Aug 2026· Improving Schools· 0 citations· 23 references
TL;DR
Applying Critical Policy Analysis and Jencks’ framework of educational opportunity, the study shows that policy silence is not the absence of governance but a governance choice, one that shapes how access, responsibility, and fairness are determined.
Abstract
This study examines how policy silence functions as a governance mechanism in the context of generative artificial intelligence (AI) in K–12 education. While existing research has focused on how districts regulate or integrate AI, far less attention has been given to what happens when formal guidance is delayed, incomplete, or unresolved. Drawing on a single-district qualitative case study of a Texas public school district, the analysis uses interviews and document analysis as primary data sources, while student survey data provide descriptive context regarding patterns of AI access and guidance. Findings show that policy silence actively redistributes interpretive authority to educators and school leaders, shifting responsibility for ethical and instructional decision-making onto individual classrooms without corresponding institutional support. This redistribution produces uneven enactment and may contribute to disparities in student access, guidance, and learning opportunities. Applying Critical Policy Analysis and Jencks’ framework of educational opportunity, the study shows that policy silence is not the absence of governance but a governance choice, one that shapes how access, responsibility, and fairness are determined. Equitable AI integration requires policies that pair clarity with support for professional judgment, positioning AI governance as central to contemporary school improvement.
As artificial intelligence (AI) becomes increasingly embedded in higher education, empirical evidence on how institutional governance shapes its equitable and responsible implementation in South African universities remains limited. This study examined how institutional policies and governance practices influence the implementation and equitable use of AI in undergraduate education while contributing to international discourse on responsible AI governance. Guided by an interpretivist research paradigm, the study adopted a qualitative approach and employed a single-case study design within one public university in South Africa. Participants comprised university leaders, academic staff, professional support staff, and undergraduate students involved in or affected by AI governance and implementation. Data were collected through semi-structured interviews, focus group discussions, and document analysis and analysed using thematic analysis supported by inductive coding. The findings indicate that limited policy transparency, context-insensitive governance frameworks, unequal access to AI technologies, and weak institutional accountability can reinforce educational inequalities, particularly among first-generation and under-resourced students. Conversely, participatory governance, transparent decision-making, stakeholder engagement, and enhanced digital literacy promote more equitable and responsible AI implementation. The study proposes a multi-level governance model integrating institutional policy, stakeholder participation, and pedagogical practice to strengthen equitable AI adoption. It concludes that higher education institutions should develop context-sensitive AI governance frameworks, strengthen institutional capacity, and expand equitable access to AI technologies to advance fairness, inclusion, and responsible AI implementation.
R. Lumadi· International Journal of Stu...· 0 citations
As generative artificial intelligence becomes embedded in digital education, universities face a governance problem that institutional guidance alone cannot resolve: how should acceptable AI use be defined within particular courses and assessments? This study examines syllabi as formal governance texts through which university principles become student-facing rules.
Using qualitative comparative document analysis, the study analyzed 35 syllabi collected from a large U.S. public research university, including 22 from Education and 13 from other fields. Course-level statements were compared with the institutional governance framework. The analysis distinguished primary governance stances and examined institutional alignment, policy rationales, discourse registers, and variation across course contexts.
The institutional framework delegated substantial authority to instructors while emphasizing communication, attribution, verification, and student responsibility. Course-level enactment was highly heterogeneous: 11 syllabi were silent on student AI use, 11 were prohibitive, five permitted specified uses, two broadly permitted AI with responsibility safeguards, and six treated AI or machine learning as an object of pedagogical or professional learning. Six syllabi were explicitly aligned with institutional guidance, seven implicitly aligned, eight elaborated the institutional framework, three provided minimal guidance, and 11 remained silent; none directly contradicted a specific institutional requirement. Authentic or independently produced work was the most common rationale, appearing in 18 of 24 non-silent syllabi. Governance patterns crossed disciplinary boundaries, while all pedagogical cases were concentrated in AI-adjacent courses.
The findings challenge simple disciplinary explanations and show that course-level AI governance is shaped by the interaction of assessment design, authorship expectations, course purpose, AI adjacency, and instructor discretion. Variation is best understood as an outcome of delegated digital governance whose educational value depends on clarity, justification, and alignment with the intellectual work students are expected to perform.
