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A Hybrid Methodological Approach to Choral Practice for Older

Adults in Cuenca, Ecuador: Integrating Artificial Intelligence and

Digital Resources

Enfoque Metodológico Híbrido para la Práctica Coral en Adultos Mayores en

Cuenca, Ecuador: Integración de Inteligencia Artificial y Recursos Digitales

María Belén Neira Landívar

belenela6.97@hotmail.com

https://orcid.org/0009-0006-1438-537X

Universidad de Cuenca

Cuenca – Ecuador

Artículo recibido: 11/06/2026

Aceptado para publicación: 18/07/2026

Conflictos de Intereses: Ninguno que declarar

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ABSTRACT

This study examines the integration of artificial intelligence (AI)-based digital tools

into the choral practice of older adults in Cuenca, Ecuador. Using a qualitative descriptive

approach, the research draws on a literature review, analysis of local choral experiences, and

interviews with choral directors. The study proposes a hybrid pedagogical model that

combines traditional choral methodologies with AI-supported digital resources to enhance

vocal training, repertoire preparation, and learner autonomy. Findings suggest that AI tools

can support individual musical learning and aural skills development; however, their

implementation is constrained by factors such as digital literacy, access to technology, and

teacher training. The study contributes to the field of music education by proposing an

innovative framework for integrating AI into community-based choral practice for older

adults.

Keywords: artificial intelligence, music education, older adults

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RESUMEN

Este estudio examina la integración de herramientas digitales basadas en inteligencia

artificial (IA) en la práctica coral de adultos mayores en Cuenca, Ecuador. Utilizando un

enfoque descriptivo cualitativo, la investigación se sustenta en una revisión de literatura, el

análisis de experiencias corales locales y entrevistas con directores corales. El estudio

propone un modelo pedagógico híbrido que combina metodologías corales tradicionales con

recursos digitales apoyados por IA para mejorar el entrenamiento vocal, la preparación del

repertorio y la autonomía del aprendizaje. Los hallazgos sugieren que las herramientas de IA

pueden favorecer el aprendizaje musical individual y el desarrollo de habilidades auditivas;

sin embargo, su implementación está limitada por factores como la alfabetización digital, el

acceso a la tecnología y la formación docente. El estudio contribuye al campo de la educación

musical al proponer un marco innovador para la integración de la IA en la práctica coral

comunitaria dirigida a adultos mayores.

Palabras clave: inteligencia artificial, educación musical, adultos mayores

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INTRODUCTION

Choral practice is one of the most widely recognized forms of collective musical participation

in both educational and community contexts.In the case of older adults, choral singing

constitutes an activity that fosters socialization, cognitive stimulation, and emotional well-

being (Clift & Hancox, 2010; Creech et al., 2013).

Various studies have demonstrated that participation in collective musical activities

contributes to improving quality of life in later years, strengthening social networks, and

reducing symptoms associated with social isolation (Hallam, 2015). In particular, choral

singing has been linked to psychological, cognitive, and physiological benefits among older

individuals (Welch et al., 2014).

In the city of Cuenca, Ecuador, several choral groups composed of older adults function as

important artistic and community spaces.including the choir of the University for Older

Adults (UPAM), the Santa Catalina Choir, and the Agua Capulí Choir, which function as

artistic and community spaces for their members. However, recent research highlights the

lack of systematized pedagogical methodologies specifically oriented toward choral work

with older adults (Neira, 2024).

In recent years, digital technologies—particularly artificial intelligence—have significantly

transformed music education practices.In particular, artificial intelligence has begun to be

applied in processes such as audio analysis, automatic music generation, and vocal feedback

supported by machine learning algorithms (Briot et al., 2020).

Nevertheless, the integration of these technologies into community choral practice—and

specifically within ensembles of older adults—remains scarcely documented in the academic

literature.

In this context, the present study aims to analyze the pedagogical potential of integrating

artificial intelligence–based digital tools into the choral practice of older adults in the city of

Cuenca, Ecuador.

More specifically, this research seeks to identify possible applications of these tools within

the choral teaching and learning process, as well as to explore their pedagogical implications

in community-based music education contextsAccordingly, this study addresses the following

research question ¿How can the incorporation of artificial intelligence–based digital tools

contribute to musical learning within choral ensembles of older adults?

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This study contributes to the field of music education by addressing a gap in the literature

regarding the integration of artificial intelligence in community-based choral practice for

older adults. It proposes a hybrid pedagogical model that combines traditional choral

methodologies with AI-supported tools, offering a framework that may be adapted to similar

socio-cultural contexts.

