Bridging Montessori Education and Artificial Intelligence: A Music-Enhanced Approach to Intelligent Learning Environments

Cao Yuan

doi:doi:10.11648/j.ijecs.20251005.14

Research Article |

| Peer-Reviewed

Bridging Montessori Education and Artificial Intelligence: A Music-Enhanced Approach to Intelligent Learning Environments

Cao Yuan^*

Published in International Journal of Education, Culture and Society (Volume 10, Issue 5)

Received: 15 July 2025 Accepted: 5 September 2025 Published: 19 September 2025

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

Abstract

The research explores the use of artificial intelligence (AI) in Montessori education using music-enriched, intelligent learning spaces that support the fundamentals of the pedagogy that include autonomy, sensory engagement, and child-led discovery. Unlike conventional digital learning systems, where the screen and predefined app navigation act as determinants to the flow of learning, this research proposes non-intrusive, tactile, and screenless AI ways acting as a “silent guide” incorporated within classroom resources and activities. AI can collect the data on learner engagement, interaction with music, and preferred sense via sensors and microcontrollers embedded in them without distracting concentration and superseding teacher judgment. Through the data-driven customization like principal component analysis, the study proves the connection between engagement and language development with the improvement of pronunciation and developing expressivity, mainly in music learning. The musical instruments with the customization of pitch, rhythm, and touch, due to the real-time feedback, can individualize the learning path, and the visual representation of data can be utilized by the teachers to make decisions and cooperate with similar organizations worldwide. The ethical aspect of this research is top priority, and most of the focus is on the protection of privacy, inclusiveness in the design of music AI, and avoidance of bias or over-engineering. The study identifies possible risks of digital inequity, cultural homogenization, and ways to mitigate them by using modular, cost-efficient tools and a community-driven design process. The requirements for implementation are technical minimalism, local processing of data, and education of educators within the Montessori philosophy and AI literacy.

Published in	International Journal of Education, Culture and Society (Volume 10, Issue 5)
DOI	10.11648/j.ijecs.20251005.14
Page(s)	279-285
Creative Commons	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.
Copyright	Copyright © The Author(s), 2025. Published by Science Publishing Group

Keywords

Artificial Intelligence, Montessori Education, Smart Music Teaching Aids, Customized Learning Environment

1. Introduction

In recent years, AI has shifted from being a peripheral innovation to a main component in the reimagination of global educational systems. In a range of learning environments, AI delivers relevant and personalized material, and quick responses, automates administrative roles, and assists with analyzing student performance. The innovations in education generally support old forms of teaching, where it’s centered on standardized programs, linear assessments, and teacher-directed instruction. Despite improving education delivery, the integration fails to account for learner diversity, individual pacing, and non-linear development that’s central to alternative educational philosophies like Montessori.

Montessori education was pioneered by Dr. Maria Montessori, inspiring a century of practice and research in child development fields. Existing literature mainly focuses on sensorial learning, highlighting how sensational materials matter for cognitive processes and fine motor skills. Another key area is autonomy and self - directed learning; studies show Montessori settings foster intrinsic motivation, decision - making, and autonomy. Mixed - age learning and social interaction in these contexts also yield positive peer learning and social togetherness outcomes. Recently, neurodiversity has been a topic, with the structured yet flexible Montessori model shown to suit kids with ASD/ADHD.”

Nonetheless, there are remaining areas that are of primary concern. The introduction of new digital technologies, especially artificial intelligence (AI), is not properly explored within Montessori environments. Screens, fixed task design, and reinforcing systems that form the backbone of most edtech products will clash with the Montessori ideas of self-regulation and the hands-on approach to learning. Moreover, there is no literature evidence of how music, AI, and individualized learning can be combined because music education represents one of the fundamental aspects of the Montessori schooling. Further, the AI ethical dimensions of early childhood are associated with privacy, cultural inclusivity, and teacher autonomy, which are not analysed in detail. Lastly, there has been minimal effort on examining cross-cultural and data sharing models that would integrate the practices of Montessori across the globe in the digital era.

