Extended CAITIZEN: A PLS-SEM Study of Sustainable AI-Assisted Citizenship Innovation
DOI:
https://doi.org/10.55965/setp.6.11.a4Keywords:
ai-assisted sustainable citizenship, innovation for sustainable development, critical artificial intelligence literacy, pls-sem, higher educationAbstract
Context. Artificial intelligence is transforming higher education by reshaping learning, creativity, decision-making, and civic participation. This study examines CAITIZEN—Citizenship Assisted by Artificial Intelligence for Sustainable, Ethical, and Networked Formation—as an extended model for validating AI-assisted sustainable citizenship as an innovation for sustainable development, aligned with SDG4 and SDG9.
Problem. Although the original CAITIZEN model was qualitatively grounded as an ethical–cognitive–social framework, its explanatory and predictive capacity had not been empirically tested. AI education still prioritizes efficiency, automation, and technical adoption, with limited evidence on how critical AI literacy, ethics, data justice, human–AI collaboration, and metacognitive prompting contribute to sustainable AI-assisted citizenship.
Purpose. This study validates the extended CAITIZEN model through PLS-SEM by examining how Critical Artificial Intelligence Literacy (CAIL) enables Ethical Awareness and Responsibility (EAR), Awareness of Fairness and Data Justice (AFDJ), Human–AI Creative Collaboration (HAIC), and Metacognitive Transparency in Prompting Practices (MTPP), and how these capacities predict CAITIZEN.
Methodology. This research builds on a previous qualitative phase conducted in Guadalajara, Jalisco, Mexico, during July–December 2025, and complements it with an explanatory-predictive quantitative design using SmartPLS 4.1.1.8 to assess reflective constructs and predictive relevance through PLSpredict.
Theoretical and Practical Findings. Results show that CAIL significantly predicts EAR, AFDJ, HAIC, and MTPP, confirming its role as foundational antecedent. AFDJ, HAIC, and MTPP significantly predict CAITIZEN, whereas EAR does not show a direct effect. Predictive relevance is confirmed because all Q²_predict values for CAITIZEN indicators are positive and all PLS-LM RMSE differences favor PLS-SEM.
Originality. The study transforms the qualitative CAITIZEN model into an empirically validated explanatory-predictive structure.
Conclusions and Limitations. The extended CAITIZEN model provides a measurable framework for responsible AI education and sustainable innovation. Limitations include non-probabilistic sampling, cross-sectional design, and student sample.
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References
Cohen, J. (1992). A power primer. Psychological Bulletin, 112(1), 155–159. https://www2.psych.ubc.ca/~schaller/528Readings/Cohen1992.pdf DOI: https://doi.org/10.1037/0033-2909.112.1.155
Córdova-Esparza, D.-M. (2025). AI-powered educational agents: Opportunities, innovations, and ethical challenges. Information, 16(6), 469. https://doi.org/10.3390/info16060469 DOI: https://doi.org/10.3390/info16060469
Decker, M., Wegner, L., & Leicht-Scholten, C. (2025). Procedural fairness in algorithmic decision-making: The role of public engagement. Ethics and Information Technology, 27, Article 1. https://doi.org/10.1007/s10676-024-09811-4 DOI: https://doi.org/10.1007/s10676-024-09811-4
Demirchyan, G. (2025). Algorithmic fairness: Challenges to building an effective regulatory regime. Frontiers in Artificial Intelligence, 8, Article 1637134. https://doi.org/10.3389/frai.2025.1637134 DOI: https://doi.org/10.3389/frai.2025.1637134
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104 DOI: https://doi.org/10.1177/002224378101800104
Franke, G., & Sarstedt, M. (2019). Heuristics versus statistics in discriminant validity testing: A comparison of four procedures. Internet Research, 29(3), 430–447. https://doi.org/10.1108/IntR-12-2017-0515 DOI: https://doi.org/10.1108/IntR-12-2017-0515
