Abstract for: Evidence and Innovation in SD-Based Interactive Learning Environments: A Scoping Review with SD Modeling

Public health practitioners face complex challenges requiring systems thinking, yet systematic evidence on system dynamics-based interactive learning environments (ILEs) remains fragmented. This work synthesizes evidence on ILE effectiveness through scoping review and develops an integrated conceptual model combining system dynamics with social marketing theory—directly informing development of a lead-poisoning prevention ILE for an urban US region. We conducted a scoping review following the Joanna Briggs Institute framework, searching six databases (PubMed, Web of Science, Scopus, ERIC, PsycINFO, ProQuest Dissertations & Theses). Approximately 1,150 citations were screened examining SD-based ILEs for adult public health learners. Data extraction addresses ILE design features, theoretical frameworks, learning outcomes, and implementation factors. Findings inform a tabular logic model extending Sterman's (2000) learning process with social marketing principles to guide ILE development. Preliminary screening reveals substantial heterogeneity in ILE types and evaluation approaches across ~877 unique citations. Initial patterns suggest emphasis on immediate learning outcomes over sustained use and behavior change, with limited integration of behavior change theory in tool design. The integrated logic model depicts causal mechanisms linking ILE design features, adoption dynamics, systems thinking development, decision quality, and health outcomes—advancing theory for effective decision support development. This work advances SD methodology through a scoping model approach—using SD to synthesize scoping review findings. The integrated SD-social marketing framework addresses why many ILEs fail to achieve sustained real-world use despite sound technical design. Findings directly inform development of an urban lead poisoning prevention ILE (NIH R01 planned), demonstrating a replicable model for creating effective public health decision support tools that ensure both scientific rigor and practitioner adoption. AI being used in combination with review software (Covidence) and analysis software (R) for data extraction from primary studies.