Gamified Digital Public Education as a Policy Direction for Shaping the Civic Culture of Youth: A Case Study of Interaction between the Parliament, the University, and an Expert-Methodological Organization

The subject of this study is the inter-institutional mechanism of gamified digital public education for young people, emerging from the interaction of legislative authorities, universities, and expert-methodological organizations, and the educational outcomes it produces. The empirical material is the science-education project "How to Understand the Machine Mind" (Saint Petersburg, 2025), implemented with grant support from Rosmolodezh. The project combined twelve video podcasts co-hosted by nine deputies of the Legislative Assembly of Saint Petersburg and fourteen experts, a gamified educational platform, and offline formats; the total number of participants reached 3,456. Particular attention is paid to the transfer of the course into the formal curriculum: the discipline "Artificial Intelligence Systems" covered 70 students of technical and humanities programs at the Baltic State Technical University "Voenmeh". The methodology includes an embedded case study, primary analysis of platform diagnostics data (analytical sample of 3,323 participants), comparative analysis of twelve thematic blocks, component-based analysis of AI literacy, and participant observation. The novelty lies in the development of a politico-pedagogical model of digital public education. A four-link institutional configuration "parliament — university — expert organization — grant operator" is empirically verified. AI literacy is conceptualized both as a cognitive component of civic culture and as an integrative educational outcome comprising cognitive, motivational-value, emotional-volitional, and operational-activity components. The format of co-presentation of knowledge by a deputy and a scholar is identified; a reward architecture separating material incentives from the learning cycle is substantiated. The average share of correct answers rose from 32.7% to 94.9%; the share of participants with a high level of AI understanding grew from 43.2% to 96.3%, with the best results in data privacy and critical analysis. Pedagogical conditions for effective digital public education and recommendations for scaling the model across Russian regions are formulated.