Enhancing Medical Education with AI-Enabled Simulations: Leveraging Scaffolded Learning and Adaptive Technology

The Intersection of AI and Medical Training

Medical education is continually evolving due to rapid technological advancements, transformations in healthcare environments, and the growing expectations placed upon medical trainees. Traditionally, the cornerstone of medical teaching was lectures and textbooks. In 2025, however, learners require dynamic, interactive experiences to effectively bridge the gap between foundational knowledge and real-world clinical practice. Artificial intelligence (AI) has emerged as a tool for reshaping and bolstering medical education. Specifically, AI-powered healthcare simulations (HCS) offer promising opportunities to significantly enhance individualized learning, accelerate development of clinical reasoning, and foster students’ sense of autonomy and preparedness(1).

Research shows that technology-based teaching methods have become increasingly important as medical curricula grow more complex, placing increasing demands on both educators and students. AI-powered learning tools have gained popularity for various applications in medical education, such as personalized feedback and assessment, case-based learning, and test-preparation (2,3). One such platform that embodies these principles is DDx by Sketchy, which provides adaptive guidance through immersive, AI-driven clinical scenarios designed explicitly to scaffold the learner’s experience.

Here, we’ll cover a few of these key developments in more detail: the concept of scaffolded learning within medical education, the essential roles of adaptive AI in teaching clinical reasoning skills, and how platforms like DDx utilize these approaches to foster self-directed learning and produce stronger, more independently capable future clinicians.

Scaffolded or Mastery Learning in Medical Education

To address the tension between covering vast amounts of information and effectively developing learners' critical reasoning skills, many health sciences programs have adopted educational frameworks that actively support learners through graduated, responsive interventions, such as scaffolded learning and mastery learning (4).

Historically rooted in the educational theories of Vygotsky, scaffolded learning is an instructional framework in which students are provided tailored support and guidance as they progress in their learning journey. The idea is to offer structured assistance at the beginning of the learning process and gradually reduce support as learners gain competence and confidence (5, 6). A related concept is the idea of mastery learning, in which students must achieve a set level of proficiency before advancing to more complex tasks or concepts (7). By integrating both scaffolded learning and mastery learning, educators can ensure that learners build a robust foundation of knowledge and skills at each stage, ultimately fostering deeper clinical reasoning and better performance in demanding healthcare environments.

How Scaffolded Learning is Implemented in Medical Education

1. Early Exposure to Foundational Knowledge: At the start of medical education, students are introduced to core concepts through lectures, textbooks, and guided discussions. Instructors deliberately sequence learning activities to gradually build in complexity. By breaking content into manageable steps, providing timely feedback, and offering tools such as concept mapping, interactive visual aids, and guided reading assignments, educators ensure that each new skill builds on previously mastered ones (5).
2. Simulation-Based Learning: As students advance, they engage in simulation-based learning, where they apply theoretical knowledge to practical, real-world scenarios. Simulations, ranging from standardized patient encounters to high-fidelity mannequins, provide a safe space for learners to experiment, make decisions, and learn from mistakes without real patient consequences. AI-powered simulations enhance this experience by offering dynamic feedback, adaptive difficulty levels, and personalized case progression (8).
3. Progressive Reduction of Guidance: As learners become more proficient, instructors reduce direct guidance, allowing students to take the lead in clinical reasoning and decision-making. This stage is crucial for transitioning from knowledge acquisition to real-world application. Students may be tasked with managing complex patient cases with minimal prompts, fostering confidence and independence (4).
4. Peer Learning and Collaboration: Scaffolded learning is also reinforced through peer discussions, mentorship programs, and case-based learning sessions. Senior students or educators provide initial guidance, but as learners progress, they engage in collaborative problem-solving, deepening their understanding through discussion and reflection (9, 10).
5. Assessment and Reflective Practice: Regular assessments, including self-assessment, formative quizzes, and instructor feedback, help students track their progress. AI-powered platforms use data-driven insights to identify knowledge gaps and suggest personalized learning pathways, ensuring continuous improvement (11).

The Power of AI: Individualized Adaptive Learning in Healthcare Simulation

Technology plays a pivotal role in shaping how students learn, apply knowledge, and develop clinical reasoning skills. The integration of AI into HCS has generated new opportunities for students to engage in individualized learning outside of a traditional classroom setting. AI-driven simulations provide a structured yet flexible approach to mastering complex clinical scenarios, allowing learners to practice decision-making in a risk-free environment.

For medical educators, promoting self-directed learning (SDL) is essential for preparing students to become competent, reflective healthcare professionals. By encouraging learners to identify gaps in their knowledge and actively seek out resources, instructors help cultivate lifelong learning skills. Integrating AI-powered simulation platforms—such as DDx by Sketchy—into curricula can further strengthen SDL through personalized guidance, timely feedback, and interactive case-based experiences.

DDx by Sketchy: An Example of AI-Powered Scaffolded Medical Education

Interactive Case Guidance

A key element of scaffolded learning is providing learners with personalized support at the right times. DDx by Sketchy exemplifies this principle through its case guidance feature, which offers incremental hints and prompts to guide clinical reasoning without simply presenting the final answer. From an instructor’s perspective, this approach helps for the following reasons:

• Adaptive Support: Hints are personalized based on the learner's responses, ensuring targeted assistance rather than generic explanations.

• Encourages Critical Thinking: Instead of spoon-feeding solutions, the platform nudges students toward the correct thought process, reinforcing diagnostic skills.

• Confidence Building: By receiving just the right amount of support when needed, learners build confidence in their ability to navigate complex clinical cases independently.

• Progressive Skill Development: As students become more proficient, they rely less on hints, fostering autonomy and mastery over clinical reasoning.

