The Use and Effectiveness of Digital Tools in Elementary Music Education: A Systematic Review

Introduction

Digital tools, encompassing a wide range of hardware and software such as mobile devices, digital instruments, and applications (Rice, 2003), play a crucial role in enhancing the teaching and learning experience in elementary music classrooms. Similarly, in music education, digital tools refer to technological resources and software designed to enhance music education by facilitating various aspects of teaching and learning (Sánchez-Jara et al., 2023; Sularso et al., 2023). These tools, collectively referred to as Information and Communication Technology (ICT), include (but are not limited to) web-based platforms; software applications for mobile and computer devices; digital games; video resources; assistive technologies; immersive technologies such as virtual reality (VR) and augmented reality (AR) systems; and tangible hardware such as projection, tablets, computers, and robotic devices. These tools integrate a variety of media forms (e.g., text, graphics, animation, and audio/video) that enrich and diversify learning and teaching content, methods, and their effectiveness (Ruan, 2009). These various media forms are claimed to provide students with multiple modes of engagement, such as visual, auditory, and kinesthetic experiences, enhancing overall comprehension and retention (Revano & Juanatas, 2024). Due to this variety, it is necessary to map out the current landscape of the tools being used and the evidence for their effectiveness to foster future intervention and education practices.

Students using digital tools in elementary music classrooms often demonstrate a better understanding of musical concepts, increased interest in learning, and enhanced creative skills, particularly in composition and improvisation (Gagica Rexhepi et al., 2024). These digital tools can support personalized learning by allowing students to progress at their own pace, engage with interactive platforms, and access extensive digital resources, catering to creativity and collaborative learning. Digital tools can improve engagement, support diverse learning needs, and foster independent learning and creativity (Roman et al., 2024). Some tools, such as GarageBand, Sequencer Software (Lam, 2024), and MIDI controllers (Kladder, 2020), enable hands-on composition, and facilitate real-time musical exploration, and interactive tutorials provide guided instruction. Besides, adaptive music technologies, such as haptic metronomes and visual notation software, assist students with hearing impairments by providing alternative ways to experience rhythm and pitch, ensuring they can actively participate in instrument playing and ensemble activities (Lafuente & Jurado, 2018).

Furthermore, digital tools help students with self-regulation (e.g., managing their practice schedules, setting personal learning goals, and adjusting their performance based on feedback (Saarikallio, 2016) and provide immediate feedback, which is crucial for self-assessment and improvement (Moldovan & Nedelcut, 2022). Digital music recordings function as instructional models that guide students in skill acquisition and serve as self-corrective tools, fostering enhanced auditory discernment and creative development (Chao-Fernández et al., 2020). Moreover, digital tools support music-making activities that can foster students’ social skills, such as negotiation (e.g., deciding on song arrangements in collaborative software), empathy (e.g., responding to peers’ musical ideas in a shared digital workspace), and verbal communication (e.g., discussing creative choices while using online music platforms) (Hallam & Himonides, 2022). Additionally, tools such as practice logs, note feedback tools, and music software can help track students’ progress and provide real-time feedback by offering a sense of achievement and encouraging consistent practice. These tools can promote engagement by making practice more interactive and personalized, helping students stay focused and invested in their musical development (Wan & Gregory, 2018).

Moreover, digital tools can enhance students’ motivation in learning music. In the context of elementary music education, motivation plays a crucial role in student engagement and learning outcomes. For example, the Makey application has been shown to significantly increase student motivation, particularly by nurturing their emotional engagement (Serra-Marín & Berbel-Gómez, 2021). Studies assessing motivational aspects (e.g., Danso et al., 2021; Song et al., 2024; Szabó et al., 2019) have used various frameworks, measuring components such as autonomy, delight, stress, interest, emotional expressions, task-related behaviors, and robot-related behaviors. However, the operational definitions of these constructs vary across studies, with some research not explicitly specifying subscales or indicators. This systematic review aims to synthesize how these motivational aspects are assessed and their relevance to digital tools in music education.

However, challenges remain regarding the use of digital tools in elementary music education. In certain regions, such as rural areas in China, factors such as limited resources and insufficient infrastructure hinder their integration (F. L. Wang et al., 2009). A similar situation exists in Germany, where digital tools are rarely used in music education (Förster & Lepa, 2023). One of the reasons can be that teachers often lack sufficient knowledge and confidence to use these tools effectively (García et al., 2021). Thus, there is a need for professional development to help educators effectively integrate these tools. Additionally, equitable access to technology remains a significant challenge, as schools with greater financial resources are more likely to afford digital tools and infrastructure, while underfunded schools, particularly in rural areas, face barriers such as limited devices, poor internet connectivity, and inadequate technical support (Sularso et al., 2023).

The lack of domain-specific knowledge among music teachers (Ruiz et al., 2023) and the rare use of digital tools in music classrooms highlight the need to develop accessible digital tools designed for classroom music education and to identify music teachers’ positive attitudes toward the use of these digital tools as part of their teaching practices in music classrooms (Förster & Lepa, 2023). Not only do the teachers have some challenges, but the students should also have equitable access to technology. Not all students have the same level of access to digital tools, which can create disparities in learning opportunities (Sularso et al., 2023). Also, while digital tools offer significant benefits in music education, their effectiveness depends on factors such as usability, accessibility, and ease of integration into classroom settings (Stefan et al., 2020). Some tools are difficult to use, require extensive teacher training, or present technical challenges that hinder their potential benefits (Peretti et al., 2025). Therefore, a critical evaluation of digital tools is necessary to ensure they truly enhance learning rather than introducing new obstacles.

Despite the importance of digital tools for students’ musical skills development, enhancing their engagement, creativity, and learning outcomes, specific musical instruments (e.g., recorders, xylophones, ukuleles) and digital tools in elementary music classrooms (e.g., GarageBand, SmartMusic, or interactive whiteboards) remain underexplored and inconsistently implemented. Moreover, although there are several digital assessment tools, few studies have systematically evaluated specific musical skills of students through the self-developed digital tools (e.g., developing video games, Music Island app for active participation), especially in the context of young learners’ cognitive and developmental needs. For example, Weatherly et al. (2024) wrote a systematic literature review about digital games for music education but did not target any age range and did not conduct a critical appraisal to evaluate the strengths of the evidence. Existing studies often focus on general benefits without systematically assessing specific musical skills, engagement levels, or long-term learning outcomes (Förster & Lepa, 2023). Additionally, while digital assessment tools are available, few studies critically examine their reliability and validity in measuring students’ musical development. A systematic review synthesizing findings on the benefits of digital tools, challenges, and assessment effectiveness is essential to address these gaps. By systematically analyzing existing literature, this study will provide a comprehensive overview of how digital tools impact elementary music education, what specific skills they enhance, and how they are assessed.

