and provide tailored feedback, addressing diverse learner needs [111,112].

In medical education, AI-enabled assessment and feedback mechanisms can be incorporated to significantly enhance the inclusion of Indigenous medical students by addressing their specific challenges, including the need for culturally contextualised feedback, reinforcing cultural competence in medical practice. For instance, Mirchi et al. leveraged AI to develop simulation-based training for medical trainees, introducing a Virtual Operative Assistant to deliver automatic feedback to students based on performance metrics grounded in a formative educational paradigm. Their approach integrates virtual reality and AI to classify students according to proficiency performance benchmarks. The system offers constructive feedback to facilitate improvement, enabling students to refine their skills [113].

AI technologies can also help close the feedback gap in medical education, where timely, constructive feedback is crucial for learning [110,114]. For example, AI-driven surgical training systems can analyse video performances, offering insights to trainees and experienced surgeons, enabling continuous learning based on performance metrics [115].

ITSs are informed by cognitive psychology and educational theories that replicate the benefits of one-on-one tutoring. Research shows that personalised feedback leads to better outcomes than traditional classroom settings [116,117]. These systems update student models dynamically, evolving with learner progress [67,111]. This multi-modal feedback mechanism, as discussed by Skinner et al., further enhances learning through diverse forms of assessment and support [118]. Furthermore, AI-driven platforms use data analytics to analyse performance and engagement, offering personalised recommendations to meet individual needs [119]. Moreover, AI’s scalability addresses the challenges of high student–teacher ratios, which hinder personalised feedback. As enrolments continue to grow, AI will be beneficial for seamlessly providing timely and tailored feedback and support. This support particularly benefits Indigenous medical students, fostering a more inclusive learning environment that respects their diverse experiences.

3.3. Challenges in Integrating GAI for the Inclusion of Indigenous Students

3.3.1. Capacity, Infrastructure, and Training Gaps

The integration of generative AI into medical education presents a unique set of challenges [97], particularly regarding promoting inclusive educational practices for Indigenous medical students. Several barriers must be addressed to ensure that AI effectively enhances medical education for this demographic.

One of the primary challenges is the lack of adequate training and resources for faculty members and students [120,121,122]. Many medical schools struggle to incorporate AI into their curricula due to insufficient faculty expertise and the absence of structured recommendations from educational authorities [120,121,123]. This particularly concerns Indigenous students, who could benefit from culturally relevant AI applications that address their specific learning needs and contexts. The absence of such tailored educational frameworks can lead to a disconnect between the knowledge being transmitted and the required clinical competencies.

Indigenous students also face compounded challenges due to restricted access to technology and digital resources. Additionally, financial constraints further exacerbate the digital divide, hindering equitable participation and underscoring the need for targeted solutions [7,8,9]. Moreover, studies have identified issues such as the lack of high-quality, unbiased data necessary for effective generative AI integration. A primary concern is that most AI systems are trained on limited datasets that fail to represent the experiences of minority individuals, including Indigenous students [124,125,126]. This further perpetuates the existing biases and highlights the need for inclusive dataset development, culturally responsive AI design, and continuous bias monitoring and mitigation.

3.3.2. Ethical Considerations and Cultural Sensitivity

The ethical implications of AI in medical education need to be carefully considered, as some medical students have expressed concerns about the ethical challenges posed by AI technologies, particularly regarding bias and the potential for dehumanisation in patient care [127,128,129]. For Indigenous students, who may already face systemic biases in healthcare, these concerns are particularly salient, and addressing these ethical considerations through a culturally sensitive lens is essential for fostering trust and engagement among Indigenous medical students [28,89].

In addition, AI systems are often designed without Indigenous involvement, resulting in culturally inappropriate and ineffective solutions. The limited engagement of Indigenous scholars in developing AI-driven equity support programs hinders the creation of frameworks that respect Indigenous knowledge systems while leveraging technological advancements [130]. Codesigning and collaborating with Indigenous stakeholders are crucial for ensuring AI solutions’ cultural responsiveness and effectiveness; however, prioritising these aspects is often overlooked [130,131,132].

