Jul 2026· Journal of Non-Formal and Digital Education· Vol 2, pp. 2-7· 0 citations· 6 references
TL;DR
Artificial intelligence is best understood as an amplifier of pedagogy, rather than a replacement for teachers or human judgment, and its benefits are conditional on AI literacy, transparent governance, and equitable access.
Abstract
Artificial intelligence (AI) has moved rapidly from a specialised research topic into a routine presence in classrooms, training programmes and self-directed study, a shift accelerated by the public release of generative tools. This paper examines the role of AI across both formal and non-formal education, with the objective of clarifying where the technology adds genuine pedagogical value, where it introduces risk, and what conditions are needed for responsible adoption. The study adopts a narrative and critical review of peer-reviewed literature, foundational scholarship and policy guidance published mainly between 2016 and 2024. Sources were identified through academic databases and synthesised thematically around application domains, reported benefits and recurring concerns, rather than through statistical meta-analysis. Four broad application areas emerge: personalised and adaptive learning; intelligent tutoring and automated assessment; generative AI for content creation and dialogue; and AI-supported non-formal and lifelong learning. Reported benefits include wider access, individualised pacing and reduced routine workload for educators. Persistent concerns cluster around academic integrity, algorithmic bias, data privacy, the digital divide, and a possible erosion of independent reasoning when learners over-rely on automated output. In conclusion, AI is best understood as an amplifier of pedagogy, rather than a replacement for teachers or human judgment. Its benefits are conditional on AI literacy, transparent governance, and equitable access. Non-formal settings, being flexible and learner-driven, are particularly well placed to exploit AI, provided the same ethical safeguards apply.
Artificial Intelligence (AI) has brought about significant changes in the educational landscape, driven primarily by the advancement of generative models and the increasing availability of tools capable of supporting teaching and learning processes. This study aimed to analyze the main possibilities and challenges associated with AI in pedagogical practice through a structured narrative literature review. Searches were conducted in the Web of Science Core Collection, Scopus, ERIC, SciELO, and Google Scholar, prioritizing publications from 2019 to 2026, while earlier foundational sources were retained when methodologically or conceptually relevant. The final interpretive corpus comprised 31 core sources, including peer-reviewed empirical studies, systematic and scoping reviews, meta-analyses, and institutional guidance. Evidence was synthesized into four themes: evolution and educational applications of AI, pedagogical potential, ethical and institutional challenges, and the changing role of teachers. The literature identifies opportunities for personalized learning, pedagogical planning, accessibility, formative assessment, and teaching-material development, but also recurring concerns involving information reliability, academic integrity, privacy, algorithmic bias, digital inequality, and teacher preparedness. Recent evidence indicates that positive outcomes are heterogeneous and depend on pedagogical scaffolding, human verification, institutional governance, and AI literacy. AI should therefore be understood as a supportive educational technology rather than a substitute for teacher mediation. Its integration requires critical, ethical, and pedagogically grounded use that preserves student autonomy, assessment validity, and educational equity.
A. F. da Silva, Rosimeire Rozendo, Cristiane Aparecida Simão Silverio· Brazilian Journal of Science· 0 citations
The review shows that AI offers substantial potential for personalised learning, scalable feedback, administrative automation and curriculum innovation, but that effective integration requires transparent governance, educator training, reliable evidence, accessibility and a commitment to ethical and human-centred use.
Pu Chen, Sherry Bawa, N. Islam et al.· Higher Education Studies· 1 citation
Artificial intelligence is being incorporated into education at a pace that exceeds the development of stable pedagogical evidence, institutional capacity, and enforceable governance. Its educational value is often framed through personalisation, rapid feedback, and efficiency, yet these affordances do not necessarily produce active learning and may instead encourage cognitive offloading, dependency, or superficial task completion. This critical narrative review integrates three bodies of scholarship that are commonly treated separately: active-learning theory and evidence, resistance to pedagogical and technological change, and ethical frameworks for artificial intelligence in education. Literature published from 1 January 2010 to 5 June 2026 was examined, with earlier foundational sources included where conceptually necessary. The synthesis indicates that artificial intelligence supports active learning most plausibly when it elicits explanation, prediction, retrieval, critique, revision, and peer dialogue rather than supplying polished answers. Meta-analyses of intelligent tutoring systems and recent experimental studies of generative artificial intelligence suggest beneficial average effects, but confidence is constrained by short interventions, locally developed assessments, weak evidence on retention and transfer, uneven disciplinary coverage, and rapid technological obsolescence. Resistance is not adequately understood as reluctance or deficient digital competence. It may signal concerns about pedagogical legitimacy, professional identity, workload, surveillance, reliability, inequity, and the erosion of human relationships. Effective mitigation therefore requires participatory design, transparent purpose, protected alternatives, assessment alignment, professional learning, and institutional support. Ethical principles are necessary but insufficient unless translated into lifecycle governance covering educational necessity, data minimisation, fairness testing, human oversight, contestability, disclosure, incident response, and periodic withdrawal decisions. An integrated framework is proposed in which pedagogical alignment, learner agency, adoption conditions, and accountable governance are treated as interdependent design requirements. Artificial intelligence can catalyse educational improvement, but only when it is subordinated to defensible learning purposes and when institutions accept responsibility for both pedagogical and social consequences.
