Purpose: This study aimed to design and validate a conceptual model for the integration of artificial intelligence into science communication. The research sought to identify key components, assess their perceived importance from experts’ perspectives, and propose a strategic framework to inform policy-making and decision-making in this field.
Methodology: An exploratory sequential mixed-methods design was employed. In the qualitative phase, the main components were extracted through expert-based data analysis. In the quantitative phase, a researcher-developed questionnaire comprising 44 items across 11 components was designed using a five-point Likert scale and completed by 26 scientific experts. The data were analyzed using descriptive statistics (mean and standard deviation) and confirmatory factor analysis (CFA).
Findings: The results indicated that all identified components were rated as highly important. The component “countering scientific misinformation” achieved the highest mean score. This was followed by “content simplification and comprehensibility,” “enhancement of scientific literacy,” and “content personalization.” Confirmatory factor analysis supported the satisfactory fit of the multidimensional structure of the proposed model.
Conclusion: The proposed three-tier model categorizes AI integration strategies in science communication into “fundamental and critical,” “important and enabling,” and “innovative and evolutionary” levels. The model can serve as a practical roadmap for policymakers, science communicators, and institutional leaders in designing and implementing AI-driven science communication programs.