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Artificial Intelligence in Music Education
International Journal of Human-Computer Interaction
... ChatGPT-4 exhibits considerable potential within the realm of education and has garnered recognition in the literature for its capacity to support and enhance music education outcomes (McGee, 2023). An example of a potential instructional strategy involves facilitating student engagement with ChatGPT-4 through dialogs, which can serve to augment their understanding of complex music theory principles (Javaid et al., 2023;Li & Wang, 2023;McGee, 2023). This interactive approach encompasses a wide range of topics, including scales, chords, harmony, rhythm, and various other related concepts. ...
... Notwithstanding its use among learners in different fields on a global scale, a thorough comprehension of the impact of ChatGPT technology on students' music learning outcomes remains an issue that needs to be firmly established (McGee, 2023). Furthermore, it is worth noting that there is a notable number of empirical studies that provide substantial evidence supporting the advantages linked to the incorporation of ChatGPT-4 technology into various educational settings that focus on music learning (Li & Wang, 2023;McGee, 2023). In order to bridge these gaps identified in the research, the present study aims to investigate the potential influence of ChatGPT-4 technology on augmenting the quality of students' music learning experience. ...
... Having the flexibility to create changes makes for a more engaging and beneficial learning environment (Baidoo-Anu and Owusu Ansah, 2023;Hariri, 2023). The ChatGPT-4 system has the potential to act as a live, online, music instructor, guiding and critiquing students as they hone their music skills (Li & Wang, 2023). The system can be used to monitor students' instrumental performances, evaluate their technique, and offer helpful feedback for improvement (Li & Wang, 2023). ...
The application of artificial intelligence technologies, such as ChatGPT-4, offers a substantial chance to improve the learning process. The promising prospect of utilizing artificial intelligence and ChatGPT-4 to enhance educational interactions by offering tailored and engaging experiences is highly enticing. The promise of ChatGPT-4 resides in customizing teaching materials to correspond with the distinct learning objectives and musical preferences of individual students. Nevertheless, only a restricted amount of research has examined the effectiveness of ChatGPT-4 in improving students’ learning outcomes. The present study aims to investigate the influence of ChatGPT-4 on augmenting students’ learning outcomes concerning music. To this end, an experiment was conducted with 74 undergraduate students hailing from Chugye University for the Arts, Korea. The participants were randomly assigned to two distinct groups. One experimental group was assigned to utilize ChatGPT-4, while the control group received traditional teacher-led instructions. Additionally, a questionnaire was employed. Data analysis was conducted using SPSS 22. The results reveal that the experimental group exhibits a higher level of musical knowledge acquisition in comparison to the control group. Moreover, the results reveal that the experimental group demonstrates enhanced proficiency in the acquisition of musical knowledge when assessing their scores prior to and following the implementation of the intervention. The results indicate that the students demonstrate a propensity to utilize ChatGPT-4 as a tool for enhancing their music learning abilities. The influence of ChatGPT-4 on students’ music learning outcomes is noteworthy, as it possesses the capacity to foster a perception of tranquility and ease among students. A multitude of suggestions and recommendations were proffered.
... However, in music education, the main subject of assessment is the ability to play a musical instrument. The specifics of music education present some challenges to the introduction of AI technologies, for example, the increased complexity of working with sound (Li & Wang, 2024). ...
... Other researchers have tested the use of AI chatbots in music training and revealed the positive impact of this technology on learning outcomes (an increase of 15%). However, since the study lacks information about the specifics of using the chatbot, it is impossible to fully compare the results (Li & Wang, 2024). In another study, the author demonstrates the positive effect of using the DeepBach generative model for creating polyphonic music in the learning process. ...
Artificial intelligence (AI), due to its extensive capabilities for analyzing incoming information and providing personalized and detailed feedback, has significant potential for use in education. This study aims to investigate an application that analyzes the performance of musical exercises and provides instant AI-based feedback. The main task is to explore the impact of using this application on learning, as well as on the indicators of self-efficacy and academic locus of control. These indicators reflect an individual’s beliefs about their ability to determine their own success in learning activities. According to the results, in the experimental group that studied using instant feedback provided by AI, the self-efficacy score on the Self-Efficacy for Musical Learning scale increased from 51.13 to 57.38. A dependent sample t-test confirmed the significance of differences at the level of p = 0.012. In turn, the control group studied according to the classical curriculum and showed no significant changes (53.46 in post-test vs. 52.78 in pre-pest, p = 0.756). Additionally, in the experimental group, the total score on the test of musical skills significantly increased from 19.89 to 34.08 out of 50, compared to an increase from 20.13 to 28.26 in the control group. A Student’s t-test was used to compare the post-test results between the experimental and control groups. The comparison revealed significant differences in self-efficacy and musical skills in favor of the experimental group. At the same time, there was no significant effect of the studied educational intervention on academic locus of control measured using the Academic Locus of Control Scale. The results can be useful for curriculum developers and researchers studying the impact of AI technologies, particularly those that provide instant feedback for error correction. Further research may focus on exploring the longer-term effects of regular use of AI-based feedback in music training.
... Wissner (2024) proposed a pedagogical approach using generative AI to enhance students' learning of music history: individuals can use chatbots 1 to talk with simulated composers from different musical periods, co-creating music with generative AI tools 2 based on these composers' styles, and discussing the authenticity of the generated musical content. In a quasi-experimental study, Li and Wang (2024) found that music students who used a chatbot app designed by the team for piano learning achieved better learning outcomes than those who attended conventional classes. Similar findings were reported by Lv (2023), who found that university students enrolled in a piano course that employed the use of an assisted learning tool built on a deep learning neural network (Barreto et al., 2016) achieved better results than those in a control group. ...
... The effective implementation of these recommendations depends on the competencies of the teachers who design and deliver AI-integrated pedagogies in the music classroom (Cheng, 2024). Apart from a basic understanding of generative AI and its educational affordances as guided by existing frameworks (e.g., Cheng et al., 2024;Mishra et al., 2023), professional development for music teachers should also include the awareness on stylistic outputs and creative capacity of generative AI (Carnovalini & Rodà, 2020;Civit et al., 2022), emerging music practices (Miranda, 2021), pedagogical approaches for empowering students in music learning (Li & Wang, 2024), curriculum and assessment design integrated with generative AI (Wei et al., 2022), copyright and ethical considerations , and the impact of generative AI on diversity and equity in students' learning (Barenboim et al., 2024). Given that they may suffer from lower self-efficiency, as well as being less likely to integrate technology into classroom practices compared to their younger colleagues (Leong, 2017;Sarıkaya, 2022), professional development programs that focus on these attributes are deemed to be particularly crucial for senior music teachers. ...
The widespread application of generative artificial intelligence (AI) poses both opportunities and challenges in formal music education. Students can now effortlessly compose using simple text prompts with AI music generators, a situation which encourages innovative pedagogical approaches and democratizes creativity in the music classroom. However, concerns about cultural bias, originality, equity, and the ethical use of generative AI in school music education require careful regulation. This article highlights both the potential benefits and risks through a review of current applications of generative AI in music education, in the process proposing a set of policy recommendations that can serve to ethically and effectively guide its use. These include enhancing AI literacy among students and teachers, developing assessment frameworks that reflect the collaborative nature of AI-assisted music creation, defining acceptable boundaries in terms of ensuring equitable access to AI tools, and providing professional development opportunities for music teachers. By maintaining an awareness of technological advancements and their impact on students’ musical engagement, school music education can remain relevant to young people’s evolving musical experiences while simultaneously preparing them to engage confidently, creatively, and critically with the complex landscape of generative AI.