This study aims to fill the research gap mentioned above and proposes how to create an environment for children's music learning under the condition of artificial intelligence by using the Montessori education method, with a focus on cultivating children's sensory awareness and the autonomy of learners. This paper explores how to utilize AI as a "silent guide" to observe and respond to children's current music and emotional patterns without interfering with the prepared environment. These sections will be supported by simple statistical analysis and pictures to back up the arguments, and further divided into several chapters.

2. AI and Personalized Montessori Learning

2.1. Sensor Technologies for Individual Learning Paths

2.1.1. Integration of Sensor Technologies

The integration of sensor technologies into Montessori learning environments represents a better connection of artificial intelligence with its pedagogy tenets like autonomy, observation-based instruction, and sensorial exploration

[1]

(Bose, 2016). When children reach for or use certain instruments, motion sensors like accelerometers and gyroscopes detect their physical gestures and movement patterns like clapping, tapping, and rhythmic movements

[2]

(Holland, 2013) highlights that the data points record activities and help AI systems interpret sensorimotor development, rhythmic timing, and consistency in practice. In addition, the presence of pressure sensors in drums and piano keys allows for measuring how strongly children play with sound-creating objects, helping evaluate their coordinated movements and sensitivity to different textures

[3]

(Grosshauser & Troster, 2013). With sound sensors recording pitch, frequency, tempo, and sound strength, AI models can determine the learner’s auditory profile, including the type of musical engagement using loudness, duration, and tonality.

Download: Download full-size image

Figure 1. Design of a prototype with the (a) location of pressure sensors and b cross-section of the strap.

2.1.2. Visual and Tactile Feedback Mechanisms

Montessori is distinguished from traditional educational technology by its capacity to translate abstract musical phenomena into tangible, visible, and responsive feedback mechanisms. Through AI, the data gathered from sensors can be aggregated into useful visual formats like graphs for keeping track of pitch changes, heat maps for instrument frequency, and bars that beat in rhythm accuracy. These visualizations assist teachers in tracking developmental trajectories while giving students a self-correcting, explorative environment.

In this case, if a child hits a xylophone key at incorrect pitch intervals, the system uses light-based cues and tactile feedback that guides the child to practice accurate pitch and tonal sequence. As a result, the process supports Montessori’s principles of control of error, where the environment and not the teacher guides in the correction while allowing them to preserve their independence.

The chart visually presents how AI technology can enhance the principles of Montessori education, thereby breaking the limitations of standardization in Montessori education and achieving personalized adaptation through real-time data.

Download: Download full-size image

Figure 2. AI-Driven Music Adaptation in Montessori Learning.

2.2. Development of Smart Music Teaching Aids

2.2.1. AI-Powered Pitch Blocks and Height-Based Sound Learning

Montessori’s music aids are traditionally tactile, including rhythm sticks, bells, and tone bars that help children internalize rhythm, scales, and musicality through experience. Building upon the foundation of sensor integration, AI can help develop intelligent music teaching tools that are interactive and engaging for kids, and further Montessori principles through novel, multisensory formats

[4]

(Mavric, 2020). The teaching tools include an AI-powered pitch block system that utilizes pressure sensors to measure the height at which modular blocks are stacked

[5]

(George, 2016). Each block features diverse pitch levels, and when the blocks are stacked, the system calculates the vertical configuration to pick out a musical scale and tune. AI translates the stack’s height into real-time auditory output, and the bigger the stack, the higher the pitch. The height-to-pitch translation is visually supported by flashing lights; it indicates vibrations that users can feel, thereby providing kinesthetic feedback.

Working with objects is essential in Montessori learning as children use their hands, observing cause-and-effect relationships as they build knowledge and discover how sounds are made via structured play.