3.3.3. Evaluation and Sustainability

Evaluating the effectiveness of AI in promoting equity and support for Indigenous students is also a significant challenge, as robust assessment frameworks are lacking, making it difficult to measure the impact of AI solutions. Moreover, strategies for scaling and sustaining AI solutions in medical education and healthcare are underdeveloped, with inadequate resource allocation and infrastructure planning [133,134]. In summary, while AI holds significant potential to transform medical education, its integration must be approached cautiously, particularly in the context of Indigenous medical students. Addressing infrastructural and resource-related challenges is crucial to fully realising the potential of AI to enhance inclusive learning experiences for Indigenous medical students.

4. Discussion

This study significantly advances our understanding of the intersection between educational technology and inclusivity in medical education, particularly concerning the role of GAI in promoting equity and supporting Indigenous medical students. By synthesising the current literature, this study highlights the potential of GAI to address systemic barriers and foster an inclusive learning environment. The findings underscore the importance of leveraging technology to support diverse learning needs and promote cultural sensitivity in medical education.

The study reveals promising opportunities for enhancing the educational experience of Indigenous students. However, it highlights critical limitations and challenges, including the need for culturally relevant training data and the risk of exacerbating existing inequities if not implemented thoughtfully. These insights are crucial for educators, policymakers, and technologists seeking to harness GAI in a way that genuinely supports Indigenous learners.

Moving forward, it is essential to prioritise the development of GAI tools that are co-created with Indigenous communities, ensuring that these technologies are culturally safe and responsive to the unique needs of Indigenous students. Furthermore, ongoing evaluation and research are necessary to assess the long-term impact of GAI on Indigenous student success and identify best practices for implementation.

Ultimately, this review contributes to a growing recognition of the need for inclusive and equitable medical education. By exploring the potential of GAI to support Indigenous medical students, this study highlights the importance of innovation guided by principles of equity, cultural humility, and community engagement. As medical education continues to evolve, embracing technologies prioritising inclusivity will be essential for fostering a more diverse and supportive learning environment.

To fully realise the potential of technology-enhanced inclusivity in medical education, institutions must move beyond the classroom in simply adopting new tools and platforms and, instead, undergo a more fundamental transformation. This requires a comprehensive and multifaceted approach that addresses the cultural biases, curricular reforms, and infrastructural changes necessary to support the effective integration of technology in medical education. The recommendations in Figure 3 outline the necessary changes to create an environment fostering innovation, inclusivity, and excellence in medical education.

4.1. Culture Shift and Institutional Commitment

Firstly, institutions must undergo a culture shift that embraces technology as a transformative tool for enhancing medical education, which requires a fundamental change in how educators and administrators think about teaching and learning [135]. This culture shift involves recognising the potential of technology to improve student outcomes, increase access to education, and enhance the overall learning experience. It also requires a willingness to experiment and take calculated risks to identify innovative solutions that work best for the institution.

4.2. Investment in Infrastructure, Faculty Development, and Capacity Building

Medical schools need to address the digital divide by providing Indigenous students with the necessary tools and resources, such as access to technology and reliable internet. Additionally, financial assistance programs in the form of scholarships should be established to ensure equitable participation in AI-driven medical education [7,8,9]. Initiatives could include providing digital devices, subsidised internet access, and support systems for students who face financial barriers.

To ensure the long-term sustainability of AI initiatives in medical education, medical schools should also allocate adequate resources for infrastructure development, including IT support, AI system maintenance, and continuous improvements. Resource allocation should also account for scaling AI solutions to benefit a broader range of students, ensuring that both Indigenous and non-Indigenous students receive equal access to AI-driven learning tools [135].