Methods

Selection Criteria

Table 1 presents the inclusion criteria and exclusion criteria of the studies included in this review.

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Table 1.

Selection criteria.

Item	Inclusion criteria	Rationale
Bibliographic and study context	Include 1: Peer-reviewed journal articles or conference proceedings published in English between 2014–2024. Exclude 1: Non-peer-reviewed works, book chapters, dissertations, or publications in other languages.	Focusing on the past decade captures recent advancements in rapidly evolving digital education tools. Limiting to peer-reviewed English publications ensures academic rigor and global relevance.
Digital tools	Include 2: At least one digital tool (software or hardware). Exclude 2: No digital tools or use of only traditional methods.	Including all digital tools, both software and hardware, ensures a broad evaluation of their applications and effectiveness while excluding traditional methods focuses on the study's digital aspect.
Study population	Include 3: Elementary students or children aged 6–12 years. Exclude 3: Studies without specific results in the targeted age range, such as mixed results for both elementary and secondary students.	The age range of 6–12 aligns with the developmental and educational characteristics of elementary school students, ensuring findings are relevant to this critical stage of learning.
Experiment context	Include 4: In any public or private primary school, general music classroom, or other venue related to music instruction. Exclude 4: Intervention studies that did not specify the intervention site and that were not in primary school music classes	Focusing on elementary school settings ensures greater uniformity, but no specific requirements are set for music classrooms or general-purpose classrooms due to educational variations.
Research design	Include 5: Studies employing experimental or quasi-experimental designs. Exclude 5: Studies that do not involve the direct manipulation of a digital tool by researchers (including observational, correlational, and qualitative study designs), and literature reviews.	Experimental research demonstrates the causal impact. Thus, non-manipulation studies do not provide the necessary evidence to address the review's objectives
Evaluation instruments	Include 6: The study must utilize valid assessment tools such as self-report questionnaires, teacher reports, tests, tasks, or other objective instruments for evaluating outcomes. Exclude 6: Studies lacking clearly defined assessment tools or relying solely on anecdotal evidence or descriptive data will be excluded.	The inclusion of valid tools ensures reliable measurement of musical skills and motivation, supporting the review's methodological rigor.
Outcome measures	Include 7: Report measurable results of the intervention program, focusing on the development of musical abilities within the music education setting. Exclude 7: No measurable outcomes or focus outside music education.	Focusing on measurable outcomes related to musical skills ensures relevance to the study's objectives, avoiding speculative findings. While musical skills are explicit outcomes of using digital tools, motivational aspects are important as well in motivating students’ learning to reach the expected outcomes.
Reliability and validity	Include 8: Tools demonstrate validity and reliability (e.g., content validity, internal consistency). Exclude 8: Do not provide sufficient information on the validity or reliability of their assessment tools	Requiring validity and reliability in assessment tools ensures the credibility and robustness of the included studies.

Study Selection

The PRISMA flow diagram (Figure 1) illustrates the data selection and screening process. After the retrieved search results were imported into Rayyan and duplicates removed, the article screening process commenced, identifying 1,898 relevant studies. Two authors had independently screened the titles, excluding those unrelated to the three screening dimensions. In the second stage, abstracts were independently reviewed, and when disagreements arose between the two authors, a third author was consulted to discuss and resolve all conflicts. Subsequently, 49 records were subjected to full-text screening based on the eligibility criteria outlined in Table 1. To ensure a thorough and unbiased evaluation, the researchers independently assessed these records. Inter-rater reliability, measured using Cohen's Kappa (κ = 0.89), indicated strong agreement among the raters. Ultimately, 34 articles were excluded for not meeting the inclusion criteria, resulting in 15 studies being included in the review.

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Figure 1.

PRISMA 2020 flow diagram of the study selection process.

Results

Coding Process

Table 2 presents the 15 studies’ key characteristics based on the research questions and inclusion criteria. Detailed data and responses to all research questions across the different analytical sections are provided in the subsequent sections.

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Table 2.

Study characteristics.

Study	PT	Study design	Intervention design	Sample size	Duration	Types of digital tools	Measured outcomes
1.(Rizqyawan & Hermawan, 2015)	CP	One-group pre-test, post-test	One-group	40 Age 9–12	N/A	Software	Theory (theoretical knowledge), Relative pitch
2. (Huang et al., 2015)	JA	Non-equivalent groups pre-test, post-test	Digital tool vs. traditional method comparison	186 Grade 3,4,5	3 months	Software	Learning performance, Learning attitude, Learning works
3. (Pećanac et al., 2016)	JA	Non-equivalent groups pre-test, post-test	Digital tool vs. traditional method comparison	78 Grade 4	1 year	Hybrid	Musical literacy
4.(Hernández-Bravo et al., 2016)	JA	RCT	Digital tool vs. traditional method comparison	90 Grade 5–6 (10–12 years)	24 weeks	Hybrid	Music aptitude, Competence in music, Marks in music
5. (Addessi et al., 2017)	JA	RCT	Multi-condition	47 Age 6–7	5 weeks	Software (platform)	Improvisation
6. (Innocenti et al., 2019)	JA	Non-equivalent groups pre-test, post-test	Digital tool vs. traditional method comparison	36 Aged 10–11	4 weeks	Software (application)	Genre identification, Association between musical instruments and genre, Spatial arrangement of instruments in the performance space
7. (Szabó et al., 2019)	CP	Non-equivalent groups pre-test, post-test	Digital tool vs. traditional method comparison	246 Grade 1	1 year	Software (application)	Musical perception and reproduction, Musical reading and writing, Motivation for learning music
8. (Lesser, 2020)	JA	Cluster RCT	Multi-condition	82 Grade 5–6	3 weeks	Software	Aural Perception, Rhythm identification, Pitch/rhythmic identification
9. (Jeremić et al., 2020)	JA	Non-equivalent groups pre-test, post-test	Digital tool vs. traditional method comparison	129 Grade 4	7 months	Software	Learning music theory, Listening skills
10. (Kang & Yoo, 2021)	JA	Within-Subjects Design post-test only	One-group	37 Grade 4–5	12 weeks	Software	Music composition
11. (Danso et al., 2021)	JA	Non-equivalent groups pre-test, post-test	Multi-condition	42 Aged 8–9	6 weeks	Hybrid	Musical Knowledge, Behavioral engagement, User experience
12. (Song et al., 2021)	JA	Within-Subjects Design post-test only	Multi-condition	31 Aged 9–12	50–60 min	Hardware	Motivation, Persistence, Engagement with robots
13. (Wang, 2023a)	JA	Non-equivalent groups pre-test, post-test	Multi-condition	50 Aged 9–11	1 h	Hybrid	Listening skills, Sight-reading skills, Comprehensive skills
14. (Wang, 2023b)	JA	Non-equivalent groups pre-test, post-test	Multi-condition	34 Aged 10–12	50–60 min	Software	Recognition of the sound of notes, Sight-reading skills, Melody recognition
15. (Song et al., 2024)	JA	Within-Subjects Design post-test only	Multi-condition	31 Aged 9–12	50–60 min	Hardware	Rhythm accuracy, Pitch accuracy, General musical performance

