Jun 2026· International Journal of Active & Healthy Aging· 0 citations
TL;DR
It is concluded that GenAI should serve as a pedagogical assistant for teachers, not a replacement, and effective implementation requires robust AI governance mechanisms, continuous teacher professional development, ethical safeguards, and the promotion of AI literacy.
Abstract
Generative Artificial Intelligence (GenAI), including Large Language Models (LLMs) such as OpenAI’s ChatGPT, Google Gemini, and Anthropic Claude, is rapidly transforming the educational landscape from classroom instruction to intelligent learning systems. This study systematically reviews the pedagogical, cognitive, ethical, and governance implications of integrating GenAI into education. A systematic literature review was conducted using publications from Scopus, Web of Science, IEEE Xplore, ERIC, and Google Scholar spanning 2022–2025. An initial list of 78 studies was identified, and 42 peer-reviewed articles, policy reports, and institutional publications were included for thematic synthesis. The results indicate that GenAI significantly enhances adaptive learning, formative assessment, and personalized instructional support. Empirical findings show that AI-supported writing tasks led to more coherent and organized content. AI-mediated formative feedback positively influenced subsequent learner performance, yielding a 63% increase in coherence and organization in writing tasks and a 27% reduction in revision turnaround time compared to non-AI groups. Furthermore, AI-supported scaffolding boosted engagement and reduced task drop-off rates by 19% in STEM learning environments. Despite these benefits, the review highlights persistent concerns, including academic integrity violations, algorithmic bias, hallucinated information, data privacy risks, and over-reliance on AI-generated content. The study concludes that GenAI should serve as a pedagogical assistant for teachers, not a replacement. Effective implementation requires robust AI governance mechanisms, continuous teacher professional development, ethical safeguards, and the promotion of AI literacy. These measures are essential to ensure equitable, responsible, and sustainable use of intelligence-enabled learning in today’s educational landscape.
The rapid advancement of Generative Artificial Intelligence (GenAI), particularly Large Language Models (LLMs) such as ChatGPT, has significantly transformed instructional practices in higher education. Beyond serving as instructional support tools, GenAI systems are increasingly conceptualized as collaborative co-instructors capable of assisting educators in curriculum planning, personalized instruction, formative assessment, and learning analytics. Despite growing scholarly interest, existing reviews often examine these applications independently and provide limited integration of pedagogical, ethical, governance, and sustainability perspectives. This study addresses this gap through a PRISMA-guided systematic literature review of 20 empirical and evidence-based scholarly publications published between 2023 and 2025. Articles were retrieved from Scopus, Web of Science, ERIC, and Google Scholar using predefined search protocols and were analyzed through directed qualitative content analysis. The synthesis identified three dominant instructional functions of GenAI: co-planning, co-instruction, and co-assessment, each demonstrating measurable improvements in instructional efficiency, learner engagement, personalized learning, and formative feedback. Simultaneously, the review highlights significant challenges related to academic integrity, algorithmic bias, data privacy, teacher autonomy, and student overreliance on AI-generated content. Comparative analysis across studies indicates that effective implementation consistently depends on sustained human oversight through a Teacher-in-the-Loop (TiTL) framework, which positions educators as pedagogical decision-makers while leveraging AI to augment instructional effectiveness. The review further demonstrates that responsible GenAI integration contributes to sustainable higher education by improving resource efficiency, reducing faculty workload, expanding equitable access to learning, and supporting resilient digital education ecosystems. The study contributes a synthesized conceptual perspective that integrates pedagogical, ethical, governance, and sustainability dimensions into a unified framework for responsible AI-augmented higher education. The findings provide evidence-based guidance for educators, institutional leaders, and policymakers seeking to implement GenAI responsibly while preserving academic quality, educational equity, and human-centered teaching.
