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With the emergence of technologies such as cloud computing, the Internet of Things(IOT), and educational big data (BD), personalized teaching (PT) has got more technical support. This article aims to explore how to apply emerging technologies to the teaching process, help teachers to carry out PT, stimulate students’ interest in learning, meet students’ individual needs, and break through traditional teaching methods. Through examining and collating a large number of literatures such as education BD and English PT platform, this paper analyses the status quo and needs, and builds a platform based on BD on this basis. The personalized English teaching platform in the campus network is used in practical teaching, and the effect of this platform in practical teaching is tested by comparative analysis. The results show that the English PT platform has realized PT well, enhanced the pertinence and initiative of students’ learning, and improved students’ comprehensive ability.

This research investigates the application of artificial intelligence (AI) optimization algorithms in higher education management and personalized teaching. Through a comprehensive literature review, theoretical analysis, and empirical study, the potential, effectiveness, and challenges of integrating AI algorithms into educational processes and systems are explored. The study demonstrates that AI optimization algorithms can effectively solve complex educational management problems and enable personalized learning experiences. An empirical study conducted over one academic semester shows significant improvements in students’ learning outcomes, engagement, satisfaction, and efficiency when using AI-driven personalized teaching compared to traditional approaches. The research also identifies challenges and limitations, including data privacy issues, algorithmic bias, and the need for human-AI interaction. Recommendations for future research directions are provided, emphasizing the importance of developing more adaptive algorithms, investigating long-term effects, and establishing ethical frameworks for AI in education.

In order to increase the effectiveness and personalization of music instruction, this paper aims to create a deep reinforcement learning (DRL)-based framework for creating music resources. Therefore, a Melody Generation Model in Music Education Based on Actor-Critic Framework (AC-MGME) is proposed. This model analyzes students’ learning status in real time through AC-MGME algorithm, generates melodies that match their abilities, and enhances the polyphonic generation effect by using multi-label classification and attention mechanism. According to the testing results, the proposed model clearly outperforms the baseline Deep Q-Network (DQN) algorithm, achieving 95.95% accuracy and 91.02% F1 score in melody generation quality with a generation time of 2.69 s. Therefore, the constructed model can not only generate high-quality personalized melody, but also shows a significant improvement in improving user experience and learning effect, providing reference direction for the generation and optimization of intelligent resources in music teaching.

To improve the current oil painting teaching mode in Chinese universities, this study combines deep learning technology and artificial intelligence technology to explore oil painting teaching. Firstly, the research status of individualized education and related research on image classification based on brush features are analyzed. Secondly, based on a convolutional neural network, mathematical morphology, and support vector machine, the oil painting classification model is constructed, in which the extracted features include color and brush features. Moreover, based on artificial intelligence technology and individualized education theory, a personalized intelligent oil painting teaching framework is built. Finally, the performance of the intelligent oil painting classification model is evaluated, and the content of the personalized intelligent oil painting teaching framework is explained. The results show that the average classification accuracy of oil painting is 90.25% when only brush features are extracted. When only color features are extracted, the average classification accuracy is over 89%. When the two features are extracted, the average accuracy of the oil painting classification model reaches 94.03%. Iterative Dichotomiser3, decision tree C4.5, and support vector machines have an average classification accuracy of 82.24%, 83.57%, and 94.03%. The training speed of epochs data with size 50 is faster than that of epochs original data with size 100, but the accuracy is slightly decreased. The personalized oil painting teaching system helps students adjust their learning plans according to their conditions, avoid learning repetitive content, and ultimately improve students' learning efficiency. Compared with other studies, this study obtains a good oil painting classification model and a personalized oil painting education system that plays a positive role in oil painting teaching. This study has laid the foundation for the development of higher art education.

Big data is a valuable resource for the Internet education platform. Big data mining is an important technology for Internet education platforms to provide personalized services for learners [1-3]. Using big data related technology to explore the inherent rules among students, teachers, courses and grades can provide reference for decision-makers of education and teaching, and can also provide guidance for the school’s teaching tasks and teaching plans [4, 5]. This paper briefly introduces the concept of data mining and personalized teaching, and studies the application of data mining technology in the personalized teaching of Internet education platform, in order to improve the school’s teaching management level and students’ academic performance.

