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.
Abstract
Generative artificial intelligence (GenAI) has entered classrooms faster than teachers have been prepared to use it well, producing a GenAI literacy lag in which technological diffusion outpaces educators'conceptual, pedagogical, and ethical readiness. Established AI literacy frameworks predate the widespread adoption of large language models and, while acknowledging ethics, position it as a discrete competency rather than a constitutive commitment, with equity and agency as supplementary design principles. Recent GenAI-specific efforts address isolated features but remain fragmented. We introduce the Responsible AI Literacy in Education (RAIL-Ed) framework, developed through a systematic review and qualitative framework analysis of 67 studies (2023-2025), grounded in critical, pragmatist, sociocultural, and human-centered traditions (Freire, Dewey, Vygotsky, Shneiderman). RAIL-Ed specifies six interdependent pillars: Technical Fluency, Critical Evaluation, Human-AI Collaboration, Contextual Awareness, Ethical Reasoning, and Empowered Agency, marked by three commitments. It is integrative: the absence of any pillar produces a characteristic pedagogical failure. It is developmental: a three-level rubric (Emerging, Competent, Advanced) specifies how each pillar matures across the K-12 teacher-preparation continuum. It is dialectical: the same generative affordance can deepen or displace learning depending on the literacy a teacher brings to it, making the cultivation of that literacy, not the adoption of the tool, the object of design. By treating ethics, equity, and agency as constitutive, RAIL-Ed offers a theoretically grounded basis for curriculum design, teacher education, and policy, aligned with the UNESCO AI Competency Framework for Teachers and the OECD/European Commission AILit Framework. The framework is conceptual, advancing falsifiable propositions for empirical validation.
The rapid emergence of generative artificial intelligence (AI) technologies has transformed academic writing, learning, and knowledge production in higher education. This constructivist grounded theory study explored how future teachers construct responsible AI-mediated academic literacy in their academic work. The study examined how pre-service teachers use AI-assisted tools, negotiate authorship and ownership, protect academic integrity, balance efficiency with learning, preserve personal voice, and respond to institutional and social expectations. Data were generated through semistructured interviews, observations, field notes, and analytic memoing involving teacher education students with experience using generative AI tools such as ChatGPT, Grammarly, and related platforms.
Analysis followed constructivist grounded theory procedures, including initial coding, focused coding, constant comparative analysis, theoretical sampling, memo writing, and theoretical integration.
Findings generated five major categories: AI as a supportive learning and writing resource rather than a replacement for human thinking; negotiating authorship, ownership, and academic integrity in AIassisted writing; balancing efficiency, learning, and dependence through ethical decision-making;
preserving personal voice, authenticity, and human agency; and navigating institutional expectations, policies, and social influences in responsible AI use. These categories converged into the core category of constructing responsible AI-mediated academic literacy through Human-Guided Ethical Engagement. The study generated the Responsible AI-Mediated Academic Literacy Framework (RAALF), which explains responsible AI use as a cyclical, reflective, and human-directed process. The findings suggest that AI literacy in teacher education must extend beyond technical tool use toward ethical self-regulation, authorship preservation, critical evaluation, transparency, and professional responsibility.
Erlinda C. Caerlang· QualiSearch Journal of Educa...· 0 citations
The rapid integration of Artificial Intelligence (AI) into academia and industry necessitates a fundamental transformation of higher education curricula. Moving beyond purely technical proficiency, this paper proposes a comprehensive framework for embedding AI Literacy and Critical Discourse Analysis (CDA) as transdisciplinary core competencies, positioning AI outputs not as neutral tools but as ideologically laden discourse requiring rigorous scrutiny. Drawing on a qualitative multiple-case study involving undergraduate Communication (n=45) and Business (n=38) courses at a Zambian university, this research demonstrates how a pedagogy synergistically combining CDA with hands-on AI interaction fosters deeper digital citizenship, enhances critical thinking, and improves employability skills. Over a three-week intervention, students applied Fairclough’s three-dimensional CDA model to AI-generated press releases and marketing content. The findings reveal three transformative outcomes: first, students adeptly identified embedded societal biases such as gendered leadership stereotypes; second, they reconceptualised AI from an authoritative “oracle” into a fallible, dialogic partner requiring ongoing interrogation; and third, they developed sophisticated, ethically-informed prompting strategies that directly corrected identified biases, a synergistic loop between critique and creation. This approach ensures graduates emerge as critical, innovative, and ethical producers rather than passive consumers of technology. The paper recommends curriculum design, including transdisciplinary modules, faculty development, open-access resources, and reformed assessment strategies that reward critical engagement over mere output generation.
Pethias Siame· International Journal of Mul...· 0 citations
Background: The rapid integration of artificial intelligence (AI) into educational systems worldwide presents significant opportunities for personalised learning, inclusive pedagogy, and data-informed instructional practice, yet AI systems designed without an understanding of how learners acquire knowledge, sustain motivation, regulate emotions, and respond to diverse social and cultural contexts may unintentionally reinforce existing educational inequalities.
