It is argued that the teacher’s role in AI-based instruction is being reconfigured rather than diminished, offering implications for teacher professional development and the design of teacher-facing AI systems.
Abstract
The rapid advancement of artificial intelligence has made AI-based instruction, including intelligent tutoring systems, learning analytics dashboards, adaptive learning platforms, and generative AI, increasingly prevalent in K-12 education. However, AI-generated information does not automatically lead to pedagogical action; it gains instructional meaning only when teachers interpret, judge, and translate it into situated classroom support. Following PRISMA guidelines, this systematic review analyzed 29 peer-reviewed English-language studies published between 2016 and 2025 across six databases to examine how teacher intervention is constituted in K-12 AI-based instruction and what conditions shape its reported effects. The synthesis was organized around three dimensions: process, strategy, and effect. Findings indicate that teacher intervention operates as a cyclical process of monitoring, judgment, intervention, and orchestration, through which AI-generated information is transformed into pedagogical action and subsequent classroom adjustment. Three broad intervention strategies were identified: pedagogical translation of AI outputs, design of learning support, and reconstruction of interaction structures. Reported benefits for student learning and teacher orchestration were generally promising but conditional, varying according to the interpretability of AI information, intervention timing, target level, teachers’ implementation feasibility, and students’ autonomy. By presenting an integrated conditional framework, this review argues that the teacher’s role in AI-based instruction is being reconfigured rather than diminished, offering implications for teacher professional development and the design of teacher-facing AI systems.
Artificial intelligence (AI) is increasingly embedded in English as a Foreign/Second Language (EFL/ESL) education, yet existing research remains fragmented across tools, skills, and short-term outcomes. This review examines how AI is integrated into EFL/ESL education across language skills, instructional domains, and educational contexts.
This PRISMA-guided systematic review synthesised 221 unique peer-reviewed publications. Moving beyond a tool-centred inventory, the review analysed AI through four interrelated dimensions: pedagogical roles, mediating processes, reported outcomes, and contextual constraints.
AI systems increasingly operated as multifunctional pedagogical actors rather than isolated instructional aids. The most frequently coded roles were teacher orchestration/support, content and materials generation, assessment or diagnosis, coaching or practice companionship, tutoring or scaffolding, and conversational partnership. AI-mediated learning was especially concentrated in writing and speaking/communication, where text-based, voice-based, multimodal, immersive, and adaptive systems supported feedback, revision, rehearsal, and learner–system interaction. Reported benefits included expanded practice opportunities, accelerated feedback cycles, redistributed instructional labour, skill development, learner autonomy, affective support, assessment and monitoring, collaboration, and multilingual engagement. Recurring challenges included technical reliability and feedback quality, teacher readiness, privacy and data security, learner over-reliance, infrastructural inequality, bias and cultural mismatch, authorship and academic-integrity concerns, and methodological weaknesses.
Interpreted through a three-layer framework of efficiency, pedagogy, and ideology, the synthesis conceptualises AI in EFL/ESL education as a pedagogical ecology in which tools, learners, teachers, feedback regimes, assessment practices, institutional infrastructures, and governance arrangements interact. The review provides a theory-informed framework for analysing, designing, and governing AI-mediated language education beyond simple claims of technological effectiveness.
Arash Javadinejad, M. Davari· Frontiers in Education· 0 citations
One of the first comprehensive syntheses of AI integration across the entire CAR cycle is offered, linking it explicitly to critical-thinking development within a reflective, teacher-led inquiry framework, an intersection that remains underexplored in the extant literature.