The pedagogical elegance is notable in the suggestive guidance of students without correcting them. If a child constructs a melody sequence that does not belong to a musical scale, the system doesn’t show any mistakes but encourages playing different notes by lowering the lights and softening noises. According to

[6]

(Daboll, 2024), the correction aligns with Montessori’s emphasis on intrinsic motivation and independent discovery.

2.2.2. Emotional Expression Through Music Drawing Boards

The emotional music drawing board presents art and music in a way that helps people learn through multiple senses. Emotion-responsive tools join the senses in a way that helps children grasp feelings through what they watch, hear, and experience physically

[7]

(Lee & Lin, 2023). Equipped with capacitive touch sensors and technology on the board allows it to measure drawing intensity, speed, and color choice

[8]

(Swargiary, 2024). These AI algorithms analyze the data and search for links between the learner’s feelings and musical emotions

[9]

(Chen & Sun, 2024). For instance, fast, bold strokes in warm hues are perfect for presenting a strong, upbeat, staccato musical accompaniment that demonstrates excitement and joy, whereas gently flowing, soft lines drawn in lighter, cooler shades might yield a legato, minor key melody that represents contemplation and sorrowful tunes. Educational toy trains children to relate music, images, and emotion, which improves their emotional intelligence. Rather than correcting or grading the child, the system encourages and extends musical ideas of what they already play in real-time

[10]

(Greaney, 2021). If haptic feedback, motion sensors, and light-emitting parts are used, these tools can be converted into machines that allow people to join in and use their senses

[11]

(Fröhlich, 2021). Montessori’s approach to learning helps children gain control of their feelings and emotions through integrated modalities.

Download: Download full-size image

Figure 3. Color-to-sound mapping. Images are drawn while music plays (Mirriam, 2016).

2.3. Game-Based Music Learning with Embedded AI

Although Montessori environments traditionally focus more on self-study and free choice, AI-designed music games can reinforce the learning processes in the Montessori classroom-particularly in rhythm education, which demands practice, variation, and structured challenges.

With popular fruit-themed games like Fruit Ninja as a guide, it’d be possible to dream up a musical slice game, where musical notes fall from the top of a screen and must be clapped or tapped in perfect timing at a certain spot

[12]

(Troiano et al., 2020). The AI engine analyzes accuracy in rhythm patterns, note lengths, and styles like jazz, classical, and folk as the child progresses

[13]

(Sanganeria & Gala, 2024). In addition, every session is different because the content adapts to how the learner performs, which means the material and learning paths are always personalized.

Based on the above design, I have created a game classroom. (as shown in Supplementary material 2)

Over-digitalizing Montessori educational environments may reduce tactile engagement, thereby creating pressing issues. When applying artificial intelligence in educational technology, traditional designs should be avoided; instead, data-based customization should be used to enable its seamless integration into the learning environment. With the help of elementary logical programming, non-invasive sensors, and visual data representations blend into the prepared environment. For instance, a system might use color-coded LED bars to show the number of times a child has listened to a musical piece throughout the year

[14]

(Chilvers, 2025). Instead of commanding or instructing students, patterns in the chart are gradually shown, making it easy for everyone to track engagement trends over time. The tools serve to guide the child's actions without actually controlling them which supports Montessori’s idea of child independence.

3. Challenges and Measures

3.1. Protecting Child Data and Privacy

Integrating artificial intelligence into Montessori educational environments carries an ethical responsibility to safeguard children's data. Regardless of the role that AI systems play in Montessori settings, the methods of data collection, storage, and processing should be transparent. Ethical AI should operate in the background, meaning that it collects learning signals without the need to verify and track them

[15]

(Chakraborty, n.d). For example, developmental feedback by the rhythm detection of an embedded sensor in a musical exercise and a musical exercise does not store personally identifiable information. This aligns with the Montessorian philosophy of securing an independent, transformative, and respectful environment in which the child would develop.