The rapid integration of artificial intelligence (AI) into higher education is transforming how universities are governed, managed, and held accountable. While existing scholarship has focused primarily on the pedagogical applications of AI and the ethical implications of algorithmic technologies, less attention has been devoted to how AI reshapes institutional governance and decision-making processes. Addressing this gap, this paper advances the concept of AI managerialism to explain the growing influence of algorithmic systems on university governance and organizational control.The study employs a critical narrative review and conceptual policy analysis, synthesizing scholarship on AI governance, managerialism, and higher education administration. It further examines three purposively selected cases representing key domains of algorithmic governance: the Ofqual algorithm controversy in the United Kingdom, Purdue University’s Course Signals learning analytics system, and the University of Sydney’s response to generative AI. Through cross-case thematic analysis, the study identifies recurring governance issues related to accountability, transparency, participation, and institutional autonomy.Findings suggest that AI-enabled systems can improve administrative efficiency, predictive capacity, and evidence-informed decision-making while simultaneously generating risks associated with opacity, surveillance, stakeholder exclusion, and the centralization of managerial authority. In response, the paper proposes an Ethical AI Governance Framework for Higher Education built on five principles: mission alignment, transparency and explainability, participatory governance, equity auditing, and bounded scope. Extending existing AI ethics frameworks, the model explicitly incorporates institutional mission, shared governance, and organizational accountability into AI oversight processes. The framework provides practical guidance for university leaders and policymakers seeking to balance technological innovation with academic values and democratic governance. The paper concludes that effective AI governance requires institutionally grounded arrangements that ensure AI supports, rather than undermines, the educational mission of higher education.
Tian-Zi Sun, Mark Joseph D. Pastor· American Journal of Educatio...· 0 citations
The present study examines the effectiveness of the public policy in the context of digitally unequal environments and its impact on human rights protection in the framework of the Center-Periphery Theory and the role of AI-driven learning societies. AI's swift penetration into the field of education, governance, and public administration has raised several issues and concerns, such as digital inequality, algorithmic bias, accessibility, and social exclusion, especially in developing societies where technological resources are not equally available across various regions. This study aims to analyze the relationships between AI Accessibility, Digital Inequality, AI Governance, Public Policy Effectiveness, Human Rights Protection, and the moderating effect of an Inclusive AI-Driven Learning Society. This study was quantitative with a cross-sectional research design engaged with structured questionnaires given to digitally active respondents, such as students, teachers, policy makers, and researchers. Structural Equation Modeling (SEM) was employed to analyze the data in SPSS and SmartPLS to test the hypotheses presented and investigate direct relationships and moderating relationships. The results showed that AI Accessibility (β = 0.379, p = 0.000) and Digital Inequality (β = 0.385, p = 0.000) had a significant relationship with Public Policy Effectiveness, and Public Policy Effectiveness had a strong relationship with Human Rights Protection (β = 0.436, p = 0.000). The moderation effect of Inclusive AI-Driven Learning Society, however, was not statistically significant (β = -0.020 and p = 0.339). On the theoretical side, the study extends the Center-Periphery Theory to the realm of AI governance studies, while on the practical side, it draws attention to the need for inclusive policy frameworks, digital accessibility, and ethical governance of AI in the context of socially sustainable digital transformation and protection of human rights in AI-informed societies.
Altantuya Dashnyam, Ariungerel Tseden-Ish, Burmaa Natsag et al.· International journal of res...· 0 citations
Background
Research-policy partnerships are widely used to support evidence-informed reform, yet how evidence actually shapes policy and practice in low-income education systems remains poorly understood. This article asks how, and under what relational and institutional conditions, evidence generated by Research on Improving Systems of Education (RISE) Ethiopia contributed to national policy on equity, learning and accountability.
Methods/data
The study uses qualitative data, drawing on 22 semi-structured interviews with senior federal policy makers, regional officials, development partners and local non-governmental organisations (NGOs), complemented by documentary analysis of RISE outputs, education sector plans and policy materials.
Approach
Interview transcripts and documents were analysed using a thematic analysis framework, combining deductive coding organised around bounded mutuality, sustained interactivity and policy adaptability with inductive coding of emergent evidence-to-policy contribution pathways.
Results
RISE evidence contributed to the bottom-up design of the four-year Education Transformation Programme and its implementation vehicle, Education Transformation Operation for Learning (2025-2029), supported a reframing of national discourse from schooling expansion towards foundational learning, helped develop an equity narrative grounded in observed learning gains among disadvantaged students within a school year, and informed COVID-19 school-reopening deliberations. Contributions were clearest where trusted relationships, timely decision windows and actionable evidence converged; where any of these elements was weak, policy impact was correspondingly less direct.
Conclusion
The article shows that policy contribution was enabled by relational conditions that allowed evidence to be heard, trusted and acted upon. It offers a transferable framework for embedding research in policy systems and underscores the need for sustained institutional investment in the capacity for evidence use.
M. Araya, Pauline Rose, D. Tiruneh et al.· Evidence & Policy: A Journal...· 0 citations
This study synthesizes fragmented research on artificial intelligence (AI) in higher education governance and identifies key gaps for future research and policy and provides a useful lens for interpreting institutional adaptation.
Xinyi Jiang, Zuraidah Abdullah· Frontiers in Education· 2 citations