... Machine learning applications in music listening have significantly benefited music education [1]- [3], musicology [3]- [6], and performance theory [7], [8]. Advances in this area are predominantly data-driven, emphasizing the importance of comprehensive datasets [9]- [11]. ...
... Machine learning applications in music listening have significantly benefited music education [1]- [3], musicology [3]- [6], and performance theory [7], [8]. Advances in this area are predominantly data-driven, emphasizing the importance of comprehensive datasets [9]- [11]. ...
Guitar-related machine listening research involves tasks like timbre transfer, performance generation, and automatic transcription. However, small datasets often limit model robustness due to insufficient acoustic diversity and musical content. To address these issues, we introduce Guitar-TECHS, a comprehensive dataset featuring a variety of guitar techniques, musical excerpts, chords, and scales. These elements are performed by diverse musicians across various recording settings. Guitar-TECHS incorporates recordings from two stereo microphones: an egocentric microphone positioned on the performer's head and an exocentric microphone placed in front of the performer. It also includes direct input recordings and microphoned amplifier outputs, offering a wide spectrum of audio inputs and recording qualities. All signals and MIDI labels are properly synchronized. Its multi-perspective and multi-modal content makes Guitar-TECHS a valuable resource for advancing data-driven guitar research, and to develop robust guitar listening algorithms. We provide empirical data to demonstrate the dataset's effectiveness in training robust models for Guitar Tablature Transcription.
... The Manchus, who number about 4.2 million in China today, serve as a warning to China's ethnic minorities to protect their culture and language, as only about 50 people still speak their native Manchu language. The advancement of AI technologies provides a promising solution to these challenges by creating systematic, scalable methods for documenting, analyzing, and preserving ethnic music [14][15][16]. AI applications in ethnomusicology allow for the detailed capture of audio data, the automatic extraction of musical features, and the generation of new musical compositions that reflect traditional styles. For culturally rich but endangered music forms, AI enables large-scale digitization and cataloging of musical materials, making preservation efforts more sustainable and accessible. ...
... For culturally rich but endangered music forms, AI enables large-scale digitization and cataloging of musical materials, making preservation efforts more sustainable and accessible. Additionally, AI can support The advancement of AI technologies provides a promising solution to these challenges by creating systematic, scalable methods for documenting, analyzing, and preserving ethnic music [14][15][16]. AI applications in ethnomusicology allow for the detailed capture of audio data, the automatic extraction of musical features, and the generation of new musical compositions that reflect traditional styles. For culturally rich but endangered music forms, AI enables large-scale digitization and cataloging of musical materials, making preservation efforts more sustainable and accessible. ...
This study focuses on the construction and evaluation of a high-quality Chinese Manchu music dataset designed to facilitate Artificial Intelligence (AI) research and applications within cultural heritage and ethnomusicology. Through a systematic collection and organization of diverse Manchu music resources, including folk songs, dance music, and ceremonial pieces, this dataset effectively represents the cultural breadth of Manchu music. The dataset includes digitized and preprocessed audio data, with comprehensive metadata annotations, such as essential information, musical features, and cultural context, creating a robust foundation for AI-based analysis. Experimental evaluations highlight the dataset’s utility across various AI-driven applications: in music classification, using a CNN model, an accuracy of 90% was achieved in the “folk ensemble” category, with an overall accuracy of 85.7% and a precision of 82.3%. For music generation, a Generative Adversarial Network (GAN) model yielded a quality score of 7.8/10 and a Fréchet Audio Distance (FAD) of 0.32. In emotion recognition, the Random Forest model achieved 87% accuracy in identifying the emotion “joy”. These results underscore the dataset’s potential in supporting digital preservation and expanding AI applications in ethnic music classification, generation, and emotional analysis, contributing to both cultural heritage preservation and AI advancement in ethnomusicology.
... Literature [19] emphasizes the importance of music subject to the development of students, pointing out that the traditional music teaching mode is teacher-oriented, too monotonous, and lacks teaching resources, which seriously affects students' learning of music skills. Literature [20] artificial intelligence technology can reform music education, through experiments show that compared with the traditional music classroom, the use of the Internet in music teaching can make students achieve better learning results. ...
... Due to a large amount of similarity computation, the string-matching retrieval of pitch features is usually performed first, and the similarity computation is performed on the hit results. Assume that (19) Using the results obtained from the similarity calculation, the correlation of the hit results can be calculated by using equation (20): ...
Music education, as an important part of art education, can also take advantage of artificial intelligence technology to achieve more efficient personalized teaching. The direction of the application of artificial intelligence technology in music course learning is explored in this paper. Therefore, a music data technology interactive learning system is proposed. The music recognition module’s design utilizes a deep neural network model to model the complex problem of speech signal recognition. The encoder formula is obtained by representing the implicit layer feature vectors in the sample through a mathematical model. After encoding and decoding, as well as designing the activation function, the HMM algorithm is introduced to realize the application of DNN-HMM in acoustic modeling. Using a digital filter, the spectrum of the speech signal is smoothed, and the spectrogram is obtained by Fourier variation to visualize the representation of the speech frequency domain. The design of a music Internet teaching course is based on the method proposed in this paper. The melody recognition accuracy of the system is tested through simulation experiments, in which the distribution of auditory feature points of the piano ranges from 0.66 to 0.69. The distribution of rock music is above 0.7, and there is no overlap between the two audio datasets, which indicates that the system proposed in this paper has good recognition accuracy of audio features. Using the speech analysis module, the students’ music learning performance is analyzed. After the model designed in this paper to assist music learning, students’ music performance mean value is 4.397, and the control group’s performance is 3.565. The difference is 0.832. The system designed in this paper is more effective for music learning.
... Li and Wang [26] have conducted research on the efficacy of AI-driven music instruction. The purpose of the study is to replace piano instruction at seven different music schools with AI-powered chatbots, then evaluate the effect on student performance. ...
... Step 1: Initialization At the initialization phase, the Lyrebird population is represented by the following equation: (26) Here Z denotes the LOA population matrix, n indicates the count of lyrebirds, H indicates the count of decision variables, correspondingly. ...
Colleges and universities play a crucial role in nurturing talent and providing highly skilled individuals for various sectors of society. Through modifications over time, the model of music education at colleges and universities has advanced. However, there are still numerous issues that demand careful consideration. This manuscript proposes a hierarchically gated recurrent neural network (HGRNN) optimized with the lyrebird Optimization Algorithm (LOA) for predicting music teaching mode of colleges and universities (MTM-HGRNN-LOA). Initially, the data is collected via real time basis. Afterward, the data is fed to an unscented trainable kalman filter (UTKF) based pre-processing process. In the pre-processing segment, it enhances training rate and eliminates the of batch size dependency. The pre-processing output is given to modified spline-kernelled chirp let transform (MSKCT). The input signal undergoes feature extraction to derive the primary features, which are subsequently combined to yield more comprehensive features in an efficient manner. After that, the extracted features are given to a hierarchically gated recurrent neural network and lyrebird optimization algorithm for effectively classifying the music teaching mode for best, good, normal, satisfactory and poor. The weight parameters of hierarchically gated recurrent neural network are optimized using the lyrebird optimization algorithm. The proposed method is implemented in python and evaluated their performance with existing methods. The performance metrics, like precision, F1-score, accuracy, specificity, sensitivity, and ROC is analysed to the proposed method's performance. The proposed MTM-HGRNN-LOA methods of accuracy are provide 97% best, 98% good, 95% normal, 98% satisfactory and 97% poor music teaching mode. The existing methods MTM-CNN, MTM-BPNN and MTM-GNN, the specificity becomes 90%, 70%, 79% best, 77%, 75%, 65% good, 66%, 85%, 84% normal, 59%, 58%, 70% satisfactory, 61%, 79%, 81% poor music teaching mode. The results show that the proposed MTM-HGRNN-LOA method outperforms other existing techniques, such as online vocal music teaching quality using Back Propagation neural network and convolutional neural network based College-Level Music Teaching Quality Evaluation.
