Using AI to Design and Develop Online Educational Modules to Enhance Lung Cancer Screening Uptake Among High-Risk Individuals

TL;DR

AI-designed online modules improved lung cancer screening knowledge and participation among high-risk individuals.

Contribution

AI-generated educational modules based on health theory to boost lung cancer screening uptake in high-risk populations.

Findings

Modules showed high content validity and strong user satisfaction.

Participants demonstrated significant improvements in knowledge, stigma, and health beliefs.

Over half of participants completed low-dose computed tomography screening after three months.

Abstract

This study describes the development and pilot testing of artificial intelligence-generated online educational modules designed to improve lung cancer screening knowledge, attitudes, and beliefs among high-risk individuals. Guided by the Health Belief Model, five interactive modules were created to address lung cancer risk, prevention, screening guidelines, and screening procedures. Content validity and usability testing demonstrated high expert agreement and strong user satisfaction. Preliminary findings showed significant improvements in knowledge, lung cancer-related stigma, and health beliefs following module completion, with over half of participants reporting completion of low-dose computed tomography screening at the three-month follow-up. These results suggest that AI-assisted, theory-guided digital education is a feasible and promising approach to support lung cancer screening awareness and participation in high-risk populations.

Background: Despite clear evidence supporting low-dose computed tomography (LDCT) for lung cancer screening, the participation rate among eligible high-risk individuals remains low. Educational interventions that address gaps in knowledge, attitude, and beliefs may improve screening uptake. Objective: This study describes the systematic use of artificial intelligence to design and develop a series of online educational modules aimed at improving knowledge, attitudes, and beliefs toward lung cancer screening among high-risk individuals. Methods: Guided by the Health Belief Model and principles of digital health education, five interactive online modules were developed by artificial intelligence technology to address key topics: (1) lung cancer epidemiology, etiology, signs, and symptoms; (2) lung cancer treatment and care; (3) lung cancer prevention methods; (4) screening guidelines, benefits, and risks; and (5) screening procedures and results interpretation. The design process included literature review, individual cognitive interviews, expert consultation, and pilot testing among target users. Qualitative individual interviews were conducted with 12 high-risk individuals. Content validity was evaluated by an expert panel (n = 7) using a content validity index (CVI), and pilot usability testing was conducted with 25 high-risk individuals. Results: All five modules achieved high content validity (I-CVI range = 0.90–1.00; S-CVI = 0.96). Usability and satisfaction testing showed that participants rated the modules as clear, engaging, and relevant (mean System Usability Scale score = 88/100, mean satisfaction score = 18.32/20). Participants demonstrated significant improvements in knowledge (p < 0.001), lung cancer stigma (p < 0.001), and health beliefs (p < 0.001) after module completion. Of the 22 participants who completed the 3-month follow-up (88%), 13 (59.1%) reported obtaining LDCT screening. Conclusions: The developed online modules demonstrated strong content validity and usability, indicating their feasibility for use in future intervention studies to promote lung cancer screening knowledge, attitude, beliefs, and participation among high-risk individuals.

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