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
The integration of generative artificial intelligence (AI) tools in higher education is reshaping academic writing practices, particularly within Open and Distance e-Learning (ODeL) contexts marked by uneven digital access and emerging forms of AI support. This study examines how South African ODeL students describe and rationalise their use of large language model tools in academic writing, with specific attention to paraphrasing as a cognitive and ethical practice. Drawing on sociocultural learning theory, discourse theory, and AI and verification literacy frameworks, the study conceptualises paraphrasing not merely as textual rewording but as a discursive practice through which students negotiate authorship, responsibility, and academic legitimacy. Using a qualitative interpretive design, open-ended responses from 70 students enrolled in mathematics-related modules at a South African ODeL institution were analysed. The findings indicate that students consistently frame paraphrasing as an ethical practice aligned with institutional expectations, even when their engagement with AI involves varying degrees of automation. These accounts suggest that ethical engagement with AI is enacted discursively through self-positioning rather than through demonstrable verification of content. By foregrounding student discourse, the study contributes to Global South scholarship on responsible AI use by highlighting how integrity, authorship, and learning are negotiated in AI-mediated ODeL environments.
M. Ngoveni, M. Graham, Mathelela Steyn Mokgwathi· Journal of Education and Tra...· 0 citations
Generative artificial intelligence (AI) is increasingly shaping how university students search for information, write, create, communicate, and solve problems. In higher education, this situation requires not only operational skills for using AI tools, but also broader competencies such as critical evaluation, information literacy, ethical judgment, self-regulation, and collaborative reflection. This study examines 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. The activity was conducted at two Japanese universities. Students received a common instructional presentation on digital safety, misinformation, AI risks and benefits, cognitive bias, and digital well-being. They then discussed the question, “What competencies should university students develop in the AI era?” and created collaborative mind maps using Personary. Student-generated mind maps and written reflections were analyzed through interpretive map analysis and text-mining-assisted qualitative analysis. The results show that students understood AI-era competencies as multidimensional capacities rather than as technical skills alone. Their maps and reflections emphasized critical evaluation of AI-generated information, media and data literacy, autonomous thinking, communication, ethical responsibility, appropriate AI use, and adaptability. Personary supported the externalization and organization of these ideas, while the AI-assisted mode provided additional prompts for expanding selected branches. The study demonstrates how AI-supported mind mapping can function as a reflective learning activity for visualizing, sharing, and reorganizing students’ understanding of AI literacy in higher education.
The rapid integration of generative Artificial Intelligence (AI) into higher education writing instruction is outpacing pedagogical frameworks, creating profound disruptions in how writing is taught, assessed, and valued. While AI shifts writing from individual production to collaborative human–AI processes, instructors face escalating challenges, including the erosion of traditional authorship, uncertainty in evaluating AI-mediated work, threats to assessment validity, and growing student dependency on AI tools. These tensions expose a widening gap between technological adoption and pedagogical preparedness, placing faculty at the center of unresolved ethical, instructional, and institutional dilemmas. This systematic review synthesizes empirical research published between 2023 and 2025 on generative AI (e.g., ChatGPTand other GPT-based systems) in higher education writing instruction. Following PRISMA guidelines and SPIDER framework, 19 peer-reviewed studies were analyzed. Findings suggest 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. The review contributes theoretically by reconceptualizing writing pedagogy for AI-mediated processes and practically by providing guidance for instructional design, assessment strategies, and institutional policy. Gaps identified include a lack of longitudinal studies, limited exploration of faculty perspectives, and inconsistent integration of AI literacy and ethical considerations. Implications for research, practice, and policy are discussed.
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
Generative artificial intelligence (GenAI) research has largely focused on text generation, feedback, and writing enhancement, overlooking the cognitive and epistemic processes underlying academic knowledge construction. This paper proposes the AI-mediated knowledge construction (AMKC) framework to explain how GenAI may support graduate students' reading-to-write development. Integrating academic literacies, reading-to-write research, sociocultural theory, and dialogic approaches, the framework positions GenAI as a cognitive mediator and dialogic partner across dialogic reading, knowledge transformation, source integration, disciplinary meaning-making, critical reflection, and academic writing. Six theoretical propositions elaborate the mechanisms of AI-mediated literacy development, while pedagogical implications and a future research agenda address implementation and empirical validation. By shifting attention from writing assistance to knowledge construction, AMKC provides a theoretically grounded account of GenAI-mediated academic literacy development in higher education.
Yang Jiao, Jing Huang· Region - Educational Researc...· 0 citations