Note. PT = publication type; CP = conference proceeding; JA = journal article; RCT = randomized controlled trial; N/A = information is not available in the publication.

Musical Skills and Motivational Aspects

Musical skills represent a broad and multifaceted concept in music education. Although challenging to define comprehensively, music learning is best understood as an integrated process that relies on audition—the internal ability to hear and comprehend music—while incorporating performance and creative practices to deepen and expand musical understanding (Gordon, 2012). This study categorizes musical skills into three primary dimensions—understanding, performance, and creativity—drawing on Bloom's Taxonomy (Anderson & Krathwohl, 2001) to better conceptualize and analyze musical skills. Each dimension represents a distinct yet interconnected aspect of music learning, providing a comprehensive framework for examining the development of musical abilities. The process can be categorized into three primary dimensions: understanding, performance, and creativity.

The understanding dimension focuses on students’ comprehension of pitch and rhythm (Gordon, 2012) and the integration of visual and auditory methods to learn music notation, writing, and symbols (Tóth-Bakos, 2016). These foundational knowledge areas, rooted in perception and cognition, are defined in this study as musical literacy, aligning with theoretical skills.

The performance dimension builds on musical literacy, emphasizing the application and expression of this foundational knowledge. It encompasses the development of instrumental, singing, rhythm, and listening skills through auditory input, imitation, and practice (Gordon, 2012). In this study, these abilities are collectively referred to as performance skills.

The creativity dimension represents the advancement from foundational musical skills to improvisation and composition. It involves the natural development of creative abilities, such as composing and improvising, following the mastery of basic auditory patterns (Gordon, 2012). Here, creative skills denote the fostering of students’ innovative capacities in music education, primarily through composition and improvisation.

The purpose of this categorization is to systematically identify the types of skills measured across the 15 selected studies. This framework facilitates the observation of research focuses and the identification of prevalent trends while also serving as a basis for the classification analysis in the Effectiveness Analysis section (3.6). Table 3 presents the categorized dimensions and their key descriptors.

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Table 3.

Categories of music skills among studies.

Skill category	Skill type	Studies	Key definitions
Performance skills	Instrumental skills	(Studies 4, 9 & 15)	Involve exercises related to playing various instruments and the ability to perform simple melodies or rhythms on them.
	Singing skills	(Studies 4 & 9)	Ability to sing accurately in tune, with correct pitch and rhythm.
	Rhythmic skills	(Studies 7, 8, 9, 13 & 15)	Recognizing and reproducing different rhythms, including maintaining a steady beat.
	Listening skills	(Studies 1, 4, 7, 9, 13 & 14)	Ability to identify and differentiate musical sounds, such as pitch and rhythm, by listening.
Theoretical skills	Music literacy	(Studies 1, 2, 3, 4, 6, 7, 8, 9, 11, 13 & 14)	Music literacy includes essential theoretical skills such as note reading, music notation, and music comprehension, along with the foundational abilities for musical perception and understanding. These elements collectively form the core of music theory.
Creative skills	Composition and improvisation	(Studies 2, 5 & 10)	Primarily refers to composition and improvisation, while also encompassing innovation in musical expression.

Similarly, to facilitate the observation and analysis of the effectiveness of digital tools, this section reports on studies related to motivational aspects. The report is based on the content measured in 15 studies, without discussing the theoretical concepts themselves.

Skills Assessed in the Studies

Studies 5, 6, 10, and 12 focused on a single music skill. Studies 1, 2, 3, 8, 11, 14, and 15 addressed two types of music skills. Studies 4, 7, 9, and 13 examined three or more types of musical skills.

Performance Skills were the most frequently measured category: Instrumental skills, rhythmic skills, vocal skills, and listening skills are addressed in multiple Studies (1, 3, 4, 6, 7, 8, 9, 11, 13, 14, and 15). These skills are often central to music education due to their observable and measurable nature.

Theoretical Skills were also a common focus in studies, such as music literacy (Studies 1, 2, 3, 4, 6, 7, 8, 9, 11, 13, and 14), which reflects the importance of theoretical understanding in music education. These skills highlight students’ cognitive abilities and comprehension of musical knowledge.

Creative Skills received a moderate level of attention: Composition and improvisation (Studies 2, 5, and 10) were discussed in three studies, indicating a growing interest in fostering creative abilities in music education.

Motivational Aspects Assessed in the Studies

Of the 15 included studies, five measured the motivational aspects of students in classrooms. One study measured motivation without specifying further details of the subscale and content (Study 7). Also, another study observed the behaviors of students, including motivation, without specifying details of aspects, indicators, or themes (Study 11). Measured aspects in this study included autonomy, delight, stress, interest, emotional expressions, task-related behaviors, and robot-related behaviors. One study measured concentration-related behavior through observation, including off-task behaviors and on-task behaviors (Study 12). One study assessed learning attitudes with cognitive components, affective components, and behavioral tendency components (Study 2).

