The advent of artificial intelligence (AI) has brought significant and transformative alterations to traditional music education. This study examines the progress of AI technology in music education by conducting a systematic review using the PRISMA methodology. Articles were selected for inclusion based on the criterion of specifically describing the utilization of AI in the instruction and acquisition of music. The search was performed on April 9, 2024, via the Web of Science and SCOPUS databases. The search terms “music education” and “artificial intelligence” were employed to ascertain relevant scholarly research. The group of papers underwent scrutiny by various researchers to ascertain their adherence to the established criteria. The articles that were verified by a minimum of two researchers were chosen. 31 articles were finally screened, and the results were divided into two sections: the development of AI in music education and innovative music pedagogy based on AI. A key finding is that the implementation of bibliometric analyses suggests that AI research in music education is still in its infancy. Prior research has primarily concentrated on music instruction at the university level, with a particular emphasis on the integration of AI in music education in China. In addition, this study identifies four specific facets of AI through the reshaping of music pedagogy: enhancing personalized music teaching, providing timely feedback on learning, supporting interactive experiences, and providing organized digital materials.

Educational technology has altered conceptions of effective learning. The combination of virtual and physical instruction is constantly developing and growing, making blended learning a growing trend. While blended learning has been extensively reviewed in various fields, there is a lack of research on its application in music education. This study aims to understand the current development of blended learning in the field of music education; this paper will collect literature on blended learning from the past ten years. Following the PRISMA guidelines, we have systematically analysed the blended learning strategies and outcomes in music education. This study examined Web of Science, Scopus, and ERIC for pertinent strings. Thirteen publications passed the strict screening procedure and were included in the analysis of blended learning in music education. This study found that the flipped classroom blended learning model has emerged as the prevailing method in music education, enhancing learners’ musical perception, performance, and overall experience. The findings reveal a need for more research on blended learning in music education.

The rapid emergence of generative artificial intelligence (GenAI) in higher education has prompted critical discussions regarding its impact on faculty workload. Existing evidence is fragmented and lacks systematic synthesis, leaving it unclear whether GenAI alleviates or intensifies academic work. To address this gap, this study conducted a systematic review of 30 empirical studies following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, with the methodological quality of included studies assessed using the Mixed Methods Appraisal Tool (MMAT). Findings reveal that GenAI's impact on faculty workload is not a simple increase or decrease but represents a workload transformation. It improves efficiency in routine and procedural tasks (e.g., teaching material preparation, basic grading), while simultaneously introducing new professional responsibilities (e.g., quality review of AI outputs, ethical judgement, academic integrity monitoring) that may increase workload in specific areas. The direction and extent of workload change depend on both individual and organisational conditions. At the individual level, digital literacy, teaching experience, and AI judgement ability shape faculty members' capacity to use GenAI effectively. At the organisational level, policy clarity, systematic training, ethical guidance, and reliable technical infrastructure determine whether GenAI functions as a work resource or becomes an additional burden. These findings highlight the conditional and context-dependent nature of GenAI's effects, providing actionable guidance for higher education institutions to integrate AI technologies effectively while supporting faculty development and maintaining educational standards.

This study examined the influence of lecturers’ artificial intelligence (AI) leadership on postgraduate student teachers’ motivation to integrate AI into their curricula in Malaysian higher education. Using a sample of 62 participants, the study employed a mixed-methods approach to explore ethical implications and the alignment of AI with traditional teaching practices. By means of open-ended questions and online surveys, the study generated both quantitative and qualitative understanding of how leadership influences acceptance of AI in educational settings. Key findings showed that transformative and visionary AI leadership approaches not only improve feedback systems and tailored learning opportunities but also inspire teachers by means of interactive, game-like learning activities. AI leadership enables early identification of learning gaps by means of real-time analytics, enabling targeted interventions and a more inclusive learning environment. However, over-reliance on AI highlights the need for strategic planning to ensure that AI complements rather than replaces traditional teaching methods. The research emphasized the need for strategic leadership and professional development in embedding AI ethically and successfully inside curricula, offering a framework for both curriculum design and educator training programs. These results support current debates on educational innovation and place leadership as key in promoting a balanced, ethical AI integration matched with present educational aims.

