The Future of Education

Digital governance of education has led to the flourishing of algorithmic digital infrastructures and data-driven tools which increasingly shape decision-making in schools and educational institutions. As Williamson (2015 p.84) notes, education is “increasingly governed through data managed by actors and manipulated using software technologies that remain hidden and little understood.” While digital tools promise streamlined administrative processes of governance, improved learning outcomes, and personalized experiences, they also raise concerns about democratic accountability. Critics argue that relying on “self-evident data” suspends democratic deliberation and minimizes the role of critical, culturally sensitive, and contextualized knowledge (e.g. do Amaral, 2020 pp.4-5).

Despite literature exploring how algorithmic digital infrastructures reshape educational governance (e.g. Sefton-Green & Pangrazio, 2022; Williamson et al., 2024), a gap remains regarding the challenges and tensions in ensuring democratic accountability. This article reviews literature on the tensions between human-centered, democratic decision-making and algorithmic digital infrastructures, guided by two research questions: (1) What characterizes the tensions in the literature on digital governance when ensuring democratic accountability in educational institutions? and (2) What challenges emerges in the literature when maintaining democratic accountability within digital governance frameworks?

Grounded in a theoretical framework of deliberative democratic evaluation (Ryan, 2005), the review identifies three areas of tensions: (1) the exclusion of tacit and experiential knowledge in algorithmic decision-making; (2) the difficulty of ensuring democratic deliberation in opaque digital systems (Williamson, 2015); and (3) the potential erosion of professional autonomy due to data-driven governance. The findings reveal that algorithmic digital infrastructures potentially limit inclusive deliberation by prioritizing quantifiable data over context-sensitive, experiential and tacit knowledge. Moreover, machine learning and artificial intelligence potentially restricts stakeholders’ ability to scrutinize processes of decision-making, raising concerns about democratic accountability as educational institutions increasingly depend on automated tools.

A key challenge lies in balancing algorithmic efficiency with professional autonomy. This study underscores the need for governance frameworks that integrate quantitative analytics with qualitative, context-sensitive deliberation to preserve democratic accountability in educational governance.

Keywords:  Democratic accountability, digital governance, algorithmic digital infrastructure, deliberative democracy, inclusion.

References:

do Amaral, M. P. (2020). Digital Governance and Education. In M. A. Peters & R. Heraud (Eds.), Encyclopedia of Educational Innovation (pp. 1–5). Springer Singapore.

Ryan, K. E. (2005). Making Educational Accountability More Democratic. American Journal of Evaluation, 26(4), 532–543.

Sefton-Green, J., & Pangrazio, L. (2022). The Death of the Educative Subject? The Limits of Criticality under Datafication. Educational Philosophy and Theory, 54(12), 2072–2081.

Williamson, B. (2015). Governing software: networks, databases and algorithmic power in the digital governance of public education. Learning, Media and Technology, 40(1), 83–105.

Williamson, B., Komljenovic, J., & Gulson, K. N. (2024). World yearbook of education 2024 : digitalisation of education in the era of algorithms, automation and artificial Intelligence. Routledge.