Abstract
The integration of artificial intelligence (AI) into music education has introduced transformative possibilities for piano pedagogy, particularly in the areas of technical development and musical expression. This study examines the role of AI-assisted tools in enhancing piano instruction by analyzing their impact on learning efficiency, performance accuracy, and interpretative depth. Through a synthesis of existing literature and pedagogical practices, this paper explores how AI technologies—such as real-time feedback systems, motion analysis software, and intelligent accompaniment platforms—contribute to individualized learning experiences. AI-assisted piano pedagogy offers significant advantages in technical training by providing immediate, objective, and data-driven feedback. These systems enable students to identify errors in rhythm, articulation, and dynamics more efficiently than traditional methods. Furthermore, AI technologies support the development of expressive performance by analyzing phrasing, timing, and tonal variation, thereby guiding students toward more nuanced interpretations. However, the study also addresses potential limitations, including over-reliance on technology, reduced teacher-student interaction, and challenges in capturing the subjective and emotional dimensions of musical expression. The balance between technological assistance and human artistic guidance remains a critical concern in contemporary pedagogy. This paper argues that AI should not replace traditional teaching but rather function as a complementary tool that enhances both technical precision and expressive awareness. By integrating AI into piano pedagogy, educators can create more adaptive, efficient, and engaging learning environments while preserving the essential artistic and humanistic aspects of musical training.
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Published in
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Humanities and Social Sciences (Volume 14, Issue 3)
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DOI
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10.11648/j.hss.20261403.19
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Page(s)
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282-287 |
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Creative Commons
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This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
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Copyright
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Copyright © The Author(s), 2026. Published by Science Publishing Group
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Keywords
Artificial Intelligence, Piano Pedagogy, Music Education, Performance Practice, Musical Expression, Technology in Education
1. Introduction
In recent years, artificial intelligence (AI) has emerged as a significant force in transforming educational practices across various disciplines, including music education. Within the field of piano pedagogy, AI-assisted technologies have begun to reshape traditional teaching models by introducing new methods of feedback, analysis, and individualized instruction. These developments raise important questions regarding the role of technology in artistic training, particularly in balancing technical precision with expressive depth.
Traditionally, piano instruction has relied heavily on the expertise of the teacher, who provides personalized guidance on technique, interpretation, and musicality. While this approach remains fundamental, it is inherently limited by time constraints and subjective evaluation. AI technologies, by contrast, offer real-time, objective analysis of performance parameters such as rhythm accuracy, articulation, dynamics, and tempo stability. This allows students to engage in more efficient and targeted practice, potentially accelerating technical development.
Moreover, AI systems have expanded beyond mechanical assessment to address aspects of musical expression. Through advanced algorithms and machine learning models, these tools can analyze phrasing, timing variations, and tonal balance, offering insights into interpretative choices. Such capabilities suggest that AI may play a role not only in technical training but also in fostering deeper musical understanding.
However, the integration of AI into piano pedagogy also presents challenges. Questions arise concerning the extent to which technology can replicate the nuanced judgment of human instructors, particularly in matters of artistic interpretation and emotional communication. Additionally, there is a risk that excessive reliance on AI may diminish the development of critical listening skills and independent musical thinking.
This paper aims to explore the potential of AI-assisted piano pedagogy in enhancing both technique and musical expression. By examining its applications, benefits, and limitations, the study seeks to provide a balanced perspective on the evolving relationship between technology and musical artistry.
2. AI in Piano Pedagogy: Concepts and Technologies
2.1. Overview of AI in Music Education
Artificial Intelligence (AI) has increasingly become an integral component of contemporary music education, particularly in the domain of piano pedagogy. Broadly defined, AI in music education encompasses technologies such as machine learning algorithms, pattern recognition systems, and data-driven analytical tools that are capable of processing large volumes of performance data. These systems can identify subtle nuances in musical execution, including timing deviations, dynamic inconsistencies, and articulation patterns, thereby offering a level of analytical precision that was previously unattainable through traditional pedagogical methods. Moreover, AI-driven platforms facilitate personalized learning by adapting instructional content according to individual learners’ abilities and progress. Through continuous data collection and analysis, these technologies enable the creation of adaptive learning environments in which instructional strategies evolve dynamically, aligning with each student’s technical level and musical understanding. As a result, AI not only enhances teaching efficiency but also transforms the traditional teacher-centered model into a more learner-centered paradigm
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2.2. Real-Time Feedback Systems
One of the most significant contributions of AI to piano pedagogy lies in the development of real-time feedback systems. These systems utilize audio recognition and signal processing technologies to evaluate key performance parameters such as pitch accuracy, rhythmic stability, tempo consistency, and articulation clarity. Unlike conventional teaching scenarios, where feedback is often delayed until after a performance, AI-based systems provide immediate and continuous responses during practice sessions. This immediacy allows students to identify and correct errors at the moment they occur, thereby reinforcing correct motor patterns and reducing the likelihood of ingraining mistakes. Furthermore, real-time feedback enhances practice efficiency by enabling focused and goal-oriented repetition. Students can engage in self-directed learning outside of formal lessons, receiving guidance that closely simulates the presence of a teacher. Consequently, these systems not only supplement traditional instruction but also promote greater learner autonomy and self-regulation in the practice process.
