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With the development of modern science and technology, computer technology is more and more applied to the learning and development of martial arts. In order to study and expand the way of martial arts, this paper analyzes the role of the traditional way of martial arts teaching mode and computer-aided teaching mode. Computer technology breaks old ideas, breaks traditional teaching methods, and promotes the learning and development of martial arts. The way of martial arts collection is the combination of a series of movements, which means the protective significance of boxing and attack. Based on the analysis of the current emotional state of students and its reasons, this paper studies the positive influence of various elements of traditional martial arts on students’ emotional states and puts forward specific measures to enhance the positive effect of traditional martial arts on students’ emotional states.

The creation of a personalized ideological and political education resource suggestion system has significant practical implications for boosting the efficiency of ideological and political education. Many challenges are preventing ideological and political education from progressing smoothly in the online environment, and the mainstream values of education are being affected as an effect. The educational model is reversed, there is a single way to communicate, and the educational content is solidified. It constructs a personalized recommendation user model for educational resources, and then builds a recommendation collaborative filtering algorithm using Data Mining (DM), that produces personalized suggestion of ideological and political education resources, improving the Collaborative Filtering Algorithm (CFA) allows for personalized recommendations of teaching materials for ideological and political education. As a result, DM-CFA has become a tool for education professionals can gain a better understanding of the learning and life characteristics of current college students through the collaborative operate of ideological and political education entities in the network environment, that likewise offers a diverse range of educational materials and varieties. As a result of the curriculum’s ideological function in mounding students’ brains, the ruling class’s history, gender, religion and nationality develop in society. Developing various learning channels for pupils can increase their interest for ideological and political research and help students grow holistically.

To explore the realm of English education through contemporary advancements in artificial intelligence (AI), language education methods have developed significantly traditional approaches that usually have trouble with individualized learning needs and cannot handle diverse proficiency levels. This paper reveals the most prominent problem of inefficiency and non-personalized learning experiences in college English as a significant concern in the existing literature. In response, it suggests a novel method called Artificial Intelligence based English Teaching Method (AI-ETM). It has linked intelligent algorithms that can dynamically adapt teaching strategies and learning materials according to individual student’s needs, rate of comprehension and proficiency levels. The main aim of AI-ETM is to integrate AI technologies with hybrid particle swarm optimization (PSO) into English language teaching to improve effectiveness and efficiency and provide personalized learning experiences for students. This paper expects several outcomes from the implementation of AI-ETM. Among these is enhanced student engagement and motivation due to personalized learning experiences customized according to personal preferences and level of competence. Additionally, AI-ETM is expected to improve learning outcomes by offering targeted feedback and adaptive learning resources. This paper suggests that once AI-ETM is introduced, efficiency in delivering language instruction services will improve, thereby optimizing the allocation of educational resources.

The joint predictive-posterior approach to the study of the reliability of an engineering system, under a stress-strength model, is presented in this article. This new combined approach is original, is supported by charts and graphs and can be quite useful since it allows the realistic forecasting of system reliability, with known credibility levels, ahead of real experimentation and testing results. In this article, it is concretely illustrated by several engineering applications related to a highway bridge study and a quality control problem.

Colleges and universities increasingly incorporate ideological and political (IP) concepts into their courses as a fundamental prerequisite and a rising IP education trend under changing conditions. Students have difficulty sifting through the ever-growing amount of online information to locate what they need in learning resources. Technology-enhanced learning encompasses any technology that helps students study more effectively. This paper suggests a personalized learning resource recommendation system (PLRRS) for IPC. Personal learning recommendation systems (PLRSs) that do their task well will help students cope with the existing information overload. They will make sure that they receive the correct information at the right time and in the right format for their particular needs. E-learning systems that intentionally personalize their courses to the preferences, objectives, skills, and interests of the students they serve are engaging in personalized learning. In the last several years, researchers have been looking at ways to assist instructors in enhancing e-learning. Personalized learning scenarios are created by picking the most relevant learning objects based on an individual’s profile. A test score greatly improved for students in IPC after using the model in this research, which suggests that this model has a strong promotion value.

The integrated chatbot for artificial intelligence customizes the learning experience for the students in higher education platform. The chatbot tests the answers of the students and offers insights for the students to improve their learning skills. Further, It helps to improve students’ thinking ability and expectations in higher education platform. The demanding factors for the students in the higher education platform include the need for interactive learning in a non-linear world, enhancing the thinking ability and expectations of the students. This paper proposes Artificial Intelligence Based Inquiry Evaluation Student Learning System (AI-IESLS) to increase interactive learning experience in a non-linear environment. The principal aim of this system is to improve the learning ability of the student on a specific subject using concept mapping in the chatbot. In addition, mapping has been validated for students based on the probability distribution analysis using a concept mapping. Besides, the probability graph’s curve generated by this system assesses the student’s understanding of the topic. The simulation results have been analyzed among students based on concept mapping using the AI-IESLS system in correlation with the traditional approach based on assessment ratio, feedbacks, reciprocity, timing analysis, expectation ratio, and active learning factor.