Theoretical Framework

Choral Conducting and Music Pedagogy

Choral conducting constitutes a discipline that integrates musical, pedagogical, and

communicative knowledge aimed at leading vocal ensembles (Durrant, 2018). The choral

conductor fulfills interpretative, pedagogical, and organizational functions within the vocal

ensemble.

Some authors have pointed out that the construction of choral sound depends on factors such

as vocal technique, collective listening, understanding of the repertoire, and the conductor’s

gestural communication (Jordan, 2012). In this sense, the choral rehearsal is conceived as a

space for collaborative learning in which singers develop musical skills through interaction

and active listening (Phillips, 2015).

Within the field of Latin American choral pedagogy, the contributions of Méndez (2003) and

Sánchez and Guerra (1982) have supported the development of methodological models

oriented toward choral practice. These approaches emphasize a progressive process that

begins with unison singing and advances toward polyphonic structures, prioritizing aspects

such as intonation, breathing, phrasing, and tonal homogeneity.

Likewise, music education methodologies have influenced contemporary choral practice,

including the approaches of Dalcroze, Kodály, Orff, and Suzuki, which integrate the

development of musical hearing, rhythmic perception, and bodily expression into the music

learning process.

Characteristics of the Aging Voice

The aging process produces physiological transformations in the human voice known as

presbyphonia. These modifications include changes in the structure of the phonatory system,

alterations in the elasticity of laryngeal tissues, and variations in vocal fold vibration, which

may result in a reduced vocal range and decreased vocal intensity (Retuert et al., 2017).

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In the choral context, these characteristics require adaptations in vocal technique, repertoire

selection, and rehearsal organization in order to prevent vocal fatigue and promote comfort

within the singers’ vocal range (Welch et al., 2014). In this regard, the choral conductor must

consider the physiological characteristics of the singers when planning the ensemble’s vocal

and musical work.

Artificial Intelligence and the Evolution toward Modern Artificial Intelligence

Artificial intelligence (AI) is defined as the field of computer science dedicated to the design

of systems capable of performing tasks that typically require human intelligence, such as

pattern recognition, decision-making, and learning from data (Russell & Norvig, 2021). Since

its origins in the mid-twentieth century, AI research initially focused on symbolic approaches

based on rules and logical representations of knowledge. This paradigm, known as classical

or symbolic AI, relied on explicit knowledge bases and inference engines to solve specific

problems.

Although these approaches achieved advances in areas such as planning and expert systems,

they encountered difficulties in addressing complex environments and processing large

volumes of data. Beginning in the 2010s, significant progress emerged with the rise of

machine learning and, in particular, deep learning—techniques that enable computational

systems to identify patterns directly from large-scale datasets (Briot et al., 2020).

This new approach, commonly referred to as modern artificial intelligence, is based on

statistical models and artificial neural networks capable of identifying regularities in complex

data and improving their performance through automated training processes. These

technologies have had a significant impact on fields such as speech recognition, computer

vision, machine translation, and content generation.

In the musical domain, modern artificial intelligence has enabled advances in areas such as

automatic audio analysis, source separation, computational music generation, and automated

vocal feedback. For instance, Chandna et al. (2022) developed a deep learning–based model

capable of separating individual voices within polyphonic choral recordings, facilitating the

analysis of each vocal line and the independent study of repertoire.

Similarly, recent studies have shown that AI-assisted vocal feedback systems can improve

intonation accuracy and support self-regulation processes in music learning (Li et al., 2025).

In the field of music generation, deep learning models make it possible to create

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accompaniments, harmonic progressions, and personalized practice materials for music

students (Herremans et al., 2017).

Consequently, modern artificial intelligence offers new possibilities for music education by

providing tools that support aural training, vocal analysis, and individual musical practice.

However, some authors note that these systems present limitations related to creativity,

musical control, and user interaction; therefore, their use should be understood as

complementary rather than as a substitute for human interpretative processes (Briot et al.,

2020).

In the context of choral practice with older adults, artificial intelligence can serve as a

complementary resource to support individual repertoire study, strengthen aural training, and

enhance preparation prior to choral rehearsals.

Applications of artificial intelligence in music education and choral practice

With the growth of research on the use of digital technologies and Artificial Intelligence (AI)-

based tools in music and music education, these resources have become instrumental in

analyzing sound data, identifying musical patterns, and generating musical material through

computational models (Briot et al., 2020).

