The review found frequent use of supervised machine learning, regularized regression, EFA, CFA, SEM, and learning analytics, but limited reported use of explainable AI, subgroup fairness analysis, multimodal validation, and longitudinal designs.
Abstract
This scoping review maps how artificial intelligence (AI) is being connected to teacher competence in recent research. The review was based on 33 peer-reviewed articles published in 2022–2026 and identified through a bounded Web of Science search. Its purpose was not to evaluate intervention effectiveness, but to describe the extent, range, and nature of the available evidence on AI, machine learning (ML), and learning analytics (LA) in teacher assessment, modeling, and professional development within this indexed corpus. The mapped literature suggests two broad lines of work. One uses AI, ML, LA, and computational psychometrics to assess teaching practice, model teacher development, or measure AI-TPACK-related competence. The other treats AI as part of what teachers themselves need to know and do. Instruments represented in the corpus, such as TAICS, T-GAIC, AI-SRLS, AI-TPACK, and RAIS, broaden the concept of competence to include AI literacy, self-efficacy, ethical reasoning, readiness, and teacher–AI co-teaching. The review found frequent use of supervised machine learning, regularized regression, EFA, CFA, SEM, and learning analytics, but limited reported use of explainable AI, subgroup fairness analysis, multimodal validation, and longitudinal designs. Quality appraisal indicated stronger support for measurement-structure claims than for causal claims about professional-development effectiveness or high-stakes AI deployment.
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) has rapidly transformed language education by providing innovative tools that support teaching, learning, and assessment. In English for Specific Purposes (ESP) and English for Academic Purposes (EAP), AI technologies such as ChatGPT, automated writing evaluation, grammar assistants, machine translation, and intelligent tutoring systems have increasingly been adopted to facilitate academic and professional communication. However, evidence regarding AI implementation in ESP/EAP remains fragmented across educational technology, applied linguistics, artificial intelligence in education, and language assessment research. This study aims to map current AI-mediated pedagogical practices, identify major challenges, and propose future research directions for AI integration in ESP and EAP. A scoping review methodology guided by the Joanna Briggs Institute framework and PRISMA-ScR reporting guidelines was employed. Literature was identified using the Population–Concept–Context (PCC) framework, resulting in 45 relevant sources consisting of empirical studies, review articles, policy reports, methodological references, technical papers, and ESP/EAP theoretical works. The findings reveal that AI has primarily been utilized for writing feedback, revision support, materials development, prompt-based learning, simulated communication, AI literacy, and assessment redesign. Nevertheless, concerns remain regarding hallucination, linguistic bias, disciplinary authenticity, privacy, academic integrity, and learner overdependence. The review concludes that effective AI implementation in ESP/EAP requires human verification, genre-based pedagogy, AI literacy, learner agency, and transparent assessment practices. These findings provide practical implications for language teachers, curriculum developers, and higher education institutions seeking responsible AI integration.
Luluk Iswati, Abd. Rajab· Jurnal Pendidikan dan Sastra...· 0 citations
AI's ability to change educational leadership and supervision has garnered attention due to its rapid expansion. Early research has examined AI applications in educational administration, leadership decision-making, and learning analytics, but research on its effects on educational supervision is fragmented. Authors will map the current state of AI in educational supervision and leadership, identify new trends, potential, and issues, and provide an integrated conceptual framework for future study and practice. PRISMA-ScR scoping review was used in this investigation. A comprehensive search of major academic databases yielded 29 publications published between 2021 and 2026 that met the inclusion criteria for final analysis. The results were analysed using thematic coding and six domains: supervisory transformation, human–AI collaboration, data-driven governance, sociotechnical preparation, ethical governance, and future-oriented innovation. The results show that AI is making instructional supervision data-driven, predictive, and collaborative. The literature emphasises the growing importance of human–AI collaboration, learning analytics, evidence-based decision-making, and ethical governance in educational growth. However, digital preparation, algorithmic transparency, data privacy, and organisational competency issues hinder uptake. The AI-Supervision Framework, an integrative model of AI-enabled educational supervision as a human-centered ecosystem involving technology, data, leadership, organisational preparation, and ethical governance, is presented in this review. This review contributes to the field by consolidating fragmented scholarship on AI, educational leadership, learning analytics, and instructional supervision into a coherent evidence base. It advances theoretical understanding by conceptualising AI-enabled supervision as a human-centred sociotechnical ecosystem and provides an analytic foundation for future empirical research.
Rachmat Satria· International Journal of Mul...· 0 citations
This study examines and refines an existing competence model by incorporating perspectives from diverse stakeholder groups and elaborated to provide an empirically grounded, didactically actionable framework for teacher education and future research.
Luca Mikula· The European Educational Res...· 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
This study systematically reviews the current evidence on AI in academic writing using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) guidelines and contributes a comprehensive synthesis of current research by integrating pedagogical, assessment, and human perspectives through the TPACK, assessment, and Technology Acceptance Model frameworks.
Saidatul Akmar Zainal Abidin, Noor Hanim binti Rahmat· International journal of res...· 0 citations
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