Georgieva, I., & Georgiev, G. V. (2025). Exploring the use of generative text AI in design creativity inquiries. Computers in Human Behavior: Artificial Humans, 6, Article 100219. https://doi.org/10.1016/j.chbah.2025.100219 DOI: https://doi.org/10.1016/j.chbah.2025.100219
González-Argote, J., Maldonado, E., & Maldonado, K. (2025). Algorithmic bias and data justice: Ethical challenges in artificial intelligence systems. EthAIca, 4, Article 159. https://ai.ageditor.ar/index.php/ai/article/view/159 DOI: https://doi.org/10.56294/ai2025159
Gunasekara, L., El-Haber, N., Nagpal, S., Moraliyage, H., Issadeen, Z., Manic, M., & De Silva, D. (2025). A systematic review of responsible artificial intelligence principles and practice. Applied System Innovation, 8(4), 97. https://doi.org/10.3390/asi8040097 DOI: https://doi.org/10.3390/asi8040097
Haidar, H., Suryoputro, G., & Safi’i, I. (2025). Impact of the integration of metacognitive prompts by generative artificial intelligence (GenAI) in collaborative and individual learning in improving writing skills and metacognitive awareness. International Journal of Learning, Teaching and Educational Research, 24(6), 232–250. https://doi.org/10.26803/ijlter.24.6.11 DOI: https://doi.org/10.26803/ijlter.24.6.11
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). Sage. https://us.sagepub.com/en-us/nam/a-primer-on-partial-least-squares-structural-equation-modeling-pls-sem/book270548 DOI: https://doi.org/10.1007/978-3-030-80519-7
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203 DOI: https://doi.org/10.1108/EBR-11-2018-0203
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43, 115–135. https://doi.org/10.1007/s11747-014-0403-8 DOI: https://doi.org/10.1007/s11747-014-0403-8
INEGI. (2023). Encuesta Nacional sobre Disponibilidad y Uso de Tecnologías de la Información en los Hogares (ENDUTIH) 2023. Instituto Nacional de Estadística y Geografía. https://www.inegi.org.mx/programas/endutih/2023/
Kong, S. C., & Zhu, J. (2025). Developing and validating an artificial intelligence ethical awareness scale for secondary and university students: Cultivating ethical awareness through problem-solving with artificial intelligence tools. Computers and Education: Artificial Intelligence, 9, Article 100447. https://doi.org/10.1016/j.caeai.2025.100447 DOI: https://doi.org/10.1016/j.caeai.2025.100447
Long, D., & Magerko, B. (2020). What is AI literacy? Competencies and design considerations. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1–16). Association for Computing Machinery. https://doi.org/10.1145/3313831.3376727 DOI: https://doi.org/10.1145/3313831.3376727
Mejía-Trejo, J. (2025a). Innovating sustainable artificial intelligence citizenship: A qualitative study of the CAITIZEN model using ATLAS.ti. Scientia et PRAXIS, 5(10), 126–154. https://doi.org/10.55965/setp.5.10.a5 DOI: https://doi.org/10.55965/setp.5.10.a5
Mejía-Trejo, J. (2025b). Inteligencia artificial y su repercusión en la educación superior. AMIDI Editorial. https://doi.org/10.55965/abib.9786076984543 DOI: https://doi.org/10.55965/abib.9786076984543
Miao, F., & Cukurova, M. (2024). AI competency framework for teachers. UNESCO. https://doi.org/10.54675/ZJTE2084 DOI: https://doi.org/10.54675/ZJTE2084
Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2021). Conceptualizing AI literacy: An exploratory review. Computers and Education: Artificial Intelligence, 2, Article 100041. https://doi.org/10.1016/j.caeai.2021.100041 DOI: https://doi.org/10.1016/j.caeai.2021.100041
OECD. (2025). Bridging the AI skills gap: Is training keeping up? OECD Publishing. https://www.oecd.org/content/dam/oecd/en/publications/reports/2025/04/bridging-the-aiskillsgap_b43c7c4a/66d0702e-en.pdf
OECD & European Commission. (2025). AI literacy framework for primary and secondary education. https://learnworkecosystemlibrary.com/initiatives/ai-literacy-framework-for-primary-secondary-education-oecd-ec/
OECD & Eurostat. (2005). Manual de Oslo: Guía para la recogida e interpretación de datos sobre innovación (3.ª ed.). OECD Publishing. https://doi.org/10.1787/9789264065659-es DOI: https://doi.org/10.1787/9789264065659-es
