Governing the Unseen: A Systematic Review of AI Literacy among Language Teachers in Higher Education

Synthesis: Deng, Çelik and Duran (2026) systematically review 32 empirical and conceptual studies (Dec 2022 – Mar 2026) on AI Literacy among language teachers in Higher Ed, framed by the "governing the unseen" lens that prefigures institutional policy. Guided by PRISMA 2020 and a critical-policy/sociomaterial thematic synthesis of ERIC, British Educational Index and Web of Science records, they find AI Literacy is overwhelmingly conceptualised through competency-based, multi-dimensional models whose critical and domain-specific dimensions remain underdeveloped. Teacher Education is largely unstructured, Assessment leans on self-report tools, and weak institutional support plus unclear responsibility blunt gains such as confidence and innovative teaching. The review concludes that durable, equitable AI literacy demands structural Governance rather than individual upskilling.

Key Findings

Conceptualisation. AI literacy among language teachers is most often modelled as a set of competencies spanning technological, pedagogical and (less so) critical-ethical dimensions. Domain-specific literacy — how AI reshapes language teaching's subject matter — is comparatively thin, echoing the wiki's broader finding that AI Literacy frameworks routinely under-specify disciplinary content.

Four governance patterns. Across the corpus, the authors identify four recurring "governing the unseen" patterns: (1) institutional invisibility of AI literacy in workload models, promotion policies, and resource allocation; (2) fragmented professional development rather than coherent, sustained PD; (3) ungoverned ethical ambiguity — unclear guidelines leave teachers to navigate moral and professional dilemmas individually; and (4) displaced accountability, where responsibility for AI literacy is shared or unclear across institutions, teacher-training programmes and policy agencies.

Professional development. Training is frequently ad-hoc, unstructured and unevenly planned, mirroring the "individual effort and experimentation" pattern documented across Faculty Development. Despite growing tool availability, the review finds little evidence of coherent, sustained PD models.

Assessment. Evaluation of teacher AI literacy relies heavily on self-report instruments, with limited attention to ethical and critical skills — consistent with Assessment Validity concerns about measuring literacy via perception rather than performance.

Structural barriers. Limited institutional support, unequal access to resources, unclear ethical guidelines, and shared/unclear responsibility reduce positive individual outcomes (confidence, innovation). The authors frame these as governance failures rather than individual deficits: unless there is a conscious effort to govern AI literacy, it will remain unevenly distributed, perpetuate existing inequalities, and place teachers in moral and professional dilemmas.

Conclusion. Sustainable AI literacy development requires an integrated, system-wide strategy — embedding AI literacy in the curriculum, formalising it in workload and appraisal policies, and offering long-term, subject-specific professional education and ethical support — over occasional workshops and individual upskilling. The paper calls for longitudinal, multi-method, non-Anglophone, performance-based and governance-oriented research on language-teacher AI literacy.

Connected Concepts

Connected Articles

Edurev 100741 TPACK GenAI Review — TPACK in the GenAI era: a systematic review
Teacher Education AI Literacy Sdt 2026 — Teacher education and AI literacy through self-determination theory
AI TPACK Teacher Multi Agent Workflow — AI-TPACK and teacher multi-agent workflows
GenAI Literacy Training Teacher Education Dbr 2026 — GenAI literacy training in teacher education (design-based research)

Citation

Deng, Y., Çelik, F., & Duran, V. (2026). Governing the Unseen: A Systematic Review of AI Literacy among Language Teachers in Higher Education. Computers and Education: Artificial Intelligence, 100658. https://doi.org/10.1016/j.caeai.2026.100658