The increasing availability of low-cost environmental sensors and the integration of Artificial Intelligence (AI) into data processing are reshaping citizen-driven environmental monitoring. This study explores public engagement with such technologies, focusing on the willingness of different population groups to participate in monitoring activities and the trust they place in AIsupported sensor data. By combining citizen science approaches with AI-assisted interpretation, the research aims to assess how individuals perceive the reliability, usefulness, and accessibility of environmental information. A quantitative survey was conducted using a 15-item online questionnaire distributed to four groups: university students, general citizens, active participants in citizenscience projects, and IT/data professionals. The survey included multiple-choice, Likert-scale, and short open-ended questions to capture a comprehensive picture of familiarity with environmental monitoring, attitudes toward participation, and perceived role of AI in enhancing data credibility. The collected data were analyzed using descriptive statistics and comparative group analysis. All anonymized data, survey instruments, and analysis files have been made publicly available in the AIMIS-Survey-2025 GitHub repository (https://github.com/oljak-cyber/AIMIS-Survey-2025), ensuring reproducibility and transparency. Results indicate that participants are generally willing to engage in citizen-led monitoring, with IT and active citizen-science participants demonstrating the highest levels of trust and readiness. AI-assisted validation of sensor data was perceived as a significant factor in enhancing confidence and interpretability, particularly among technically proficient respondents. Main barriers identified included cost, lack of knowledge, and time constraints, highlighting the importance of accessible technology and educational guidance for broader adoption. Overall, the study underscores the potential of combining low-cost sensors with AI tools to empower citizens, improve environmental awareness, and generate reliable datasets for informed decision-making. Future initiatives should focus on public education, transparent AI models, and scalable sensor deployments to maximize engagement and ensure data quality.
The increasing availability of low-cost environmental sensors and the integration of Artificial Intelligence (AI) into data processing are reshaping citizen-driven environmental monitoring. This study explores public engagement with such technologies, focusing on the willingness of different population groups to participate in monitoring activities and the trust they place in AIsupported sensor data. By combining citizen science approaches with AI-assisted interpretation, the research aims to assess how individuals perceive the reliability, usefulness, and accessibility of environmental information. A quantitative survey was conducted using a 15-item online questionnaire distributed to four groups: university students, general citizens, active participants in citizenscience projects, and IT/data professionals. The survey included multiple-choice, Likert-scale, and short open-ended questions to capture a comprehensive picture of familiarity with environmental monitoring, attitudes toward participation, and perceived role of AI in enhancing data credibility. The collected data were analyzed using descriptive statistics and comparative group analysis. All anonymized data, survey instruments, and analysis files have been made publicly available in the AIMIS-Survey-2025 GitHub repository (https://github.com/oljak-cyber/AIMIS-Survey-2025), ensuring reproducibility and transparency. Results indicate that participants are generally willing to engage in citizen-led monitoring, with IT and active citizen-science participants demonstrating the highest levels of trust and readiness. AI-assisted validation of sensor data was perceived as a significant factor in enhancing confidence and interpretability, particularly among technically proficient respondents. Main barriers identified included cost, lack of knowledge, and time constraints, highlighting the importance of accessible technology and educational guidance for broader adoption. Overall, the study underscores the potential of combining low-cost sensors with AI tools to empower citizens, improve environmental awareness, and generate reliable datasets for informed decision-making. Future initiatives should focus on public education, transparent AI models, and scalable sensor deployments to maximize engagement and ensure data quality.
The increasing availability of low-cost environmental sensors and the integration of Artificial Intelligence (AI) into data processing are reshaping citizen-driven environmental monitoring. This study explores public engagement with such technologies, focusing on the willingness of different population groups to participate in monitoring activities and the trust they place in AIsupported sensor data. By combining citizen science approaches with AI-assisted interpretation, the research aims to assess how individuals perceive the reliability, usefulness, and accessibility of environmental information. A quantitative survey was conducted using a 15-item online questionnaire distributed to four groups: university students, general citizens, active participants in citizenscience projects, and IT/data professionals. The survey included multiple-choice, Likert-scale, and short open-ended questions to capture a comprehensive picture of familiarity with environmental monitoring, attitudes toward participation, and perceived role of AI in enhancing data credibility. The collected data were analyzed using descriptive statistics and comparative group analysis. All anonymized data, survey instruments, and analysis files have been made publicly available in the AIMIS-Survey-2025 GitHub repository (https://github.com/oljak-cyber/AIMIS-Survey-2025), ensuring reproducibility and transparency. Results indicate that participants are generally willing to engage in citizen-led monitoring, with IT and active citizen-science participants demonstrating the highest levels of trust and readiness. AI-assisted validation of sensor data was perceived as a significant factor in enhancing confidence and interpretability, particularly among technically proficient respondents. Main barriers identified included cost, lack of knowledge, and time constraints, highlighting the importance of accessible technology and educational guidance for broader adoption. Overall, the study underscores the potential of combining low-cost sensors with AI tools to empower citizens, improve environmental awareness, and generate reliable datasets for informed decision-making. Future initiatives should focus on public education, transparent AI models, and scalable sensor deployments to maximize engagement and ensure data quality.