3.2. Preserving Human Judgment and Observation

Observation is both an art and a science within Montessori philosophy. There is a growing inclination to entrust complex pedagogical decisions—determining what another person ought to do—to artificial intelligence, especially when these systems can produce data visualizations and forecasts of children’s behavior. However, these outputs must be treated as supplementary tools to complement information- not facts. Children are not datasets to be streamlined but rather individuals whose development unfolds in unpredictable and deeply human ways. The over-reliance on AI presents a risk to the Montessori philosophy of working as a three-dimensional person and transforming the vision into a system of mechanical measures, depriving the child of his / her agency and making the task of an educator the work of a technician. Montessori stressed the preparedness of the teacher’s environment and spirit. The readiness cannot be replicated by the code and algorithmic presented by AI. However, the AI systems ought to encourage reflective teaching, and not automate it. For instance, AI can suggest that a child has unusually spent much time with a specific musical instrument but the teacher will put the situation into perspective and contextualize the situation. As a result, the teacher can query to determine questions like; Is a child developing an interest in a new topic? Are they employing music as a way of controlling their emotions? Or are they shying away from other activities?

4. Conclusion

The paper explores the multifaceted intersection between artificial intelligence and Montessori education with a special focus on how music can be applied as a developmental and integrative tool.. These include the sensor technologies that observe the musical and motor interactions with the kids without disturbing their concentration. Moreover, smart teaching aids like the AI-powered pitch blocks and emotional music drawing boards reform the effects of physical manipulations into multisensory feedback. Game-based learning with embedded AI to continue with the habits of Montessori regarding the need to be internally motivated and also engage in child direction. Distinguishing the proper creation of these systems, with mindfulness and faithfulness to Montessori ideals, enables educators to observe and respond to children’s needs more effectively while maintaining a sense of autonomy and respect for the individual pace and interests of each learner. The paper discussed how AI could be integrated and applied into the Montessori environments as the concept of silence guides an environment that does not interfere with learning activities and aids in a prepared environment.

AI demands algorithmic design, inclusivity in user testing, and a critical dedication toward the ethics of data, especially in targeting vulnerable communities of young children and neurodiverse learners. Moreover, responsible innovation implies involving teachers, parents, and children in the co-designing process. Their thoughts and lived experiences cannot be highly valued in creating useful tools that are fulfilling. AI should provide the flexibility to work in a variety of cultural, economic, and pedagogical settings without imposing a standardized pattern of instruction. The next natural step involves the integration of AI into education which needs to use it in a manner that is not insertive, but natural as part of the rich fabric of the Montessori philosophy of being humble, responsive, and sustaining. The tools built must have the same tone as the procedure itself--natural, placid, sane, and in harmony with the pace of human growth. The foundations of technological development in long-standing educational values that educators and parents can make sure that they have innovation at the service of the child.

Acknowledgments

The thesis was written by me myself after carefully reviewing a large number of relevant documents. During this process, I couldn't have done it without the help of my supervisor and family.

Author Contributions

Cao Yuan is the sole author. The author read and approved the final manuscript.

Funding

This work is not supported by any external funding.

Data Availability Statement

The data supporting the outcome of this research work has been reported in this manuscript.

Conflicts of Interest

The author declares no conflicts of interest.

Appendix

Download: Download full-size image

Figure 4. AI Augmented Montessori Music Education Framework.

Supplementary Material

Below is the link to the supplementary material:

