Jul 2026· Frontiers in Education· 1 citation· 7 references
TL;DR
By reframing GenAI adoption as a sociotechnical design-and-governance problem rather than tool uptake, RAiLE offers a pathway for building plural, accountable, and agency-preserving futures for language education.
Abstract
Generative artificial intelligence (GenAI) is rapidly becoming infrastructural in language education, supporting drafting, revision, translation, and interactional rehearsal. Yet most institutional deployments remain organized around personalization and efficiency logics that treat learning as an optimization problem. This article argues that personalization-first GenAI, when implemented without explicit pedagogical and governance constraints, may amplify three risks in language education: epistemic substitution (outsourcing judgement to the system), normative compression (privileging dominant varieties and genre conventions as “neutral”), and governance opacity (data capture and surveillance drift). Drawing on rhizomatic learning theory and recent work on epistemic authority redistribution in AI-mediated learning, the article proposes Rhizomatic AI for Language Education (RAiLE), a testable sociotechnical framework integrating pedagogy, system design, and governance. RAiLE specifies evaluable design commitments and indicators beyond narrow proficiency metrics and outlines a research agenda for empirical refinement through design-based, discourse-analytic, and policy studies. By reframing GenAI adoption as a sociotechnical design-and-governance problem rather than tool uptake, RAiLE offers a pathway for building plural, accountable, and agency-preserving futures for language education.
The rapid adoption of generative artificial intelligence (GenAI) in higher education has outpaced institutional readiness, creating urgent ethical and regulatory challenges that threaten academic integrity, data privacy, and educational equity. While global frameworks like UNESCO’s Guidance for Generative AI in Education (2023) advocate for human-centric design, and national laws such as FERPA mandate student data protection, no existing model systematically integrates these domains into a cohesive governance structure. This study addresses this critical gap by proposing the Ethico-Regulatory Governance (ERG) Framework, a conceptual model designed to bridge global ethics with local compliance. Developed through a systematic synthesis of 68 peer-reviewed studies, policy documents, and institutional guidelines, the ERG Framework consists of four interlocking layers: Foundational Principles (UNESCO values), Regulatory Anchors (FERPA/GDPR alignment), Institutional Mechanisms (audits, disclosure, training), and Pedagogical Integration (process-based assessment, prompt engineering). The framework transforms abstract principles into actionable practices, enabling institutions to move beyond reactive policies toward proactive, accountable governance. Key findings demonstrate that effective AI integration requires not only technical oversight but also stakeholder co-design, bias mitigation, and continuous feedback loops. By operationalizing ethics through enforceable mechanisms, the ERG Framework offers a scalable, adaptable solution for universities navigating the complexities of GenAI. Its implementation can safeguard core academic values while fostering innovation, ensuring that AI serves as a partner—not a replacement—for human judgment in teaching, learning, and research.
Christian Roberto Cabezas Freire, Nayana Desai· Revista Hambatu Science· 0 citations
The Responsible AI Literacy in Education (RAIL-Ed) framework is introduced, developed through a systematic review and qualitative framework analysis of 67 studies (2023-2025), grounded in critical, pragmatist, sociocultural, and human-centered traditions.
S. Hossain, S. Ahmadi, Leqi Li et al.· 0 citations
This paper critically examines how two major international organizations, the OECD and UNESCO, frame “AI literacy” in their key policy documents. Using text-mining and network text analysis, we compare their visions and the strategic selectivities they promote for reshaping national education systems. Our analysis, informed by Jessop’s strategic-relational approach, reveals a profound socio-technical shift: rather than integrating technology into society, these frameworks depict society being integrated into GenAI. In this new education landscape, students act as “cybernetic” co-producers of knowledge, continuously interacting with AI systems in feedback loops, while teachers are largely marginalized, reduced to roles of basic monitoring instead of pedagogical mediation. Similarly, educational institutions are emptied of their pedagogical role and reduced to legitimizing compliance. We argue these selectivities represent a radical departure from traditional educational technology policy. In these documents, GenAI is leveraged as a totalizing force that restructures classrooms around productivity and testing, while systematically obscuring crucial questions of teacher agency, institutional context, and critical engagement. Particularly in the OECD/EU framework, schools become both testing grounds and sites of extraction for GenAI systems, fundamentally altering educational priorities and actor roles in a world dominated by oligarchic powers.
Michele Martini, S. Robertson· European Educational Researc...· 0 citations
Generative artificial intelligence (GenAI) is rapidly reshaping engineering education, yet prevailing debates frame its impact narrowly as either pedagogical innovation or epistemic threat. In this article, we argue that such framings misdiagnose the problem by treating GenAI as a tool rather than as a sociotechnical reorganization of epistemic authority, agency, and professional formation. Drawing on sociological theory, philosophy of science, labor economics, and recent empirical studies, we show that GenAI lowers epistemic floors by expanding access to competent performance while leaving epistemic ceilings, those associated with judgment, authority, and problem framing, largely intact. This asymmetry produces new forms of stratification, redistributes responsibility without redistributing control, and stabilizes plausibility‐based forms of knowledge in place of warrant‐based engineering reasoning. We advance three central contributions. First, we conceptualize a shift from warrant to plausibility in engineering knowledge, in which fluent, algorithmically generated outputs increasingly circulate as legitimate despite bypassing core practices of verification, critique, and accountability. Second, we extend the concept of the AI wall beyond productivity to describe a ceiling of epistemic development, where further learning and judgment are not institutionally rewarded without deeper expertise, authority, or complementary organizational resources. Third, we theorize how engineering education participates in stabilizing these dynamics through what we term the social campus, the institutional space in which curricular norms, assessment regimes, professional signaling, and peer comparison converge to normalize AI‐mediated epistemic authority. Together, these dynamics position engineering education at a critical juncture. We argue for a reorientation toward epistemic stewardship, in which engineering programs deliberately cultivate the capacities needed to interrogate, govern, and responsibly deploy AI systems rather than merely operate within them. The question we want to highlight is not whether engineering education will adapt to AI, but whether it will do so as a steward of epistemic responsibility or as an accelerator of epistemic stratification.
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.
This study examines how the 50 U.S. universities ranked as most innovative by U.S. News & World Report articulate policy, guidance, and support for generative artificial intelligence (GenAI) in teaching and learning. Using qualitative document analysis and inductive thematic analysis of official institutional websites, the study identifies five convergent patterns: instructor-led, syllabus-level governance; disclosure and attribution expectations; privacy-oriented data guardrails and vetted tools; caution toward AI-detection systems; and expanding investment in AI literacy. It also identifies important divergences in default permission stances, the maturity of enterprise governance, and tooling strategies, including campus-hosted platforms and consumer tools governed by risk controls. Across institutions, GenAI support is distributed through teaching centers, libraries, IT/security units, and academic integrity offices. The findings suggest that academic integrity in the GenAI era is shifting from a primarily punitive model toward a pedagogy-first ecosystem that combines clear expectations, assessment redesign, equitable access to vetted tools, and iterative governance. The study offers an early comparative baseline for longitudinal tracking and evidence-informed institutional policy design.
Yufeng Qian· International Journal for Ed...· 0 citations