Aug 2026· Journal of Natural Language and Linguistics· Vol 4, pp. 12-24· 0 citations· 34 references
TL;DR
The findings indicate that AI positively influences academic performance primarily through enhanced engagement, personalization, predictive analytics, and self-efficacy.
Abstract
Artificial intelligence (AI) is increasingly embedded in higher education, with growing interest in its effects on students’ academic performance. Despite its rapid adoption, evidence regarding its benefits, risks, and implementation conditions remains limited. This systematic literature review synthesizes recent empirical evidence on the impact of AI on academic performance in higher education, and identifies the key opportunities and challenges associated with AI integration. Following the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, a systematic search of the Scopus database was conducted for English language open-access journal articles published between 2023 and 2025. Using predefined inclusion criteria, 3,344 records were screened, resulting in 20 eligible studies for qualitative synthesis. Data were analyzed thematically across methodologies, contexts, and outcomes. The findings indicate that AI positively influences academic performance primarily through enhanced engagement, personalization, predictive analytics, and self-efficacy. Predictive models have achieved high accuracy in identifying at-risk students and supporting early intervention. However, challenges, including AI dependency, academic integrity concerns, data privacy risks, and unequal benefits across student groups, have consistently been reported. AI has substantial potential to enhance academic performance in higher education, but its effectiveness depends on thoughtful pedagogical integration, institutional readiness, and ethical governance. Balanced, inclusive, and well-regulated AI adoption is essential for maximizing benefits while mitigating risks.
Artificial intelligence is increasingly used in higher education, yet evidence concerning its relationship with academic performance remains fragmented across adoption studies, predictive models and evaluations of learning outcomes. This study maps and synthesises Scopus-indexed research published between 2020 and 2025. A bibliometric analysis of 84 records was conducted using VOSviewer and Microsoft Excel, while 29 eligible studies were examined through a PRISMA-guided systematic review and structured content analysis. The review analysed publication patterns, influential contributors, AI application types, educational purposes, research designs, outcome measures and theoretical frameworks. The findings show rapid publication growth, strong reliance on quantitative and predictive designs, and increasing attention to generative AI, adaptive learning, and learning analytics. However, evidence of improved academic performance varies according to whether studies measured perceptions, prediction accuracy, correlations or direct learning outcomes. Important gaps remain in the limited use of pedagogical frameworks, including TPACK, and the scarcity of experimental, longitudinal, and geographically diverse research. The study contributes an outcome-centred synthesis that connects bibliometric patterns with the methodological and pedagogical strengths of the available evidence.
Mohammed Adi Al-Battashi, M. A. M. Adnan, Hassan Abuhassna et al.· International Journal of Lea...· 0 citations
Artificial Intelligence (AI) as a representation of technological advancement, has been recognized for its potential to positively and negatively impact the educational sphere. In mathematics education, AI revolutionizes learning by offering students more flexible and adaptive learning experiences. This study aims to provide insights into the impact of AI integration in mathematics education through a systematic literature review (SLR). The primary focus of this research includes trends in AI usage based on year, country, and education level, types of AI integration, and the impacts and challenges encountered in implementing AI in mathematics learning. The research data comprises empirical articles collected using the PRISMA approach, drawing from databases such as Scopus, ERIC, and SAGE over the past five years. This results in the analysis of 13 selected articles. The findings reveal dynamic trends in AI utilization, particularly in Indonesia and Australia. In Indonesia, AI is primarily used to expand access to online education, while in Australia, it is leveraged for personalized learning. AI is predominantly integrated at the middle school and university levels, with ChatGPT as the primary tool to assist students in understanding mathematical concepts. The benefits of AI include improvements in cognitive, affective, and teaching abilities. The challenges of AI identified include low user awareness, limited educator skills, cognitive impacts on students, and technological access disparities that exacerbate educational inequalities.
Lukman Hakim Muhaimin, T. Turmudi, Asllan Vrapi et al.· Knowledge Management & E...· 0 citations
A systematic literature review aims to synthesize existing evidence on the evolving roles of mathematics teachers within AI-enhanced educational contexts and to develop a comprehensive framework explaining role transformation, and contributes a holistic conceptualization of teacher role transformation.