Due to the fast-growing Internet speed, processing power, and the use of sophisticated algorithms, information is generated at a very fast speed. This information is broad in scope and covers a variety of fields, including the medical field, transportation sector, business firms, and education institutes. Due to the abundance of information, it is challenging to identify useful materials in general, but finding the right materials for students is particularly challenging. To address this issue, this paper aims to study the design of a personalized sports teaching resource recommendation system using a fuzzy clustering technique. To do so, we collected relevant data from entities such as students and teachers, which includes a range of attributes related to physical education, including curricular materials, student profiles, past performance records, and resource metadata. The collected data were then preprocessed to prepare it for further analysis. The features, preferences, and learning styles of each student are examined to develop student profiles based on the data that have been collected. A database schema was created that stored all the information related to physical education teaching resources, students, and teachers. The fuzzy C -means clustering algorithm is used to improve the collaborative filtering recommendation algorithm and reduce the data sparsity of the teaching resources recommendation algorithm. Through a series of experiments, it has been proven that the system designed in this paper can recommend suitable learning resources for different learners and has good performance. At the same time, the recommended method has higher recommendation accuracy and can effectively improve the quality of physical education teaching.

This paper uses Python technology and Slope One algorithm to realize a personalized teaching resource-sharing platform for non-heritage cultural learning resources. Intangible cultural heritage is introduced into the art design specialty and then incorporated with digital teaching techniques to propose an innovative approach to the construction of the art design specialty. By collecting relevant information to understand and analyze the current situation of non-heritage and art design professional construction, its characteristics and development trends are summarized. The results show that “non-heritage culture” + “art design” retrieved 184 articles, accounting for 0.25%. The average teaching evaluation is 4.48, which is a high level. Digitalized non-fiction culture is becoming more and more common. The number of teachers in higher vocational colleges who are over 40 years old and have lower education is only 8. In the analysis of the satisfaction survey of the personalized teaching resource-sharing platform, most of the students were satisfied with the updating and pushing of teaching resources, the diversification of the classroom form, and so on, and the interactivity was still lacking. Therefore, in the context of the digitization of non-heritage culture, the method of this paper provides a new path for the construction of the art design profession.

Promoting students’ individualized development is an important issue in the current stage of curriculum reform, and at the same time, promoting students’ individualized development also puts forward new requirements for teachers’ professionalism. This paper focuses on teachers’ personalized teaching level, takes elementary school English teachers as the research object, and adopts a combination of questionnaire survey and interview to understand the language evaluation literacy level of elementary school English teachers as well as the impact of personalized teaching literacy on the evaluation of teaching quality. Guided by the constructivist learning theory, the multiple intelligence theory and the personalized teaching theory, we analyzed the connotation of teachers’ teaching evaluation literacy, and designed the ten dimensions of foreign language teachers’ language evaluation literacy. The Language Evaluation Literacy Scale was utilized to determine the overall level of language evaluation literacy of elementary school English teachers. Explore the language evaluation literacy of teachers of different genders and different age stages. Using regression methods to analyze the influence of teachers’ basic information on the evaluation of personalized teaching quality. The overall language evaluation literacy of elementary school English teachers is at a medium level. Teachers’ basic information has a significant effect on the evaluation of personalized teaching quality (sig = 0.005 < 0.05), in which the training related to personalized teaching and teaching age have a significant negative effect on the evaluation of classroom teaching quality link.

Personalized music teaching in universities improves students’ learning and efficiency through adaptive guidance. This adaptability requires large study data and intelligent decisions based on the learner’s ability. This article introduces a Definitive Teaching Support System (DTSS) exclusive to music learning to augment this concept. This system is designed to increase the adaptability of music learning based on student interest and ability. The system is powered by a fuzzy decision system for identifying maximum teaching adaptability to personalized processes. Low-to-high-sorted personalization provides new endorsements for further music sessions in the fuzzy derivative process. Maximum adaptability is the target for new personalized sessions in the universities. This differs for various students from which a common adaptability level for monotonous recommendations is identified. The identified adaptability is set as a global maximum solution towards music learning personalization. The defuzzification reduces the chances of low adaptability by expelling the stationary adaptability outcomes.

The traditional Agent teaching and resource integration encounters troubles for it is only suited to closed teaching systems (systems do not permit Agents to dynamically join), but not suited to the heterogeneous Agents (Agents with different standards) participated, open (permit Agents to dynamically join), non –deterministic (with self-interest Agents), intelligent (Agents with mental states), and personalized (according to personal requirements) teaching environment. In this paper, aiming at the virtual community/VO based policy-driven teaching as a main line, and with the purpose to construct an open, heterogeneous Agents supported and cooperative teaching infrastructure, and the personalized teaching and resource integration application, we propose the key techniques including teaching ontology modeling, teaching mediation, teaching negotiation, and the teaching law and government. We design the experiment using the prototype of intelligent teaching and learning system based on two parts platform, and using the success rate of the task performance to compare the proposed system with the other system. The experiment shows that the proposed system has an average success rate enhancement of 60.9%, and supports heterogeneous Agents participated, open, intelligent, and personalized teaching and resource integration very well.