Embedding AI Literacy in Education: A Multidimensional Approach to Fostering Cognitive Liberation
DOI:
https://doi.org/10.58445/rars.3485Keywords:
Generative Artificial Intelligence (GenAI), Cognitive liberation, AI LiteracyAbstract
Generative Artificial Intelligence (GenAI) has changed the way schools work very, but it has also raised concerns about how students grow independently and critically. If students rely too much on AI tools, they may lose their autonomy to regulate, be creative, and be conscious with their own way of thinking. This paper contends that proficient AI literary education serves a mechanism of cognitive liberation, providing learners with intellectual and ethical independence – such as critical thinking, self-efficacy, autonomy, and ethical awareness – to responsibly explore AI-influenced worlds as ideal citizens. Using Paulo Freire’s theory of critical pedagogy, the study combines recent empirical research such as AI literacy frameworks suggested through four interconnected aspects: cognitive, metacognitive, affective, and social. Altogether, these areas help students reflect their behaviors toward using AI, control their emotions, and make more ideal moral decisions. Also, this paper argues that AI literacy can be added to the current school curricula through interdisciplinary learning, project-based models, and test assessment tools such as the AI Literacy Questionnaire (AILQ). The paper demonstrates that well-structured AI literacy education can not only make students more competent in the use of AI technologies, but also more aware. This changes students from passive users of technology into active thinkers who can make moral choices in a technology-driven world.
References
References
Freeman, J. (2025). Student generative ai survey 2025. Higher Education Policy Institute: London, UK.
Freire, P. (1968). Pedagogy of the oppressed. Marxists Internet Archive. https://www.marxists.org/subject/education/freire/pedagogy/
Freire, P. (1970). Pedagogy of the Oppressed. New York: Seabury Press.
Gu, X., & Ericson, B. J. (2025, August). AI literacy in K-12 and higher education in the wake of generative AI: An integrative review. In Proceedings of the 2025 ACM Conference on International Computing Education Research V. 1 (pp. 125-140).
Hou, I., Man, O., Hamilton, K., Muthusekaran, S., Johnykutty, J., Zadeh, L., & MacNeil, S. (2025, June). 'All Roads Lead to ChatGPT': How Generative AI is Eroding Social Interactions and Student Learning Communities. In Proceedings of the 30th ACM Conference on Innovation and Technology in Computer Science Education V. 1 (pp. 79-85).
Ju, Q. (2023). Experimental evidence on negative impact of generative AI on scientific learning outcomes. arXiv preprint arXiv:2311.05629.
Klarin, J., Hoff, E., Larsson, A., & Daukantaitė, D. (2024). Adolescents’ use and perceived usefulness of generative AI for schoolwork: exploring their relationships with executive functioning and academic achievement. Frontiers in Artificial Intelligence, 7, 1415782.
Kong, S. C., Cheung, M. Y. W., & Tsang, O. (2024). Developing an artificial intelligence literacy framework: Evaluation of a literacy course for senior secondary students using a project-based learning approach. Computers and Education: Artificial Intelligence, 6, 100214.
Kong, S. C., & Yang, Y. (2025). Developing and validating an artificial intelligent empowerment instrument: evaluating the impact of an artificial intelligent literacy programme for secondary school and university students. Research & Practice in Technology Enhanced Learning, 20.
Laupichler, M. C., Aster, A., Schirch, J., & Raupach, T. (2022). Artificial intelligence literacy in higher and adult education: A scoping literature review. Computers and Education: Artificial Intelligence, 3, 100101.
Ng, D. T. K., Wu, W., Leung, J. K. L., Chiu, T. K. F., & Chu, S. K. W. (2024). Design and validation of the AI literacy questionnaire: The affective, behavioural, cognitive and ethical approach. British Journal of Educational Technology, 55(3), 1082-1104.
Ng, D. T. K., Xinyu, C., Leung, J. K. L., & Chu, S. K. W. (2024). Fostering students' AI literacy development through educational games: AI knowledge, affective and cognitive engagement. Journal of computer assisted learning, 40(5), 2049-2064.
Promma, W., Imjai, N., Usman, B., & Aujirapongpan, S. (2025). The influence of AI literacy on complex problem-solving skills through systematic thinking skills and intuition thinking skills: An empirical study in Thai gen Z accounting students. Computers and Education: Artificial Intelligence, 8, 100382.
Sengul, T., & SARIKÖSE, S. (2025). The effect of artificial intelligence literacy on self-directed learning skills: The mediating role of attitude towards artificial intelligence: A study on nursing and midwifery students. Nurse Education in Practice, 104516.
Su, J., & Yang, W. (2023). Unlocking the power of ChatGPT: A framework for applying generative AI in education. ECNU Review of Education, 6(3), 355-366.
Tseng, Y. J., & Yadav, G. (2023). ActiveAI: Introducing AI Literacy for Middle School Learners with Goal-based Scenario Learning. arXiv preprint arXiv:2309.12337.
Usher, M., & Barak, M. (2024). Unpacking the role of AI ethics online education for science and engineering students. International Journal of STEM Education, 11(1), 35.
Vasconcelos, M. A. R., & Santos, R. P. D. (2023). Enhancing STEM learning with ChatGPT and Bing Chat as objects to think with: A case study. arXiv preprint arXiv:2305.02202.
West, P., Lu, X., Dziri, N., Brahman, F., Li, L., Hwang, J. D., ... & Choi, Y. (2023). The Generative AI paradox:" What it can create, it may not understand". arXiv preprint arXiv:2311.00059.
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