... Customised learning materials are tailored based on students' evaluations, addressing their strengths and weaknesses (Hopcan et al., 2022). Li and Wang (2023) propose using advanced technology to create a comfortable communication environment, fostering learner networks with increased information accessibility for future generations. ...
... It is crucial to understand in detail how AI can influence them, improving the quality of the learning process and academic outcomes. Based on the analysis of the literature (Das et al., 2015;Asakura et al., 2020;Al Braiki et al., 2020;Chen, Chen and Lin, 2020;Nuseir, Basheer and Aljumah, 2020;al-Zyoud, 2020;Ouyang and Jiao, 2021;Cope, Kalantzis and Searsmith, 2021;Zhang and Aslan, 2021;Hopcan et al., 2022;Khosravi et al., 2022;Crompton, Jones and Burke, 2022;Yang et al., 2022;Han, Park and Lee, 2022;Chiu et al., 2023;Southworth et al., 2023;Li and Wang, 2023;Skavronskaya, Hadinejad and Cotterell, 2023;Crescenzi-Lanna, 2023;Ley et al., 2023), two main research questions have been formulated, namely: Q1: Have you used AI in educational activities? ...
Understanding the impact of artificial intelligence (AI) on education is vital for guiding teachers in developing educational tools. AI in education (AIEd) comes not only with opportunities but mostly with challenges for both educators and learners. Finding the proper tools to integrate AI into the learning framework represents a test for current and future generations. Even if most students acknowledged AI as a valuable tool, their interaction with AI in education seems more limited than expected. They mainly concentrated on few tools with higher awareness.This paper examines AI’s support for educational activities, key drivers, and tools for business education. Survey data collected from 254 learners were analysed using multivariate binary logistic regression. Two research questions were formulated to verify if AI supports educational activities and what AI tools support business educational activities. Results show learners appreciate AI for aiding teachers in administrative tasks, personalising learning plans, and saving time. However, learners are unfamiliar with most benefits of AI tools, except computer vision, edge computing, and AI chatbots. The paper highlights the need to increase the use of AI in education to make students more familiar with AI tools and capitalise on them in business education.
... Music teaching occupies an important position in today's education system, which not only enriches students' campus life, enhances students' aesthetic ability, but also provides students with diversified learning and thinking styles [1][2]. However, many music classrooms still adopt the traditional way in teaching methods, which often neglects students' individual differences and subjective initiative, making the effect of music teaching unsatisfactory [3][4][5]. ...
In recent years, the active attempts and breakthroughs of artificial intelligence in music applications and music education have been amazing. The study proposes a lightweight music score recognition method, CRNN-lite, which achieves both lightweight and improved accuracy. In order that the method can be better and faster migrated to be applied to music education, the article designs a new multimodal domain adaptation algorithm based on differential learning, which effectively utilizes the variability of different modal models for multimodal domain adaptation. Finally, the performance comparison analysis and practical application effects of the proposed method in this paper are discussed. Comprehensive experiments show that the multimodal domain adaptation algorithm DLMM based on differential learning proposed in this paper both achieve better recognition results than other methods, and compared with the original recognition algorithm CRNN-Lite, CRNN-Lite+DLMM precision rises by 2.9%, and the recall rate rises by 1.1%, mAP@0.5 increased by 1.3%.
... In recent years, the integration of artificial intelligence (AI) in education has garnered significant attention due to its potential to revolutionize traditional learning methodologies [1]. Particularly in music education, AI offers the opportunity to personalize learning experiences, adapt content to individual needs, and provide real-time feedback that can accelerate skill development [2]. For beginner musicians, the challenge of mastering an instrument often involves overcoming common hurdles such as poor pitch recognition, inconsistent rhythm, and low motivation. ...
This paper presents the development and implementation of an AI-driven personalized learning platform designed specifically for beginner musicians. The platform leverages advanced machine learning techniques, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), to offer real-time feedback on musical performance, focusing on pitch recognition, rhythm accuracy, and note classification. By adapting to the individual progress of each learner, the system provides tailored exercises, personalized recommendations, and motivational elements, such as gamification, to enhance engagement and improve learning outcomes. The study evaluates the platform’s effectiveness through empirical testing, measuring improvements in performance accuracy and user satisfaction. Results indicate that the AI system significantly aids beginner musicians in overcoming common learning challenges, fostering confidence, and accelerating skill development. This research contributes to the growing field of AI in music education, addressing the gap in personalized learning tools for beginners and offering a scalable solution for music instructors and learners alike. The findings suggest that AI-driven platforms hold considerable potential to revolutionize music education by providing adaptive, interactive, and personalized learning experiences.
... The integration of Artificial Intelligence (AI) in Music Education (Music Educ.) represents a transformative advancement in instrumental pedagogy [1][2][3]. Traditional music instruction faces inherent limitations in providing continuous, objective feedback during individual practice sessions, potentially leading to the development of improper techniques and physical strain [4][5][6][7]. According to recent studies, 65%-80% of professional musicians experience playing-related musculoskeletal disorders during their careers, predominantly stemming from poor posture and technique developed during formative training years [8][9]. ...
This paper presents an innovative Artificial Intelligence (AI)—based system for real-time posture analysis and correction in instrumental music training. The system integrates OpenPose-based Convolutional Neural Networks (CNN) for skeletal tracking, Dynamic Time Warping for motion pattern analysis, and K-Nearest Neighbors (K-NN) for posture classification. Through a 16-week experimental study involving 18 music students, the system demonstrated significant improvements in learning outcomes compared to traditional methods. Key findings include (a) 33.3% faster technique acquisition in AI-assisted learning compared to traditional methods; (b) 18.6% higher posture improvement rates by week 16; (c) 40.2% better self-correction capabilities; and (d) 95.1% retention rate of correct posture after 6 months. The system processes video input at 120 fps with a total latency of 30 ms, achieving 94.3% accuracy in posture detection and 91.2% in motion pattern matching. The research establishes a comprehensive framework for integrating AI technology in music education, providing continuous, objective feedback during practice sessions. This approach addresses the critical gap between supervised instruction and individual practice, potentially reducing the risk of performance-related injuries through early detection of posture deviations.
... Literature [17] investigated the practicality of AI music teaching and its impact on students' performance. The experiment applied artificial intelligence to the teaching of piano lessons in several music schools, and the results showed that students' learning level was significantly improved. ...