A. Rushdi, S. S. Zagzoog, Ahmad Ali Rushdi· Asian Journal of Education a...· 0 citations
The rapid expansion of higher education enrolment has created unprecedented challenges in managing large classrooms, prompting institutions to explore artificial intelligence (AI) as a transformative solution. This systematic review, conducted in accordance with PRISMA guidelines, synthesises current research on AI applications in large-class management within higher education settings. A comprehensive search of academic databases yielded 47 studies that met the inclusion criteria, published between 2018 and 2024. Thematic analysis revealed five key domains: automated assessment and feedback systems, intelligent tutoring and personalised learning, student engagement monitoring, administrative task automation, and predictive analytics for student success. Findings indicate that AI technologies significantly enhance instructor efficiency, improve student engagement, and enable personalised learning at scale. However, implementation challenges, including technological infrastructure, faculty training needs, ethical considerations, and concerns about data privacy, emerged as critical barriers. The review identifies a notable gap between AI's theoretical potential and practical implementation in resource-constrained institutions. This study contributes to understanding how AI can address scalability challenges in higher education while highlighting the need for evidence-based implementation frameworks, ethical guidelines, and inclusive design principles. Recommendations for practitioners, policymakers, and researchers are provided to guide the responsible integration of AI in large classroom contexts.
Zaffar Ahmad Nadaf, S. Jamal· Journal of Education Method...· 0 citations
This systematic literature review examines how audio-based artificial intelligence (AI) tools are being used to support learning across educational contexts and what challenges and opportunities arise from their implementation. Using a structured search across major e-scientific databases, this study identified and analysed empirical and conceptual works on AI assisted audio learning, including audio feedback, voice tutors/assistants, adaptive audio modules, and AI generated podcasts. VOSviewer was employed to map keyword co-occurrences and thematic clusters, while a large language model (LLM)-based notebook was used to assist in summarising abstracts, methods, and key findings, and to group studies by educational level and AI technology type. The findings indicate that audio-based AI interventions generally contribute positively to cognitive outcomes, motivation, and engagement, particularly by enabling frequent, personalised feedback and flexible, on-the-go learning opportunities. At the same time, the review reveals several challenges related to instructional design, teacher orchestration, data privacy, and the risk of learner overreliance on AI feedback. The existing evidence is heavily concentrated in language learning and a few specialised domains, with a predominance of short-term studies and self-report measures, limiting the generalisability of current conclusions. This review contributes by offering an updated mapping of AI-assisted audio learning research, highlighting underexplored contexts and design configurations, and outlining clear directions for future studies, including longitudinal designs, comparative trials of audio versus text-based feedback, and investigations into how teachers and students negotiate the role of AI in classroom practice.
Muhammd Fauzan Hawari, Afifah Mesha Putri, Akmal Andri Yantama et al.· Business System & Innova...· 0 citations
Artificial Intelligence (AI) is increasingly transforming education by enabling personalized learning, intelligent assessment, virtual experimentation, and data-informed instructional support. This paper examines the opportunities, challenges, and future directions of AI integration in Integrated Science education. A structured literature review approach was adopted, using scholarly literature published between 2020 and 2026 and identified primarily through the Education Resources Information Center (ERIC) and the Directory of Open Access Journals (DOAJ), with Google Scholar used as a supplementary source. Relevant literature was screened using predefined eligibility criteria and synthesized thematically. The review indicates that AI can support personalized and adaptive learning, virtual laboratories, scientific inquiry, immediate feedback, intelligent assessment, and teacher decision-making. However, effective integration is constrained by inadequate digital infrastructure, limited teacher preparedness and AI literacy, data privacy and ethical concerns, unreliable AI-generated content, academic integrity issues, and learner overdependence on automated systems. The review further identifies limited interdisciplinary evidence specifically addressing AI in Integrated Science, particularly in developing and resource-constrained contexts. Future research should therefore emphasize human-centred AI integration, teacher professional development, context-sensitive and inclusive research, longitudinal empirical studies, and responsible and explainable AI. Overall, AI offers significant potential for strengthening Integrated Science education when implemented responsibly, equitably, and in alignment with curriculum objectives and human pedagogical judgement.
O. O., Dike Obiageri Ijioma, Okanume Chioma M. et al.· International journal of re...· 0 citations
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