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.
Abstract
This article presents a scoping review of the emerging transformative role of Artificial Intelligence (AI) in higher education. Using a Population-Concept-Context (PCC) review design, we focus on the implications of AI for three major stakeholder groups: students, educators and institutions. Based on academic studies and grey literature from 2010 onwards, the review covers AI technologies, including machine learning, natural language processing, intelligent tutoring systems, learning analytics, chatbots and generative AI, and examines their pedagogical, administrative and ethical impacts. We analyse stakeholder demand, teaching and learning innovations, AI literacy education, institutional implementation barriers, data privacy, algorithmic bias, academic integrity and policy governance. 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. The article concludes with best-practice and policy recommendations for responsible AI implementation and adoption in higher education.
Artificial Intelligence (AI) is changing higher education through individualized learning, intelligent tutoring systems, learning analytics, automated assessment and generative AI applications. AI is being adopted by universities around the world to improve the quality of teaching, learning, research, and institutional administration [1], [2]. AI-based systems increase student engagement with adaptive learning, provide immediate feedback, and help educators create instructional content. Generative AI tools have also boosted digital transformation by helping academic writing, programming and knowledge discovery [3], [4]. However, the adoption of AI is still hampered by issues including data privacy, algorithmic bias, academic integrity and ethical governance [5]. This review summarizes recent advances in AI for higher education and discusses major applications, benefits, obstacles, and prospects for the future with a special focus on the Indian higher education context. The review concludes that AI should complement educators rather than replace them and that responsible implementation supported by institutional policies, faculty development, and ethical guidelines is essential for sustainable educational transformation.
Keywords: higher education; educational technology; artificial intelligence; generative AI; intelligent tutoring systems; Learning Analytics
Jestin James M· International Journal of Tec...· 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, Sajid Jamal· Journal of Education Method...· 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
With the rapid development of artificial intelligence (AI), generative artificial intelligence (GenAI) has been widely applied in higher education, specifically bringing both opportunities and potential challenges. This review focuses on the application of GenAI in teaching, learning, assessment and institutional governance within the higher education context. By adopting a literature review approach, this paper reviews and analyses research on GenAI in education. The findings reveal that GenAI can effectively improve teaching efficacy, enable personalised learning experiences, and streamline assessment procedures. However, its implementation also draws attention to concerns regarding academic integrity, data privacy, algorithmic bias, and ethical governance. In accordance, higher education institutions should strengthen AI literacy training for faculty and students, while improving institutional guidelines and establishing responsible governance mechanisms. Future research is recommended to conduct longitudinal investigations, cross-cultural comparative analyses, and in-depth studies on teacher professional development, in order to support the sustainable and long-term integration of GenAI into higher education.
Xi Bi· Exploring Science Academic C...· 0 citations
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.
D. Bîrsan· Journal of Non-Formal and Di...· 0 citations
Artificial Intelligence (AI) is increasingly reshaping curriculum development in higher education as a
socio-technical, multidimensional phenomenon rather than a neutral technological tool. This study explores how AI
influences ethical governance, pedagogical design, institutional capacity, and contextual dynamics within curriculum
systems. Despite growing scholarly attention, existing literature remains fragmented, with ethical, pedagogical, and
institutional dimensions often examined in isolation, limiting a comprehensive understanding of their interdependencies.
Using a narrative integrative review design, this study synthesizes findings from 55 peer-reviewed studies
retrieved from major academic databases, including Scopus, Web of Science, ERIC, IEEE Xplore, and ScienceDirect.
The analysis employed systematic coding and thematic synthesis across ethical-policy, pedagogical-design,
and technical-institutional domains. Findings reveal that AI introduces systemic ethical risks, including algorithmic
bias, transparency deficits, and data governance challenges, while simultaneously transforming pedagogical
practices through personalization and adaptive learning. However, these advances also raise concerns regarding
epistemic narrowing and the redistribution of human agency in teaching and learning processes. At the institutional
level, AI implementation is constrained by infrastructure limitations, governance misalignment, and dependence on
external platforms. Contextual factors further demonstrate that AI curriculum implementation is highly cultureand
institution-specific, challenging the feasibility of universal models of adoption. The study identifies a persistent
fragmentation in existing research and proposes the Integrated Challenge Model for AI Curriculum Development
(ICM-AI-CD), which conceptualizes AI integration as a dynamic socio-technical system comprising interdependent
ethical-policy, pedagogical-design, and technical-institutional domains. The study concludes that AI in curriculum
development represents a systemic transformation of higher education, requiring integrated frameworks that capture
its cascading and interdependent effects. This framework provides a foundation for future research, policy development,
and institutional planning in AI-enabled curriculum systems.
Abdul Malik· Applied Business: Issues &am...· 0 citations