Keywords
Algorithmic Governance
Discipline
Digital Governance
Brazilian Education
Educational Autonomy
Datafication
AI Policy
How to Cite
Abstract
Objective: This study presents the Algorithmic Discipline Recalibration Model (ADRM), a theoretically integrated framework that articulates the critical tradition of discipline with the contemporary literature on algorithmic governance to examine how artificial intelligence systems reconfigure institutional dynamics within Brazilian education. It investigates whether the incorporation of AI represents a rupture, an intensification, or a recalibration of historically sedimented disciplinary mechanisms, as well as how this process generates structural tensions between institutional governance and pedagogical autonomy.
Method: A structured integrative conceptual review combined with comparative conceptual analysis was employed. Foundational works in genealogical theory, sociology of reproduction, critical data studies, and AI-in-education research were systematically mapped and analyzed to identify convergences and theoretical tensions. Concepts were organized into analytical categories, allowing cross-traditional comparison and synthesis into a unified interpretative model.
Findings: The analysis indicates that AI integration does not displace disciplinary rationality but digitally recalibrates it through datafication, predictive analytics, continuous visibility, and performance metrics. Algorithmic mediation translates normalization and classification into statistically mediated governance mechanisms. This transformation produces a structural tension between professional autonomy and institutional oversight, with outcomes contingent upon regulatory design, institutional architecture, and ethical safeguards.
Value: This study contributes an original conceptual framework (ADRM) that reframes AI in education as a process of digitally mediated disciplinary recalibration. By integrating classical critical theory with contemporary AI governance debates, it advances a context-sensitive interpretation of algorithmic integration in Brazilian education.
Practical Implications: The ADRM offers policymakers, administrators, and educators a theoretically grounded lens to design participatory governance structures, safeguard professional discretion, and implement ethically responsible AI strategies within educational systems.
References
Althusser, L. (1970). Ideology and ideological state apparatuses (Notes towards an investigation). In Lenin and philosophy and other essays (pp. 127–186). Monthly Review Press.
Baker, T., Smith, L., & Anissa, N. (2019). Educ-AI-tion rebooted? Exploring the future of artificial intelligence in schools and colleges. Nesta. https://www.nesta.org.uk/report/education-rebooted/
Bourdieu, P., & Passeron, J.-C. (1977). Reproduction in education, society and culture. Sage.
Buckingham Shum, S., & Ferguson, R. (2012). Social learning analytics. Educational Technology & Society, 15(3), 3–26.
Castells, M. (1996). The rise of the network society. Blackwell.
Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2023 - https://doi.org/10.1080/14703297.2023.2190148).
Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International, 61(2), 228–239. https://doi.org/10.1080/14703297.2023.2190148
Dewey, J. (1916). Democracy and education. Macmillan.
Dwivedi, Y. K., et al. (2024). So what if ChatGPT wrote it? Multidisciplinary perspectives on generative AI in research and education. International Journal of Information Management.
Foucault, M. (1977). Discipline and punish: The birth of the prison. Pantheon Books.
Foucault, M. (1980). Power/knowledge: Selected interviews and other writings, 1972–1977. Pantheon Books.
Freire, P. (1970). Pedagogy of the oppressed. Continuum.
Giroux, H. A. (1983). Theory and resistance in education: A pedagogy for the opposition. Bergin & Garvey.
Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.
Holmes, W., Persson, J., Chounta, I.-A., Wasson, B., & Dimitrova, V. (2022). Artificial intelligence and education: A critical view through the lens of human rights, democracy and the rule of law. Council of Europe Publishing. https://book.coe.int/en/education-policy/11333-artificial-intelligence-and-education-a-critical-view-through-the-lens-of-human-rights-democracy-and-the-rule-of-law.html
Illich, I. (1971). Deschooling society. Harper & Row.
Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Müller, K.-R., Narciss, S., Pfeiffer, T., Poquet, O., Sailer, M., Schmidt, A., … Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274
Knight, S., Buckingham Shum, S., & Littleton, K. (2014). Epistemology, assessment, pedagogy: Where learning meets analytics in the middle space. Journal of Learning Analytics, 1(2), 23–47. https://doi.org/10.18608/jla.2014.12.3
Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Pearson.
Lyotard, J.-F. (1984). The postmodern condition: A report on knowledge. University of Minnesota Press.
Miao, F., & Holmes, W. (2023). Guidance for generative AI in education and research. UNESCO. https://doi.org/10.54675/EWZM9535
Mollick, E. (2024). Co-Intelligence: Living and Working with AI. Penguin.
O’Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. Crown.
OECD. (2023). Digital education outlook 2023: Towards an effective digital education ecosystem. OECD Publishing. https://doi.org/10.1787/8fbf5e68-en
Pasquale, F. (2015). The black box society: The secret algorithms that control money and information. Harvard University Press.
Perrotta, C., & Selwyn, N. (2020). Deep learning goes to school: Toward a relational understanding of AI in education. Learning, Media and Technology, 45(3), 251–269. https://doi.org/10.1080/17439884.2020.1686012
Selwyn, N. (2011). Education and technology: Key issues and debates. Bloomsbury.
Selwyn, N. (2019). Should robots replace teachers? AI and the future of education. Polity Press.
UNESCO. (2024). Guidance for generative AI in education and research. UNESCO.
Williamson, B. (2017a). Big data in education: The digital future of learning, policy and practice. Sage.
Williamson, B. (2017b). Education policy and the datafication of schooling. Palgrave Macmillan.
Williamson, B., Eynon, R., & Potter, J. (2020). Pandemic politics, pedagogies and practices: Digital technologies and distance education during COVID-19. Learning, Media and Technology, 45(2), 107–114. https://doi.org/10.1080/17439884.2020.1761641
Williamson, B., Macgilchrist, F., & Potter, J. (2024). Re-examining AI governance in education. Learning, Media and Technology.
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education. International Journal of Educational Technology in Higher Education, 16, Article 39. https://doi.org/10.1186/s41239-019-0171-0
Zuboff, S. (2019). The age of surveillance capitalism. PublicAffairs.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright (c) 2025 Deivison Shindi Takatu, Fabrício Tonetto Londero