Objective: This paper proposes a conceptual framework that positions educational psychology as the primary foundation, rather than a secondary consideration, for the design and evaluation of AI-supported educational systems.
Methods: The framework was developed through a systematic narrative synthesis of peer-reviewed literature, international policy documents, and recent empirical evidence, drawing on searches of PsycINFO, ERIC, Scopus, and Web of Science (2011–2025), supplemented by forward and backward citation tracking and review of international policy reports. The search yielded approximately 620 potentially relevant sources, of which approximately 65 were incorporated following title/abstract screening and full-text review against explicit inclusion criteria.
Results: The resulting framework comprises three interconnected components — a psychologically informed understanding of learner diversity, AI-enabled inclusive and innovative pedagogical practices, and reflective teaching within intelligent learning environments — mapped, through a reference table, against specific AI applications, illustrative empirical evidence, and inclusion implications. A comparative analysis across East Asia, Europe, Sub-Saharan Africa, and South Asia further identifies region-specific challenges, policy contexts, and persistent gaps in cross-cultural validation, particularly regarding generative AI and adaptive learning tools.
Conclusion: The framework offers AI designers, teacher educators, and policymakers a psychologically grounded, empirically mapped, and internationally contextualised basis for developing AI-supported educational systems that advance equity, learner engagement, and meaningful educational transformation, provided that access, teacher readiness, cultural validity, and ethical safeguards are adequately addressed.
Arpana Koul· Review of Artificial Intelli...· 0 citations
Language Assessment Literacy (LAL) has long been central to teachers’ professionalisation, yet the field has given relatively little attention to digitalisation. The rapid emergence of generative AI in language teaching and assessment now compels a reconceptualisation of teacher LAL to incorporate AI related knowledge, skills and ethical awareness. This paper maps key affordances—automated item generation, scalable scoring, and timely individualised feedback—alongside critical challenges including overreliance, academic integrity threats, bias, data protection and equity concerns. It argues that teachers require prompting skills, critical evaluation of AI output, understanding of automated scoring systems, and pedagogical strategies to redesign assessments, and that these competences must be embedded in initial and in service teacher education. Sustainable implementation demands coordinated action by policymakers, researchers, test developers, teacher educators and school leaders to provide localised frameworks, professional development, and time for practice. New conceptions of teacher LAL have to integrate a dynamic AI component, with AI literacy and related requirements representing a moving target and LAL levels being underdeveloped in many educational contexts.
Karin Vogt· Language Testing in Focus: A...· 0 citations
Generative artificial intelligence (AI) is transforming reading and writing practices in and out of educational contexts, yet few frameworks exist to support students' responsible engagement with these tools. This conceptual paper proposes a taxonomy of literacy practices for engaging with AI, grounded in the new literacies of online reading comprehension (Leu et al., 2004, 2015); Coiro, 2021). Using Leu et al.'s (2015) five processing practices and Coiro's (2021) multifaceted heuristic as an analytical lens, we identify seven interconnected practices students enact when reading and writing with AI. We conceptualize these not as hierarchical competencies but as socially situated practices enacted differently across contexts and purposes. For each practice, the taxonomy provides a description, an observable indicator, and ethical considerations embedded as intrinsic dimensions. This framework extends established new literacies scholarship into AI-mediated environments, providing educators with language and observable markers for supporting students' evolving literacy practices.
Amy Hutchison, Marissa J. Filderman, Chinecherem Ezeihejafor· TechTrends· 0 citations
Background: Generative artificial intelligence (GenAI) is changing higher education by expanding access to feedback, simulated interaction, and personalized support. In foreign language education, however, its pedagogical value remains contested because the same tools that may scaffold oral and written production can also encourage dependence, superficial correctness, authorship ambiguity, and uncritical automation. Objective: This conceptual article proposes an ethical and pedagogical framework for integrating GenAI into foreign language education, with specific implications for engineering and technology programs in higher education. Methods: The study develops an integrative conceptual synthesis based on transparent literature mapping conducted with Elicit and cross-checked through scholarly and institutional sources published mainly between 2022 and 2026, alongside foundational works on communicative competence, autonomy, mediation, and assessment. Results: The analysis yields the MAICA Framework, a Mediated AI Communicative Agency model organized around five interdependent dimensions: pedagogical mediation, communicative production, disciplinary transfer, learner agency, and ethical and critical AI literacy. The article also formulates a six-stage AI mediated communicative task cycle that guides learners from task orientation, guided prompting, and drafting or rehearsal to critical revision, transparent submission or performance, and reflective transfer. Conclusions: GenAI should not be treated as a shortcut for language performance or a replacement for teacher expertise. Its educational contribution depends on thoughtful task design, transparent assessment, teacher mediation, and students’ capacity to question, adapt, verify, and ethically declare AI assistance. The framework offers a practical conceptual contribution for language educators working with students who need communicative competence and responsible technological agency
Dionelio Jesús Moreno Villalobos· International Journal of Edu...· 0 citations