This research aims to systematically review the integration of artificial intelligence (AI) in English language teaching and learning. It specifically seeks to analyze the current literature to identify how AI could be utilized in English language classrooms, the specific tools and pedagogical approaches employed, and the challenges faced by educators. Using the PRISMA-guided Systematic Literature Review (SLR) methodology, articles were selected from Scopus, Science Direct, and ERIC, and then analyzed thematically with NVivo software. Findings reveal that AI enhances English teaching through tools like grammar checkers, chatbots, and language learning apps, with writing assistance being the most common application (54.55% of studies). Despite its benefits, challenges such as academic dishonesty, over-reliance on AI (27.27% of studies), linguistic issues, and technical problems remain significant. The study emphasizes the need for ethical considerations and teacher training to maximize AI’s potential. It also highlights societal concerns, including the digital divide, underscoring the importance of equitable access to AI-powered education for learners of all socioeconomic backgrounds.

Artificial intelligence (AI) has revolutionized higher education. The rapid adoption of artificial intelligence in education (AIED) tools has significantly transformed educational management, specifically in self-directed learning (SDL). This study examines the factors influencing Indonesian higher education students' intention to adopt AIED tools for self-directed learning using a combination of the Theory of Planned Behavior (TPB) with additional theories. A total of 322 university students from diverse academic backgrounds participated in the structured survey. This study utilized machine learning it was Artificial Neural Networks (ANN) to analyze nine factors, including attitude (AT), subjective norms (SN), perceived behavioral control (PBC), optimism (OP), user innovativeness (UI), perceived usefulness (PUF), facilitating conditions (FC), perception towards ai (PTA), and intention (IT) with a total of 41 items in the questionnaire. The model demonstrated high predictive accuracy, with SN emerging as the most significant factor to IT, followed by AT, PBC, PUF, FC, OP, and PTA. User innovativeness was the least influential factor due to the lowest accuracy. This study provides actionable insights for educators, policymakers, and technology developers by highlighting the critical roles of social influence, supportive infrastructure, and student beliefs in shaping AIED adoption for self-directed learning (SDL). This research not only fills an important gap in the literature but also offers a roadmap for designing inclusive, student-centered AI learning environments that empower learners and support the future of SDL in digital education.

This study aims to develop a competency framework for teaching natural science under the blended learning (BL) model for Natural Science education students at Thai Nguyen University of Education. Recognizing the increasing importance of BL in the context of modern education and the challenges teachers face during implementation, the modified Delphi method was employed to collect expert opinions, involving three rounds of surveys with 50 participants, including university lecturers and secondary school educational administrators. The research identifies seven core competency groups, including specialized knowledge, lesson design and evaluation competencies, classroom organization and management, student assessment and feedback, information technology competencies, experiment and simulation utilization in teaching, and basic knowledge of BL. The findings highlight the necessity of blending traditional teaching methods with modern technology to effectively implement the BL model, enhancing both the teaching process and students' learning outcomes. This framework is expected to serve as a crucial basis for teacher training universities to adjust their curricula and support educational administrators in fostering and enhancing the capacity of natural science teachers at the secondary level. This competency framework aims to support the professional development of Natural Science teachers and education students, ensuring their preparedness for the evolving demands of modern education. Furthermore, the study provides insights into the skills and knowledge that teachers need to acquire to adapt to the continuously evolving educational environment.