2.3. Motion and Gesture Analysis
In addition to auditory analysis, AI technologies have made significant advancements in the field of motion and gesture analysis within piano education. By employing motion capture systems and computer vision techniques, AI can track and evaluate physical aspects of piano performance, including hand positioning, finger movement trajectories, wrist flexibility, and overall posture. Such detailed biomechanical analysis provides valuable insights into the technical foundations of piano playing, which are often difficult to observe with the naked eye alone. Importantly, these systems contribute to injury prevention by identifying inefficient or potentially harmful movements, such as excessive tension or improper alignment. At the same time, they support the refinement of technique by offering corrective suggestions based on established pedagogical principles. Through the integration of visual and kinetic feedback, students develop a more comprehensive awareness of their physical interaction with the instrument, leading to improved technical control and expressive capability.
2.4. Intelligent Accompaniment and Interactive Systems
Another notable application of AI in piano pedagogy is the development of intelligent accompaniment and interactive performance systems. These systems are designed to respond dynamically to a performer’s tempo fluctuations, expressive timing, and dynamic shaping, thereby creating a responsive and musically engaging accompaniment environment. Unlike traditional fixed-tempo backing tracks, AI-powered accompaniment can adjust in real time, closely following the performer’s interpretative choices. This capability enables students to experience ensemble playing in a solo practice context, fostering skills such as rhythmic coordination, listening sensitivity, and expressive communication
. Additionally, interactive systems may incorporate elements of gamification and immersive learning, further enhancing student motivation and engagement. By simulating collaborative musical experiences, AI technologies bridge the gap between individual practice and ensemble performance, ultimately contributing to a more holistic and interactive approach to piano education.
3. Enhancing Technical Skills Through AI
3.1. Precision in Rhythm and Timing
Rhythm and timing constitute the foundational elements of piano performance, and their accurate execution is essential for both technical proficiency and musical expression. In this regard, AI technologies demonstrate a remarkable capacity for detecting even the most subtle rhythmic deviations. By employing advanced audio analysis and temporal modeling techniques, AI systems can precisely identify inconsistencies in tempo, irregular note durations, and misaligned rhythmic patterns. These tools often present feedback through both visual interfaces—such as waveform displays or rhythmic grids—and auditory cues that highlight discrepancies between intended and actual performance. Such multimodal feedback enables students to develop a more refined internal sense of pulse and temporal control. Over time, consistent interaction with these systems facilitates the internalization of stable tempo and rhythmic clarity, which are crucial for performing complex repertoire. Furthermore, AI-assisted rhythmic training supports the development of ensemble skills, as students become more adept at maintaining synchronization and responding to temporal variations.
3.2. Development of Finger Technique
The cultivation of refined finger technique is a central objective in piano pedagogy, encompassing aspects such as finger independence, agility, strength, and coordination. AI-driven platforms contribute significantly to this area by offering systematic analysis of performance data across repeated practice sessions. Through continuous monitoring, these systems can detect patterns of uneven finger usage, excessive tension, or inefficient movement sequences. Based on such analyses, AI tools generate targeted exercises designed to address specific technical weaknesses. For instance, if a student demonstrates inconsistency in executing rapid passages, the system may recommend customized drills that focus on improving finger dexterity and control. Additionally, some platforms integrate motion-sensing technologies to provide feedback on hand positioning and movement economy, further enhancing technical development. By combining repetition with intelligent adaptation, AI facilitates a more efficient and individualized approach to mastering fundamental pianistic techniques.
