Artificial intelligence is revolutionizing education as a whole, as AI brings new technologies that offer learners new ways of learning, thinking, and relating to learning content. AI systems are a major component of learners’ academic activities, ranging from tutoring to automatic feedback systems, because AI affects various aspects of student learning, including research, writing, and problem-solving activities. Sejdiu and Sejdiu(1) stated that education is in a “quiet transformation” as adaptive learning systems provide targeted education services according to learners’ performance. Interconnected adaptive learning combined with experiential learning is enhancing learners’ applications of course concepts in practical settings. Learning activities in various forms, including internships, act as platforms that help learners gain the skills needed in the job industry.(2) AI integration with experiential learning improves learners’ performance as AI blends theory in education with practice in students’ experiences.
Experiential Learning in Public Health and Health Administration
Generative AI is a derivative of AI that has the ability to generate new material given a prompt. It is set to be incorporated into scholarly as well as professional education. Its adoption in experiential learning presents a whole new set of possibilities that range from customization to immersion in training. Because AI is able to provide realistic simulations, as highlighted by Salinas-Navarro, et al.,(3) authentic assessments can be done in simulations, with a feedback process that imitates that of authentic systems in reality.
Artificial Intelligence and Experiential Learning Opportunities
AI-based experiential learning practice is increasingly being adopted in higher education. AI promotes student engagement, creativity, and motivation in learning. Murniarti and Siahaan(4) observed that AI integration improves creativity driven by high levels of internal motivation, a critical factor within experiential learning. Effective implementation of a combination of AI technology and experiential learning results in a revolutionary education experience and encourages in-depth learning and innovation. Nevertheless, there are some drawbacks. Overdependence on AI is believed to lead to insufficient face-to-face interactions between individuals, resulting in a loss of “soft skills,” including interpersonal communication skills, which play a crucially important role in the field of healthcare administration and public health. Moreover, inappropriate implementation of AI technologies will lead to surface-level learning rather than the development of problem-solving skills.
Despite these factors, the collaboration between AI technology and experiential learning holds great promise. Students enrolled in AI-based experiences display a higher level of engagement and creativity. For example, a survey-based study conducted by Murniarti and Siahaan(4) revealed that a significant percentage of students (75%) experienced higher creativity levels because of AI integration in experiential learning activities. This model of learning not only enables students to gain expertise in a particular field, as in the medical sector, but also trains them to be adaptive in a way that enables them to easily interact with new systems.
Digital Twins, Simulations, and Artificial Intelligence in Experiential Learning
Digital twins are virtual copies of real-world systems that use real-time data to model, simulate, and predict outcomes. These tools provide a virtual setting that is optimal for experiential learning, because students can use them to experiment with decisions that they can see the outcomes of before they are implemented in the real world. According to Kreuzer, et al.,(5) digital twins represent a combination of machine learning and real-time simulation whereby AI uses data pattern analysis to predict outcomes and improve the accuracy of decisions. Machine learning is a field of AI that specifically concentrates on employing algorithms that use data to predict outcomes.(6) One type of advanced machine learning is deep learning, which enables a digital twin to process large amounts of data, making its use appropriate in the healthcare sector.(7)
In a learning environment, a digital twin enables students studying health administration and public health to participate in simulations that recreate situations akin to those that may be experienced in a healthcare setting. For example, a student can participate in a simulation process that entails a hospital management simulation, population analysis, or a crisis simulation. Badach(8) shows that a digital twin enables a student to model a “digital shadow” of a healthcare setting where the student can offer a solution that is instantly reflected. Using AI in the context of experiential learning enables a student to solve a problem by interpreting data, looking for a pattern, and suggesting a solution.(5,8)
Digital Twins and Simulations in Health Administration and Public Health
In the realm of health administration education in public health, the use of digital twins and AI simulations effectively bridges the gap between theoretical education in a classroom setting and practical implementation in the field. Findings presented by Neo, et al.,(9) as well as those presented by Chan, et al.,(10) show that AI-based simulation in health education allows learners to experience a set of practical scenarios that are imbued with a high degree of realism relevant to public health management, with learners exposed to relevant expertise in managing aspects of patient flow. Negi, et al.,(11) as well as De Jong, et al.,(12) and Lee, et al.,(13) highlight that competency-based skills can only result from simulation-based education that uses AI systems, thus making learners qualified to face practical issues in management that pertain to implementing new policies and emergency management, as well as providing improvements in quality.
Generative Artificial Intelligence and SL Examples
Generative AI (GenAI) is the name given to those AIs that possess the ability to generate any form of content, including text, pictures, and simulations, in response to user input.(14) In the education sector, students increasingly use GenAI tools to help in understanding subject matter, while brainstorming, and in simulating problem-solving activities. Keeping in mind that AI not only assists in giving personalized feedback, GenAI tools help in self-directed learning.(3) Therefore, in experiential learning, the use of GenAI tools works as a “smart partner.”
