In the context of globalization, oral English proficiency has become increasingly important, and the junior high school stage is a crucial period for cultivating students' oral English skills. However, traditional English oral teaching lacks real-life contexts, monotonous teaching methods, and insufficient personalized guidance. Artificial intelligence has injected new vitality into multimodal teaching and provided new solutions to these problems. This study adopts a literature review method, systematically retrieved mainstream databases such as CNKI, Science Citation Index, and Google Scholar, and reviewed relevant literature on artificial intelligence, multimodal teaching, English oral teaching, and junior high school English published in the past decade. It analyzed the theoretical basis, the current status of application, the impact on students' oral English, and the existing challenges. This study shows that multimodal teaching based on artificial intelligence is based on constructivism, situated cognition, and input-output hypotheses. It creates a virtual, immersive environment and provides personalized support and intelligent feedback, thereby effectively improving students' oral skills and their willingness to communicate. However, there are still challenges, such as technological dependence, the digital divide, changes in teacher roles, and ethical issues. Future research should optimize human-machine collaboration, track long-term effects, and improve the assessment system.

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In the context of digital transformation, technology has infused new vitality into Vygotsky's Zone of Proximal Development (ZPD) theory in English teaching. By examining key Chinese literature from 2015 to 2025, this article follows how ZPD and English instruction are integrated within technology-aided setups. The results indicate a ten-year shift from using single tools to having anintegrated, multi-technology ecosystem. This transformation, which is centered on personalized paths, immersive scenarios, and collaborative learning, boosts the efficiency as well as the students' capabilities.However, there are still quite a number of severe challenges existing, such as the unbalance between tool rationality and value rationality, the conflict between personalization and impartiality, and the gap between data and theory.This paper puts forward that future advancement ought to go beyond simply making use of technology. It endeavors to create a human - oriented, adaptable and equitable intelligent learning ecosystem, and also puts forward practical approaches and policy recommendations to guide the high - quality development of this domain.

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As the digital transformation of global higher education deepens, the widespread adoption of artificial intelligence (AI)-assisted tools is profoundly reshaping educational paradigms and academic collaboration models. However, most current research focuses solely on single technical attributes such as accuracy or processing speed, with a significant lack of exploration into the interplay between institutional policies, cultural orientations, and individual socioeconomic status. This study employs the Technology Acceptance Model (TAM) as its core analytical framework—effectively explaining individual adoption—to investigate the combined effects of technological attributes, institutional environments, and individual differences on university students' willingness and depth of AI tool adoption. It examines three primary cases: the campus proliferation of Grammarly, the Russell Group's ethical policy formation process, and ChatGPT's progressive payment model. Findings reveal that perceived value is a necessary condition for technology adoption, while meso-level institutional orientations and normative ethical reasoning serve as key variables moderating students' technological readiness and reducing adoption anxiety. Notably, income disparities are emerging as an invisible technological barrier.

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The rapid expansion of digital learning management systems (LMS) has shaped teaching, learning, and school management worldwide, yet their adoption and impact vary across countries. This study develops a conceptual framework to understand how LMS affect teaching quality and school management in different national contexts, using China and the United States as comparative cases. Drawing on socio-technical systems theory and comparative institutional analysis, LMS are conceptualized as educational infrastructures shaped by the interaction of technology, governance, and teacher practice. Rather than testing causal relationships, this study presents a set of theoretically based propositions to explain cross-national differences in LMS adoption patterns, teaching impacts, and management outcomes. It proposes that LMS effectiveness depends on national policy environments, governance models, and teacher professionalism. Centralized education systems tend to emphasize the standardization and governance functions of LMS, while decentralized systems are more likely to leverage LMS to improve administrative efficiency and coordination. By shifting focus to institutional embeddedness, this study contributes to research in comparative education and educational technology and provides theoretical guidance for future empirical studies on digital learning systems.

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