Real-Time Feedback and Performance Analytics

A key feature of DDx is its ability to engage students in dynamic conversations that challenge them to think critically, justify their clinical decisions, and identify key findings. These interactive dialogues simulate real-world clinical discussions, requiring students to articulate their diagnostic reasoning and consider alternative possibilities. The student receives real-time feedback, highlighting errors and offering explanations for incorrect responses. This immediate feedback loop is crucial for SDL, as it allows learners to understand their mistakes, adjust their reasoning, and improve their decision-making skills.

DDx utilizes this feedback to generate performance analytics, allowing educators to track students' progress over time, identify areas needing improvement, and provide immediate guidance to support students’ learning goals and refine their study strategies.

Scenario-Based Learning and Clinical Decision-Making

DDx presents learners with diverse clinical cases, each requiring unique decision-making approaches. By engaging with a wide array of patient scenarios, students develop diagnostic acumen, refine treatment planning skills, and learn to adapt their approach based on evolving clinical presentations. Crucially, instructors have the flexibility to select cases from the DDx library according to complexity, subject matter, or curricular focus, ensuring that the simulated experience meets learners’ specific needs and challenges them appropriately. This strategic choice of scenarios is invaluable in preparing students for real-world patient care, where no two cases are identical.

On-Demand Learning and Flexibility

Traditional classroom settings have limitations in terms of scheduling and availability of instructors. DDx overcomes these barriers by allowing students to engage in learning at their own pace, anytime and anywhere. This flexibility supports SDL by enabling learners to revisit difficult cases, practice repeatedly, and deepen their understanding without time constraints.

Broader Impact: Encouraging Self-Directed Learning and Lifelong Clinical Competence

The rise of AI-powered learning highlights an increasing emphasis on learner autonomy and self-efficacy as core expectations. Providing students access to highly individualized and dynamic resources diminishes their reliance solely on traditional classroom materials, fostering a more empowered, independent learning process.

The scaffolding methods demonstrated by AI-driven platforms like DDx help students develop the essential SDL behaviors needed for lifelong professional development. Utilizing analytics from adaptive AI platforms, instructors can help support learners in developing valuable metacognitive abilities, encouraging students to actively identify strengths and weaknesses, set goals, and self-regulate (12, 13).

The Future of Medical Education is Adaptive, Scaffolded, and AI-Powered

AI-powered simulations are revolutionizing medical education by providing dynamic, interactive, and personalized learning experiences. DDx by Sketchy exemplifies how AI-driven tools can incorporate scaffolded learning principles to strengthen learners’ knowledge and encourage critical thinking. Through strategic implementation of AI-powered tools like DDx, medical educators can strengthen students’ SDL, cultivate advanced clinical reasoning skills, and ultimately prepare them for the nuanced complexities of patient care.

References:

1. Hamilton A. Artificial Intelligence and Healthcare Simulation: The Shifting Landscape of Medical Education. Cureus. 2024;16(5):e59747. doi:10.7759/cureus.59747
2. Chan KS, Zary N. Applications and Challenges of Implementing Artificial Intelligence in Medical Education: Integrative Review. JMIR Med Educ. 2019;5(1):e13930. doi:10.2196/13930
3. Preiksaitis C, Rose C. Opportunities, Challenges, and Future Directions of Generative Artificial Intelligence in Medical Education: Scoping Review. JMIR Med Educ. 2023;9:e48785. doi:10.2196/48785
4. Masava B, Nyoni CN, Botma Y. Standards for Scaffolding in Health Sciences Programmes: A Delphi Consensus Study. J Med Educ Curric Dev. 2023;10:23821205231184044. doi:10.1177/23821205231184045
5. Masava B, Nyoni CN, Botma Y. Scaffolding in Health Sciences Education Programmes: An Integrative Review. Med Sci Educ. 2023;33(1):255-273. doi:10.1007/s40670-022-01691-x
6. Vygotsky LS, Cole M. Mind in Society: Development of Higher Psychological Processes. Harvard university press; 1978.
7. Bloom B. Human Characteristics and School Learning. McGraww-Hill; 1968.
8. Bauer E, Heitzmann N, Fischer F. Simulation-based learning in higher education and professional training: Approximations of practice through representational scaffolding. Studies in Educational Evaluation. 2022;75:101213. doi:10.1016/j.stueduc.2022.101213
9. Visser CL, Wouters A, Croiset G, Kusurkar RA. Scaffolding Clinical Reasoning of Health Care Students: A Qualitative Exploration of Clinicians’ Perceptions on an Interprofessional Obstetric Ward. J Med Educ Curric Dev. 2020;7. doi:10.1177/2382120520907915
10. Burtson KM, Wilson KR, Kiger ME, Jung E, Hartzell JD, Meyer H. Academic Coaching to Promote Self-Directed Learning in Graduate Medical Education. J Gen Intern Med. Published online February 13, 2025. doi:10.1007/s11606-025-09424-7
11. Wang HS, Chen S, Yen MH. Effects of metacognitive scaffolding on students’ performance and confidence judgments in simulation-based inquiry. Phys Rev Phys Educ Res. 2021;17(2):020108. doi:10.1103/PhysRevPhysEducRes.17.020108
12. Xu X, Wang X, Zhang Y, Zheng R. Applying ChatGPT to tackle the side effects of personal learning environments from learner and learning perspective: An interview of experts in higher education. PLoS One. 2024;19(1):e0295646. doi:10.1371/journal.pone.0295646
13. Hwang GJ, Cheng PY, Chang CY. Facilitating students’ critical thinking, metacognition and problem-solving tendencies in geriatric nursing class: A mixed-method study. Nurse Educ Pract. 2025;83:104266. doi:10.1016/j.nepr.2025.104266

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