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Primary or secondary music directors, college or collaborative music directors, and music educators are trained in music education. Students in music education research and develop new methods for teaching and learning music. Through peer-reviewed publications and instruction at the University of Music for music teachers, students in the field of music conduct their research. Using artificial intelligence in the classroom is a challenge because music is an available domain that requires students and teachers to innovate and solve problems. In this paper, Music Education and Teaching based on AI (MET-AI) techniques are increasingly comprehensive with modern science and technology, enhancing music education. The use of artificial intelligence in music education breached the conventional paradigm of music education, particularly electronic music and innovative music software in private colleges, which has significantly enhanced the standard of teaching music and the teaching model for music education. The experimental results show us that the network teaching platform Music Major should be continuously improved based on artificial intelligence technologies. AI can make more optimized environments and professional music classes so that teachers and students can make the most of this and ensure smooth improvement in the network's teaching model. The MET-AI scores students' learning outcome rate of 95.2%, an efficiency ratio of 98.1%, the mean square error rate of 17.9%, accuracy ratio of 95.3%, teaching performance analysis ratio of 90.7%, the false-positive rate of 18.6%, true positive rate 95.7% and flexibility ratio of 92,1%. [ABSTRACT FROM AUTHOR]

Advances in Interactive Teaching Methodology (ITM) quickly transformshigher education and learning where ITM incorporates web-based face-to-face instruction. IfITM policies are expanding, teachers, lecturers, and administrators must discuss the theoretical foundations of ITM Research. The intellectually enhanced ability makesmore learning choices in some adverse circumstances.Using methods such as hands-on demonstrations, audio-visual aids, and regular teacher-student interaction, teachers actively involve their students in their learning through interactive Teaching. Students are constantly urged to take an active role in class discussions.This paper examines the Interactive Teaching Framework using Human-Machine Interaction (ITF-HMI) for online education Higher Education Systems.In the literature survey, the critical hypotheses used in prior research are the ad hoc models for embracing technologies, the performance studies of knowledge systems, the unified ideology of ITM deployment, the utilization of technology, and the propagation of progress theories. In higher education institutions' instructional phase, sophisticated information and communication technologies with virtual technologies allow an integrated collaboration. This framework discusses how the MOOC platform allows organizing e-learning, taking electronic classroom classes, following online courses, and carrying out synchronous and asynchronous learning. The online tasks created permitted the students to track their progress in all educational activities. The experiment findings helped direct a follow-up analysis in this area with a high acceptance rate amongst the participants. The simulation result of the proposed method enhances performance analysis (98.27%), prediction analysis (97.45%), accuracy analysis (96.27%), resiliency analysis (94.9%), efficiency analysis (98.8%). [ABSTRACT FROM AUTHOR]

• The concept and vision of Education 4.0 in higher education are presented. • Four core components are integrated to shape the proposed concept of Education 4.0: (i) Competencies, (ii) Learning Methods, (iii) Information and Communication Technologies, and (iv) Infrastructure. • The components of Education 4.0 can apply to different educational levels and non-formal and informal educational environments. • Three different case studies were presented to illustrate how current programs adopt the concept and vision of Education 4.0. Technological progress and its rapid evolution have positively affected the industrial sector and different productive/service sectors. One of the service-sectors that have benefited the most has been Education. In this sector, the implementation of current and emerging technologies combined with innovative pedagogical procedures and best practices is known as Education 4.0, which, in this paper, is described and mapped according to the well-known periods of the four industrial revolutions and related to higher education. Likewise, four core components of Education 4.0 to be used as a reference for the design of new projects in educational innovation are proposed (i) Competencies, (ii) Learning Methods, (iii) Information and Communication Technologies, and (iv) Infrastructure. Finally, three case studies applied to Engineering Education illustrate how the proposed components are considered in educational programs' designs. [Display omitted] [ABSTRACT FROM AUTHOR]

• Academic institutions need to be more efficient in supporting students at risk. • Automated machine learning helps in identifying optimal prediction models. • Ensemble models with balanced data enhances prediction using pre-admission data. Students' success has recently become a primary strategic objective for most institutions of higher education. With budget cuts and increasing operational costs, academic institutions are paying more attention to sustaining students' enrollment in their programs without compromising rigor and quality of education. With the scientific advancements in Big Data Analytics and Machine Learning, universities are increasingly relying on data to predict students' performance. Many initiatives and research projects addressed the use of students' behavioral and academic data to classify students and predict their future performance using advanced statistics and Machine Learning. To allow for early intervention, this paper proposes the use of Automated Machine Learning to enhance the accuracy of predicting student performance using data available prior to the start of the academic program. Image, graphical abstract [ABSTRACT FROM AUTHOR]