AI-Driven Civic Education Platform for Inclusive Community Engagement

Thesis Abstract

This study addresses the gap in effective, inclusive civic education in digital societies by developing and evaluating an AI-driven platform designed to enhance citizen engagement, information credibility, and participatory decision-making among diverse urban communities. The aim is to assess whether a personalized, AI-assisted civic education platform can improve civic knowledge, critical media literacy, and inclusive participation across socio-demographic groups. Specific objectives include (i) designing an adaptive content engine that curates civic education modules aligned with user profiles, (ii) evaluating changes in civic knowledge and attitudes using pre- and post-tests, (iii) examining differences in engagement and participation across age, gender, education, and income brackets, (iv) investigating the platform’s impact on perceived inclusivity and trust in local institutions, and (v) identifying ethical and governance considerations for deploying AI in civic education. The study adopts a mixed-methods design grounded in the theory of situational awareness and social constructivist learning, complemented by Elaboration Likelihood Model to account for persuasion pathways in information literacy. The research population comprises adult residents in three metropolitan districts with heterogeneous socio-economic profiles. A stratified random sample of 600 participants will be recruited, ensuring proportional representation of age groups (18–34, 35–54, 55+), gender, educational attainment, and immigrant status. Data collection will integrate quantitative instruments and qualitative insights (a) a standardized civic knowledge scale and a media literacy questionnaire administered at baseline and after a 12-week intervention, (b) platform usage analytics capturing engagement metrics, module completion, and peer collaboration, and (c) semi-structured interviews and focus groups with a purposive subsample of 60 participants to explore experiences of inclusivity, perceived barriers, and trust. Instrument validity and reliability will be established through pilot testing (n=60) with Cronbach’s alpha targets ?0.80 for scales and confirmatory factor analysis to verify construct validity. Data collection will occur in three phases baseline assessment, 12-week intervention, and a three-month follow-up. Quantitative data will be analyzed using descriptive statistics, repeated-measures ANOVA to detect changes over time, and multiple regression to identify predictors of improved civic knowledge and participation. Moderation analyses will test whether socio-demographic factors moderate the effect of platform use on outcomes. Qualitative data will undergo thematic analysis guided by Braun and Clarke’s framework, with coding reliability checked by inter-coder agreement (Cohen’s kappa ?0.70). Triangulation will integrate quantitative and qualitative findings to provide a robust understanding of mechanisms driving inclusive engagement. A secondary analytic track will apply a structural equation model to test a hypothesized pathway AI-driven personalization ? enhanced information credibility ? increases in civic knowledge and participatory intentions, moderated by digital access and prior civic engagement. Key expected findings include significant gains in civic knowledge (effect size d ? 0.45–0.60) and media literacy scores post-intervention, higher self-reported civic engagement among underrepresented groups, and evidence that perceived inclusivity mediates the relationship between platform use and participatory attitudes. Usage analytics are anticipated to show higher engagement for modules that employ conversational AI agents and interactive simulations, suggesting that adaptive, AI-guided experiences outperform static content in fostering sustained participation. Qualitative insights are expected to reveal actionable design implications, such as localized content relevance, multilingual support, transparent AI governance, and ethical considerations regarding algorithmic fairness and data privacy. The study contributes to knowledge by integrating AI-driven personalization with civic education to operationalize inclusive community engagement, advancing empirical understanding of digital interventions in urban governance, and providing a validated mixed-methods framework for evaluating civic technology initiatives. It offers practical implications for municipal policymakers and platform designers regarding scalable deployment, ethical guidelines, and metrics for assessing inclusivity and engagement. The main conclusion anticipates that a thoughtfully designed AI-driven civic education platform can broaden participation and strengthen trust in local institutions among diverse urban residents, provided that issues of accessibility, transparency, and local relevance are centrally addressed. Recommendations include adopting open-source AI components with explainable decision-making, partnering with community organizations to co-create content, implementing ongoing equity audits, and conducting longitudinal studies to assess long-term impacts on civic outcomes.