OECD & Eurostat. (2018). Oslo manual 2018: Guidelines for collecting, reporting and using data on innovation (4th ed.). OECD Publishing. https://doi.org/10.1787/9789264304604-en DOI: https://doi.org/10.1787/9789264304604-en
Papagiannidis, E., Mikalef, P., & Conboy, K. (2025). Responsible artificial intelligence governance: A review and research framework. The Journal of Strategic Information Systems, 34(2), Article 101885. https://doi.org/10.1016/j.jsis.2024.101885 DOI: https://doi.org/10.1016/j.jsis.2024.101885
Pham, N., Pham Ngoc, H., & Nguyen-Duc, A. (2025). Fairness for machine learning software in education: A systematic mapping study. Journal of Systems and Software, 219, Article 112244. https://doi.org/10.1016/j.jss.2024.112244 DOI: https://doi.org/10.1016/j.jss.2024.112244
Rafner, J., Zana, B., Bang Hansen, I., Ceh, S., Sherson, J., Benedek, M., & Lebuda, I. (2025). Agency in human-AI collaboration for image generation and creative writing: Preliminary insights from think-aloud protocols. Creativity Research Journal, 1–24. https://doi.org/10.1080/10400419.2025.2587803 DOI: https://doi.org/10.1080/10400419.2025.2587803
Salma, Z., Hijón-Neira, R., & Pizarro, C. (2025). Designing co-creative systems: Five paradoxes in human–AI collaboration. Information, 16(10), 909. https://doi.org/10.3390/info16100909 DOI: https://doi.org/10.3390/info16100909
Sarstedt, M., Ringle, C. M., & Hair, J. F. (2021). Partial least squares structural equation modeling. In C. Homburg, M. Klarmann, & A. Vomberg (Eds.), Handbook of market research (pp. 587–632). Springer. https://doi.org/10.1007/978-3-319-57413-4_15 DOI: https://doi.org/10.1007/978-3-319-57413-4_15
Southworth, J., Migliaccio, K., Glover, J., Glover, J. N., Reed, D., McCarty, C., Brendemuhl, J., & Thomas, A. (2023). Developing a model for AI across the curriculum: Transforming the higher education landscape via innovation in AI literacy. Computers and Education: Artificial Intelligence, 4, Article 100127. https://doi.org/10.1016/j.caeai.2023.100127 DOI: https://doi.org/10.1016/j.caeai.2023.100127
Tsakeni, M., Nwafor, S. C., Mosia, M., & Egara, F. O. (2025). Mapping the scaffolding of metacognition and learning by AI tools in STEM classrooms: A bibliometric–systematic review approach (2005–2025). Journal of Intelligence, 13(11), 148. https://doi.org/10.3390/jintelligence13110148 DOI: https://doi.org/10.3390/jintelligence13110148
United Nations. (2015). The 17 Sustainable Development Goals. https://sdgs.un.org/goals
UNESCO & Cámara Nacional de la Industria Electrónica, de Telecomunicaciones y Tecnologías de la Información. (2025, November 4). UNESCO and CANIETI, with the Microsoft support, implement a model for ethical and responsible artificial intelligence in Mexican companies. UNESCO. https://www.unesco.org/en/articles/unesco-and-canieti-microsoft-support-implement-model-ethical-and-responsible-artificial-intelligence
Waaler, P. N., Hussain, M., Molchanov, I., Bongo, L. A., & Elvevåg, B. (2025). Prompt engineering an informational chatbot for education on mental health using a multiagent approach for enhanced compliance with prompt instructions: Algorithm development and validation. JMIR AI, 4, Article e69820. https://doi.org/10.2196/69820 DOI: https://doi.org/10.2196/69820
Wang, C., & Wang, Z. (2025). Investigating L2 writers’ critical AI literacy in AI-assisted writing: An APSE model. Journal of Second Language Writing, 67, Article 101187. https://doi.org/10.1016/j.jslw.2025.101187 DOI: https://doi.org/10.1016/j.jslw.2025.101187
Wang, N., Kim, H., Peng, J., & Wang, J. (2025). Exploring creativity in human–AI co-creation: A comparative study across design experience. Frontiers in Computer Science, 7, Article 1672735. https://www.frontiersin.org/journals/computer-science/articles/10.3389/fcomp.2025.1672735/full DOI: https://doi.org/10.3389/fcomp.2025.1672735
World Economic Forum. (2025). Future of Jobs Report 2025. https://www.weforum.org/publications/the-future-of-jobs-report-2025/
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