In the process of researching innovative methods of music teaching, this paper takes the theory of multiple intelligences as the theoretical support of AI-assisted teaching methods to assist music teaching design ideas. The multiple intelligence theory is used to propose an auxiliary Q&A system for music education. After conducting basic theoretical research on the seven dimensions of the multiple intelligence theory, the preliminary design of the auxiliary Q&A method for music education is carried out, and finally, an auxiliary Q&A system for music teaching is proposed, which mainly consists of four major modules, namely, question-answering, question-retrieval, question-browsing, and backstage management. Through empirical testing, this paper concludes that in the post-test comparison experiment of the intelligence of students in the experimental class and the control class, the experimental class students’ bodily-kinesthetic intelligence and musical intelligence, etc., have been significantly improved (P<0.01).
... AI, extensively discussed, emerges as a game-changer, personalizing learning through adaptive platforms that tailor instruction to individual strengths and weaknesses . Chatbots offer immediate feedback and support, enhancing engagement and performance (Li & Wang, 2023). VR and AR burst onto the scene, shattering traditional boundaries with immersive experiences. ...
The use of artificial intelligence (AI) in education has become an increasingly popular topic in recent times. Since its conceptualization, research on AI education has gained significant popularity across multiple disciplines. However, there have been few attempts to undertake a critical scoping review and synthesis of the accumulated knowledge investigated throughout the years. The main research objectives of this chapter are (a) to identify theories, methodologies, and AI techniques that have informed prior AI education research and (b) to examine the emerging themes investigated in previous studies and identify the strength, weakness, opportunities and threats (SWOT)analysis for AI education research. To fulfill the research objectives, 32 research articles were analyzed, synthesized, and assessed based on the themes, methods, and frameworks applied in AI education research. The review found four key themes (development phase, application phase of ChatGPT, SWOT analysis phase, and human–AI interaction phase) necessary to address some limitations in current AI education research. Also, we found the progression of emerging technologies into three phases: the short-term (2021–2025), mid-term (2025–2030), and long-term (2031+). Therefore, this study represents a significant contribution and serves as a valuable reference for future AI and emerging technologies studies in education.
... Traditional methods assess these benefits by using surveys and observations. However, these methods may not capture all aspects of emotional and psychological health and can be subjective and time-consuming [8]. To highlight the importance of musical education in mental health and view its impact on the social, moral and academic behaviour of students this study utilizes a Machine Learning (ML) algorithm namely the Q-learning technique. ...
Musical education has a positive impact on psychological health. It enhances emotional expression and contributes to constructive transformation of mental health. This study explores the use of a machine learning technique known as Q-learning to assess these effects. The research process commences by collecting data from music students. This data includes psychological health status, emotional expression levels and progress in musical education. Surveys and regular assessments are used for this purpose in which Students report their psychological health and emotional experiences. It also tracks and record their progress in musical education. Secondly, a Q-learning algorithm is implemented to analyze the collected data. It demonstrates how different musical education activities influence psychological health and emotional expression. The algorithm works in the form of iterations and can learn from interactions and make decisions based on rewards. Thirdly, the algorithm processes the information and identifies which activities have the most positive impact on musical education by identifying patterns. It also assists in suggesting different types of improvements and methods in teaching methods. To evaluate the performance of the study different performance metrics are used. These indicators include psychological health scores, levels of emotional expression, progress in music skills, attendance rates, participation in class activities and student engagement levels. It also depicts what kinds of activities are particularly beneficial in increasing impact of the musical education. The study shows that students deeply engaged in music have better psychological health and exhibit higher levels of emotional expression.
... It has been suggested an ITS is potentially more effective in helping students than traditional teacher-to-student tutoring (Clancey & Hoffman, 2021) and can increase interest in certain school subjects (Jauhiainen & Guerra, 2023). Empirical research has shown an ITS can lead to growth for individual piano students (Li & Wang, 2023) and is a cost-effective solution to providing high quality music education in rural settings (Zhang & Song, 2023), presuming the rural setting has access to high-speed internet and reliable electricity. Detailed frameworks have been proposed for developing AI for preschool music classrooms (Lin & Ding, 2020) and self-paced, online music learning systems (Yu et al., 2023). ...
This study explored the potential of artificial intelligence (ChatGPT) to generate lesson plans for music classes that were indistinguishable from music lesson plans created by humans, with current music teachers as assessors. Fifty-six assessors made a total of 410 ratings across eight lesson plans, assigning a quality score to each lesson plan and labeling if they believed each lesson plan was created by a human or generated by AI. Despite the human-made lesson plans being rated higher in quality as a group ( p < .01, d = 0.44), assessors were unable to accurately label if a lesson plan was created by a human or generated by AI (55% accurate overall). Labeling accuracy was positively predicted by quality scores on human-made lesson plans and previous personal use of AI, while accuracy was negatively predicted by quality scores on AI-generated lesson plans and perception of how useful AI will be in the future. Open-ended responses from 42 teachers suggested assessors used three factors when making evaluations: specific details, evidence of classroom knowledge, and wording. Implications provide suggestions for how music teachers can use prompt engineering with a GPT model to create a virtual assistant or Intelligent Tutor System (ITS) for their classroom.
... Concurrently, the positive educational outcomes of such applications improve the overall satisfaction and educational experience for students, educators, and parents alike (Qiusi, 2022). This enhancement in learning outcomes and overall academic performance pertains not only to the acquisition of practical musical applications and skills but also to the familiarization and instruction of theoretical aspects of music education and music theory (Li & Wang, 2023). ...
This article presents the findings of a research study focused on understanding the perception of a specific artificial intelligence model, ChatGPT-4, regarding its capabilities in the field of music education at the primary level. These findings are derived from a series of dialogic sessions, wherein researchers directly interact with the AI model, inquiring specifically about its potential and prospects in enhancing music education. The study analyzes how ChatGPT-4 can introduce both basic and advanced concepts of music theory, offering a dynamic and interactive learning experience that adapts to the diverse needs of young students. The article emphasizes the integration of ChatGPT-4 with digital tools such and e-learning systems, providing an enriched experience in music composition and performance. Furthermore, it examines the AI model's ability to facilitate personalized learning, enhancing a deeper understanding of musical concepts through adaptive responses and interactive dialogues. In summary, the article demonstrates the significant contribution of ChatGPT-4 to educational practice and proposes pathways for the further incorporation of artificial intelligence in music education. Through this research approach, the article contributes to the understanding of the capabilities and limitations of AI in the field of music education, offering valuable perspectives for the utilization of technology in this critical area.
... Music composition education constitutes a crucial component of university music curricula. By integrating artificial intelligence technology, it is feasible to enhance the quality of university music education, thus attracting more students to engage in the field of music composition [5]. ...
... For institutions dedicated to music education-a field inherently woven with human emotion and expression-the incorporation of AI evokes a mix of excitement and concern. While AI promises efficient management, tailored learning experiences, and pioneering teaching methods [4], it also introduces concerns regarding the quality of education, the authenticity of musical encounters, and potential shifts in time-honored pedagogical frameworks [5]. Given the depth of its impact and the swift pace of its evolution, there is an imperative to delve into the ramifications of integrating AI into music education, especially from an organizational and managerial stance. ...
... While the acceleration of technological developments significantly alters the functioning of societies, it also highlights modern technologies in education and increases the interest in Artificial Intelligence (AI) (Li & Wang, 2023). AI, which has started to be used in many fields ranging from preschool (Su & Yang, 2023;Yang, 2022) to higher education (McGrath et al., 2023;Popenici & Kerr, 2017), from mathematics (Hwang & Tu, 2021;Mohamed et al., 2022) to science (Darayseh, 2023), is also increasing its popularity in the field of language education (Liang et al., 2021). ...