Instruments for Assessing the Impact of Digital Tools on Musical Skills and Motivational Aspects

The overall characteristics of the instruments used in the included studies are provided in Table 4. Almost all instruments in the 15 articles were uniquely developed or tailored to the specific context and research questions. Among them, 24 unique instruments were identified, and only one instrument was repeatedly employed in Studies 13 and 14. Thirteen unique tests were reported, and it was the most common type of instrument. There were four scales to measure learning motivation, four scoring guides, two structured observations using coding schemes, and one unstructured observation; no interviews were used to assess musical abilities or musical learning motivation.

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Table 4.

Characteristics of the reported instruments measuring musical abilities and motivational aspects.

Study	Instrument	Type of instrument	Reported original author(s)	Reported subscales/dimensions/criteria	Number of items employed	Reported validity and reliability
1	Pitch test	Test	Self-designed	N/A	N/A	N/A
1	Theoretical test	Test	Self-designed	N/A	N/A	N/A
2	Music composition cognition scale – learning performance	Test	Self-designed	Rhythm, tempo, melody, and other concepts	N/A	Content validity
2	Music composition learning questionnaire – learning attitude	Scale	Self-designed	Cognitive components, affective components, and behavioral tendency components	13	Content validity Cronbach's alpha Factor analysis with Principal axis factoring
2	Music composition scoring rubric – learning works (skills)	Scoring guide	Self-designed	Rhythm, melody, sense of cadence, and length of composition	N/A	Content validity
3	Scale for assessing vocal abilities (SPVS)	Test	Jeremić, 2011	Only subtests I and IV were used, including tones, notes, melodic, rhythm knowledge	N/A	N/A
4	Musical aptitude test (MA)	Test	Seashore, Lewis & Saetveit, 1956	Tone, intensity, time, timbre, rhythm, tonal memory	N/A	Cronbach's alpha Interrater reliability by Percentage of agreements
4	Musical competence test	Test	Hernández-Bravo et al., 2016	Audition, vocal expression, instrumental expression, musical language, movement, dance, and arts and culture	62	Face validity Interrater reliability by percentage of agreements
5	Test for ability to improvise (TAI) – scoring guide	Scoring guide	McPherson, 1993	Instrumental fluency, musical organization, creativity, musical quality, musical dialogue, reflexive interaction	5	Interrater reliability, Cronbach's alpha and Pearson's correlation
6	Written music assessments	Test	Teacher designed	Knowledge of musical genres, instrument identification, and the spatial arrangement of instruments on stage	3	N/A
7	Musical aptitude test (tablet-based)	Test	Surján & Janurik, 2018	Music discrimination and music production	94	Cronbach's alpha
7	Music notation test	Test	Self-designed	Rhythm writing and melody writing	76	Cronbach's alpha
7	Motivation scale	Scale	Self-designed	N/A	5	Cronbach's alpha
8	Music skills and knowledge test	Test	Self-designed	Aural perception (pitch matching), rhythmic perception (beat matching), and pitch/rhythm identification	20	N/A
8	Observation of behavior, including motivation	Unstructured observation	Self-designed	N/A	N/A	N/A
9	Scale for the assessment of vocal abilities (SAVA)	Test	Jeremić et al., 2020	Basics of music literacy, performance of music by playing, listening to music, musical rhythm, and performance of music by singing	N/A	N/A
10	Consensual assessment technique (CAT)	Scoring guide	Amabile, 1996	creativity, aesthetic appeal, and craftsmanship	N/A	Interrater reliability, Cronbach's alpha
11	Test of knowledge	Test	Self-designed	Aural skill, pitch identification, remembering musical notation names, identifying piano keys, and rhythmical markings	5	Face validity
11	Concentration-related behavior	Structured observation	Self-designed	Off-task behaviors and on-task behaviors	2	Interrater reliability, Cohen's k
12	FunQ questionnaire	Scale	Tisza & Markopoulos, 2021	Autonomy, delight, and stress	9	Cronbach's alpha
12	Situational motivation scale (SIMS)	Scale	Guay et al., 2000	Interest	3	Cronbach's alpha
12	Observation of the motivation coding scheme	Observation coding scheme	Berhenke et al., 2011	Emotional expressions, task-related behaviors, and robot-related behaviors	27	Interrater reliability, Cohen's k
13	Pre- and post-test	Test	Self-designed	Listening test, sight-reading test, and comprehensive test	24	Face validity
14	Pre- and post-test	Test	Self-designed	Listening test, sight-reading test, and comprehensive test	24	Face validity
15	Performance ratings – scoring guide	Scoring guide	Self-designed	Rhythm, pitch, and general impression	N/A	Interrater reliability, Cronbach's alpha

Note. N/A = did not report or did not report clearly in the publication.

Of the 23 uniquely identified instruments, 9 lacked specifications of the number of items employed. For the other instruments, the total number of items ranges from 3 to 94. When considering the total number of tested items in a study, the number ranges from 3 to 175, a huge number of test items for primary students.

Of the 15 included studies, 9 used at least a self-designed measurement tool (Studies 1, 2, 6, 7, 8, 11, 13, 14, and 15). Among them, one test was designed by the music teacher who also taught participants in the experiment (Study 6). Seven studies employed at least an adapted measurement (Studies 3, 4, 5, 7, 9, 10, and 12). Four studies employed at least one measurement tool that had been previously developed by one of the authors (Studies 3, 4, 7, and 9). Specifically, three studies conducted observations to study the behaviors of students related to motivation in learning (Studies 8 and 12) or concentration-related behaviors (Study 11). Among them, two studies provided coding schemes for observing students’ behaviors (Studies 11 and 12).

Regarding examiners of the tests, three studies utilized at least one measurement based on teacher judgments: marks in music (Study 4), consensual assessment technique (Study 10), and performance ratings (Study 15). One study (Study 5) assessed students’ ability based on judgments of university professors using a pre-defined scoring guide (based on the Test for Ability to Improvise). In the remaining studies, the tests were either marked by researchers or groups of researchers; however, it was not specified whether the researchers were blinded to the treatment condition or not.

