Currently submitted to: Journal of Medical Internet Research
Date Submitted: Sep 3, 2026
Open Peer Review Period: Sep 4, 2026 - Oct 30, 2026
(currently open for review)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Design of the interdisciplinary course “AI-Empowered Proactive Health” based on the "Teacher–Student–AI" tripartite educational ecosystem: An exploratory pedagogical study
ABSTRACT
Background:
As healthcare shifts toward disease prevention and whole-life-cycle health management, artificial intelligence (AI) offers key technological enablers for proactive health management. However, medical education lacks systematic curriculum models that integrate real-world proactive health challenges with AI applications. This study aims to develop and iteratively refine an interdisciplinary curriculum framework, titled "AI-Empowered Proactive Health," based on a "Teacher–Student–AI" tripartite educational ecosystem.
Objective:
To address this gap, this study aims to develop and iteratively refine an interdisciplinary curriculum framework, titled “AI-Empowered Proactive Health,” based on a “Teacher–Student–AI” tripartite educational ecosystem.
Methods:
A sequential, three-phase iterative curriculum development method was conducted from March to December 2025. In Phase 1, an initial curriculum framework was developed via expert panel meetings using the Modified Nominal Group Technique (NGT). In Phase 2, semi-structured interviews were conducted with senior nursing undergraduates for formative evaluation. In Phase 3, two rounds of expert meetings were held to review feedback item-by-item, leading to the refinement of the 25-credit-hour curriculum content, real-world project workflows, multidimensional assessment schemes, and the online digital learning platform architecture.
Results:
The finalized curriculum is grounded in a "1-2-3-4-5" framework: guided by active health competencies (1 Leading Force), driven by healthcare transformation and AI advancement (2 Driving Forces), executed through instructor–student–AI collaboration (3 Roles), spanning four project phases (Problem Definition, Evidence Integration, Knowledge Graph & Agent Development, and Outcome Presentation), and integrating across five interdisciplinary domains. Student feedback resulted in reducing projects from 11 to 6 and extending contact hours from 18 to 25. The digital platform incorporates ten integrated modules (e.g., Evidence-Based Practice Workbench, Knowledge Graph Laboratory, Intelligent Agent Laboratory) to support seamless workflow execution. Assessment spans process tracking (40%), project outcomes (45%), and critical AI literacy (15%).
Conclusions:
Grounded in a tripartite educational ecosystem, the interdisciplinary "AI-Empowered Proactive Health" course provides a structured, practice-oriented model that bridges medical knowledge, evidence-based methodologies, and AI technology. This curriculum framework offers a valuable reference for cultivating proactive health professionals and reforming medical education in the digital era. Clinical Trial: the Ethics Committee of Beijing University of Chinese Medicine (Ethics Approval No.: 2025BZYLL0103)
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