3.3. Error Detection and Correction
Error detection and correction represent another domain in which AI significantly enhances piano learning. Traditional practice often relies on a student’s subjective awareness or delayed teacher feedback, which may limit the timely identification of mistakes. In contrast, AI systems are capable of continuously monitoring performance and instantly flagging errors related to pitch, rhythm, articulation, and dynamics. This real-time diagnostic capability allows learners to focus their attention on problematic passages immediately, preventing the reinforcement of incorrect habits. Moreover, AI tools often provide detailed explanations or suggested corrective strategies, guiding students toward more effective solutions. For example, when repeated inaccuracies are detected in a specific musical phrase, the system may isolate that segment and propose slower practice tempos or alternative fingering approaches. Such targeted intervention not only enhances accuracy but also encourages a more analytical and reflective approach to practice, fostering deeper musical understanding
| [3] | Hallam, S. (2006). Music Psychology in Education. Institute of Education. University of London. |
[3]
.
3.4. Practice Optimization and Learning Efficiency
Beyond individual technical components, AI plays a crucial role in optimizing overall practice strategies and improving learning efficiency. By collecting and analyzing longitudinal performance data, AI platforms can track a student’s progress over time and identify trends in technical development. This data-driven insight enables the creation of personalized practice plans that prioritize areas requiring improvement while reinforcing existing strengths. In addition, adaptive algorithms adjust the level of difficulty and complexity of exercises according to the learner’s evolving capabilities, ensuring an appropriate balance between challenge and attainability. Such structured and goal-oriented practice not only maximizes time efficiency but also enhances motivation by providing clear indicators of progress. Furthermore, the integration of gamified elements—such as achievement tracking and performance scoring—can increase engagement and sustain long-term commitment to practice. Ultimately, AI-supported practice environments represent a shift toward more systematic, efficient, and individualized learning processes in piano pedagogy
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4. AI and Musical Expression
4.1. Analysis of Phrasing and Dynamics
Musical expression constitutes a central dimension of piano performance, extending beyond technical accuracy to encompass phrasing, dynamics, and expressive shaping. In recent years, AI technologies have begun to demonstrate promising capabilities in analyzing these expressive elements. By processing performance data through advanced audio feature extraction and pattern recognition algorithms, AI systems can evaluate dynamic contrasts, note grouping, and phrase contours. These systems often compare a student’s performance with established interpretative models, identifying areas where phrasing may lack direction or dynamic variation appears insufficient. Based on such analysis, AI tools can offer suggestions for shaping musical lines, such as highlighting climactic points, adjusting dynamic gradients, or refining articulation to enhance expressive coherence. While these recommendations do not replace artistic decision-making, they provide valuable reference points that help students develop a more conscious and structured approach to musical expression.
4.2. Tempo Flexibility and Rubato
Tempo flexibility, particularly in the form of rubato, represents one of the most nuanced aspects of expressive piano performance. Unlike strict tempo control, expressive timing involves subtle deviations that convey musical intention and emotional depth. Advanced AI systems are increasingly capable of analyzing such temporal variations by tracking micro-level tempo fluctuations throughout a performance
. Through this analysis, students can visualize how their tempo changes over time and compare these patterns with stylistically informed interpretations. For instance, AI tools may reveal whether a student’s rubato is applied consistently or whether it disrupts the overall musical flow. Additionally, these systems can provide guidance on stylistic conventions, helping learners understand how tempo flexibility differs across musical periods and composers. By making abstract expressive concepts more concrete and measurable, AI facilitates a deeper comprehension of timing as an expressive device.
4.3. Interpretation and Style Recognition
Another significant application of AI in the realm of musical expression is interpretation and style recognition. Machine learning models trained on extensive datasets of historical recordings are capable of identifying stylistic characteristics associated with different composers, performers, and musical eras. Through comparative analysis, AI systems can highlight interpretative tendencies such as preferred tempo ranges, articulation styles, dynamic shaping, and phrasing approaches. For students, this provides an opportunity to engage with a wide spectrum of interpretative possibilities, broadening their artistic perspective. Some platforms even allow users to emulate particular stylistic profiles, offering real-time feedback on how closely their performance aligns with a given interpretative model. This function not only enhances stylistic awareness but also encourages critical listening and informed decision-making. Consequently, AI serves as a bridge between historical performance practice and contemporary pedagogy, enriching the interpretative dimension of piano education
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4.4. Limitations in Expressive Evaluation
Despite these advancements, significant limitations remain in the application of AI to musical expression. Unlike technical parameters, expressive qualities such as emotional depth, artistic intention, and individuality are inherently subjective and context-dependent. While AI systems can quantify certain aspects of expression—such as dynamic range or tempo variation—they lack the capacity to fully comprehend the underlying emotional or cultural meanings embedded in a performance. Furthermore, excessive reliance on algorithmic feedback may lead to overly standardized interpretations, potentially limiting creative exploration. It is therefore essential to recognize that AI should function as a supportive tool rather than an authoritative evaluator in matters of artistic expression. The role of the human teacher remains indispensable in guiding students toward developing a personal musical voice and cultivating interpretative sensitivity. In this sense, the integration of AI into piano pedagogy must be approached with a balanced perspective, acknowledging both its analytical strengths and its expressive limitations.