Furthermore, institutions need to prioritise faculty development and provide ongoing training and support to ensure that educators are well-equipped to integrate technology into their teaching practices effectively. Medical schools should also invest in ongoing professional development programs that equip faculty and students with the necessary skills to incorporate GAI into curricula. This includes providing structured recommendations from educational authorities and ensuring that both educators and students are proficient in AI technologies [120,121,122,136,137]. Training programs should be tailored to meet the specific needs of Indigenous medical students, ensuring culturally relevant AI applications that reflect their learning contexts.

4.3. Curriculum Reform and Pedagogical Innovation

Furthermore, institutions need to redesign their curriculum to integrate technology-enhanced learning and align with learning objectives and outcomes, which requires a comprehensive review of the curriculum to identify areas where technology can enhance student learning [133,138]. This redesign involves developing new learning objectives and outcomes that reflect the use of technology in medical education and ensuring that assessments and evaluations align with these objectives. It also requires the development of new pedagogical approaches that leverage technology to enhance student engagement and learning [139].

4.4. Ethical, Inclusive, and Culturally Responsive AI Integration

Furthermore, there is a need to develop more inclusive and diverse datasets that accurately represent the experiences of minority individuals, including Indigenous students [124,125,126]. This can be achieved through partnerships with Indigenous communities, allowing their involvement in data collection and AI system development. Regular bias monitoring and mitigation processes must be implemented to prevent the perpetuation of existing inequalities in AI applications, ensuring AI tools provide fair and unbiased outcomes.

In addition, AI systems should be codesigned with Indigenous scholars and stakeholders to ensure cultural appropriateness and responsiveness [130]. Collaborative frameworks should respect Indigenous knowledge systems while leveraging AI advancements to support equity in medical education. Prioritising Indigenous involvement will help create AI-driven programs that address the specific educational needs of Indigenous medical students while fostering trust and engagement in the AI integration process.

Also, AI in medical education should be guided by ethical principles, especially in addressing bias and preventing dehumanisation in healthcare [127,128,129]. Developing ethical guidelines through a culturally sensitive lens will ensure that AI technologies support, rather than hinder, inclusive practices. Ethical AI frameworks should also incorporate feedback from Indigenous medical students, focusing on their concerns and experiences to foster greater inclusivity in AI applications.

4.5. Continuous Quality Improvement and Evaluation

Medical institutions should prioritise continuous quality improvement and ongoing evaluation to ensure that technology-enhanced learning is effective, inclusive, and aligned with institutional goals, which involves regularly reviewing and updating technology-enhanced education strategies to ensure they remain effective [86]. Medical schools also need to create comprehensive frameworks to evaluate the effectiveness of GAI in promoting equity and support for Indigenous medical students. These frameworks should focus on assessing both AI-driven learning tools’ short- and long-term impacts. Student feedback on these evaluations will help refine AI solutions and improve their effectiveness in addressing the needs of Indigenous students. Ongoing improvements also require applying data and analytics to inform decision-making and drive improvements in technology-enhanced health education [140]. It also requires ongoing feedback from students, the faculty, and other stakeholders to identify areas for improvement and ensure that technology-enhanced learning remains aligned with institutional goals. By prioritising continuous quality improvement, institutions can ensure that technology is used to support student learning and drive innovation in medical education.

5. Conclusions

In conclusion, while Generative AI holds immense potential to revolutionise medical education, its successful integration requires addressing several critical challenges, particularly for Indigenous medical students. Overcoming barriers, such as inadequate faculty training, the digital divide, biased AI systems, and the lack of Indigenous involvement in AI design, is essential for creating an inclusive learning environment (Figure 3). Collaborative efforts involving educators, researchers, and Indigenous stakeholders are needed to develop culturally responsive AI systems that respect Indigenous knowledge systems and address their unique educational needs. Through targeted interventions, medical schools can ensure that AI technologies enhance medical education and contribute to the equitable and inclusive participation of Indigenous students. Ultimately, this can lead to better academic outcomes, increased graduation rates, and a more diverse healthcare workforce better equipped to meet the needs of indigenous communities.