Aug 2026· European Educational Research Journal· 0 citations· 21 references
TL;DR
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 to reveal a profound socio-technical shift.
Abstract
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.
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.
Ali Khodi, Samantha M. Curle· Frontiers in Education· 1 citation
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.
Yufeng Qian· International Journal for Ed...· 0 citations
As generative artificial intelligence becomes one of the most significant systems of knowledge production in our society today, questions relating to who can access and shape that production grow increasingly important in our discourse. This paper argues that the existing frameworks for AI literacy, which are dominated by technical competency and responsible-use principles, are insufficient because they enforce a"consumer"orientation toward AI rather than fostering genuine epistemic agency. Based upon Foucault's concept of power-knowledge, Freire's pedagogy of critical consciousness, and scholarship of digital literacy, this paper proposes a reconceptualization of AI literacy as a critical practice that equips individuals not just to use AI systems, but to critically evaluate them, resist their structuring assumptions, and participate in their governance. The paper further argues that unequal access to AI tools in society recapitulates longstanding epistemic injustices, and that a literacy framework oriented toward empowerment must account for these structural inequities. A three-part framework of AI literacy based on the notions of contextual use, critical interrogation, and participatory governance frames this literacy as a cultivation of epistemic"agents"rather than the training of competent consumers of AI-generated information.
Generative artificial intelligence is reshaping the organization of knowledge, classroom interaction, assessment evidence, and institutional arrangements in ideological and political education (IPE). In this article, IPE refers to a form of higher education that integrates theoretical learning, civic responsibility, and social practice. This conceptual article examines how generative AI changes the conditions under which educational judgment is formed in IPE classrooms. Drawing on educational technology studies, human agency theory, responsible AI governance, and critical AI literacy, it adopts conceptual analysis and theoretical synthesis to develop a four-stage pedagogical redesign model for the generative AI era. The model contains problem generation, negotiated interpretation, evidence verification, and practice transfer. The article identifies four innovation pathways: issue-based knowledge organization, human-AI collaborative dialogue, situated learning environments, and process-based assessment. It also specifies four risk boundaries: knowledge compression, cognitive dependence, relational weakening, and excessive datafication. The main contribution is to argue that generative AI should not be positioned as an autonomous educational subject, but as a conditional medium that supports interpretation, deliberation, and responsible practice under curricular purpose, teacher judgment, transparent rules, critical AI literacy, and institutional safeguards.
The arrival of generative artificial intelligence (GenAI) in educational settings has sparked debate over how it will transform teaching and learning. Language education is already grappling with teacher shortages, workload intensification, and shifting program viability. While conceptual work has outlined theoretical use cases, little empirical research has examined how world language teachers actually use GenAI or the challenges they encounter. In this study, we used a mixed‐methods design to survey U.S. world language teachers, followed by interviews with eight teachers who were highly proficient in GenAI implementation. Findings revealed various factors shaping GenAI adoption and challenged claims that GenAI diminishes teacher expertise or undermines critical thinking. Access to GenAI and policies for its use were uneven across school districts and grade levels. Without institutional guidance, many teachers were self‐taught, underscoring the need for targeted professional development. We discuss how, with appropriate guidance, emerging AI‐integrated pedagogical models have the potential to revitalize and sustain language education programs through strategic, teacher‐led use of GenAI.
Jue Wang, K. Davin, Scott Kissau et al.· Foreign language annals· 1 citation
Despite growing enthusiasm around generative AI (GenAI) as a transformative force in education, access to these technologies remains profoundly unequal. This qualitative study examines how educators and students in under‐resourced Chinese educational contexts experience unequal access to GenAI and interpret its consequences for language learning, teaching and local knowledge production. Informed by critical sociolinguistics and political economy, the study draws on ten semi‐structured interviews with six teachers/educators and four students from local universities, rural communities and linguistically minoritised settings in China. The analysis shows that GenAI access is rarely a binary matter of presence or absence, but a layered set of hurdles that participants had to navigate actively: tools are visible through media, training sessions and peer networks, but unstable connectivity, outdated devices, paywalls, institutional permissions and limited local integration meant that turning visibility into sustained use required ongoing effort, workaround strategies and situated forms of agency. Participants also reported English‐centred performance, weak support for local dialects and minority languages and culturally generic or stereotyped outputs. Although empirically situated in China, the study uses this contextually bounded dataset to contribute analytically to wider debates on global digital inequality, linguistic hierarchy and algorithmic colonialingualism.
Jinming Du, Qinghua Chen, Wei Wei· International Journal of App...· 0 citations