Montadzah A. Abdulgani, Jonathan M. Mantikayan· International Journal of Lat...· 0 citations
The rapid development of generative artificial intelligence (AI) has significantly transformed university English as a Foreign Language (EFL) education, creating new opportunities for language learning, teaching, and assessment. This review synthesizes recent studies published between 2023 and 2025 to provide an overview of current research trends, major findings, research gaps, and pedagogical implications of generative AI in university EFL contexts. A critical narrative review with a systematic literature search was conducted using studies retrieved from Scopus, Web of Science, and Google Scholar. The reviewed literature indicates that research has expanded rapidly following the emergence of ChatGPT, with writing instruction, learner perceptions, and language performance being the most frequently investigated topics. Across the reviewed studies, generative AI was found to enhance language learning through personalized feedback, increased learner autonomy, and greater learning engagement. However, concerns regarding academic integrity, AI literacy, ethical issues, and institutional readiness remain significant challenges to its sustainable implementation. The review also identifies several research gaps, including the limited diversity of AI applications investigated, the dominance of short-term quantitative studies, and the lack of longitudinal and classroom-based research. Overall, this review highlights the importance of integrating generative AI through responsible pedagogical practices and provides directions for future research and AI-enhanced university EFL education.
N. H. Hong Nhung· International journal of soc...· 0 citations
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
Generative artificial intelligence (GenAI) has brought the issue of teacher digital competence into focus again, although it is possible to see that most of the frameworks list the skills and do not make any distinction between the tasks of teaching where AI applications are used. The research was based on six platforms provided to determine the expressions of five capabilities in which teachers were able to perform tasks. There were 12,861 records included in the analytic sample and they came from 300 source URLs. The highest capability expressions were efficiency (41.87%) and content development (34.76%). Positive associations of all five task areas were found in pedagogical assistance; the greatest estimates were made with classroom enactment and professional learning. Task-replacement language was less represented in classroom enactment and professional learning after multiplicity adjustment. The results confirm the competence model, which is task sensitive and is based on assessment of capability, orchestration of instruction, the verification of evaluation, mediation of the learner and inquiry of the profession. The corpus explains the expectations of discourse instead of the competence of teachers or effects of their instruction.
Na Zhao· Advances in Social Behavior...· 0 citations
The rapid integration of artificial intelligence (AI) technologies in higher education is transforming teaching, learning, and assessment practices. Generative AI systems, such as ChatGPT and Google Gemini, enable students to generate ideas, summarize academic materials, and refine written work, challenging the validity of conventional assessments designed to measure independent intellectual effort. Despite this potential, limited research has explored how assessment can be reconceptualized to accommodate AI-assisted learning while maintaining academic rigor. This study addresses the question, how student assessment can be aligned with AI-mediated learning in higher education. Guided by Extended Mind Theory, which conceptualizes cognition as distributed across human and technological agents, the study examined assessment in Tanzanian higher learning institutions. Using a qualitative multiple case study design, data were collected from 68 participants through interviews, focus groups, open-ended questionnaires and document reviews. Thematic analysis revealed that students integrate AI tools as cognitive extensions, enhancing understanding, argumentation and metacognitive reflection, while lecturers noted benefits alongside challenges in evaluating independent learning. Emerging approaches including oral presentations, reflective journals, project-based tasks and AI-transparent reporting were identified as effective. The findings underscore the need for AI-inclusive assessment frameworks emphasizing process, reflection and critical engagement.
Nabahani Kimboka, Abeid Hussein Rashid· Journal of Ethics in Higher...· 0 citations
The rapid expansion of generative artificial intelligence (Gen AI) in higher education is reshaping not only learning practices but also the cultural conditions through which students engage with knowledge, academic communities, and their responsibilities as learners. While existing research has predominantly examined Gen AI through technological, pedagogical, and ethical perspectives, its role in transforming learning culture remains insufficiently synthesised. This study aims to synthesise how Gen AI reshapes learning culture in higher education by examining transformations in learning practices, academic norms, academic identity, learning interactions, and self-regulated learning. Guided by the JBI methodology and reported in accordance with PRISMA-ScR, this scoping review identified, appraised, and thematically synthesised 25 peer-reviewed studies published between 2020 and 2026. The synthesis indicates that AI-mediated learning culture emerges through dynamic interactions among Gen AI affordances, academic norms, learner identity, learning interactions, and self-regulation mechanisms. Gen AI therefore operates not merely as a learning tool, but as a socio-cultural mediator that influences how students study, produce knowledge, collaborate, and negotiate academic responsibility. Drawing on these findings, the review proposes an AI-Mediated Learning Culture Framework that maps the interrelationships between technological affordances, academic practices, and learner agency. This conceptual synthesis offers directions for empirical research and supports higher education institutions in designing AI-responsive learning environments.
Rachmat Satria· IQRO Journal of Islamic Educ...· 0 citations