The present study aims to determine the anxiety and attitudes of pre-service Turkish language teachers towards artificial intelligence and to examine the relationship between the two. The sample group of the study, which was based on a correlational survey design, consisted of 232 pre-service Turkish language teachers studying at 14 different universities in different provinces of Türkiye. The study data were collected using the ‘Personal Information Form’, the ‘Artificial Intelligence Anxiety Scale’ and the ‘General Attitudes toward Artificial Intelligence Scale’. SPSS 23.0 package program was used for data analysis. Based on the findings, it was determined that the pre-service Turkish language teachers’ positive and negative attitudes towards artificial intelligence were at moderate levels while their anxiety was below moderate levels in the learning dimension, but above moderate levels in the dimensions of job replacement, sociotechnical blindness and artificial intelligence configuration. The relationship between anxiety and attitudes towards artificial intelligence was found to be negatively significant. It was determined that the variables of gender and time spent on the internet did not make a significant difference on the pre-service teachers’ anxiety and attitudes towards artificial intelligence. However, although there was no difference between the pre-service language teachers’ attitudes towards artificial intelligence in terms of grade level, differences were observed in the job replacement and sociotechnical blindness dimensions of anxiety.
... The advances in AI in the last 20 years, however, made many tasks and processes in education institutions automatable which triggered significant research on the topic (Bearman et al., 2023;Chen et al., 2020;Chiu et al., 2023;Crompton & Burke, 2023;Escotet, 2023;Pham & Sampson, 2022;Popenici, 2022;Teng et al., 2023;Yang et al., 2021;Zawacki-Richter et al., 2019;Zhou, 2023). Studies have focused on the application of AI in diverse programmes of study such as STEM (Leavy et al., 2023;Van Slyke et al., 2023), medical education (Masters, 2019), sports management (Keiper et al., 2023), business studies (Sollosy & McInerney, 2022), tourism and hospitality (Ivanov & Soliman, 2023), music education (Li & Wang, 2023), art and design (Fan & Li, 2023), online higher education (Ouyang, Zheng, et al., 2022), etc., confirming the applicability of AI for educating students from various programmes and delivery modes. More recently, research has focused on the impacts of generative AI (e.g. ...
La integración de la inteligencia artificial en la educación musical ofrece nuevas oportunidades para mejorar el aprendizaje de estudiantes autistas. Este artículo de revisión sistemática tuvo como objetivo analizar las herramientas de Inteligencia Artificial que promueven el desarrollo autónomo en estudiantes con Trastorno del Espectro Autista en el contexto de la educación musical. Para ello se analizaron 8 estudios publicados a partir del 2020. Se empleó el método PRISMA para la estructuración del proceso. Los hallazgos indicaron mejoras en el aprendizaje y la autonomía de los alumnos. Las herramientas presentadas como algoritmos especializados, pantalla elástica, árboles de decisión, robots y aplicaciones interactivas, demostraron ser efectivos para adaptar la educación a las necesidades individuales. Sin embargo, se identificaron barreras como la falta de formación docente y la infraestructura tecnológica insuficiente. Se concluye, que es necesario la promoción de herramientas tecnológicas debido a la escasez de investigaciones en este campo.
In the age of rapid technological advancement, artificial intelligence (AI) has begun to play a transformative role in various sectors, including education. This chapter, “The Evolving Role of Teachers in the AI-Powered Classroom,” explores how AI technologies are reshaping the educational landscape and the role of teachers. This chapter begin with an overview of AI in education, we then provide a historical context, tracing the evolution of teaching methods and technology, and the impact of early educational technologies. The chapter examines the current state of AI in education along with the benefits and challenges they bring to the classroom. Ethical considerations will also be addressed, including privacy concerns, equity and access issues, and the critical role of teachers in advocating for ethical AI use. Through case studies of AI-powered classrooms, we offer insights from educators and students, sharing lessons learned from these implementations.
This research aims to analyze and reflect on the impact of artificial intelligence (AI) advancements on the field of music education in South Korea. A review of current research trends reveals a quantitative increase in AI-related studies, primarily driven by the growing demand for AI integration in music education. Most research has focused on exploring the philosophical directions of music education and developing educational programs, with relatively few empirical studies verifying the practical impacts of AI on music education. Methodologically, the research has shown a significant reliance on literature reviews, with limited focus on studies that directly involve teachers and students as participants. Despite AI discourse remaining somewhat limited within the field of music education, interdisciplinary studies involving AI are gradually expanding. Future research should address these gaps by exploring qualitative case studies, validating AI’s effectiveness, examining teachers’ practices and students’experiences, and pursuing interdisciplinary approaches. This broader approach is necessary for advancing AI’s role in music education.
Introduction
The study investigates the integration of artificial intelligence (AI) in higher education (HE) and its impact on pre-service teachers at the University of Latvia (UL) by exploring pre-service teachers' perceptions of the benefits and challenges of AI in both their academic learning and their future professional roles as educators, particularly regarding the promotion of inclusive education.
Methods
Data was collected via an online survey of 240 pre-service teachers across various disciplines at the UL. The survey included demographic details, AI usage patterns, and perceived benefits and challenges. Responses were analyzed using descriptive statistics, Kruskal-Wallis H tests, Spearman's correlation, and thematic analysis.
Results
Less than half of the participants used AI in their studies, with many expressing ambivalence or opposition toward AI. Benefits included language assistance and accessibility to global knowledge, while challenges involved reduced critical thinking and concerns over plagiarism. Despite recognizing AI's potential to promote inclusivity, most pre-service teachers have not applied it in practice. No significant differences in AI perceptions were found based on age, gender, or study level.
Discussion
The findings highlight a low adoption rate of AI among pre-service teachers and a gap between theoretical recognition of AI's potential and its practical application, particularly for inclusion. The study emphasizes the need for HE institutions to enhance AI literacy and readiness among future teachers.
Conclusion
AI is underutilized by pre-service teachers in both HE learning and teaching environments, which has implications for teacher preparation programs that better integrate AI literacy and inclusive practices.
El objetivo del artículo es analizar cómo la Inteligencia Artificial (IA), específicamente la función Designerde Copilot, puede ser utilizada en la elaboración e interpretación de imágenes para mejorar la enseñanza y el aprendizaje en el ámbito educativo. En el estudio, se pidió a seis estudiantes de educación primaria de la escuela rural de Juscaymarca en Ayacucho, Perú, que utilizaran la IA para generar imágenes basadas en su imaginación. Los resultados del estudio sugieren que la IA puede ser una herramienta valiosa en la educación, ayudando a los estudiantes a explorar su creatividad y a entender su mundo de una manera más profunda y significativa. Además, las imágenes resultantes, que ilustran la vida cotidiana en un entorno rural, no sólo sirven como representaciones visuales, sino también como testimonios de la vida en Ayacucho. PALABRAS CLAVE: Inteligencia artificial; enseñanza; creatividad estudiantil; vida rural; Copilot
This study explored Korean music teachers’ use of technology and views on integrating technology into music classrooms. Using a mixed methods approach, the researchers administered a survey to 105 music teachers and conducted semi-structured interviews with five music teachers. The results showed that about one-fourth of survey respondents indicated that they rarely incorporate music technology when teaching singing and playing, and generally, music teachers reported that they use music technology infrequently for main class activities. In addition, these Korean music teachers demonstrated limited knowledge of fourth industrial revolution smart technologies, despite viewing AI and the metaverse as potentially helpful for music teaching. Notably, this study found that music teachers consider it necessary to utilize music technologies that enable them to broaden the scope of music curricula.
