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The empowerment of educational reform and innovation through AI technology has become a topic of increasing interest in the field of education. The advent of AI technology has made comprehensive and in-depth teaching evaluation possible, serving as a significant driving force for efficient and precise teaching. There were few empirical studies on the application of high-quality precision teaching models in the field of compulsory education, and the learning difficulty of technology and the teaching burden on teachers have become significant factors hindering the use of technology to support education. This study analyzed teaching models from the perspectives of teachers' teaching burdens and students' learning obstacles, and was committed to relying on intelligent technology to construct a new precision teaching model, an educational diagnosis–feedback–intervention path that covered the entire teaching process, from the dimensions of teacher behavior, student behavior, and parent behavior, aiming to assist teachers in efficient teaching and students in personalized learning. This study was conducted with nine science classes, including about 540 people in the second year of high school at a Middle School in China; six classes were the intervention groups while the last three classes were control groups, and a survey of 19 teachers from the intervention classes was carried out. The results showed that this model can significantly improve students' academic performance in science subjects, especially in mathematics and chemistry. It has increased the proportion of high-achieving students, reduced the proportion of low-achieving students, stimulated students' self-directed learning ability, cultivated a positive attitude towards science learning, and explained the key points of using a precision teaching model in different disciplines. It has achieved a deep integration of education and technology, helping to increase the efficiency and reduce the burden of teaching. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) has had a significant impact on language education, arousing the related studies from different fields and perspectives. However, the extant review articles only focused on a certain aspect in the field, thus neglecting the multi-disciplinary and multi-perspective features of AI-enhanced language education. The aim of the current study is to bridge this gap by reviewing the 812 articles selected from Web of Science via CiteSpace. The study found that the existing studies were mainly conducted from the perspectives of computer science, linguistics and psychology. Besides, the individualized development of language education supported by technology was not obvious, and the degree of human-computer interaction was weak. Moreover, previous studies focused more on the advantages of AI-enhanced language education than the disadvantages, and the empirical research to analyze specific problems was insufficient. In the future, the research tends to strengthen the cooperation among related disciplines and focus on the individualized development of language education. In addition, empirical research should be strengthened to explore the efficiency of AI-enhanced language education in order to promote the reform of language education.

The empowerment of educational reform and innovation through AI technology has become a topic of increasing interest in the field of education. The advent of AI technology has made comprehensive and in-depth teaching evaluation possible, serving as a significant driving force for efficient and precise teaching. There were few empirical studies on the application of high-quality precision teaching models in the field of compulsory education, and the learning difficulty of technology and the teaching burden on teachers have become significant factors hindering the use of technology to support education. This study analyzed teaching models from the perspectives of teachers’ teaching burdens and students’ learning obstacles, and was committed to relying on intelligent technology to construct a new precision teaching model, an educational diagnosis–feedback–intervention path that covered the entire teaching process, from the dimensions of teacher behavior, student behavior, and parent behavior, aiming to assist teachers in efficient teaching and students in personalized learning. This study was conducted with nine science classes, including about 540 people in the second year of high school at a Middle School in China; six classes were the intervention groups while the last three classes were control groups, and a survey of 19 teachers from the intervention classes was carried out. The results showed that this model can significantly improve students’ academic performance in science subjects, especially in mathematics and chemistry. It has increased the proportion of high-achieving students, reduced the proportion of low-achieving students, stimulated students’ self-directed learning ability, cultivated a positive attitude towards science learning, and explained the key points of using a precision teaching model in different disciplines. It has achieved a deep integration of education and technology, helping to increase the efficiency and reduce the burden of teaching.

Artificial Intelligence (AI) has had a significant impact on language education, arousing the related studies from different fields and perspectives. However, the extant review articles only focused on a certain aspect in the field, thus neglecting the multi-disciplinary and multi-perspective features of AI-enhanced language education. The aim of the current study is to bridge this gap by reviewing the 812 articles selected from Web of Science via CiteSpace. The study found that the existing studies were mainly conducted from the perspectives of computer science, linguistics and psychology. Besides, the individualized development of language education supported by technology was not obvious, and the degree of human-computer interaction was weak. Moreover, previous studies focused more on the advantages of AI-enhanced language education than the disadvantages, and the empirical research to analyze specific problems was insufficient. In the future, the research tends to strengthen the cooperation among related disciplines and focus on the individualized development of language education. In addition, empirical research should be strengthened to explore the efficiency of AI-enhanced language education in order to promote the reform of language education., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)