Citizen participation for social innovation and co-creating urban regeneration proposals can be greatly facilitated by innovative IT systems. Such systems can use Open Government Data, visualise urban proposals in 3D models and provide automated feedback on the feasibility of the proposals. Using such a system as a communication platform between citizens and city administrations provides an integrated top-down and bottom-up urban planning and decision-making approach to smart cities. However, generating automated feedback on citizens’ proposals requires modelling domain-specific knowledge i.e., vocabulary and rules, which can be applied on spatial and temporal 3D models. This paper presents the European Commission funded H2020 smarticipate project that aims to achieve the above challenge by applying it on three smart cities: Hamburg, Rome and RBKC-London. Whilst the proposed system architecture indicates various innovative features, a proof of concept of the automated feedback feature for the Hamburg use case ‘planting trees’ is demonstrated. Early results and lessons learned show that it is feasible to provide automated feedback on citizen-initiated proposals on specific topics. However, it is not straightforward to generalise this feature to cover more complex concepts and conditions which require specifying comprehensive domain languages, rules and appropriate tools to process them. This paper also highlights the strengths of the smarticipate platform, discusses challenges to realise its different features and suggests potential solutions.

Citizen science now relies heavily on digital platforms to engage the public in environmental data collection. Yet, many projects face declining participation over time. This study examines the effect of three elements of gamification—points, daily streaks, and real-time leaderboards—on student engagement, achievement, and immersion during a five-day campus-wide intervention utilising the GAME and a spatial crowdsourcing app. Employing a convergent mixed-methods design, we combined behavioural log analysis, validated psychometric scales (GAMEFULQUEST), and post-experiment interviews to triangulate both quantitative and qualitative dimensions of engagement. Results reveal that gamified elements enhanced students’ sense of accomplishment and early-stage motivation, which is reflected in significantly higher average scores for goal-directed engagement and recurring qualitative themes related to competence and recognition. However, deeper immersion and sustained “flow” were less robust with repetitive task design. While the intervention achieved only moderate long-term participation rates, it demonstrates that thoughtfully implemented game mechanics can meaningfully enhance engagement without undermining data quality. These findings provide actionable guidance for designing more adaptive, motivating, and inclusive citizen science solutions, underscoring the importance of mixed-methods evaluation in understanding complex engagement processes. While the sample size limits the statistical generalizability, this study serves as an exploratory field trial offering valuable design insights and methodological guidance for future large-scale, controlled citizen science interventions.