Aug 2026· Asian Journal of Education and Social Studies· 0 citations
TL;DR
The review concludes that artificial intelligence should be treated as a socio-technical component of curriculum and assessment rather than a stand-alone productivity tool, because its educational legitimacy depends on whether institutions can preserve epistemic agency, valid judgement of learning, equitable access, and accountable human responsibility.
Abstract
Artificial intelligence has moved rapidly from specialised analytics and tutoring applications to widely accessible generative systems capable of producing text, code, images, explanations, and feedback. In higher education, this shift has created a closely coupled set of pedagogical opportunities and integrity risks. This critical narrative review examines how artificial intelligence is reshaping student learning, assessment, and academic integrity, with emphasis on the conditions under which educational value is strengthened or weakened. Literature published from 1 January 2019 to 30 May 2026 was identified through accessible scholarly indexes, citation searching, authoritative institutional sources, and verification against DOI and journal records. The evidence indicates that artificial intelligence can expand access to explanation, formative feedback, language support, ideation, and practice, and controlled studies increasingly report benefits for selected learning outcomes. Yet effects are heterogeneous, often short term, and highly sensitive to task design, student expertise, prompting skill, feedback literacy, and the degree of human oversight. Gains in efficiency or performance do not necessarily demonstrate durable understanding, metacognition, or independent capability. Assessment is therefore the pivotal institutional problem: generative systems can assist feedback and evaluation while simultaneously weakening the validity of unsupervised products as evidence of individual achievement. Automated detection is not a dependable solution because accuracy varies by detector, text type, language background, and model evolution, creating risks of false accusation and unequal treatment. The most defensible response is not unrestricted adoption or blanket prohibition, but an aligned model combining explicit AI literacy, process-rich and dialogic assessment, proportionate disclosure rules, human judgement, data governance, and fair procedures for investigating suspected misuse. The review concludes that artificial intelligence should be treated as a socio-technical component of curriculum and assessment rather than a stand-alone productivity tool. Its educational legitimacy depends on whether institutions can preserve epistemic agency, valid judgement of learning, equitable access, and accountable human responsibility.
This document outlines the conceptual, theoretical, and methodological underpinning of the AI-Augmented Pedagogy Integration Model (AAPIM), which has now been further supported by a growing evidence base of 2025–2026 meta-analyses and systematic reviews.
Ahnaf Afsin, Rumaysha Tahan Towaa, Kasif Suhail Ayate et al.· Frontiers in Computer Scienc...· 0 citations
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
This paper conducts a systematic review of artificial intelligence integration in programming, history, and English education, focusing on technological applications, practical outcomes, and existing challenges. Against the backdrop of global educational digitalization, AI technologies such as generative models, adaptive learning systems, intelligent tutoring systems, and AI-enhanced learning management platforms have addressed long-standing issues in these disciplines, including low engagement in programming, abstract historical instruction, and one-size-fits-all English teaching. By analyzing 10 recent scholarly works, this study first categorizes mainstream AI educational technologies, then compares their discipline-specific uses in code generation, historical simulation, argument feedback, language practice, and adaptive assessment. It also discusses how these tools influence learner autonomy, teacher workload, classroom interaction, and educational equity. Findings reveal that AI enhances personalized learning but requires pedagogical alignment to avoid superficial knowledge acquisition, student overreliance, data bias, and integrity risks. Future research should prioritize interdisciplinary AI education frameworks, ethical governance mechanisms, teacher training, and long-term impact assessments to promote sustainable AI integration in K-12 and higher education.
En-Gui He· Advances in Engineering Inno...· 0 citations
Artificial Intelligence (AI) is reshaping higher education through adaptive learning, intelligent tutoring, generative tools, learning analytics, research assistance and administrative automation. This structured narrative review synthesizes recent academic and institutional literature on the influence of AI on teaching, learning, research and university management. The evidence indicates meaningful potential for personalization, timely feedback, accessibility and operational efficiency, but also identifies risks involving inaccurate output, academic integrity, algorithmic bias, data privacy, unequal access and insufficient faculty preparedness. The review argues that AI should augment rather than replace educational judgment. Sustainable adoption requires clear governance, transparent assessment rules, faculty and student AI literacy, data protection, human oversight and regular evaluation of accuracy and equity. The paper also identifies the need for longitudinal research on learning outcomes and discipline-specific implementation.
Husna Sultana, Irfan Ahmed, Jeevan G· International journal of res...· 0 citations
Educational institutions have adopted Artificial Intelligence (AI) tools at a pace that has outstripped careful study of their cognitive consequences for students. This paper synthesizes findings from 25 recent journal articles and empirical studies to examine how AI integration in school, undergraduate, and graduate settings shapes critical thinking, logical reasoning, and broader cognitive development. Generative large language models, intelligent tutoring systems, and adaptive learning platforms clearly strengthen personalized instruction and raise measurable academic performance; the evidence reviewed here, however, points to a separate risk: when such tools are adopted without safeguards, they can gradually erode independent reasoning, deductive problem-solving, and metacognitive self-regulation. One especially striking pattern emerges from the empirical literature: students who rely heavily on AI support tend to earn higher grades yet perform worse on assessments that require unassisted thinking, a divergence they term the grade-competence gap. To address this tension, the paper proposes the CLEAR Framework: contextual use, literacy, engagement, assessment reform, and reflection, a five-component approach for capturing AI's instructional value without undermining students' cognitive growth. The paper closes with practical recommendations for educators, curriculum designers, and policymakers navigating AI's growing role in education.
Ashok Sadavare, D. Kumbhar, Aditya Sadavare· Journal of Data Engineering...· 0 citations
Generative artificial intelligence (GenAI) has unsettled a central premise of higher-education assessment: that the quality of a submitted artefact is a sufficiently trustworthy proxy for the competence of the named student. This problem is acute in engineering, where text, code, calculations, models, design rationales and technical reports can increasingly be generated or transformed by general-purpose and specialised artificial intelligence (AI) systems. This critical narrative review examines how assessment in higher engineering education should be reconceptualised when GenAI is simultaneously a learning resource, an emerging professional tool and a source of construct-irrelevant assistance. Literature published from 1 January 2018 to 27 June 2026 was searched, with earlier foundational assessment research retained when conceptually necessary. Evidence was synthesised around assessment validity, engineering task vulnerability, authentic and process-based assessment, AI literacy and evaluative judgement, academic integrity and detection, feedback and grading, equity, and programme-level governance. The literature indicates that neither blanket prohibition nor unrestricted adoption provides a defensible general solution. Authenticity alone is also insufficient, because realistic take-home tasks can remain highly susceptible to undisclosed AI influence. A more robust approach separates two complementary purposes: protected evidence of independent competence in threshold and safety-relevant capabilities, and AI-integrated evidence of professional judgement in tasks where responsible tool use is itself an intended outcome. These forms of evidence should be triangulated through staged work, oral explanation, live demonstration, provenance, and programme-level assessment mapping. AI detectors are too unreliable and potentially inequitable to serve as stand-alone evidence of misconduct, while AI-assisted feedback and grading show promise but require human oversight, particularly for complex engineering work. The review proposes a Dual-Assurance Assessment Architecture as an evidence-derived organising model rather than a validated framework. Its central implication is that assessment reform should prioritise the validity of inferences about student capability, not merely the detectability of AI use.
Unknown authors· Asian Journal of Education a...· 0 citations
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