INSPIRE: a healthcare-specialized instructional design methodology for high-stakes e-learning in the era of artificial intelligence

Résumé fourni par la source

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.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.