Jun 2026· QualiSearch Journal of Educational Research and Practice· 0 citations· 10 references
Abstract
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.
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
Examination of how South African ODeL students describe and rationalise their use of large language model tools in academic writing indicates that students consistently frame paraphrasing as an ethical practice aligned with institutional expectations, even when their engagement with AI involves varying degrees of automation.
M. Ngoveni, M. Graham, Mathelela Steyn Mokgwathi· Journal of Education and Tra...· 0 citations
Examination of a classroom practice using Personary, a digital mind-mapping platform with an optional AI-assisted mode, to explore how university students conceptualize competencies needed in the AI era shows that students understood AI-era competencies as multidimensional capacities rather than as technical skills alone.
It is suggested that AI can enhance drafting, revision, and feedback processes, improving coherence, metacognition, and writing confidence, however, these benefits are accompanied by persistent concerns regarding ethical ambiguity, inconsistent policy guidance, and insufficient faculty training.
Samira Dichari, Fadi Jaber· Journal of Education and Tra...· 0 citations
Traditional second language (L2) writing instruction and assessment frequently emphasize unaided, timed production, a model that no longer fully represents the communicative realities of AI-mediated contexts. This conceptual article aims to reconceptualize the L2 writing construct for educational settings in which generative AI is routinely and legitimately used. The study uses a theory-driven integrative conceptual synthesis. Sources were located through purposive searching of Scopus, ERIC, Web of Science, and Google Scholar, supplemented by citation chaining and journal hand-searching, and screened against stated inclusion criteria across two streams: foundational scholarship on mediated cognition, genre, literacy, and validity, and work on generative AI and writing published from 2020 onward. Forty-seven sources were retained for close analysis, spanning sociocultural learning theory, activity theory, distributed cognition, multiliteracies research, computer-assisted language learning, and language assessment scholarship. Analysis proceeded through manual thematic coding of construct-relevant claims, conducted by the first author and independently reviewed by the second. The resulting orchestration model defines AI-mediated writing as the purposeful coordination of human judgment with machine-generated output under conditions of authorial responsibility. It specifies four interdependent competencies: prompting, critical evaluation, adaptation, and ethical accountability. The analysis shows that traditional dimensions of writing, including coherence, organization, language use, critical thinking, and audience awareness, are not displaced by AI-mediated writing but redistributed across these competencies. The paper also identifies specific challenges for L2 writers, especially the difficulty of evaluating and reshaping fluent AI-generated output in a language still being acquired. The article recommends process-visible assessment designs, genre-specific orchestration tasks, and empirical validation studies that examine construct structure, scoring reliability, and consequential validity.
M. Askari, A. Rahim· Polyglot: Journal of Linguis...· 0 citations
The AI-mediated knowledge construction (AMKC) framework is proposed to explain how GenAI may support graduate students' reading-to-write development and provides a theoretically grounded account of GenAI-mediated academic literacy development in higher education.
Yang Jiao, Jing Huang· Region - Educational Researc...· 0 citations