Kolb’s experiential learning cycle can be enhanced by the use of GenAI. At the concrete experience level, virtual environments enabled by AI help students experience administrative experiences in a way that may have pragmatic use in a healthcare setting, such as minimizing wait times in patient care. At the reflective observation level, students evaluate results of decisions backed by AI-assisted feedback on the observed results’ alignment with best practices. At the abstract conceptualization level, student comprehension of administrative theory is enhanced by AI-filtered literature, results-based guidelines, and AI-curated case studies. Lastly, active experimentation (AE) rounds out the learning cycle as students continue practical exposure to administrative experiences with AI-based feedback.(3)
This cycle is illustrated in a GenAI-enabled healthcare simulation by Salinas-Navarro, et al.,(3) where students played the role of administrators in a simulation under the model of Lean Healthcare. It not only illustrates that is GenAI useful in reflective learning, but it also gives feedback and updates in the simulation to promote refinement. Again, this emphasizes that AI, when combined in the framework of experiential education, is capable of simulating the realism of public health practice in a well-structured way. Neo, et al.,(9) and Janumpally, et al.,(15) emphasize that incorporating GenAI in medical as well as public health education can help in personalization of learning, simulating practical constraints, as well as ethical reflection.
Generative AI in Health Administration and Public Health
In the realm of health administration and public health, GenAI is used as a learning partner as well as a systems-level model builder. This use is supported by the contributions of authors including Ichikawa, et al.,(16) who draw attention to the inclusion of GenAI in education to teach students about the ethical and practical considerations involved in AI applications in a clinical as well as administrative context. This is further linked to AI skills as a factor of job readiness by authors Wang, et al.,(17) as well as Portocarrero Ramos, et al.,(18) Kővári, et al.,(19) further discuss this topic in relation to GenAI by pointing out that in AI-enabled collaboration, teamwork and problem-solving are enhanced during interprofessional activities in a way that is valuable in public health administration. Through AI integration, particularly GenAI, public health and health administration education can be transformed into a more interactive, adaptive, and ethical realm of experiential education. Learners will benefit from digital twins, decision feedback tools, and solution-testing platforms in virtual healthcare systems, thereby preparing them for the demands of the fast-changing healthcare sector.
Methodology
The scope and discipline of this literature review involved a systematic and multiphased approach to properly identify credible and pertinent studies on the role and application of AI in experiential learning. The first phase involved an exploratory search using the Google Scholar search engine and standard Google search terms. The purpose and scope of the search involved the use of terms such as experiential learning, applied learning, internships, AI experiential learning, AI in health education, and digital twins in education to collect a sample set of literature. The search and inquiry process quickly uncovered the problem that the available samples and sources related to the scope and discipline involved an oversight in the search terms and phases — perhaps reflecting a lack of publicly available and credible sources on the use and application of AI in experiential and applied learning experiences.
Recognizing the need for filling the gap, the next step in the methodology was the performance of a search using academic libraries that are renowned for their peer-reviewed and subject-specific contributions. Academic libraries utilized in the search include ScienceDirect, Frontiers, JMIR, Computers and Education, The Internet and Higher Education, and ResearchGate. These websites were chosen because they offer the latest studies on innovative practices in education, simulation-based learning, health training, and AI technology. The search process targeted sources that specifically deal with simulation-based education, digital twins, virtual internship experiences, generative AI, and AI-enabling competency training. Although the preexisting studies used the technology in the training and development of medical professionals, including nurses and medical experts, these studies were considered because the process of simulation, reflection, decision-making, and gaining skills applies in the field of public health and health administrations.
The third stage of purposive sampling was an effort to uncover studies that addressed known limitations in the field, namely the lack of studies using AI models in an experiential manner in the field of public health and health administration. In light of the lack of studies on the subject, the review included studies on related subjects such as medical education, simulation in health care, and technology in higher education. These studies were assessed for ideas, frameworks, and results that could reasonably be applied in health and health administration.
To make sure that the sources were credible and relevant, each article was assessed through three means:
Ensuring the sources were up-to-date: sources should have been published in the last four years, ideally in the last year.
Ensuring that sources were related to the issue under investigation: each source should offer evidence related to AI, experiential learning, and health care–related educational innovations.
Ensuring that sources reached conclusions supported by data, prioritizing scientific and theory-backed studies in lieu of opinions.
Through the use of these guidelines, a set of 18 sources was created.
Conclusion
The future involves balancing technological integration (e.g., AI, data analytics) with human needs, focusing on preventative health, streamlining costs, ensuring equitable access, and building system resilience against global health threats, all while managing a strained workforce and evolving patient expectations.
The integration of AI with experiential learning stands poised to fundamentally reshape the landscape of public health and health administration education and practice. The future implications of this synergy are profound, creating a dynamic ecosystem where theoretical knowledge is immediately actionable and data-driven insights translate directly into improved community health outcomes and efficient administrative systems.(6)
The imminent future will see AI not merely as a computational tool, but as an indispensable partner in experiential education, providing robust platforms for realistic simulations, predictive analytics, and personalized learning pathways. By leveraging AI to model complex epidemiologic scenarios and administrative challenges, students and practitioners will acquire the practical wisdom necessary to navigate a rapidly evolving healthcare environment.(5)
Ultimately, the successful convergence of AI and experiential learning ensures a workforce that is not just technically proficient, but also strategically agile and ethically grounded. Embracing these opportunities is not merely a pedagogical trend, but a critical imperative for fostering resilient public health systems capable of addressing the multifaceted challenges of the 21st century.(5) The trajectory is clear: an AI-enhanced, experience-oriented approach is the definitive future for a healthier world.
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