Agentic education · Learning science

Actionable companion · Learning design & outcomes

Learning with agency: Will artificial intelligence change medical life? Bridging the gap between innovation and medical student adoption: a cross-sectional study among university medical students

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): Higher education. Health-professions learning must be distinguished from K–12 instruction.

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

Background Artificial intelligence and robotics are transforming healthcare by improving diagnosis, treatment, and personalized medicine. Understanding medical students’ and faculty perceptions is essential for effective integration into medical education. This study examines the benefits, limitations, and impact of AI on clinical practice, education, and healthcare delivery. This research investigates the role of artificial intelligence (AI) in medicine, highlighting its advantages and limitations, with emphasis on its applications in various clinical fields and its influence on medical education and healthcare delivery. Methods Across-sectional study was conducted involving 441 medical students at Tanta University. Data was gathered by using a structured pretested self-administered questionnaire, developed from the literature, and validated by experts. It covered sociodemographic characteristics, perceptions toward AI in medical education, and knowledge and attitudes regarding AI use in healthcare. Results The survey revealed that 95.9% of participants have heard about AI, primarily through media (77.8%). with 76.0% showing a favorable attitude. Most students (58.3%) reported learning about AI through online communities and forums. However, only 37.9% learned AI techniques through courses or training. Less than one quarter (23.8%) consider Chatbot for student support the most useful. The most frequently reported benefit was its ability to speed up the management process (46.3%), followed by its capacity to capture and analyze more information than humans (42.9%) and its potential to reduce medical errors (42.9%). Students raised concerns about reduced independent thinking (52.6%) and limited interpersonal skills (47.4%). The main barriers identified were lack of proper training (51.9%), limited financial resources (49%), and lack of awareness (47.6%). Conclusion Medical students showed high awareness and positive attitudes toward AI but had limited formal training. They recognized its benefits in learning and clinical practice while expressing ethical concerns regarding ethics, reduced human interaction, and accountability. These findings highlight the need to incorporate structured AI education into medical curricula, emphasizing both technical competencies and ethical frameworks, to prepare future physicians for safe and effective AI adoption in healthcare.

What kind of evidence is this?

Conversational & pedagogical agents · Comparative / pre-post study reported

Stage: Higher education. 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

A cross-sectional study was conducted among undergraduate medical students and medical interns at the Faculty of Medicine, Tanta University, Egypt. The study included students from the first through fifth academic years as well as medical interns. Data were collected over a five-month period from November 2024 to March 2025.

Limitations and uncertainty · source passage

This study has several limitations. The findings are based on self-reported data, which may be subject to response bias and social desirability bias. In addition, the use of a non-probability convenience sampling technique and recruitment from a single medical school may limit the generalizability of the findings to medical students in other universities and geographical regions. The sample was also restricted to students from one institution, which may not fully represent the diversity of medical students across Egypt. Furthermore, the cross-sectional design precludes the establishment of causal relationships between knowledge, attitudes, and perceptions toward artificial intelligence (AI). Future multicenter studies using probability-based sampling methods, larger and more diverse samples, and longitudinal or experimental designs are recommended to validate these findings and provide deeper insights into the integration of AI in medical education.

Design the system before the pilot

Define whether the agent asks questions, gives hints, explains or assesses. Test hallucinated feedback, excessive praise, misleading confidence and responses when the learner is confused. A conversational interface alone does not establish autonomous agency.

  • 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. Choose one disciplinary task. Require a first attempt, a documented AI consultation, a critique of the output and an independent explanation. Align the comparison group and scoring rubric with the same objective. 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. Blind-score reasoning and accuracy on a new task without AI. Distinguish model examination scores, student perceptions and actual learner gains; only the last directly tests educational benefit. 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

Mirfat Mohamed Labib Elkashif; Hebatalla Abdelmaksoud Abdelmonsef Ahmed; Mohamed Sayed Abdellatif; Darelglal Ahmed Gassmelseed; Shimaa Mohamed Mohamed Koabar. Will artificial intelligence change medical life? Bridging the gap between innovation and medical student adoption: a cross-sectional study among university medical students. Frontiers in Medicine (2026-07-03). https://doi.org/10.3389/fmed.2026.1871352

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.