Vaijayanti Aphale, Ketki Kher, Vijayanta Bhurale et al.· Journal of Asia Entrepreneur...· 0 citations
Artificial Intelligence (AI), particularly Generative Artificial Intelligence (GenAI), is rapidly reshaping higher education by transforming academic research, teaching practices, learning processes, and institutional approaches to technology adoption. However, the rapid expansion of AI also raises concerns regarding academic integrity, privacy, data ownership, algorithmic bias, misinformation, and responsible use. This systematic literature review (SLR) synthesises recent evidence on the role of AI in higher education, with particular attention to four dimensions: AI adoption, AI-assisted academic research, AI ethics, and AI-enabled teaching and learning. Following the PRISMA framework, studies were identified through Scopus and Web of Science and assessed using predefined inclusion, exclusion, and quality appraisal criteria. From the initial 260 records, the screening and eligibility processes resulted in 20 studies being included in the final qualitative synthesis. The findings reveal that AI-assisted academic research represents the most prominent research area (35%), followed by AI-enabled teaching and learning (25%), AI adoption (20%), and AI ethics (20%). The reviewed studies demonstrate that AI supports literature reviews, academic writing, text revision, data analysis, referencing, personalised learning, feedback, student engagement, and research productivity. At the same time, effective implementation requires AI literacy, critical judgement, ethical awareness, institutional guidance, and appropriate training. The review identifies a significant need for integrated and longitudinal research examining AI adoption, ethical practice, learning outcomes, critical thinking, research quality, and student engagement across diverse disciplines and contexts. The study concludes that AI should function as a supportive technology that complements human judgement rather than replacing academic responsibility, providing implications for universities, educators, researchers, students, and policymakers.
Noor Hanim binti Rahmat· International journal of res...· 0 citations
As artificial intelligence (AI) tools become increasingly accessible, their adoption by undergraduate students has outpaced institutional understanding of how, why, and with what consequences students use them. This scoping review maps the nature, extent, and gaps in the peer-reviewed evidence on undergraduates’ direct use of AI in higher education. A systematic search of Scopus, Web of Science, and MEDLINE identified 35 studies published between 2022 and 2024. Data were charted across four research questions, namely patterns of AI use, factors influencing adoption, educational outcomes, and emergent concerns. The evidence base is concentrated post-2022, dominated by cross-sectional surveys with single-institution convenience samples, and geographically weighted towards non-Western contexts. Within these studies, undergraduate AI use centres on academic task support, language learning, and supplemental instruction, with students consistently valuing efficiency and accessibility. However, significant areas of contestation emerge, including unresolved questions about whether AI enhances or undermines critical thinking, inconsistent demographic influences on adoption, and heterogeneous findings from the Technology Acceptance Model. Concerns about inaccuracy, academic integrity, diminished human interaction, and uneven AI literacy persist across contexts. Several priority gaps are identified, including the absence of longitudinal designs, adequately powered experimental studies on cognitive effects, intersectional demographic analyses, research on speech-based and multimodal AI tools, student perspectives on institutional AI policies, and intervention studies testing AI literacy curricula. This review provides a structured evidence map and research agenda for a rapidly evolving field, offering a direction of research on undergraduate AI engagement in higher education.
Jenna P. A. Orsava, Athena Ma, Matthew J. Cecchini et al.· Frontiers in Education· 0 citations
Abstract
Objective: This systematic review examines the impact of Artificial Intelligence (AI) on commerce education in higher education by synthesizing existing empirical evidence on its opportunities, challenges, and future implications for teaching and learning.
Design: A systematic literature review design was adopted following the PRISMA 2020 guidelines. Relevant peer-reviewed studies published between 2020 and 2026 were identified from major academic databases, including Scopus, Web of Science, Google Scholar, ERIC, and IEEE Xplore.
Methods: Studies were selected based on predefined inclusion and exclusion criteria focusing on AI applications in commerce and business education. Data were extracted and analyzed using a thematic synthesis approach to identify major patterns related to AI adoption, educational benefits, implementation challenges, ethical concerns, and future research directions.
Results: The review indicates that AI technologies, including generative AI tools, intelligent tutoring systems, and adaptive learning platforms, have significantly enhanced personalized learning, student engagement, research productivity, and digital competency among commerce students. However, challenges such as academic integrity, algorithmic bias, data privacy, faculty preparedness, and overdependence on AI-generated content remain significant barriers to effective implementation. Ethical governance and institutional readiness emerged as critical factors influencing the successful integration of AI in commerce education.
Conclusion: Artificial Intelligence has the potential to transform commerce education by improving learning outcomes and fostering innovation in teaching and assessment. Nevertheless, its successful adoption requires comprehensive institutional policies, faculty training, ethical guidelines, and responsible AI governance to ensure sustainable and effective implementation in higher education.
R. S, N. B., Sowmya K et al.· International Journal For Mu...· 0 citations