Background: The prevalence rates of depression and anxiety are constantly increasing, and the aging population is growing in China. Music-based intervention is a nonpharmacological treatment that is used to treat depression and anxiety and that has very few side effects. The aim of this study was to explore the effect of Chinese 5-element music-based intervention (CFEMBI) based on the midnight-noon ebb-flow theory on depression and anxiety among older adults. Methods: A total of 86 patients were divided into the control and experimental groups. The control group underwent a traditional CFEMBI, and the experimental group underwent a CFEMBI based on the midnight-noon ebb-flow theory. Outcomes were assessed at the 4th week, 8th week, and 12th week during the intervention. Results: After the 12th week, there was a significant difference in scores on the Hamilton Depression Rating Scale (HAM-D) between the control and experimental groups (Z = −6.375, P value < 0.001). Additionally, there was a significant difference in the cure rate of depression between the 2 groups at week 12 (P < .001). After the 8th week, there was a significant difference in Hamilton Anxiety Rating Scale (HAM-A) scores between the 2 groups (t = 7.422, P value < 0.001). Furthermore, there was a significant difference in scores on the Quality of Life, Enjoyment, and Satisfaction Questionnaire-Short Form (Q-LES-Q-SF) between the 2 groups (Z = 4.991, P value < 0.001; Z = 3.023, P value < 0.01; Z = 3.023, P value < 0.01). Conclusions: CFEMBI based on the midnight-noon ebb-flow theory is effective at alleviating depression and anxiety in older adults and improving life satisfaction.

Background: Depression and anxiety are among the most prevalent mood disorders worldwide, affecting hundreds of millions of people and contributing to significant disability, particularly in older adults. Conventional treatments such as pharmacotherapy and psychotherapy are effective but often limited by side effects or accessibility. As a complementary approach, Chinese Five-Element Music (CFEM), rooted in Traditional Chinese Medicine, aims to restore emotional balance through the connection between musical tones, organ systems, and emotions. Recent studies suggest its potential to alleviate psychological symptoms, yet evidence in older populations remains limited. Methods: The systematic review identified and mapped available literature on CFEM interventions, guiding the selection and analysis of randomized controlled trials (RCTs) for meta-analysis. Databases including Web of Science, PubMed, CNKI, Wanfang, and CQVIP were searched in March 2025. Eighteen RCTs were included. Outcome measures assessed included the Hamilton Depression Rating Scale (HAMD), Hamilton Anxiety Rating Scale (HAMA), Zung Self-Rating Depression and Anxiety Scales (SDS and SAS), Pittsburgh Sleep Quality Index (PSQI), overall response rate (ORR), and Quality of Life (QOL). The risk of bias was assessed using the Cochrane Handbook criteria. Most studies reported adequate randomization, but blinding methods were often unclear. Results: A meta-analysis using a random-effects model was conducted on 18 RCTs. The results indicated that, compared to conventional care, the CFEM intervention significantly alleviated symptoms in older adults with depression and anxiety. Specifically, the outcomes were as follows: HAMD score (MD = −3.63, 95% CI: −4.97 to −2.3, P < .00001), HAMA score (MD = −3.89, 95% CI: −5.80 to −1.97, P < .0001), SAD score (MD = −6.69, 95% CI: −8.32 to −5.07, P < .00001), SAS score (MD = −6.84, 95% CI: −10.76 to −2.92, P = .0006), ORR score (95% CI: 1.61 to 4.89, P = .0003), QOL score (MD = −0.58, 95% CI: −15.65 to 14.48, P = .94), PSQI score (MD = −1.86, 95% CI: −2.54 to −1.17, P < .00001). Conclusion: CFEM may be an effective complementary intervention to alleviate depression, anxiety, and sleep problems in older adults. However, due to moderate study quality and lack of blinding, further high-quality trials are needed.