There are many genetic and environmental factors that affect cognitive development. Music education can also be considered as one of the environmental factors. Some researchers emphasize that Music is an action that requires meta-cognitive functions such as mathematics and chess and supports spatial intelligence. The effect of Music on cognitive development in early childhood was examined using the Pythagorean Fuzzy Sets(PFS) method defined by Yager. This study created PFS based on experts' opinions, and an algorithm was given according to PFS. The algorithm's results supported the experts' data on the development of spatial-temporal skills in music education given in early childhood. The algorithm's ranking was done using the Expectation Score Function. The rankings obtained from the algorithm overlap with the experts' rankings.

Recent AI-driven step-function advances in several longstanding problems in music technology are opening up new avenues to create the next generation of music education tools. Creating personalized, engaging, and effective learning experiences is a continuously evolving challenge in music education. Here we present two case studies using such advances in music technology to address these challenges. In our first case study we showcase an application that uses Automatic Chord Recognition to generate personalized exercises from audio tracks, connecting traditional ear training with real-world musical contexts. In the second case study we prototype adaptive piano method books that use Automatic Music Transcription to generate exercises at different skill levels while retaining a close connection to musical interests. These applications demonstrate how recent AI developments can democratize access to high-quality music education and promote rich interaction with music in the age of generative AI. We hope this work inspires other efforts in the community, aimed at removing barriers to access to high-quality music education and fostering human participation in musical expression.

Estimating music piece difficulty is important for organizing educational music collections. This process could be partially automatized to facilitate the educator's role. Nevertheless, the decisions performed by prevalent deep-learning models are hardly understandable, which may impair the acceptance of such a technology in music education curricula. Our work employs explainable descriptors for difficulty estimation in symbolic music representations. Furthermore, through a novel parameter-efficient white-box model, we outperform previous efforts while delivering interpretable results. These comprehensible outcomes emulate the functionality of a rubric, a tool widely used in music education. Our approach, evaluated in piano repertoire categorized in 9 classes, achieved 41.4% accuracy independently, with a mean squared error (MSE) of 1.7, showing precise difficulty estimation. Through our baseline, we illustrate how building on top of past research can offer alternatives for music difficulty assessment which are explainable and interpretable. With this, we aim to promote a more effective communication between the Music Information Retrieval (MIR) community and the music education one.

Students learning classical violin usually have to wait until a session with a music teacher to get personalized feedback on their playing. Soon they may have a new tool to use between lessons: an app that can observe them play and guide them toward better posture and form--key elements both for sounding their best and avoiding overuse injuries. Two University of Maryland researchers are drawing on very different academic backgrounds--one in classical violin and music education, the other in robotics and computer science--to develop this virtual "teacher's aide" system powered by artificial intelligence (AI) technology. In addition to expanding the market for violin instruction, it will allow students who may not have access to private lessons to receive feedback on their playing. Associate Professor of Violin in the School of Music Irina Muresanu, who is collaborating with Cornelia Fermüller, associate research scientist in UMD's Institute for Advanced Computer Studies, said the technology will be revolutionary for a field rooted in tradition.

With the rapid evolution of technology, new tools for creativity and development are constantly emerging. Musicians today are beginning to use machine learning, where computers "learn" over time by being fed large amounts of data, to create music in new and innovative ways. The computers process this data and identify patterns, allowing them to act on future data. After identifying these patterns, computers can classify new information, make predictions, or even generate novel, creative content. In the world of music, the possible applications of this technology are endless.

Music is a universal language that can bring people together from all over the world. As emerging technologies help us communicate better, artificial intelligence is beginning to overtake our hearts, minds, and even ears. AI is opening up a world that users can automate, personalize, and learn from. The music and education sectors are not exempt from the efficiency of emerging technologies. Smart bots like Amper's A.I. can now compose their own albums, while other intelligent applications like SmartMusic allow users to experiment with composition and production.