Regarding the reliability and validity of the instruments, six studies did not clearly report any form of validity and reliability; these characteristics can include pilot testing the instruments, face validity or content validity, Cronbach's Alpha for internal consistency or interrater reliability, and factor analysis. Overall, two studies assessed face validity (Studies 4 and 11), while content validity was reported in three studies (Studies 2, 13, and 14). As reported in seven studies, Cronbach's Alpha was mostly used (Studies 2, 4, 5, 7, 10, 12, and 15). Where a group of experts marked the tests, three studies reported interrater reliability by estimating the percentage of agreements (Study 4), Cronbach's Alpha (Studies 10 and 15), or combining the Pearson correlation coefficient with Cronbach's Alpha (Study 5). Two studies conducting observations based on a coding scheme reported interrater reliability between coders by estimating Cohen's kappa coefficient (Studies 11 and 12). Finally, one study performed factor analysis, but the authors did not report the results (Study 2).

The Effectiveness of Digital Tools Used in Music Education

Table 5 provides an overview of the tools used and reported outcomes of each included study, along with critical appraisal commentary and a trustworthiness indicator. Table 6 shows the specific risk of bias for each included study, using three colors to represent the trustworthiness of evidence and indicating the confidence rating for each study with color coding. The criteria for proportion standards are detailed in Section 2.4, Critical Appraisal.

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Table 5.

Measured outcomes and critical appraisal of included studies.

	Study ID	Digital tool	Measured skills and motivational aspects	Outcomes	Critical appraisal commentary & trustworthiness indicator
	1	Adventure game (software)	Theory (theoretical knowledge) Relative pitch	Improvement in theoretical test score by 79.22%. Improvement in relative pitch test by 19.23% in relative pitch test.	Non-representative participants; no validated tests; no controlling for confounders; insufficient description of the intervention, e.g., experiment duration.
Quantitative non-randomized pre/post-test	2	ACME automated composition	Learning performance: This was divided into rhythm and tempo, melody, and other concepts Learning attitude: This was divided into a cognitive component, an affective component, and a behavioral tendency component Learning works: This was divided into rhythm, melody, sense of cadence, and length of composition	The experimental group performed significantly better than the control group. However, current teaching practices still generate superior performance in terms of composition length.Using the ACME teaching materials also effectively improved the affective aspect of learning effectiveness (learning attitude).	Non-representative sample; no further control for confounders except baseline comparison. The researcher was also the teacher, who assessed students, which might raise concerns of bias.
	3	Flash MX 2004, Sibelius 6.0, PowerPoint; All digital media (Computer, Projector, DVD…)	Fundamentals of musical literacy (characteristics of the tone, melody, rhythm)	The experimental group scored significantly higher than the control group in all aspects of music literacy (note writing, melody, rhythmic understanding, etc.).	Unclear hypothesis; non-representative sample; no valid measurement tools; no further control for confounders except baseline comparison; insufficient description of how the intervention was done, with many referred tools.
	6	VR4EDU application	Genre identification Association between musical instruments and genre Spatial arrangement of instruments in the performance space	There were significantly enhanced students’ musical skills. All results were statistically significant (p-values ranging from .05 to .001).	Unclear RQs; data did not address “the most effective way” to use VR in RQ 1; no validated test; the conditions were not blinded to the teachers, and no further control for confounders except baseline comparison.
	7	Music Island application	Musical abilities (musical perception and reproduction-listening discrimination skills and reproduction skills) Musical knowledge (musical reading and writing) Motivation about learning Music (attitudes toward music lessons and interest in singing)	The experimental group made significant progress in reproduction ability, while auditory discrimination ability did not differ significantly from that found in the control group. The experimental group performed better in music notation and related skills. Students in the experimental group were significantly more motivated to learn music.	Data did not address the “how” question; no further control for confounders except limited baseline comparison for a part of the test; “several other computer applications” were used threaten the interval validity; insufficient description of the intervention.
	9	MIDI, Sibelius, Practica Musica	Basics of music literacy Performance of music by playing Listening to music Musical rhythm Performance of music by singing	The experimental group outperformed the control group in all measured musical skills and demonstrated significant improvement.	Unclear hypothesis; non-representative sample; no validated tests; no confounders were controlled; not clear how two groups were taught differently, especially when different “didactic principles” were applied like a co-intervention; insufficient description of the intervention.
	10	MuseScore2	Music composition (creativity, craftsmanship, aesthetic appeal)	Students’ creativity scores significantly improved; the craftsmanship performance of notation software was not as effective as the handwritten mode; there was no significant difference in aesthetic appeal scores between the two modes.	Non-representative sample; no confounders were accounted for; no pretest; no control group.
	13	Music fun game	Listening skills (recognizing and differentiating notes by sound) Sight-reading skills (locating notes on sheet music and understanding visual representations) Comprehensive skills (Integrating listening and sight-reading for overall music understanding)	Both versions significantly improved post-test scores for adults, whereas young learners showed significant improvement only with the simple version.Young learners had a more positive perception of the full game version, while adults showed the opposite trend.Young learners gave high ratings to both versions, with no significant difference observed. Adults rated the simple version significantly higher than the full version (p = .00).	Well-designed study except for non-representative sample and no control group (only two versions of the game were compared).
	14	Music fun game	Listening Skills (recognizing and differentiating notes by sound) Sight-reading skills (locating notes on sheet music and understanding visual representations) Comprehensive skills (Integrating listening and sight-reading for overall music understanding)	Different game types influenced the development of specific music skills in students (Paired-sample t-tests revealed that Group A and Group C had significant improvements in their test scores, while Group B showed a slight decrease).	The study was limited by a non-representative sample, a low number of participants in each group (10 < N < 13), and the absence of a control group.
	15	SocibotMini robot	Rhythm accuracy Pitch accuracy General musical performance	Children showed significant improvement in rhythm, pitch, and general performance when practicing with the non-evaluative robot compared to practicing alone or with the evaluative robot.	Levels of learners were identified, but results did not account for that; inconsistency in study design description: mixed description within subjects and between subjects.
Quantitative randomized controlled trials	4	Mixed ICT: YouTube, blogs, wikis, karaoke, online translator, reading online, MIDI files, PowerPoint, web quests; recorder, interactive whiteboard with notation software, video camera, laptops	Music aptitude (tone, intensity, time, timbre, rhythm, and tone memory) Competence in music (audition, vocal expression, instrumental expression, musical language, movement, and dance, arts, and culture) Marks in music	The experimental group showed higher overall musical ability (including auditory expression, singing, and instrumental performance), with significant progress in an interactive and personalized learning environment compared to the control group.	The study accounted for aptitude differences, but groups were imbalanced, and how randomization was done was not reported; the assessor was not blinded to conditions.
	5	MIROR-Impro, a component of the MIROR Platform (v.3.14)	Solo improvisation Duet improvisation	The positive impact of the reflexive interaction system on improvisation is evident, especially in the performance of the EG2 group. However, due to the lack of consistent statistical significance in some task comparisons (between CG and EG1, and between CG and EG2), the results require further validation.	No report on how randomization was conducted; no pretest to adjust for baseline.
Mixed methods	8	Four video games	Aural perception Rhythm identification Pitch/rhythmic identification Behavior observation, including motivation	All groups demonstrated significant improvements in mean scores from pre-test to post-test. Students who received treatment were observed to have a greater sense of intrinsic motivation.	Unclear RQ; no clear report on how randomization was performed; no confounders were controlled; under-designed mixed methods: no clear rationale, no clear qualitative data analysis, no interrelated quant-qual conclusions.
	11	Instrument app on iPad; KAiKU Music Glove	Musical knowledge (musical concepts, aural discrimination, instrumental knowledge, scales, intervals, and chord structures) Behavioral engagement	The two groups of students (iPad and KAiKU Music Glove) showed significant knowledge growth before and after the test, with the iPad group demonstrating more remarkable learning outcomes.Observations indicated that the students exhibited higher levels of engagement.	Non-representative sample; no controlling for confounders; insufficient qualitative data to conclude; incoherent between qualitative data analysis and interpretation.
	12	SocibotMini robot	Motivation (autonomy, delight, stress, interest) Persistence – as an indicator of motivation in music practice, observed via video recordings Engagement with robots – behavioral responses to the evaluative and nonevaluative robot conditions	The results indicate a generally positive impact of robot roles on children's motivation and persistence, with some variations based on learning stage and gender.	Well-designed quantitative part; incomplete reported observation data; misinterpretation of qualitative data; incoherent between qualitative data analysis and interpretation; neglected inconsistency results between qualitative and quantitative data.

