Music Education Researcher | Specialist in AI for Music (Berklee) | KSSR & RSL Pedagogue.

Ashley Chang, M.Ed (Music) | Linktree

🎯 CURRENT PhD PROPOSAL (2026)

 🔵 RESEARCH FOUNDATION

🟢  TECHNICAL & ACADEMIC CAPABILI

🎤PEDAGOGICAL & PRACTICE EVIDENCE

🎹 CLASSICAL FOUNDATION

This archival performance documents my achievement as a recipient of the Yamaha Piano Prize, demonstrating a strong foundation in classical piano performance. The repertoire reflects technical precision, interpretative depth, and disciplined musicianship developed through formal training.

This classical background underpins my broader musical competency and informs my approach to vocal pedagogy, particularly in areas of tone control, musical phring, and technical discipline. It provides an important artistic foundation that complements my contemporary and research-oriented work.

🏆 Award: Yamaha Piano Prize (Advanced Solo)

This vocal performance reel showcases my practical engagement with contemporary vocal styles aligned with Rockschool (RSL) standards, including pop, rock, and musical theatre. Beyond performance, the reel serves as a pedagogical reference, illustrating applied vocal techniques such as tone production, stylistic interpretation, and expressive delivery.

The inclusion of live performance context reflects my ability to bridge artistic practice and pedagogical application, reinforcing the authenticity of my research focus on contemporary vocal training. This dual perspective—performer and educator—supports the development of a research framework grounded in both technical accuracy and real-world musical relevance.

✨ Contemporary Vocal Reel. Guest Artiste: TARUMT Fashion Gala 

This channel documents my ongoing work as a music educator, featuring classroom-based teaching practices, vocal demonstrations, and educational content tailored to the Malaysian context. It serves as a reflective and developmental platform through which I engage with issues of accessibility, pedagogy, and student engagement in music education.

The channel also illustrates my commitment to knowledge dissemination and public pedagogy, aligning with broader educational goals of inclusivity and continuous professional development. As such, it complements my academic work by demonstrating the practical implementation and outreach potential of my pedagogical approach.

🏫 Cikgu Ashley YouTube Channel

This document articulates the intellectual trajectory underpinning my research development, demonstrating a coherent progression from empirical investigation to technical specialization and, ultimately, to doctoral-level inquiry.

It traces the evolution of my work from my Master’s research—where I identified key limitations in classroom vocal pedagogy—to my subsequent training in AI for Music at Berklee, where I acquired the technical tools necessary to address these challenges.

The statement positions my proposed PhD as a natural and strategic continuation of this trajectory, integrating pedagogical insight with computational approaches to develop a scalable, Human-Centered AI framework for vocal education.

💡 Statement of Research Continuity

It presents a quantitative investigation (n=32) into the factors influencing vocal competency among Year 2 students in the Bangsar/Pudu district of Kuala Lumpur. The study examined the effects of prior singing experience and gender on pitch-matching accuracy and song performance.

Findings revealed no statistically significant differences based on gender, and only a weak, non-significant relationship between prior experience and vocal competency. These results challenge common pedagogical assumptions and highlight the limitations of existing instructional approaches in developing technical vocal skills at the primary level.

Importantly, the study exposes a structural gap in classroom-based vocal pedagogyspecifically, the absence of scalable and objective feedback mechanisms. This insight forms the empirical foundation for my proposed PhD research, which seeks to address this limitation through AI-assisted, real-time vocal feedback systems tailored to the Malaysian educational context.

📜 Master of Music Education Thesis

This document provides a more detailed articulation of the proposed research design, expanding on the methodological framework outlined in the concept paper. It elaborates on the application of Design-Based Research (DBR), including iterative development cycles, data collection strategies, and evaluation mechanisms.

Particular emphasis is placed on the integration of Music Information Retrieval (MIR) techniques—such as pitch tracking, formant analysis, and spectral modelling—into a pedagogically meaningful feedback system. This document demonstrates the technical feasibility and research depth of the proposed PhD study.

📄 Extended Research Proposal / Methodology Brief

This concept paper is addressing a critical gap between Malaysia’s KSSR classroom practices and international performance standards such as Rockschool(RSL). The study identifies a systemic “feedback bottleneck” in large classroom environments, where the absence of real-time, individualized vocal feedback limits technical development.
Grounded in Design-Based Research (DBR), the project aims to iteratively design and validate an AI-assisted pedagogical framework that integrates Music Information Retrieval (MIR) techniques—such as pitch tracking, formant analysis, and spectral modelling—into classroom practice. The framework is informed by Human-Centered AI principles and constructivist learning theory, ensuring that technological intervention enhances, rather than replaces, the role of the educator.

It represents a transition from problem identification to solution, positioning the research at the intersection of music education, AI, and pedagogical innovation in the Malaysian context.

📄 Research Concept Paper

This Statement of Purpose outlines my academic motivation, research direction, and readiness for doctoral study. It articulates my transition from identifying pedagogical limitations in classroom vocal training to pursuing computationally informed solutions through Human-Centered AI.

The document highlights my interdisciplinary development—combining music education, classroom experience, and AI training—and positions my proposed PhD as a focused and intentional progression toward developing scalable, research-driven innovations in vocal pedagogy.

📄 Statement of Purpose (SOP)

This academic CV presents a comprehensive overview of my qualifications as an emerging interdisciplinary researcher at the intersection of music education and artificial intelligence. It highlights eight years of professional experience as a KSSR music educator, alongside formal training in AI for Music from Berklee College of Music (GPA 4.0).

The document details my competencies in Music Information Retrieval (MIR), including pitch tracking, formant analysis, and acoustic feature interpretation, as well as my grounding in research methodologies such as Design-Based Research (DBR) and quantitative analysis using SPSS.

Collectively, this profile reflects a balanced integration of pedagogical expertise, technical capability, and research readiness, supporting my proposed PhD direction in Human-Centered AI for vocal pedagogy.

📄 Professional Academic CV

This certification from Berklee College of Music (GPA 4.0) documents my formal training in AI applications within music, with a focus on Music Information Retrieval (MIR) and audio signal analysis. The program provided foundational knowledge in computational approaches to sound, enabling the translation of vocal pedagogy into measurable acoustic features.

This training represents a critical technical bridge in my research trajectory, equipping me with the tools necessary to develop AI-assisted feedback systems for vocal education.

🎓 Berklee Certificate – AI for Music

This document provides a focused overview of my technical and research competencies, including proficiency in pitch tracking, formant analysis, spectral feature interpretation, and basic AI-assisted modelling concepts.

It also outlines my familiarity with research methodologies such as Design-Based Research (DBR) and quantitative analysis (SPSS), demonstrating readiness to engage in interdisciplinary doctoral research that integrates pedagogy and technology.

Ashley Chang, M.Ed (Music)

Music Education Researcher | Specialist in AI for Music (Berklee) | KSSR & RSL Pedagogue.

🎯 CURRENT PhD PROPOSAL (2026)

🔵 RESEARCH FOUNDATION

🟢 TECHNICAL & ACADEMIC CAPABILI

🎤PEDAGOGICAL & PRACTICE EVIDENCE

🎹 CLASSICAL FOUNDATION

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