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.
This chapter explores artificial intelligence (AI) and its potential to revolutionize the field of education by enhancing teaching and learning. It further highlights ways in which AI technologies can support educators and students, including content creation encompassing voice and video, task automation, and the provision of personalized learning through adaptive learning platforms. Potential benefits to leveraging AI technology include improved learner engagement, automated administrative processes, and more robust data-driven insights. As AI advances, a unique opportunity emerges for schools to prepare educators to use AI in the classroom and for educators to redefine traditional teaching methodologies. Nonetheless, balancing AI innovation with ethical considerations includes considering issues in transparency and privacy in AI algorithms as well as mitigating biases in the data.
Music education is a subject that is generally thought to have much physical activity involved. However, virtual learning has been mandatary applied to most schools worldwide due to the COVID-19 pandemic. The landscape of music learning has had to be switched to online distance learning (ODL), where students learn music virtually using technological tools. Gifted students are among those affected by the implementation of music ODL throughout 2020. Thus, the purpose of this study is to identify the effectiveness of music ODL on gifted students’ motivation. The researchers framed this quantitative study by involving 81 secondary gifted students, aged 13 years, from 13 states in Malaysia. The sample was selected through random sampling, and a preexperimental design was applied to conduct the study. Respondents had been exposed to the music ODL intervention for a month. Data were collected through an adapted questionnaire, namely, the MUSIC Inventory, with a five-point scale. Data were further analysed by descriptive and inferential statistics, integrating two-way MANOVA, using SPSS Statistics version 23. Results reveal that an ODL approach to music classes is significantly effective to enhance gifted students’ motivation domains of empowerment, usefulness, success, interest, and caring. Yet, no significant difference was found in gifted students’ genders and locations on the four domains. Different approaches in music teaching could be further explored for music ODL to gifted students in future studies.
Enhancement in information technology has made the online teaching-learning process easier. However, this process is still a challenging task for teaching courses of theoretical type, music, dance, and arts. For the classes of these types, the traditional system of teaching and learning is well suited, but, in particularly complex situations, accomplishing the task is highly difficult. Hence, new media art technology is introduced to overcome the difficulties. In this research, Chinese folk music is taught online with the aid of new media art with the support of artificial intelligence. The analysis of the proposed work is carried out on the folk music dataset, which considers folk music of ethnic minority groups. A novel Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) art algorithm is implemented to perform the analysis. The performance is compared with the existing gradient descent, Adam, and AdaDelta algorithms. L-BFGS algorithm is essentially a particular recipe for designing and possibly executing an artwork, including algorithms, functions, facial expression, and other input that ultimately decides the structure the folk music and media art would then take. This contribution could be numerical, information processing, or formative. From the obtained results, it can be shown that the proposed system has provided 97% and 98% of accuracy on training and testing data, which is higher when compared to the existing algorithms.
Music is not only an important subject in general education, but it particularly serves gifted students who face various psychological issues not encountered by their ‘normal’ peers. Nonetheless, it is found that not all schools are implementing proper music lessons due to the focus on STEM subjects. This paper provides an overview of developing music enrichment activities as an approach to meet gifted students’ needs. Differentiated instruction was used as the main approach to developing a comprehensive music enrichment activity, namely ‘Music in Film’, in which gifted students integrated music and computer skills in completing a given task. Furthermore, a research survey was conducted involving 36 gifted students. The MUSIC Inventory was used to measure their motivation and engagement towards the activity to measure the methods. Five domains—empowerment, usefulness, success, interest and caring—were measured on a 6-point Likert scale. Results revealed that all five domains were rated from moderate to high by the gifted students with a minimum 4.5 mean. Although it can be concluded that this enrichment activity is apt and effective for implementation in gifted education, future studies could look at participants with different backgrounds and demographics. It is hoped that this paper will contribute to designing more enrichment activities with a differentiated instruction approach as gifted students possess high potential in various talents that need to be nurtured.
Rationale of Study-The emergence of the Internet has unlocked immense prospects for distance learning. One of the prospects is Web 3.0 tools, a web platform that enables its users to read, write and execute content. Previous studies established the use of Web 3.0 tools in higher education but this author knows of no studies on the use of Web 3.0 tools for knowledge management by distance learners in Nigeria. Therefore, this paper examines distance learners' knowledge conversion using the SECI knowledge management model and identifies appropriate Web 3.0 tools used in each quadrant.
Nowadays, many college students like all kinds of music. Students even form their own bands. Unfortunately, music education is not valued in many schools. Some famous music schools are trying to change the current situation of music education mode. The integration of artificial intelligence technology and education has led to the change of modern music teaching concept. However, the probability of challenge and opportunity brought by AI technology is the same. This paper analyzes the current situation of traditional music education. On this basis, this paper explains the innovation strategy of college music teaching mode led by artificial intelligence. I hope this article can give you some inspiration.
The article presents the conceptual basis of the ALTYN ART magazine, ways of the magazine development and implementation. Finally, it is proposed to consider the concept of further development of the publication to give information, possibly, useful for other publications of a similar thematic area. Art magazines are an effective way to conduct a dialogue between creative artists, i.e. painters, musicians, designers, etc. The author aims to improve the theoretical and practical understanding of the key elements and factors that contribute to the arrangement of the social and cultural creative environment in Kazakhstan and the development of its print media. The data collection and analysis was based on the experience of creation and publication of own cultural and informational periodical, the search for new ways to develop and promote the achievements of culture, art and professional music education. The materials of the journal give a clear idea of modern culture of Kazakhstan. The focus on articles written by experts of the field of Kazakhstan’s art may get interest students in further research in this area.
The modern trend of education development is digitalization, transition from conventional classroom offline model to distance online education. Formation of global digital society was accelerated due to the COVID–19 pandemic, when significant portion of Earth population, including students, was forced to work, to study distantly. In this regard, this article discusses the responses of Russian system of higher education to current challenges of digitalization. This article analyzes interactive educational technologies of online, digital, e-learning, visualization and gamification of education, implementation of network structures. On the basis of analysis of the modern state of Russian higher education, the unsolved key issues are demonstrated as well as the main trends of the required changes. This work describes the urgent issues of development of regulatory framework of digital education, intensification of development of digital infrastructure of universities, digital training and further training of teachers and students, analysis and actualization of existing specialties and fields. Solution of the mentioned issues requires for legislative, organizational, administrative, technological, teaching and learning efforts of the country, universities, and society. A response of the Russian system of higher education to the challenges of digitalization can be comprised of formation of unified information and education portal of national universities, designing a model of digital university.
Current neural network based algorithmic composition methods are very different compared to human brain's composition process, while the biological plausibility of composition and generative models are essential for the future of Artificial Intelligence. To explore this problem, this paper presents a spiking neural network based on the inspiration from brain structures and musical information processing mechanisms at multiple scales. Unlike previous methods, our model has three novel characteristics: (1) Inspired by brain structures, multiple brain regions with different cognitive functions, including musical memory and knowledge learning, are simulated and cooperated to generate stylistic melodies. A hierarchical neural network is constructed to formulate musical knowledge. (2) Biologically plausible neural model is employed to construct the network and synaptic connections are modulated using spike-timing-dependent plasticity (STDP) learning rule. Besides, brain oscillation activities with different frequencies perform importantly during the learning and generating process. (3) Based on significant musical memory and knowledge learning, genre-based and composer-based melody composition can be achieved by different neural circuits, the experiments show that the model can compose melodies with different styles of composers or genres.