Recent literature has demonstrated that digital tools can enhance musical learning by

facilitating processes of autonomous practice and auditory training. In the context of singing,

AI-assisted vocal analysis systems allow for the assessment of parameters such as pitch,

rhythm, and melodic accuracy, providing immediate feedback to the student (Li et al., 2025).

Another line of research is the development of algorithms for source separation, which enable

the isolation of individual instruments or voices within complex musical recordings. These

technologies are particularly useful in a choral context, as they facilitate the independent

study of each vocal line within a polyphonic work (Chandna et al., 2022).

Furthermore, advancements in automatic music generation have led to the development of

systems capable of creating melodies, accompaniments, and musical structures through

computational models, which opens new possibilities for the creation of practice materials

and educational tools in music (Herremans et al., 2017).

In educational practice, these technologies have materialized in digital applications such as

Moises.ai, Vocal Remover, and auditory training platforms like EarMaster, which allow for

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the separation of musical tracks, modification of tonalities, generation of accompaniments,

and development of auditory skills.

Nevertheless, it is noted that the use of Artificial Intelligence in music education should be

understood as a complement to the pedagogical work of the teacher and not as a substitute for

the human interaction that characterizes collective musical practice (Briot et al., 2020).

The integration of these three perspectives—choral pedagogy, research on the aging voice,

and emerging AI-based technologies—establishes a conceptual foundation for exploring new

hybrid methodologies in community choral practice. While choral pedagogy provides

methodological principles for rehearsal organization and the technical development of

singers, research on the senile voice offers the necessary physiological foundations for

adapting vocal training strategies. AI-based technologies provide new tools that can support

autonomous learning and expand the pedagogical possibilities of the choral conductor in

contemporary educational contexts.

Despite these technological advancements, there remains a lack of research addressing the

integration of artificial intelligence within community-based choral settings, particularly

among older adult populations. This gap highlights the need for pedagogical models that

meaningfully incorporate digital tools into collective music-making processes.

METHODOLOGY

The present proposal was developed using a qualitative, descriptive approach, grounded in a

literature review, the analysis of local choral experiences, and consultations with conductors

associated with the musical context of the city of Cuenca. This approach enabled an

interpretative examination of the teaching and learning dynamics within community choral

ensembles, as well as an exploration of the potential contribution of artificial intelligence–

based technological tools in these contexts.

As a part of the exploratory phase of the research, consultations were conducted with choral

directors who have experience in choral conducting and music education across diverse

educational and community contexts. The purpose of these consultations was to understand

their perceptions regarding the use of artificial intelligence–based tools in choral practice,

particularly in ensembles composed of older adults.

The consultations were carried out through direct messages via digital communication

platforms, which allowed for efficient contact with the participants. At this stage, three choral

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directors with established experience in the field of choral conducting and music education

participated.

In order to preserve the confidentiality of the participants, their identities are presented using

codes: Choral Director 1, Choral Director 2, and Choral Director 3.

The responses obtained revealed preliminary perceptions regarding the level of knowledge,

familiarity, and use of artificial intelligence tools within the choral field, which contributed to

complementing the theoretical analysis developed in this research.

In order to broaden and deepen the theoretical review, semi-structured interviews were also

conducted with directors of collective singing groups composed of older adults. Participants

were selected through purposive sampling, primarily considering their experience in directing

such ensembles and their involvement in musical projects aimed at this population.

A total of five directors from different local choral groups were consulted, either in person or

virtually, depending on their availability.

The interview questions addressed various aspects related to choral pedagogical practice,

including:

• the musical working strategies employed with older adults;

• the main pedagogical challenges identified during teaching processes;

• and perceptions regarding the use of technological tools in music learning processes.

As a methodological foundation for the development of the proposal, the model proposed by

Neira (2024), aimed at the formation of choral ensembles of older adults in the city of

Cuenca, was taken as a reference. This model is structured into four main stages:

• a diagnostic assessment, intended to identify the participants’ initial vocal and musical

conditions;

• technical-vocal exercises, aimed at the progressive development of basic vocal skills;

• progressive repertoire selection, considering the group’s technical level and the vocal

characteristics of its members;

• and a choral approach and ensemble process, focused on voice integration and

collective interpretative work.

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Based on this structure, an exploratory analysis of digital tools based on artificial intelligence

was subsequently incorporated, with the aim of identifying their potential to support music

learning processes in community choral contexts.