5. Pedagogical Implications and Challenges
The integration of artificial intelligence into piano pedagogy is not merely a technological advancement but a transformative shift that reshapes teaching philosophies, learning processes, and educational values. While AI offers unprecedented opportunities for enhancing efficiency and personalization, it also raises important pedagogical and ethical questions. This chapter explores how AI redefines the roles of teachers and students, introduces new modes of independent learning, and presents critical challenges that must be addressed to ensure a balanced and meaningful educational experience.
5.1. Redefining the Role of the Teacher
Traditionally, piano teachers have been regarded as the primary source of knowledge, responsible for diagnosing technical issues, demonstrating musical ideas, and guiding students’ interpretative development. However, with the introduction of AI-powered tools capable of providing instant feedback on pitch accuracy, rhythm, articulation, and even expressive elements, the teacher’s role is undergoing a significant transformation.
Rather than functioning solely as an authority figure, the modern piano teacher increasingly assumes the role of a facilitator, mentor, and artistic guide. AI can efficiently handle repetitive and objective aspects of instruction, such as error detection and technical assessment, thereby freeing teachers to focus on higher-level musical concerns. These include stylistic interpretation, historical context, emotional expression, and the cultivation of individual artistic identity.
Moreover, teachers are now required to develop new competencies, including digital literacy and the ability to critically evaluate AI-generated feedback. Not all algorithmic suggestions are musically appropriate; therefore, teachers must help students discern when to accept, modify, or reject AI recommendations. In this sense, the teacher’s authority is not diminished but rather redefined—shifting from knowledge transmission to critical mediation and artistic leadership.
At the same time, this transition may challenge traditional pedagogical hierarchies. Some educators may feel that their expertise is being undermined by technology, particularly when students place excessive trust in AI systems. Consequently, it is essential to emphasize that AI is a supportive tool rather than a replacement for human instruction. The teacher remains indispensable in shaping musical sensitivity, aesthetic judgment, and expressive depth.
5.2. Student Autonomy and Independent Learning
One of the most significant contributions of AI to piano education is the promotion of student autonomy. AI-driven platforms enable learners to receive immediate, detailed feedback during individual practice sessions, reducing their reliance on weekly lessons as the sole source of guidance. This continuous feedback loop encourages more efficient practice habits and allows students to identify and correct mistakes in real time.
As a result, students become more active participants in their own learning process. They can set personalized goals, monitor their progress through data visualization, and adjust their practice strategies accordingly. This aligns with contemporary educational theories that emphasize self-regulated learning, metacognition, and learner agency.
Furthermore, AI tools often incorporate gamification elements, such as progress tracking, achievement badges, and interactive challenges, which can enhance motivation and engagement—particularly for younger learners
| [7] | Widmer, G. (2003). Machine learning in music performance. Computer Music Journal. |
[7]
. By transforming practice into a more dynamic and rewarding experience, AI helps sustain long-term commitment to musical study.
However, increased autonomy also requires students to develop critical thinking skills and self-discipline. Without proper guidance, learners may misinterpret feedback or focus excessively on measurable parameters (e.g., accuracy and tempo) at the expense of musical expression. Therefore, educators must provide structured frameworks that help students use AI effectively while maintaining a holistic understanding of music.
5.3. Ethical and Educational Concerns
Despite its advantages, the use of AI in piano pedagogy raises several ethical and educational concerns that warrant careful consideration. One of the primary issues is data privacy. Many AI systems rely on recording and analyzing students’ performances, which may involve the collection and storage of sensitive personal data. Ensuring that this data is securely managed and used responsibly is essential to protect students’ rights and maintain trust
| [8] | Yang, Y. (2020). AI in music education. International Journal of Music Education. |
[8]
.
