Agentic education · Learning science

Actionable companion · Learning design & outcomes

Learning with agency: Medical Education Learning Specialists in the Age of Artificial Intelligence

AI-assisted editorial implementation guide, separate from the original research. Proposed actions are not claims or recommendations attributed to the source authors. No independent peer review of this guide is asserted.

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Evidence and context

Title and abstract screening; stage labels describe reported settings, not verified numerical ages. Design labels require full-text appraisal. Reported stage(s): Age / stage not established. Health-professions learning must be distinguished from K–12 instruction.

Original authors’ abstract, reproduced under the linked source license:

Introduction Generative Artificial Intelligence (AI) is transforming healthcare education by offering innovative solutions to enhance students' learning and skill development. Medical education learning specialists often seek effective strategies to support students in mastering content and improving study habits. This study aimed to investigate learning specialists’ perspectives on the impact of AI on their roles and interactions with healthcare education students. Methods The researchers conducted a national online survey with the Medical Education Learning Specialists (MELS) Special Interest Group, a community of professionals providing academic support to medical and health professions students. Sixty-one learning specialists from 29 states of the United States participated in the survey. Results Key findings reveal that, despite advances in generative AI, learning specialists agree that their roles in supporting students will remain valuable and irreplaceable. However, they emphasized the need for targeted training to effectively integrate AI into academic support services. Other findings suggest that medical education learning specialists see the significant promise of AI in improving personalization in medical education and student learning experiences. Conclusion As healthcare education evolves, the role of generative AI in enhancing personalized learning and efficient study strategies to address the unique needs of students will continue to grow. AI tools can equip learning specialists with the assistance needed to provide targeted support to students.

What kind of evidence is this?

Supporting AI learning science · Survey / observational study

Stage: Age / stage not established. Screening labels describe reported context and study-design language. They are not a risk-of-bias rating or proof of effectiveness for an age band.

Study methods · source passage

This study aims to investigate medical education learning specialists’ perspectives on the impact of AI on their work and interactions with healthcare education students. The researchers received Institutional Review Board (IRB) Exempt approval (FWA00001119) from the University of Arkansas for Medical Sciences, at the time of the study, and permission from the leadership of the Medical Education Learning Specialist (MELS) Special Interest Group, a community of professionals providing academic support to medical and other healthcare education students, to send a survey through the organization’s listserv while recruiting participants.

Limitations and uncertainty · source passage

Medical education learning specialists must be knowledgeable about the changes in the AI landscape and how they will impact medical education. It was outside the scope of the study to understand the extent to which medical education learning specialists are trained or prepared to use AI in academic coaching. Future research should focus on examining learning specialists’ AI literacy first, and then providing specific preparation and training for them to integrate AI into academic coaching successfully. Training to learning specialists or educators can include workshops or online modules on topics, such as prompt engineering to achieve more effective and relevant AI responses, citations for AI use or creation, ethical and responsible AI use, evaluating AI tools, AI literacy, societal and cognitive Pros and Cons of using AI, and redesigning assessments based on AI integration [22,26-27]. For example, training can be offered on these topics:

Design the system before the pilot

Use this study to inform a learning mechanism or implementation question. It is not direct evidence that autonomous agents improve learning. Keep the proposed agent behavior separate from what the source actually tested.

  • Specify a measurable learning objective, a comparator and a task without tool assistance before adopting a system.

A 90-day district pilot

  1. Days 1–15 · Appraise. Have the curriculum lead and a teacher read the methods and limitations. Record the study design, sample, comparator, outcomes and evidence gaps. Define a local question that the study can help inform.
  2. Days 16–30 · Design. Treat the paper as background evidence until its population is verified in the full text. Do not assign an age-specific intervention or extend adult results to children. Name an accountable owner and set the baseline, success threshold and budget before implementation.
  3. Days 31–60 · Test. Start with a small voluntary cohort, preserve an accessible non-AI route and require teacher review. Use synthetic or de-identified examples; approve data handling and tool terms through district processes.
  4. Days 61–90 · Decide. Extract the participant ages or education stage, recruitment, task and comparator before selecting a local pilot. If these cannot be established, retain the paper for design discussion rather than age-band efficacy claims. Compare with baseline, document uncertainty and stop or revise if learning worsens, access is unequal or critical errors persist.

The board decision brief

Report the learning problem, evidence quality, participating population, total cost including review time, measured benefit, errors, access differences and remaining uncertainties. Make a dated recommendation to stop, revise or expand. A positive perception survey alone does not establish learning improvement.

Attribution and reuse

Helen Hu; Dechantria D Wallace; Beatrice Boateng. Medical Education Learning Specialists in the Age of Artificial Intelligence. Cureus (2025-07-21). https://doi.org/10.7759/cureus.88439

Companion prepared 2026-10-30. Original authorship and DOI are preserved. The abstract is an attributed source extract; the implementation plan is new AI-assisted editorial material, offered under the same CC BY 4.0 license. This guide has not undergone independent peer review.