It is important to note that the methodological proposal presented in this study is exploratory

in nature and has not been validated through experimental or quasi-experimental studies.

Consequently, its findings should be interpreted as an initial approach aimed at generating

future lines of research in the field of music education and community choral practice.

The analysis of the data was conducted using thematic analysis, identifying categories related

to choral teaching, music learning, and the potential use of digital tools in community choral

contexts.

This procedure made it possible to organize and systematize the information into various

interpretative categories, which were subsequently contrasted with specialized literature on

music education, choral practice, and technologies applied to learning, in order to establish

connections between the empirical findings and existing theoretical frameworks.

RESULTS

The analysis conducted allowed for the identification of three main findings.

The literature review revealed that most research on choral practice among older adults has

focused on the social and psychological benefits of choral singing, while there is a limited

body of academic work aimed at the development of specific pedagogical methodologies for

this age group.

It was observed that most artificial intelligence–based applications currently used in music

education have been designed for individual learning contexts or music production, rather

than specifically for community choral practice.

The exploration of technological tools made it possible to identify various digital applications

with potential to support choral learning, particularly in aspects related to ear training and

individual repertoire preparation.

Choral Directors’ Perceptions of the Use of Digital Tools

The interviews conducted with choral directors made it possible to identify a range of

perceptions regarding the use of digital tools—including those based on artificial

intelligence—within the process of music learning in choral contexts. The directors

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interviewed indicated that audio separation applications can facilitate the individual study of

vocal lines within choral repertoire, as they allow singers to hear their part more clearly,

which may enhance interpretative confidence during rehearsals.

Additionally, some participants highlighted the potential of ear training platforms to

strengthen musical skills such as intonation, interval recognition, and the identification of

rhythmic patterns. However, the directors also pointed out certain limitations associated with

the use of technology in groups of older adults, particularly those related to digital literacy,

access to technological devices, and internet connectivity.

The consultations carried out also allowed for the identification of initial perceptions

regarding the use of tools specifically based on artificial intelligence in choral contexts. The

responses indicate that the use of such tools in choral ensembles of older adults remains

limited.

Some of the directors consulted reported that they are not familiar with artificial intelligence

applications oriented toward choral work with this age group. For instance, one of the

directors (Choral Director 1) stated: “I am honestly not aware of any artificial intelligence

applications used in choirs of older adults.” Similarly, another director (Choral Director 2)

noted that such tools are not used in her ensemble: “In my choir we do not use artificial

intelligence, and I am not aware of any choir that does.” Likewise, another participant

(Choral Director 3) indicated that they do not have information regarding the use of artificial

intelligence in choral contexts, suggesting that the incorporation of these technologies is not

yet common in such ensembles.

Taken together, these responses suggest that, although artificial intelligence has begun to be

incorporated into various areas of music education, its application in community choral

practice—particularly in choirs composed of older adults—remains at an early stage.

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Table 1. Artificial Intelligence Tools with Potential Applications in Choral Practice

Source: Authors’ own elaboration.

Based on the analysis of the identified digital tools, a methodological integration is proposed

that articulates the traditional principles of choral conducting with the use of technology-

based resources grounded in artificial intelligence. This proposal aims to support the

individual musical learning of singers and to complement the work carried out during choral

rehearsals.

Table 2. Hybrid Methodological Proposal for Choral Practice Supported by Digital Tools

STAGE OF THE CHORAL

PROCESS

PEDAGOGICAL

OBJECTIVE

SUGGESTED DIGITAL

TOOL

Vocal diagnosis

Identification of vocal

characteristics and vocal

range

Vocal analysis applications

Technical-vocal exercises

Development of breathing,

vocalization, and intonation

Singerhood; Endel

Repertoire study

Individual understanding of

the vocal line

Moises.ai; Vocal Remover

Ear training

Development of auditory

skills

EarMaster

Choral ensemble

Integration of voices within

the ensemble

In-person choral rehearsal

Source: Authors’ own elaboration.

DIGITAL TOOL

TYPE OF TECHNOLOGY

CHORAL POSIBLE

APPLICATION

Moises.ai

separación de audio

mediante IA

estudio individual de líneas

vocales

Vocal Remover

procesamiento digital de

audio

aislamiento de voces

EarMaster

entrenamiento auditivo

desarrollo de afinación e

intervalos

Singerhood

análisis vocal

práctica individual del canto

ChatGPT

IA generativa

generación de materiales

pedagógicos

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In order to synthesize the methodological proposal presented in this study, Figure 1 illustrates

a model that integrates the traditional stages of choral work with the use of digital tools based

on artificial intelligence. This model aims to complement the choral rehearsal process

through resources that promote autonomous learning, ear training, and the individual

preparation of repertoire.