Supplementary Material 1.docx

Supplementary Material 2.docx

References

[1]	Bose, A. Mobile application and data visualization for Sensei: sensing educational interaction in Montessori classrooms. Ph.D. Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/105940
[2]	Holland, S. Artificial intelligence in music education: A critical review. Readings in music and artificial intelligence, 2013, 239-274.
[3]	Grosshauser, T., & Tröster, G. Finger Position and Pressure Sensing Techniques for String and Keyboard Instruments. New Interfaces for Musical Expression. 2013, (13), 27-30.
[4]	Mavric, M. The Montessori Approach as a Model of Personalized Instruction. Journal of Montessori Research. 2020, 6(2): 13-25.
[5]	George, D. B. Distributed sensor network for sensing educational interaction in early childhood classrooms. Ph.D. Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/106023
[6]	Daboll, D. A. Preparing the Forest: Designing Intersections Between Montessori and Nature-Based Education. Master's thesis, Rutgers The State University of New Jersey, School of Graduate Studies, 2024.
[7]	Lee, L., & Lin, C. H. Digital and Traditional Learning: Learning Styles with Music and Technology for Early Childhood Education. Engineering Proceedings. 2023, 38(1), 19.
[8]	Swargiary, K. Modern Perspectives on Montessori Philosophy: Adaptations and Applications in the 2020's. Ph.D. Thesis, ERA, US; 2024.
[9]	Chen, Y., & Sun, Y. The Usage of Artificial Intelligence Technology in Music Education System under Deep Learning. IEEE Access, 2024.
[10]	Greaney, T.. Montessori at Home: A Practical Guide for Parents. America: Sourcebooks, Inc, 2021.
[11]	Fröhlich, M. Encouraging sensory exploration of 5-7 year-olds. Ph.D. Thesis, University of Art, 2021.
[12]	Troiano, G. M., Chen, Q., Alba, Á. V., Robles, G., Smith, G., Cassidy, M., Harteveld, C. Exploring How Game Genre in Student-Designed Games Influences Computational Thinking Development. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, Honolulu, HI, USA, 2020; pp. 1-17.
[13]	Sanganeria, M., Gala, R. Tuning Music Education: AI-Powered Personalization in Learning Music. arXiv Preprint 2412.13514. 2024. https://doi.org/10.48550/arXiv.2412.13514
[14]	Chilvers, O. D. Connecting The Montessori Philosophy Through EdTech. Master's thesis, Chapman University, 2025.
[15]	Chakraborty, P. P. Ethical Considerations in Deploying AI and Data-Driven Technologies for Adaptive Education. International Research Journal of Modernization in Engineering Technology and Science. 2024, 6(3), 79-84. https://doi.org/10.56726/IRJMETS45423

Cite This Article

Plain Text BibTeX RIS

APA Style

Yuan, C. (2025). Bridging Montessori Education and Artificial Intelligence: A Music-Enhanced Approach to Intelligent Learning Environments. International Journal of Education, Culture and Society, 10(5), 279-285. https://doi.org/10.11648/j.ijecs.20251005.14

Copy | Download

ACS Style

Yuan, C. Bridging Montessori Education and Artificial Intelligence: A Music-Enhanced Approach to Intelligent Learning Environments. Int. J. Educ. Cult. Soc. 2025, 10(5), 279-285. doi: 10.11648/j.ijecs.20251005.14

Copy | Download

AMA Style

Yuan C. Bridging Montessori Education and Artificial Intelligence: A Music-Enhanced Approach to Intelligent Learning Environments. Int J Educ Cult Soc. 2025;10(5):279-285. doi: 10.11648/j.ijecs.20251005.14

Copy | Download

@article{10.11648/j.ijecs.20251005.14,
  author = {Cao Yuan},
  title = {Bridging Montessori Education and Artificial Intelligence: A Music-Enhanced Approach to Intelligent Learning Environments
},
  journal = {International Journal of Education, Culture and Society},
  volume = {10},
  number = {5},
  pages = {279-285},
  doi = {10.11648/j.ijecs.20251005.14},
  url = {https://doi.org/10.11648/j.ijecs.20251005.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijecs.20251005.14},
  abstract = {The research explores the use of artificial intelligence (AI) in Montessori education using music-enriched, intelligent learning spaces that support the fundamentals of the pedagogy that include autonomy, sensory engagement, and child-led discovery. Unlike conventional digital learning systems, where the screen and predefined app navigation act as determinants to the flow of learning, this research proposes non-intrusive, tactile, and screenless AI ways acting as a “silent guide” incorporated within classroom resources and activities. AI can collect the data on learner engagement, interaction with music, and preferred sense via sensors and microcontrollers embedded in them without distracting concentration and superseding teacher judgment. Through the data-driven customization like principal component analysis, the study proves the connection between engagement and language development with the improvement of pronunciation and developing expressivity, mainly in music learning. The musical instruments with the customization of pitch, rhythm, and touch, due to the real-time feedback, can individualize the learning path, and the visual representation of data can be utilized by the teachers to make decisions and cooperate with similar organizations worldwide. The ethical aspect of this research is top priority, and most of the focus is on the protection of privacy, inclusiveness in the design of music AI, and avoidance of bias or over-engineering. The study identifies possible risks of digital inequity, cultural homogenization, and ways to mitigate them by using modular, cost-efficient tools and a community-driven design process. The requirements for implementation are technical minimalism, local processing of data, and education of educators within the Montessori philosophy and AI literacy.
},
 year = {2025}
}