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Table 6.

Risk of bias assessment for included studies.

	Screening		Quantitative					Mixed method
Study ID	Clear research questions	Address the research questions	Representative participants	Appropriate measurements	Complete outcome data	Accounted confounders in design and analysis	Intervention administered as intended	Appropriate qualitative approach	Adequate data collection methods	Findings derived from data	Results interpretation substantiated by data	Coherence in data, analysis & interpretation	Rationale for mixed methods design	Effective integration of study components	Adequate interpretation of integrated outputs	Addressed inconsistencies between qual and quant results	Adherence to quality criteria of each method	Rating
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Strong	11	12	0	9	15	5	10	2	1	1	0	0	2	1	0	2	0	0
Moderate	4	3	4	2	0	2	4	0	1	2	1	0	0	1	0	0	0	5
Limited	0	0	11	4	0	8	1	1	1	0	2	3	1	1	3	1	3	10

Note. The table summarizes the critical appraisal results using the MMAT checklist. The JBI checklist yielded similar overall ratings. Due to space constraints, full bias assessment results for both checklists are provided in Supplementary File S2.

Impact on Performance Skills

In the context of performance skills, Studies 4, 9, and 15 addressed instrumental performance skills and reported positive outcomes. However, Study 15 lacked methodological rigor; for example, although confounding variables were considered during the pre-test phase, there was inconsistency between the study design description and the result assessment methods.

Study 9 offered a more comprehensive focus on performance skills, including vocal performance, rhythm skills, and auditory abilities. All sub-skills demonstrated notable improvements in the experimental group. However, the study contained numerous ambiguities, such as unclear hypotheses and the absence of reliability testing, which limited the trustworthiness of both the study design and results.

Study 4 also measured singing and listening skills, both of which yielded higher outcomes. Nevertheless, the study failed to report the random allocation process for participants, and evaluator blinding was not ensured throughout the experiment.

Studies measuring rhythm skills also included Studies 7, 8, 13, and 15. Among these, Studies 8 and 15 demonstrated positive improvements in post-tests. However, Study 8 revealed design limitations, including an unclear rationale for the mixed-methods approach, a lack of explicit qualitative data analysis, and insufficient integration of qualitative and quantitative findings, which diminished the overall impact of the results.

In other studies, involving listening skills, Study 1 showed improvement in relative pitch in the post-test results. However, this study lacked detailed intervention descriptions and was the only one that measured a single group. Study 7 did not show significant differences in auditory discrimination skills during post-tests, but significant progress was observed in repetition tasks. Study 13 reported significant improvement only in performance on the simplified version of the digital game. Despite this limitation, the study demonstrated high design reliability, with the primary constraint being a non-representative sample. A similar pattern was observed in Study 14, where differences in outcomes across groups highlighted the varying impacts of the same digital tool. The findings were heavily dependent on group-level comparisons, underscoring the importance of contextual factors in interpreting results.

Impact on Theoretical Skills

Among studies assessing multiple musical skills, Study 6 focused specifically on improving music literacy, targeting specific knowledge points. While it compared baseline results and showed significant post-test improvement, it lacked control of confounding variables, had an unclear research question, and used unvalidated tests.

Studies 7 and 13 both measured reading skills (sight-singing) and reported significant improvements. However, Study 13, despite utilizing MIDI keyboards and other instruments, focused primarily on fundamental knowledge derived from instrumental practice. Positive effects were observed only in the simplified version of the digital application.

Study 8 assessed rhythm and pitch matching but appeared to include composition tasks as well, with research questions lacking clarity. Post-test results showed significant improvements in all groups using digital tools, but qualitative analysis lacked integration with quantitative findings. Study 14 focused on the recognition of notes and melodies, with students’ performance improvements depending on the design implementation and instructional approach with the digital tool.

Studies 2, 3, 4, and 11 assessed multiple dimensions, including tone, melody, and rhythm. Studies 3 and 4 employed multiple digital tools and considered individual student differences but failed to control for confounding variables. Despite this limitation, both studies reported positive outcomes. Study 2 used composition-focused software and compared baseline performance but did not further control for confounding variables. The results revealed significantly better performance in the experimental group. Study 11 also employed a combination of tools but uniquely compared their effectiveness. Post-test results indicated significant knowledge gains in both experimental groups using digital tools, particularly the tablet-based group. However, inconsistencies were noted between qualitative data analysis and interpretation.