The present paper reports data from an original qualitative study that investigates how music students reacted to novel remote teaching strategies that emerged during the COVID-19 pandemic. A population of twenty learners enrolled at an Italian conservatory responded to an open-ended survey, verbalising their recent learning experiences concerning three complementary aspects of their everyday practice: (i) how efficiently new remote education settings were implemented, (ii) what novel musical activities have been creatively developed with the help of technology, and (iii) how peer interaction was transformed by the lockdown period. By providing concrete examples, our participants offered insights into the benefits, challenges, and transformations this sudden pedagogical change has produced. Our findings show how different approaches to rehearsal and time management have emerged, in turn impacting on how students prioritise short-term and long-term goals, enhance their creative potential, and establish and renew interactions with peers.
Critical but often overlooked research questions in artificial intelligence (AI) applied to music involve the impact of the results for music. How and to what extent does such research contribute to the domain of music? How are the resulting models useful for music practitioners? In this article, we describe how we are addressing such questions by engaging composers, musicians, and audiences with our research. We first describe two websites we have created that make our AI models accessible to a wide audience. We then describe a professionally recorded album that we released to expert reviewers to gauge the plausibility of AI-generated material. Finally, we describe the use of our AI models as tools for co-creation. Evaluating AI research and music models in these ways illuminate their impact on music making in a range of styles and practices.
Despite its rapid development, the artificial intelligence (AI) has not been deeply applied in art teaching. Hence, this paper attempts to design strategies for applying AI in art teaching. For this purpose, the authors reviewed the current application status of AI in art teaching and summed up the problems with these applications. Next, the promotive role of AI in art teaching was analyzed in detail, and three strategies were developed to promote AI applications in modern art teaching, namely: expand the adaptability of AI-based art teaching; improve the intelligent teaching mode of art teaching; and enhance the artistic experience and atmosphere of AI-based art teaching. At the same time, to evaluate the application effect of AI in art teaching, a performance analysis model was designed based on analytical hierarchy process (AHP) and grey clustering. The proposed model can quantify the application effect of AI in art teaching, and it has a good application potential.
The teaching and learning methodology in a typical engineering education system involves a teacher teaching to a group of 30 to 60 students and hence the students have a common learning path. With the advent of technology like Artificial Intelligence/Machine Learning, past historic data in an institution and current data of student profiles and performance can be used to analyse and predict learning gaps and suggest the learning steps a student has to take to improve his/her performance. This will result in each student having a personalized learning path. As many institutions have a well-defined Quality Management System (QMS) in line with the ISO 9001: 2015 standards and past data, they can adopt this new technology and hence achieve disruption in the education system in a phased manner.
Artificial intelligence has developed rapidly and is widely used in various fields. Music robots with specific neural networks can understand, analyze, and create music. In this paper, based on the artificial intelligence technology, human music intelligence is analyzed through big data to explore and discuss the construction of various new interactive teaching music intelligent systems in the field of professional music education with music artificial intelligence technology as the platform, seeking a new mode of music perception, cognition, creation, and education.
AI teaching is an important part of Internet + education and intelligent education, and also an inevitable trend of the times. Through the automatic language and language sense recognition processing technology, the robot can sense the life scene and voice emotion of infants, and automatically recognize and play functional music. Artificial intelligence develops rapidly and is widely used in various fields. Music robot with certain neural network can understand music, analyze music and create music. In the field of professional music education, all kinds of new interactive teaching music intelligent system based on music artificial intelligence technology will be a new mode of perceiving music, cognitive music, creating music and music education.
The current wave of technological change based on advancements in artificial intelligence (AI) has created widespread fear of job loss and further rises in inequality. This paper discusses the rationale for these fears, highlighting the specific nature of AI and comparing previous waves of automation and robotization with the current advancements made possible by a widespread adoption of AI. It argues that large opportunities in terms of increases in productivity can ensue, including for developing countries, given the vastly reduced costs of capital that some applications have demonstrated and the potential for productivity increases, especially among the low skilled. At the same time, risks in the form of further increases in inequality need to be addressed if the benefits from AI-based technological progress are to be broadly shared. For this, skills policies are necessary but not sufficient. In addition, new forms of regulating the digital economy are called for that prevent further rises in market concentration, ensure proper data protection and privacy, and help share the benefits of productivity growth through the combination of profit sharing, (digital) capital taxation, and a reduction in working time. The paper calls for a moderately optimistic outlook on the opportunities and risks from AI, provided that policymakers and social partners take the particular characteristics of these new technologies into account. which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
The adsorption of ammonium ions by natural zeolite and Al2O3 under static conditions has been investigated. The reason for changing the investigated solution pH during adsorption of ammonium ions on Al2O3 has been grounded. A phase diagram of the twocomponent system has been constructed and the composition of the adsorption system in the state of equilibrium has been determined. The thermodynamic calculations of the adsorption system Al2O3-NH4Cl-H2O have been carried out. It was established that the adsorption of ammonium with aluminum oxide occurs via the mechanism of physical adsorption.
Who are these people? Seriously, who actually sits down after a long day at work and says, I’m not going to watch Lost tonight. I’m going to turn on my computer and make a movie starring my pet iguana? I’m going to mash up 50 Cent’s vocals with Queen’s instrumentals? I’m going to blog about my state of mind or the state of the nation or the steak-frites at the new bistro down the street? Who has that time and that energy and that passion? (Grossman, 2006: html). © Palgrave Macmillan, a division of Macmillan Publishers Limited 2011.
The purpose of this inquiry is to examine the potential impact of creative, digital technologies on music pedagogy in the 21st century. In the last decade, digital technologies have fundamentally changed music making, sharing, teaching, and learning and it is rapidly evolving. An unprecedented renaissance of social music making is taking place through the use of musical games, apps, and networked digital tools. Music educators must be current with these emerging trends to stay relevant with youth culture. In our paper, we share the implications these technologies may have for the future of music curriculum and praxis. We express a call for a fundamental rethinking of our basic assumptions about pedagogy and learners, as well as what we as educators view as “valid” musical expression.
The digitalisation of the music classroom has accelerated the development of learning tools that correspond to the mindset and lifestyle of digital generation. Current pedagogical approaches emphasize a diverse and multifunctional use of technology to study music theory, learn to play instruments, and create music on and offline. Solfeg.io music education app is designed according to principles of smart pedagogy that include criteria for a meaningful use of technology in the study process. The app has been developed to meet didactic, cognitive, socioemotional, and technical requirements that create a customizable, user-oriented experience in a pedagogically justified study environment. The digital learning strategy “Start playing the guitar!” (DLS) is designed for students without prior knowledge of playing the guitar. The purpose of the paper is to study applicability of DLS in learning the basics of playing the guitar. The paper contains an analysis of theoretical literature on a technology-enhanced pedagogical process, including the ideas of smart pedagogy and the basic didactic principles for developing digital education tools. The empirical study on the use of DLS includes pedagogical observation, respondent surveys, and analysis and interpretation of the obtained qualitative data.