Copy | Download

TY  - JOUR
T1  - Bridging Montessori Education and Artificial Intelligence: A Music-Enhanced Approach to Intelligent Learning Environments

AU  - Cao Yuan
Y1  - 2025/09/19
PY  - 2025
N1  - https://doi.org/10.11648/j.ijecs.20251005.14
DO  - 10.11648/j.ijecs.20251005.14
T2  - International Journal of Education, Culture and Society
JF  - International Journal of Education, Culture and Society
JO  - International Journal of Education, Culture and Society
SP  - 279
EP  - 285
PB  - Science Publishing Group
SN  - 2575-3363
UR  - https://doi.org/10.11648/j.ijecs.20251005.14
AB  - The research explores the use of artificial intelligence (AI) in Montessori education using music-enriched, intelligent learning spaces that support the fundamentals of the pedagogy that include autonomy, sensory engagement, and child-led discovery. Unlike conventional digital learning systems, where the screen and predefined app navigation act as determinants to the flow of learning, this research proposes non-intrusive, tactile, and screenless AI ways acting as a “silent guide” incorporated within classroom resources and activities. AI can collect the data on learner engagement, interaction with music, and preferred sense via sensors and microcontrollers embedded in them without distracting concentration and superseding teacher judgment. Through the data-driven customization like principal component analysis, the study proves the connection between engagement and language development with the improvement of pronunciation and developing expressivity, mainly in music learning. The musical instruments with the customization of pitch, rhythm, and touch, due to the real-time feedback, can individualize the learning path, and the visual representation of data can be utilized by the teachers to make decisions and cooperate with similar organizations worldwide. The ethical aspect of this research is top priority, and most of the focus is on the protection of privacy, inclusiveness in the design of music AI, and avoidance of bias or over-engineering. The study identifies possible risks of digital inequity, cultural homogenization, and ways to mitigate them by using modular, cost-efficient tools and a community-driven design process. The requirements for implementation are technical minimalism, local processing of data, and education of educators within the Montessori philosophy and AI literacy.

VL  - 10
IS  - 5
ER  -

Copy | Download

Author Information

Cao Yuan

School of Music, Shandong Normal University, Ji Nan, China

Contact Email

Download PDF

Submit an Article

Plain Text BibTeX RIS

APA Style

Yuan, C. (2025). Bridging Montessori Education and Artificial Intelligence: A Music-Enhanced Approach to Intelligent Learning Environments. International Journal of Education, Culture and Society, 10(5), 279-285. https://doi.org/10.11648/j.ijecs.20251005.14

Copy | Download

ACS Style

Yuan, C. Bridging Montessori Education and Artificial Intelligence: A Music-Enhanced Approach to Intelligent Learning Environments. Int. J. Educ. Cult. Soc. 2025, 10(5), 279-285. doi: 10.11648/j.ijecs.20251005.14

Copy | Download

AMA Style

Yuan C. Bridging Montessori Education and Artificial Intelligence: A Music-Enhanced Approach to Intelligent Learning Environments. Int J Educ Cult Soc. 2025;10(5):279-285. doi: 10.11648/j.ijecs.20251005.14

