Actionable companion · Learning design & outcomes

Learning with agency: INSPIRE: a healthcare-specialized instructional design methodology for high-stakes e-learning in the era 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.

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. Verify the participant population and task before transferring the findings.

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

Abstract Background Healthcare e-learning operates under pressures that are difficult to satisfy simultaneously: it must be evidence-based, regulator-acceptable, clinically credible, and delivered on time. Established instructional design models, including ADDIE, the Successive Approximation Model, Dick and Carey, and Kemp, were not developed with these combined demands in mind and predate the routine use of artificial intelligence (AI) in content production. The field lacks a methodology that combines the structured governance required for regulated healthcare training, particularly in postgraduate medical education and continuing professional development (CPD), with the responsiveness needed to keep pace with evolving clinical evidence and AI-enabled production. Methods This article develops and presents INSPIRE, a healthcare-specialized instructional design methodology, as a conceptual contribution. The methodology was developed through a structured synthesis of the strengths and limitations of established instructional design models, integration of recent evidence on artificial intelligence in medical education, and iterative practical refinement in a national health professions education setting. The framework is described in relation to its theoretical foundations, systematically mapped against four established alternatives on six dimensions relevant to healthcare, and illustrated through phase-level integration of AI-supported tasks paired with explicit human checkpoints. Results INSPIRE is a seven-phase agile-phased hybrid framework comprising Intake, Needs Analysis, Structure, Production, Implementation, Review, and End Closure. It treats early technical integration, continuous quality assurance, and human-in-the-loop use of artificial intelligence as core design commitments rather than optional additions. The framework addresses three combined gaps that no single existing model resolves: it integrates and organizes agility and governance into a single workflow suited to compliance-bound healthcare education; it formalizes the role of artificial intelligence across the lifecycle while retaining clinical and pedagogical accountability with human experts; and it makes quality review a continuous rather than terminal function. Conclusions INSPIRE integrates phase-based governance, iterative production, continuous quality assurance, and human-in-the-loop AI use in one workflow built for healthcare e-learning. The contribution is conceptual; empirical validation against time-to-delivery, learner outcomes, and accreditation-quality measures is the next step. National regulators, postgraduate training boards, and CPD providers are the intended adopters. Keywords: Instructional design, Healthcare e-learning, Continuing professional development, Artificial intelligence, Medical education, Quality assurance, INSPIRE methodology

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

Hilalah Alturkistani; Fahad Almalki. INSPIRE: a healthcare-specialized instructional design methodology for high-stakes e-learning in the era of artificial intelligence. BMC Medical Education (2026-08-18). https://doi.org/10.1186/s12909-026-10173-2

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.