Published 2026-05-12
Keywords
- AI in education,
- Critical theory,
- Metacognition,
- Vocational Ed,
- Career and Technical Education
How to Cite
Copyright (c) 2026 Geraldine Torrisi-Steele

This work is licensed under a Creative Commons Attribution 4.0 International License.
Abstract
As the AI-era continues to intensify, the traditional skill-based model of Career and Technical Education (CTE) will no longer serve the needs of society’s workforce. There is a need for learners to regulate their thinking and critically evaluate the systems shaping that thinking so that they can operate effectively and responsibly in AI-mediated work environments . The proposed Cognitive–Critical Agency Framework, extends traditional skill development to include reflective, analytical, and system-aware capabilities. The proposition is that effective preparation for AI-mediated environments requires more than technical competence; it requires the ability to think about thinking and to question algorithmic systems. This reconceptualization positions CTE as a critical site for developing autonomous, informed, and responsible participants in an increasingly algorithmic world.
References
- Azevedo, R. (2020). Reflections on the field of metacognition: Issues, challenges, and opportunities. Metacognition and Learning, 15(2), 91–98.
- https://doi.org/10.1007/s11409-020-09231-x
- Baker, R. S., & Hawn, A. (2022). Algorithmic bias in education. International Journal of Artificial Intelligence in Education, 32(4), 1052–1092.
- Bansal, G., Wu, T., Zhou, J., Fok, R., Nushi, B., Kamar, E., & Horvitz, E. (2021). Does the whole exceed its parts? The effect of AI explanations on complementary team performance. Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, 1–16.
- Beer, D. (2017). The social power of algorithms. Information, Communication & Society, 20(1), 1–13.
- Burrell, J. (2016). How the machine ‘thinks’: Understanding opacity in machine learning algorithms. Big Data & Society, 3(1), 1–12.
- Coiro, J. (2021). Toward a multifaceted heuristic of digital reading to inform assessment, research, practice, and policy. Reading Research Quarterly, 56(S1), S9–S31.
- Crawford, K. (2021). Atlas of AI: Power, politics, and the planetary costs of artificial intelligence. Yale University Press.
- Dellermann, D., Ebel, P., Söllner, M., & Leimeister, J. M. (2019). Hybrid intelligence. Business & Information Systems Engineering, 61(5), 637–643.
- Eubanks, V. (2018). Automating inequality: How high-tech tools profile, police, and punish the poor. St. Martin’s Press.
- Foucault, M. (1980). Power/knowledge: Selected interviews and other writings. Pantheon Books.
- Gmyrek, P., & Bescond, D. (2023). Generative AI and jobs: A global analysis of potential effects on job quantity and quality. International Labour Organization. ILO Working paper 96.
- https://www.ilo.org/sites/default/files/2024-07/WP96_web.pdf
- Holmes, W., Bialik, M., & Fadel, C. (2023). Artificial intelligence in education: The realities and implications for teaching and learning. Center for Curriculum Redesign.
- Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human–AI symbiosis. Business Horizons, 61(4), 577–586.
- Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Fischer, F., & Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274.
- Kellogg, K. C., Valentine, M. A., & Christin, A. (2020). Algorithms at work: The new contested terrain of control. Academy of Management Annals, 14(1), 366–410.
- Kranzberg, M. (1986). Technology and history: “Kranzberg’s laws.” Technology and Culture, 27(3), 544–560. https://doi.org/10.2307/3105385
- McAfee, A & Brynjolfsson, E. (2017). Machine, platform, crowd: Harnessing our digital future. W. W. Norton & Company.
- https://www.scirp.org/reference/referencespapers?referenceid=3876824
- 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, 100041. https://doi.org/10.1016/j.caeai.2021.100041
- Noble, S. U. (2018). Algorithms of oppression: How search engines reinforce racism. NYU Press.
- Parasuraman, R., & Manzey, D. H. (2010). Complacency and bias in human use of automation. Human Factors, 52(3), 381–410. doi: 10.1177/0018720810376055
- Rojewski, J. W. (2002). Preparing the workforce of tomorrow: A conceptual framework for career and technical education. Journal of Vocational Education Research, 27(1), 1–27.
- Schraw, G., Crippen, K. J., & Hartley, K. (2006). Promoting self-regulation in science education. Research in Science Education, 36(1–2), 111–139. https://doi.org/10.1007/s11165-005-3917-8
- Selwyn, N. (2022). Education and technology: Key issues and debates (3rd ed.). Bloomsbury Academic.
- van Dijck, J., Poell, T., & de Waal, M. (2018). The platform society: Public values in a connective world. Oxford University Press.
- Veenman, M. V. J., Van Hout-Wolters, B., & Afflerbach, P. (2006). Metacognition and learning: Conceptual and methodological considerations. Metacognition and Learning, 1(1), 3–14.