Studies 1 and 9 assessed basic theoretical skills without clearly defining sub-skills. Although both studies showed substantial post-test improvements, their intervention descriptions lacked detail, and reliability testing was absent.

Impact on Creative Skills

Studies 5 and 10 focused on creative skills. Study 5 demonstrated the positive impact of a reflexive interaction system on improvisation but highlighted the lack of consistent statistical significance between groups. This may be due to the lack of a pretest to control baseline differences. Study 10 showed significant improvements in creativity scores, but no significant differences were observed in craftsmanship and aesthetic appeal compared with the traditional handwritten method. Study 2 assessed students’ compositional outputs and reported positive results. However, the greater composition length during teaching may reflect the researcher's dual role as both instructor and assessor during the intervention.

Impact on Motivational Aspects

Studies 2, 7, 8, 11, and 12 examined motivations. Studies 2 and 7 reported significant improvements in students’ attitudes toward using tools for music learning. Studies 11 and 12 analyzed engagement through mixed methods, noting positive behavioral responses, but inconsistencies between data, findings, and interpretation undermined the trustworthiness of their conclusions. Study 8 observed indicators of intrinsic motivation among the iPad groups but failed to specify whether these indicators reflected learning motivation or merely the motivation to play. Additionally, this study failed to compare the motivational aspects across the three groups. Furthermore, Study 8 unintentionally introduced bias, potentially lowering the control group's performance and motivation when students demonstrated frustration and complained about not receiving the treatment.

Discussion

The current systematic review examined the use and effectiveness of digital tools in elementary music classrooms. Drawing on empirical evidence from 15 studies from 2014 to 2024, along with critical appraisal tools and a narrative synthesis, our analysis offers insights into the use and impact of digital tools in music education. The research addresses three key questions: (1) classification of digital tools implemented, (2) evaluation methodologies and research designs employed, and (3) assessment of tool effectiveness in developing musical competencies and enhancing student motivation.

The first question is related to the types of digital tools that are commonly used in music class experiments. Our synthesis identified three primary categories: software-based, hardware-based, and hybrid tools. Analysis revealed software tools to be the predominant choice, likely due to their accessibility, cost-effectiveness, and scalability across devices. Additionally, software tools often feature flexible functionalities, allowing them to be adapted to diverse educational objectives. In contrast, hardware tools, though less frequently employed, offer unique advantages in specific instructional contexts. For example, feedback robots excel at capturing and analyzing students’ musical performances in real time, providing tailored feedback for improvement (Song et al., 2024). Meanwhile, hybrid tools represent an integrated intervention strategy, combining the flexibility of software with the interactive capabilities of hardware. This multi-dimensional approach proves highly effective in addressing complex instructional needs, particularly by fostering collaborative learning and accommodating students with diverse skill levels (F. L. Wang et al., 2009). From the perspective of functionality, all studies measuring performance and theoretical skills utilized software tools, likely due to the diversity of skills they target. Software tools are designed with rich content that enables the integration of multiple skill objectives, utilizing visual and interactive exercises to enhance mastery (Dore et al., 2018; Kirkorian & Choi, 2017; Szabó et al., 2019). Studies focusing on creative skills exclusively employed software tools, indicating their advantage in facilitating content analysis and recording. Hardware tools, by contrast, emphasize user experience and student acceptance. For instance, Study 11 reported higher acceptance of software over hardware due to reduced technological anxiety, which promotes engagement (Gonzalez, 2013). Furthermore, multimodal combinations better support cognitive processing and improve learning efficiency (Moreno & Mayer, 1999). Interestingly, unassessed learning processes were more favored (Study 12). Without the pressure of assessment, students tended to focus on the intrinsic enjoyment and meaning of tasks rather than external rewards or grades.

The second research question addresses assessment methods and intervention designs. Analysis of the 15 studies identified 24 distinct assessment instruments, with most being self-developed or adapted. Tests emerged as the most common instrument type, followed by motivation scales, scoring guides, and structured observations. Concerning the reliability and validity of the instruments, six studies did not clearly report any form of validity or reliability. Face or content validity was assessed in a few studies (Studies 2, 4, 11, 13, and 14), while Cronbach's alpha was the most frequently used reliability measure in several studies (Studies 2, 4, 5, 7, 10, 12, and 15). Some studies reported interrater reliability through percentage agreements, Cohen's kappa, or correlation coefficients, but only one (Study 2) attempted factor analysis—without disclosing results. The lack of factor analysis and standardized validation methods in the reviewed studies limits comparability and confidence in findings (Schmitt et al., 2018). Future research should focus on developing and validating standardized instruments to ensure more reliable assessments of digital tools in music education.

From the perspective of research design and its association with digital tool use, single-group designs and post-test-only approaches were predominantly employed to evaluate isolated tool effects or short-term changes. In contrast, non-equivalent pretest-posttest designs and randomized controlled trials (RCTs) were more commonly used in studies comparing digital tools with traditional methods. This indicates a potential trend: Rigorous research designs are typically linked to comparative studies, ensuring greater control over confounding variables and stronger causal inferences (Talavera et al., 2023). Simpler designs, such as single-group or post-test-only approaches, tend to focus on isolated evaluations of specific tools, offering insights into their standalone efficacy.

We observed that an advantage of one mode of composition was also a disadvantage of the other, as notation-based software facilitated creativity, whereas handwritten notation supported craftsmanship (Kang & Yoo, 2021). This reflects a broader reality in educational research—no single technology or method consistently excels across all contexts (Agres et al., 2021). Instead, their advantages and disadvantages are often relative and context dependent. This insight serves as a reminder that intervention designs must consider specific objectives, task demands, and resource availability. Similarly, under the scrutiny of MMAT and JBI standards, while all 15 studies exhibited methodological imperfections to varying degrees, their strengths became apparent when juxtaposed with the weaknesses of others. Studies with more robust designs should carry greater weight in shaping the collective understanding of digital interventions (Louis et al., 2002). Nevertheless, the positive findings from these studies, though often localized in scope, offer valuable preliminary evidence for the promising potential of digital tools in educational contexts.