Accessibility is another critical concern. Advanced AI tools and platforms may require expensive hardware, software subscriptions, or high-speed internet access, creating disparities between students from different socioeconomic backgrounds. If not addressed, this digital divide could exacerbate existing inequalities in music education.
In addition, there is a risk of over-reliance on technology. While AI can provide valuable feedback, it may also encourage a mechanical approach to learning if students become overly focused on achieving “perfect” scores or algorithmic approval. Music, as an expressive art form, cannot be fully quantified, and excessive dependence on AI metrics may limit creativity and individuality.
Another concern involves the potential homogenization of musical interpretation. AI systems trained on large datasets of historical recordings may implicitly promote certain stylistic norms, potentially discouraging unconventional or innovative interpretations. Educators must remain vigilant in fostering diversity and encouraging students to develop their own artistic voices
| [9] | Zhao, Y. (2019). Technology in piano pedagogy. Music Education Research. |
[9]
.
5.4. Balancing Technology and Human Artistry
The successful integration of AI into piano pedagogy ultimately depends on achieving a balance between technological efficiency and human artistry. While AI excels in objective analysis and data-driven feedback, it cannot replicate the emotional depth, cultural context, and interpersonal connection that define musical expression
| [10] | Zhou, Z. (2021). Intelligent music learning systems. Computers & Education. |
[10]
.
Teachers play a crucial role in maintaining this balance by integrating AI tools in a pedagogically meaningful way. Rather than allowing technology to dominate the learning process, educators should use it selectively to complement traditional teaching methods. For example, AI can be employed for technical drills and practice monitoring, while lesson time can be devoted to interpretative discussion, expressive nuance, and collaborative music-making.
It is also important to cultivate students’ awareness of the limitations of AI. By understanding that technology is a tool rather than an authority, students can develop a more critical and reflective approach to their learning. This perspective encourages them to value both precision and expressivity, recognizing that true musical artistry lies in the interplay between technical mastery and emotional communication.
Furthermore, preserving the humanistic aspects of music education involves fostering empathy, imagination, and cultural appreciation. Live performance, ensemble playing, and teacher-student interaction remain irreplaceable components of musical development. AI should be seen as an extension of these experiences, not a substitute.
In conclusion, while AI offers powerful possibilities for transforming piano pedagogy, its integration must be guided by thoughtful pedagogical principles and ethical awareness. By redefining the roles of teachers and students, promoting autonomy while maintaining guidance, addressing ethical challenges, and preserving the essence of human artistry, educators can harness the benefits of AI while safeguarding the integrity of musical education.
6. Conclusion
The integration of artificial intelligence into piano pedagogy represents a significant advancement in music education, offering new possibilities for both technical training and expressive development. AI-assisted tools provide immediate, objective, and data-driven feedback that enhances practice efficiency and supports the acquisition of fundamental skills. Through technologies such as real-time analysis, motion tracking, and intelligent accompaniment, students gain access to resources that were previously unavailable, enabling more personalized and adaptive learning experiences.
At the same time, AI extends beyond technical instruction by engaging with aspects of musical expression. Its ability to analyze phrasing, dynamics, and timing offers valuable insights that can deepen interpretative understanding. However, these capabilities remain limited in their capacity to fully capture the emotional and artistic dimensions of performance, which are shaped by human experience, cultural context, and individual creativity.
The role of the teacher remains indispensable in this evolving landscape. Rather than replacing traditional pedagogy, AI should be viewed as a complementary tool that enhances the teaching and learning process. Educators play a crucial role in guiding students toward meaningful artistic expression, fostering critical thinking, and cultivating a personal musical voice.
Ultimately, the successful integration of AI in piano pedagogy depends on achieving a balance between technological innovation and human artistry. When used thoughtfully, AI has the potential to transform music education by making it more efficient, accessible, and engaging, while preserving the expressive depth that defines musical performance. This synthesis of technology and artistry represents a promising direction for the future of piano education.
Abbreviations
AI | Artificial Intelligence |
Author Contributions
MingChih Hsieh: Conceptualization, Investigation, Methodology, Writing – original draft, Writing – review & editing
Conflicts of Interest
The authors declare no conflicts of interest.