Figure 1. Hybrid Methodological Model for Choral Practice Supported by Artificial

Intelligence. Hybrid Methodological Model for Choral Practice with Older Adults Supported

by Artificial Intelligence–Based Digital Tools

Source: Authors’ own elaboration.

DISCUSSION

The results obtained are consistent with previous research, which indicates that digital

technologies can support autonomous learning processes in music education (Hallam, 2015).

Likewise, recent studies have shown that audio separation tools can facilitate the individual

study of vocal lines within polyphonic repertoire, contributing to the development of

intonation and the musical understanding of choral works (Chandna et al., 2022).

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However, within the context of this proposal, it is necessary to consider factors associated

with digital literacy and access to technological resources, as these may influence the

implementation of such tools in choral teaching processes.

For this reason, artificial intelligence should be conceived as a supportive tool within the

pedagogical process, rather than as a replacement for the work of the choral director.

The present study presents several limitations.

First, the proposed methodological framework is theoretical and exploratory in nature, as it

has not been implemented or evaluated through experimental or quasi-experimental

studies.Second, the analysis of artificial intelligence tools focused on publicly available

applications, many of which are offered in free versions with limited functionality.

Furthermore, it is important to note that the consultations conducted with choral directors

were exploratory and involved a limited number of participants. For this reason, the findings

should be interpreted as preliminary insights that help identify initial trends regarding the use

of artificial intelligence in choral contexts.

CONCLUSSIONS

This study examined the potential of integrating artificial intelligence–based digital tools into

the choral practice of older adults in the city of Cuenca, Ecuador. Based on a literature

review, the analysis of local choral experiences, and the systematization of a methodological

proposal grounded in principles of choral conducting, several opportunities were identified

for combining teaching and learning processes in community choral contexts.

The findings confirm that choral practice constitutes an activity of significant social,

emotional, and cognitive value for older adults. Specialized literature demonstrates that

choral singing promotes psychological well-being, strengthens social interaction networks,

and contributes to the development of cognitive skills such as memory, attention, and

auditory perception.

The analysis also highlighted the need to develop specific pedagogical methodologies

tailored to choral work with older adults. Physiological changes associated with vocal aging

require the adaptation of conducting strategies, repertoire selection, and rehearsal

organization to the particular characteristics of this age group.

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Furthermore, the study showed that recent developments in artificial intelligence–based

technologies offer new possibilities for music education. Digital tools such as audio

separation applications, ear training platforms, and vocal analysis systems may contribute to

strengthening individual repertoire learning and improving singers’ preparation prior to

choral rehearsals.

However, the integration of these technologies into community choral contexts remains at an

early stage. The literature reviewed indicates that most research on artificial intelligence in

music has focused on music production or individual music education, rather than

specifically on choral practice.

In this regard, the methodological proposal presented in this study should be understood as an

exploratory approach that seeks to articulate traditional choral conducting practices with

emerging technological tools. Artificial intelligence is not conceived as a substitute for the

pedagogical work of the choral director, but rather as a complementary resource that can

support the music learning process.

Finally, it is important to note that the proposed methodology has not been implemented or

evaluated through experimental studies. Therefore, future research should empirically

examine the impact of these technological tools on choral learning, particularly in aspects

such as collective intonation, ear training, and participant motivation.

Overall, this study provides a theoretical foundation for future research on the integration of

digital technologies in community choral practice, as well as for the design of innovative

pedagogical methodologies aimed at musical work with older adults.

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Internacional (CC BY 4.0). Esto permite el uso, distribución y reproducción en cualquier medio, incluidos fines

comerciales, siempre que se otorgue la atribución adecuada a los autores y a la fuente original.

: https://doi.org/10.65011/prismaods.v5.i3.298

Cómo citar este artículo (APA 7ª edición):

Neira Landívar, M. B. . (2026). A Hybrid Methodological Approach to Choral Practice for

Older Adults in Cuenca, Ecuador: Integrating Artificial Intelligence and Digital

Resources. Prisma ODS: Revista Multidisciplinaria Sobre Desarrollo Sostenible, 5(3), 394-

410. https://doi.org/10.65011/prismaods.v5.i3.298