In the world, the technological and industrial revolution is accelerating by the widespread application of new generation information and communication technologies, such as AI, IoT (the Internet of Things), and blockchain technology. Artificial intelligence has attracted much attention from government, industry, and academia. In this study, popular articles published in recent years that relate to artificial intelligence are selected and explored. This study aims to provide a review of artificial intelligence based on industry information integration. It presents an overview of the scope of artificial intelligence using background, drivers, technologies, and applications, as well as logical opinions regarding the development of artificial intelligence. This paper may play a role in AI-related research and should provide important insights for practitioners in the real world.The main contribution of this study is that it clarifies the state of the art of AI for future study.
Artificial Intelligence (AI) research has always been embedded in complex networks of cultural imagination, corporate business, and sociopolitical power relations. The great majority of AI research around the world, and almost all commentary on that research, assumes that the imagination, business, and political systems of Western culture and the Global North are sufficient to understand how this technology should develop in future. This article investigates the context within which AI research is imagined and conducted in the Amhara region of Ethiopia, with implications for public policy, technology strategy, future research in development contexts, and the principles that might be applied as practical engineering priorities.
Internationally, creativity is a widely discussed construct that is pivotal to educational practice and curriculum. It is often situated alongside technology as a key component of education futures. Despite the enthusiasm for integrating creativity with technologies in classrooms, there is a lack of common ground within and between disciplines and research about how creativity relates to technology in teaching and learning—especially in the uncertain space of classroom implementation. This article provides a critical thematic review of international literature on creativity and technology in the context of educational practice. We identifyfour essential domains that emerge from the literature and represent these in a conceptual model, based around: (1) Learning in regard to creativity, (2) Mean-ings of creativity, (3) Discourses that surround creativity, and (4) the Futures or impacts on creativity and education. Each of these clusters is contextualized in regard to emerging technologies and the developing scope of twenty-first century skills in classroom imple-mentation. We offer conclusions and implications for research and practice.
Article available at https://rdcu.be/cdoWA
As the core driving force of the new round of informatization development and industrial revolution, the disruptive achievements of artificial intelligence (AI) are rapidly and comprehensively infiltrating into various fields of human activities. Although technologies and applications of AI have been widely studied and factors that affect AI adoption are identified in existing literature, the impact of success factors on AI adoption remains unknown. Accordingly, this paper proposes a framework to explore the impacts of success factors on AI adoption in telecom industry by integrating the technology, organization, and environment (TOE) framework and diffusion of innovation (DOI) theory. Particularly, this framework consists of factors regarding external environment, organizational capabilities, and innovation attributes of AI. The framework is empirically tested with data collected by surveying telecom companies in China. Structural equation modeling is applied to analyze the data. The study provides support for firms’ decision-making and resource allocation regarding AI adoption.
Overcoming the current spiritual and ecological crisis plaguing the globe is impossible without changing the paradigm of aesthetic music education. Music should play an important role in achieving the goals of sustainable development, rather than be a utilitarian product of consumer society. Therefore, attention should be drawn to the music teacher education and to a new paradigm of education that is ecology-focused, aesthetic, and enables sustainable development at the urban, suburban, and rural levels. The study examines issues around the ecology-focused aesthetic music education in the context of sustainable development culture. This study aimed to substantiate an integration framework for ecological rationality, aesthetic education, and musical ecology in order to build a new educational paradigm from international experience. This is a review of the survey findings reported by the Country Music Association Foundation. The survey involved 468 music teachers from 392 primary, secondary and high schools in the United States. The weighting procedure used ensured that the sample of responding schools was nationally representative along the two dimensions of school locale/urbanicity (e.g. urban, suburban, town/exurbun, rural) and the grade levels (e.g. primary, middle, high, or some combination). The contemporary approach to education should be improved to extend ecological and integrated methods and ideas of thinking and hence bring the art of music to a new horizon with a higher goal of creating a sustainable community of ecologically responsible individuals. This study offered an integration model for ecological rationality, aesthetic education, and musical ecology in order to build a new educational paradigm. The proposal represents a framework that is built around five components of society, ecological rationality, teachers, sustainable development, and musical ecology.
This article comprehensively reviewed the development history and evolution of the Artificial Intelligence Technology (AIT), systematically demonstrated the convenience, practicality and limitations of Artificial Intelligence (AI) in music education, compared and studied the advantages and disadvantages of both traditional and AI-applied music education mode, statistically analyzed results of the study through questionnaire survey, and deduced that the combination of music education and AI will become the new trend of future music education.
This paper addresses the idea of having ‘information experiences’ in our information society, a concept that prevails in the business world but is under-explored in the sociological study of information. From the literature, I identify the three theoretical approaches to these experiences as simulated, personalized and epistemological, and adopt an interpretivist approach to explore how recent trends manifest. Simulated experience is closely related to ‘virtuality’, a concept developed in the early days of the internet, while personalized experience arises out of participation in social websites. Centring on the third, I use data from Yam’s case studies on Wikipedia and further propose three factors that sustain the discursive environment of epistemological experience.
Recent advances in artificial intelligence and computer science can be used by social scientists in their study of groups and teams. Here, we explain how developments in machine learning and simulations with artificially intelligent agents can help group and team scholars to overcome two major problems they face when studying group dynamics. First, because empirical research on groups relies on manual coding, it is hard to study groups in large numbers (the scaling problem). Second, conventional statistical methods in behavioral science often fail to capture the nonlinear interaction dynamics occurring in small groups (the dynamics problem). Machine learning helps to address the scaling problem, as massive computing power can be harnessed to multiply manual codings of group interactions. Computer simulations with artificially intelligent agents help to address the dynamics problem by implementing social psychological theory in data-generating algorithms that allow for sophisticated statements and tests of theory. We describe an ongoing research project aimed at computational analysis of virtual software development teams.
In this paper, a methodology for assessing the energy performance of residential buildings in the Algerian context using performance-based approach is performed. This proposed methodology is based on two approaches: top-down and bottom up. The first one is descriptive down to facilitate the identification of pertinent performance indicators "PIs" related to the energy performance quality of residential buildings. The second is a bottom-up approach based on a multi-criteria aggregation using weighted sum method. A coupled method, based on Analytic Hierarchy Process "AHP" and genetic algorithms "GA" method, was used to calculate the weights of selected PIs. This approach is based on the single-objective optimization of the decision matrix (or of the consistency ratio). Application of the developed approach on real case studies of residential buildings in the Algerian context was achieved. The obtained results, based on local measurements, are very interesting and underline the reliability of this approach. The low energy efficiency of Algerian residential buildings was confirmed in the majority of studied cases. The proposed optimization of the AHP method using GA reported very satisfactory results (improvement of the weighting procedure in particular) and led to a better estimation of the energy performance level of residential buildings in Algeria.
The author reviews over a decade of science and the arts programming he and his colleagues have produced and disseminated. The programs have been supported by the National Science Foundation, the American Physical Society, the Graduate Center of the City University of New York and various foundations and corporations. The author gives detailed examples of using science and the arts to reach the public and students with respect to programming related to the play Copenhagen, the opera Doctor Atomic, the Science&the Arts Series, a major international conference on Communicating Science to the Public, and many other outreach efforts.
Music composition with collaboratory AI composers
- H Kim
- Y You
- Kim H.
Sounds of science: Copyright infringement in AI music generator outputs
- E Sunray
- Sunray E.
Music theory, the missing link between music-related big data and artificial intelligence
- J A T Lupker
- W J Turkel
- Lupker J. A. T.