Actionable companion · Assessment & reasoning

Learning with agency: Assessing digital health curriculum needs: a mixed-methods study of student and faculty perspectives in a Singapore medical school

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, Adult & professional learning. Health-professions learning must be distinguished from K–12 instruction.

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

Abstract Background The rapid advancement of digital technologies has transformed healthcare delivery, creating an imperative for medical education to integrate digital health (DH) competencies into undergraduate training. Despite growing recognition of its importance, progress in embedding DH literacy within formal medical curricula has been uneven and slow. Objective This study examined DH education within an undergraduate medical programme by integrating faculty and student perspectives to identify curriculum development needs and inform evidence-based educational strategies. Design A mixed-methods study was conducted at a Singapore medical school offering a 5-year undergraduate curriculum. Semi-structured interviews were conducted with four faculty members as DH content experts, exploring curriculum design, pedagogical strategies, and competency expectations. A 24-item survey assessing knowledge, attitudes, and usage of DH tools was administered to students across pre-clinical and clinical training, with 131 responses from 848 eligible participants. Qualitative data were analysed using grounded theory-based thematic analysis, whilst survey data were analysed using descriptive statistics. Results Faculty interviews identified three key themes: curriculum gaps requiring more practical and clinically-oriented DH teaching, structural barriers including time constraints and rapid technological change, and complex stakeholder ecosystem influencing curriculum development. Student survey results revealed a persistent theory-practice gap across DH domains. Students reported high self-perceived knowledge in DH applications and artificial intelligence (AI) but consistently lower confidence in practical application, particularly in electronic health records (EHR) and telemedicine. Clinical students paradoxically rated their EHR knowledge lowest despite greater clinical exposure. Students expressed strong interest in programming, data analytics, and hands-on AI, favouring applied DH skills with direct clinical relevance. Conclusions Medical curricula struggle to translate DH conceptual knowledge into practice, reconcile divergent competency expectations between students and faculty, and remain current amid rapid technological change. Tiered competency models, coupled with experiential learning approaches and continuous faculty development, represent a promising strategy for preparing future physicians for digitally enhanced healthcare delivery. Clinical trial number Not applicable. Keywords: Artificial intelligence in healthcare, digital health, electronic health records, mixed‑methods research, undergraduate medical education

What kind of evidence is this?

Supporting AI learning science · Qualitative / mixed methods

Stage: Higher education; Adult & professional learning. 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 was conducted in a Singapore undergraduate medical school offering a newly reformed 5-year MBBS (Bachelor of Medicine and Bachelor of Surgery) curriculum. The first two years comprise predominantly campus-based learning focused on foundational biomedical sciences, clinical skills, and professional development. The subsequent three years consist of clinical placements across affiliated healthcare institutions, where students undertake workplace-based learning and clinical rotations in authentic healthcare settings. As Singapore continues to advance its national digital transformation agenda, DH has become an increasingly important component of healthcare delivery, with technology and AI tools playing growing roles in clinical decision-making and patient care. This study sought to examine current educational practices and identify opportunities to strengthen DH education within the undergraduate medical curriculum.

Limitations and uncertainty · source passage

This study is strengthened by its convergent mixed‑methods design, which triangulates student and faculty perspectives to provide a comprehensive view of DH education. However, it is limited by its single‑institution context and reliance on self‑reported data, which may not fully capture actual competence. Students with pre-existing interest in DH may have been more responsive to survey and overrepresented. Consequently, response bias cannot be excluded. Findings should therefore be interpreted as reflecting engaged student perspectives and indicative trends, rather than as estimates of the entire population. Future research should explore longitudinal outcomes and multi‑institutional comparisons to further validate these findings.

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.

Use common rubrics, blind scoring where feasible and a fresh unaided reasoning task. Document permissible assistance and avoid treating AI-detector scores as proof of misconduct.

A 90-day district pilot

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.
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.
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.
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

Saumya Bajaj; Eng Chun David Goh; Yih Yng Ng; Zheng-Wei Lee. Assessing digital health curriculum needs: a mixed-methods study of student and faculty perspectives in a Singapore medical school. Medical Education Online (2026-09-01). https://doi.org/10.1080/10872981.2026.2726067

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.