Jul 2026· Journal of education and science· 0 citations· 24 references
TL;DR
The thematic analysis demonstrates that while AI tools offer significant educational benefits — such as learning personalization, enhanced language acquisition, and the automation of administrative workloads — they present critical pedagogical and ethical challenges.
Abstract
The rapid integration of Artificial Intelligence (AI) into academic environments has accelerated significantly in the post-COVID-19 era. This study presents a systematic literature review (SLR) evaluating the reported pedagogical benefits and structural challenges of AI applications in education. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework guidelines, a comprehensive search was executed across major academic databases, targeting peer-reviewed empirical literature published between 2021 and 2026. Applying strict inclusion and exclusion criteria, a final analytical sample of twenty (N = 20) international studies was synthesized. The thematic analysis demonstrates that while AI tools offer significant educational benefits — such as learning personalization, enhanced language acquisition, and the automation of administrative workloads — they present critical pedagogical and ethical challenges. These risks focus heavily on cognitive disengagement, diminished student critical thinking, and algorithmic data privacy vulnerabilities. This review highlights that the successful deployment of AI relies on balancing technological integration with robust ethical frameworks while preserving the central instructional responsibility of human educators.
The findings indicate that AI positively influences academic performance primarily through enhanced engagement, personalization, predictive analytics, and self-efficacy.
Abdulkadir Abdullahi Mohamed, Ahmed Abdullahi Mohamud, Abdiwali Ali Addow· Journal of Natural Language...· 0 citations
The findings underscore that effective AI integration requires clear institutional policies, ethical frameworks, and equitable access, while future research should empirically investigate AI’s impact on learning, cognitive development, and educational equity.
Zafer Kadırhan· Çukurova Üniversitesi Sosyal...· 0 citations
While AI offers promising benefits for educational leaders, its adoption remains limited due to a range of challenges, including a lack of AI literacy, inadequate professional development, data privacy and fairness concerns, misinformation, the lack of capacity for emotional judgment, as well as access disparities.
M. Bellibaş, Figen Karaferye· Improving Schools· 0 citations
A framework for sustainable, human-centered integration of AI is proposed in which AI is restricted to technical verification and efficiency, while judgments on scientific merit, ethics, and paradigm-shifting research are reserved for appropriately valued human experts.
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) is increasingly used to automate administrative work, support institutional decision-making, optimise resources, and improve communication in educational organisations. Yet the empirical evidence remains fragmented, dominated by perception studies, and methodologically heterogeneous. This study synthesised empirical research published from 2020 to 2025 on AI-based educational administration and conducted an exploratory meta-analysis of statistically compatible outcomes. A structured search of scholarly indexes, publisher repositories, and citation networks identified 11 empirical studies covering school and higher-education settings. The narrative corpus represented more than 2,400 respondent- or school-level units and 150 administrative records. Only two studies (combined N = 260) reported sufficiently compatible statistics for standardised effect-size estimation. Using the author-reported pre–post effect, a random-effects model yielded Hedges’ g = 1.00, 95% CI [0.24, 1.77], with substantial heterogeneity (I² = 90.2%). A sensitivity analysis reconstructing the pre–post effect from the reported t statistic produced a more conservative pooled estimate of g = 0.65, 95% CI [0.45, 0.86]. Narrative findings indicated potential improvements in processing time, reporting accuracy, workflow coordination, decision support, and resource allocation. However, most studies were cross-sectional, single-site, perception-based, or lacked control groups, and one effect estimate showed internal statistical inconsistency. The evidence therefore supports a cautiously positive conclusion: AI can enhance educational administration when it is embedded in redesigned workflows, staff development, human validation, and risk-based governance, but the current evidence base is insufficient for strong causal or universal claims. Future research should use controlled multisite designs, standardised administrative outcome measures, longitudinal evaluation, transparent system descriptions, cost-effectiveness analysis, and explicit auditing of privacy, fairness, explainability, and accountability
Matyoqubovich Sobirov· International Education Tren...· 0 citations
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