Copy | Download

@article{10.11648/j.ijecs.20251005.14,
  author = {Cao Yuan},
  title = {Bridging Montessori Education and Artificial Intelligence: A Music-Enhanced Approach to Intelligent Learning Environments
},
  journal = {International Journal of Education, Culture and Society},
  volume = {10},
  number = {5},
  pages = {279-285},
  doi = {10.11648/j.ijecs.20251005.14},
  url = {https://doi.org/10.11648/j.ijecs.20251005.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijecs.20251005.14},
  abstract = {The research explores the use of artificial intelligence (AI) in Montessori education using music-enriched, intelligent learning spaces that support the fundamentals of the pedagogy that include autonomy, sensory engagement, and child-led discovery. Unlike conventional digital learning systems, where the screen and predefined app navigation act as determinants to the flow of learning, this research proposes non-intrusive, tactile, and screenless AI ways acting as a “silent guide” incorporated within classroom resources and activities. AI can collect the data on learner engagement, interaction with music, and preferred sense via sensors and microcontrollers embedded in them without distracting concentration and superseding teacher judgment. Through the data-driven customization like principal component analysis, the study proves the connection between engagement and language development with the improvement of pronunciation and developing expressivity, mainly in music learning. The musical instruments with the customization of pitch, rhythm, and touch, due to the real-time feedback, can individualize the learning path, and the visual representation of data can be utilized by the teachers to make decisions and cooperate with similar organizations worldwide. The ethical aspect of this research is top priority, and most of the focus is on the protection of privacy, inclusiveness in the design of music AI, and avoidance of bias or over-engineering. The study identifies possible risks of digital inequity, cultural homogenization, and ways to mitigate them by using modular, cost-efficient tools and a community-driven design process. The requirements for implementation are technical minimalism, local processing of data, and education of educators within the Montessori philosophy and AI literacy.
},
 year = {2025}
}

Copy | Download

TY  - JOUR
T1  - Bridging Montessori Education and Artificial Intelligence: A Music-Enhanced Approach to Intelligent Learning Environments

AU  - Cao Yuan
Y1  - 2025/09/19
PY  - 2025
N1  - https://doi.org/10.11648/j.ijecs.20251005.14
DO  - 10.11648/j.ijecs.20251005.14
T2  - International Journal of Education, Culture and Society
JF  - International Journal of Education, Culture and Society
JO  - International Journal of Education, Culture and Society
SP  - 279
EP  - 285
PB  - Science Publishing Group
SN  - 2575-3363
UR  - https://doi.org/10.11648/j.ijecs.20251005.14
AB  - The research explores the use of artificial intelligence (AI) in Montessori education using music-enriched, intelligent learning spaces that support the fundamentals of the pedagogy that include autonomy, sensory engagement, and child-led discovery. Unlike conventional digital learning systems, where the screen and predefined app navigation act as determinants to the flow of learning, this research proposes non-intrusive, tactile, and screenless AI ways acting as a “silent guide” incorporated within classroom resources and activities. AI can collect the data on learner engagement, interaction with music, and preferred sense via sensors and microcontrollers embedded in them without distracting concentration and superseding teacher judgment. Through the data-driven customization like principal component analysis, the study proves the connection between engagement and language development with the improvement of pronunciation and developing expressivity, mainly in music learning. The musical instruments with the customization of pitch, rhythm, and touch, due to the real-time feedback, can individualize the learning path, and the visual representation of data can be utilized by the teachers to make decisions and cooperate with similar organizations worldwide. The ethical aspect of this research is top priority, and most of the focus is on the protection of privacy, inclusiveness in the design of music AI, and avoidance of bias or over-engineering. The study identifies possible risks of digital inequity, cultural homogenization, and ways to mitigate them by using modular, cost-efficient tools and a community-driven design process. The requirements for implementation are technical minimalism, local processing of data, and education of educators within the Montessori philosophy and AI literacy.

VL  - 10
IS  - 5
ER  -

Copy | Download