Yusriani Yusriani, Andi Yuyung· ETDC: Indonesian Journal of...· 0 citations
This narrative review synthesises 20 studies on artificial intelligence (AI)-supported calculus instruction, focusing on the relationship between teacher preparation and student outcomes within U.S. STEM pipelines. The evidence suggests that AI tools, including intelligent tutoring systems, generative chatbots, and adaptive platforms, may support student engagement and, under guided instructional conditions, conceptual understanding and problem-solving. However, the findings indicate that these benefits are conditional on teacher guidance, pedagogical alignment, and active student use. The literature remains fragmented: studies of AI tools rarely examine teacher preparation, while studies of teacher readiness seldom measure student calculus outcomes. This review integrates these strands and proposes a cross-level framework linking AI design, teacher preparation, instructional implementation, and student achievement. It also identifies major gaps, including the predominance of small-scale studies, limited longitudinal evidence, limited attention to equity, and the absence of direct research on U.S. STEM pipeline outcomes. The proposed framework offers practical guidance for teacher preparation programmes and institutional policies. It suggests that professional development should focus on helping teachers interpret AI-generated feedback, design effective prompts, and align AI tools with conceptual learning goals. These findings imply that AI-supported calculus instruction should be treated as a coordinated instructional system rather than a technology add-on if it is to contribute meaningfully to the U.S. STEM pipeline.
Emmanuel Nsadha, E. Tetteh· Asian Research Journal of Ma...· 0 citations
It is suggested that AI can enhance drafting, revision, and feedback processes, improving coherence, metacognition, and writing confidence, however, these benefits are accompanied by persistent concerns regarding ethical ambiguity, inconsistent policy guidance, and insufficient faculty training.
Samira Dichari, Fadi Jaber· Journal of Education and Tra...· 0 citations
This paper examines the opportunities and risks associated with student-facing conversational artificial intelligence (AI) in primary education. It aims to evaluate how large language models (LLMs) can support personalised learning while identifying developmental, pedagogical and ethical challenges. Rather than treating benefits and risks as discrete factors, the study conceptualises AI as a socio-technical intervention that reshapes relationships between learners, teachers and knowledge.
The paper adopts a conceptual and theory-driven approach, synthesising current literature on AI in education, pedagogical theories and emerging practices in primary classrooms. The analysis is structured through a tension-oriented synthesis, identifying points of alignment and misalignment between AI affordances and core learning processes in primary classrooms. Based on this synthesis, the study develops a set of guiding principles grounded in developmental and educational considerations.
Conversational AI offers significant benefits, including personalised learning support, immediate feedback and reduced teacher workload. However, risks include cognitive offloading, overreliance on AI, misalignment with curriculum goals and ethical concerns such as bias and privacy. The analysis suggests that these are not independent challenges but reflect underlying tensions between technological capabilities and pedagogical requirements.
The study is conceptual and lacks empirical validation. Future research should focus on longitudinal and classroom-based studies to assess the actual impact of AI on primary learners' cognitive and social development. The paper highlights the need for interdisciplinary research bridging education, AI and developmental psychology.
The study proposes a set of guiding principles derived from the identified tensions, emphasising teacher-mediated interaction, developmental calibration of AI use, transparency, curriculum alignment, privacy protection and equity considerations. These principles provide a structured basis for integrating AI in ways that support learning processes while mitigating potential risks.
The adoption of AI in primary education raises concerns about equity, access and digital divides. Without careful implementation, AI may reinforce existing inequalities. Promoting critical AI literacy and ethical awareness among young learners is essential to prepare them for responsible participation in an AI-driven society.
This paper contributes a developmentally informed, tension-based conceptual framework for understanding student-facing AI in primary education. By reframing commonly identified opportunities and risks as interrelated tensions, it offers a more analytically grounded basis for guiding AI integration beyond descriptive or normative approaches.
This paper explores the preparation of teachers through the lens of modern educational and learning theories, with particular attention to how artificial intelligence (AI) can augment educational planning and professional teacher development. Three major theoretical frameworks are examined: (1) Constructivist Theory, rooted in Piaget's cognitive development model, emphasizing the learner's active role in knowledge construction; (2) Meaningful Learning Theory (Ausubel), which underscores the critical link between new knowledge and pre-existing cognitive structures; and (3) Information Organization and Processing Theory, which explains how memory encoding and retrieval affect learning outcomes. The paper further integrates the McCarthy 4MAT model of learning styles to illustrate how AI-driven, learner centred strategies can be operationalized in contemporary teacher preparation programmers. Findings suggest that effective teacher preparation must transcend content delivery and focus on equipping teachers with adaptive, student-centred methodologies grounded in cognitive science.
A. Al-Zahrani, Ghada Abdelhady· International Research Journ...· 0 citations