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APA Style
Hsieh, M. (2026). Artificial Intelligence–Assisted Piano Pedagogy: Enhancing Technique and Musical Expression. Humanities and Social Sciences, 14(3), 282-287. https://doi.org/10.11648/j.hss.20261403.19
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Hsieh, M. Artificial Intelligence–Assisted Piano Pedagogy: Enhancing Technique and Musical Expression. Humanit. Soc. Sci. 2026, 14(3), 282-287. doi: 10.11648/j.hss.20261403.19
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Hsieh M. Artificial Intelligence–Assisted Piano Pedagogy: Enhancing Technique and Musical Expression. Humanit Soc Sci. 2026;14(3):282-287. doi: 10.11648/j.hss.20261403.19
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@article{10.11648/j.hss.20261403.19,
author = {MingChih Hsieh},
title = {Artificial Intelligence–Assisted Piano Pedagogy: Enhancing Technique and Musical Expression},
journal = {Humanities and Social Sciences},
volume = {14},
number = {3},
pages = {282-287},
doi = {10.11648/j.hss.20261403.19},
url = {https://doi.org/10.11648/j.hss.20261403.19},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.hss.20261403.19},
abstract = {The integration of artificial intelligence (AI) into music education has introduced transformative possibilities for piano pedagogy, particularly in the areas of technical development and musical expression. This study examines the role of AI-assisted tools in enhancing piano instruction by analyzing their impact on learning efficiency, performance accuracy, and interpretative depth. Through a synthesis of existing literature and pedagogical practices, this paper explores how AI technologies—such as real-time feedback systems, motion analysis software, and intelligent accompaniment platforms—contribute to individualized learning experiences. AI-assisted piano pedagogy offers significant advantages in technical training by providing immediate, objective, and data-driven feedback. These systems enable students to identify errors in rhythm, articulation, and dynamics more efficiently than traditional methods. Furthermore, AI technologies support the development of expressive performance by analyzing phrasing, timing, and tonal variation, thereby guiding students toward more nuanced interpretations. However, the study also addresses potential limitations, including over-reliance on technology, reduced teacher-student interaction, and challenges in capturing the subjective and emotional dimensions of musical expression. The balance between technological assistance and human artistic guidance remains a critical concern in contemporary pedagogy. This paper argues that AI should not replace traditional teaching but rather function as a complementary tool that enhances both technical precision and expressive awareness. By integrating AI into piano pedagogy, educators can create more adaptive, efficient, and engaging learning environments while preserving the essential artistic and humanistic aspects of musical training.},
year = {2026}
}
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TY - JOUR
T1 - Artificial Intelligence–Assisted Piano Pedagogy: Enhancing Technique and Musical Expression
AU - MingChih Hsieh
Y1 - 2026/06/09
PY - 2026
N1 - https://doi.org/10.11648/j.hss.20261403.19
DO - 10.11648/j.hss.20261403.19
T2 - Humanities and Social Sciences
JF - Humanities and Social Sciences
JO - Humanities and Social Sciences
SP - 282
EP - 287
PB - Science Publishing Group
SN - 2330-8184
UR - https://doi.org/10.11648/j.hss.20261403.19
AB - The integration of artificial intelligence (AI) into music education has introduced transformative possibilities for piano pedagogy, particularly in the areas of technical development and musical expression. This study examines the role of AI-assisted tools in enhancing piano instruction by analyzing their impact on learning efficiency, performance accuracy, and interpretative depth. Through a synthesis of existing literature and pedagogical practices, this paper explores how AI technologies—such as real-time feedback systems, motion analysis software, and intelligent accompaniment platforms—contribute to individualized learning experiences. AI-assisted piano pedagogy offers significant advantages in technical training by providing immediate, objective, and data-driven feedback. These systems enable students to identify errors in rhythm, articulation, and dynamics more efficiently than traditional methods. Furthermore, AI technologies support the development of expressive performance by analyzing phrasing, timing, and tonal variation, thereby guiding students toward more nuanced interpretations. However, the study also addresses potential limitations, including over-reliance on technology, reduced teacher-student interaction, and challenges in capturing the subjective and emotional dimensions of musical expression. The balance between technological assistance and human artistic guidance remains a critical concern in contemporary pedagogy. This paper argues that AI should not replace traditional teaching but rather function as a complementary tool that enhances both technical precision and expressive awareness. By integrating AI into piano pedagogy, educators can create more adaptive, efficient, and engaging learning environments while preserving the essential artistic and humanistic aspects of musical training.
VL - 14
IS - 3
ER -
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