The last research question investigates the efficacy of digital music technology in enhancing children's skills and motivational aspects. Among all the included studies, musical literacy emerged as the most frequently measured skill, followed by performance skills, with some studies even assessing multiple sub-skills. This underscores the central role of musical literacy in music education, prioritizing the cultivation of auditory abilities and cultural awareness in early education (Buzás, 2019; Pitts, 2017). However, this does not imply that less-measured skills, such as creativity, are undervalued. In the process of learning musical skills, each sub-skill is interconnected. As reflected in the Bloom's Taxonomy framework, the progression from absorbing knowledge to creating is a robust cycle, where higher-level learning outcomes depend on a solid foundational accumulation (Anderson & Krathwohl, 2001). It also aligns with Carl Orff's pedagogical philosophy, which emphasizes an experiential approach to music learning. By integrating singing, play-based activities, and improvisation, students are encouraged to explore melodies and rhythms in an engaging environment, embodying the principle of “learning through play” (Shiyao & Noordin, 2024). Therefore, while the development of creative skills in elementary music education should not be overlooked, the foundational role of musical knowledge remains critical for students’ overall musical growth.

In synthesizing effectiveness across performance skills, theoretical skills, creative skills, and motivation, the most consistent impact was observed on motivation and engagement in music education. Most studies affirmed the motivational benefits of digital tools, which extend beyond gamification to include reflective and interactive learning experiences. This highlights the effectiveness of learning within technology-driven and entertainment-oriented digital environments, as noted by Affah (2019). However, there are concerns about whether video games actually enhance motivation to learn or only the motivation to play (Lesser, 2020). Across the 15 studies analyzed, shorter interventions were generally associated with higher levels of student motivation. However, longer interventions, such as Study 7, also demonstrated significant increases in motivation, suggesting that duration alone is not the sole determinant of motivational outcomes. As direct causal relationships between intervention duration and motivation remain challenging to establish, these findings underscore the importance of rigorous research designs. For example, Studies 13 and 14 implemented multi-condition-controlled trials with robust methodological frameworks, enabling more reliable causal inferences.

Although all studies reported either significant or modest positive impacts on measured musical skills, they generally lacked detailed descriptions of sample representativeness. This limitation was particularly evident in most studies, with Studies 4 and 5 showing moderate constraints due to sampling issues. When a digital tool encompasses a wide range of musical skills, it inevitably involves complex programming and high-level design requirements. This, in turn, demands rigorous experimental objectives and robust research designs (Renney & Gaster, 2024). Studies 7, 13, and 14 exemplify such intricate and challenging research designs, highlighting the need for methodological precision in evaluating advanced tools. Additionally, as Ivascu emphasized, tracking and evaluating application effectiveness is crucial for improving the quality and relevance of these digital tools (Ivascu et al., 2024). Supporting this notion, Studies 12 and 15, as well as 13 and 14, assessed the same digital tool across different times and experimental setups, indirectly validating its efficacy and consistency in various contexts.

It is challenging to discuss the significance of a single tool, as nearly every study incorporates multiple tools in their experiments. However, it is crucial to explicitly detail which tools are used during interventions, how they are used, and how they interact with each other to influence the outcomes to maintain internal validity. Furthermore, such complex design introduces challenges, including limited external validity due to short intervention durations and small sample sizes (Kostis & Dobrzynski, 2020). For example, while Studies 12, 13, 14, and 15 reported statistically significant results, they were potentially constrained by reduced statistical power and limited intervention effects. In contrast, Studies 3, 7, and 9, despite some confounding factors, demonstrated improved external validity through their longer intervention period and larger sample size. Among the studies analyzed, those incorporating pre- and post-tests with control groups (e.g., Studies 2, 3, 4, 6, 7, and 9) provided more compelling evidence of significant effects. Furthermore, studies that establish validity evidence, such as 2, 4, 11, 13, and 14, highlighted greater methodological rigor and reliability, underscoring their contributions to the field.

Overall, the literature highlighted that using digital tools in music education enhances students’ motivation and enjoyment of lessons. Immediate feedback, ease of use, and support for both independent learning and collaboration are among the key benefits of ICT in this field. However, several challenges related to user experience may hinder students’ learning motivation and performance, such as low ease of use, technical difficulties, low adaptability, limited functionality, and required technical assistance. Therefore, the successful improvement of musical skills relies heavily on the integration of students’ learning environments. It is crucial for students to possess the capacity to adapt to digital technologies and for educators to have strong musical expertise and ICT competencies (Peretti et al., 2025). This combination ensures that ICT-based learning is both meaningful and effective.

Implications, Suggestions for Future Research, and Limitations

This study has several implications for music education in primary classrooms. First, primary students and teachers can leverage diverse digital tools, including software, hardware, or a combination of both, to enhance musical skills and motivation. However, teachers should be mindful that while gamified tools may increase engagement in play, they do not necessarily cultivate long-term motivation for learning. When selecting tools, priority should be given to usability, students’ familiarity, and readiness for effective implementation. Tools with poor usability, such as those with steep learning curves, cluttered interfaces, or frequent technical failures, can impede student progress rather than support it. Additionally, teachers need to possess not only foundational music knowledge and skills but also adequate digital literacy. Experienced teachers are often better equipped to maximize the effectiveness of digital teaching methods. Furthermore, while combining multiple tools may seem advantageous, several studies indicate that a single, well-chosen tool can significantly enhance both performance and motivation. Thus, teachers are encouraged to begin with accessible, user-friendly tools. Through experimentation, they can determine optimal implementations.

For future research, a robust research design is foundational. Researchers must clearly define their objectives, employ rigorous methods, and ensure intervention measure reliability. While integrating multiple technologies reflects real-world classroom practices, it hinders isolating specific tools’ impacts, making it challenging for teachers to evaluate their effectiveness. Researchers should adopt carefully designed studies that address these complexities, enabling clearer insights into the effectiveness of individual tools.

This study has some limitations. First, we identified many irrelevant studies during the screening process that deviated from the search logic, suggesting inaccuracies in the search strategy or systemic database issues. We suggest that future reviews should be mindful of these concerns and develop more effective methods to improve the results obtained from search databases. Second, the assessment methods used in the included studies were limited. Most studies relied on pre- and post-test designs, which, while useful for measuring learning outcomes, do not capture real-time changes in student engagement or learning processes. This limits understanding of how digital tools influence students during actual use. Although some studies used structured observations, only a few employed coding schemes to systematically analyze behavior. Future research should use real-time data (e.g., log data, eye-tracking, continuous behavioral observations) to provide deeper insights into the effects of digital tools on learning performance and motivation. Finally, appraisal tools (MMAT and JBI checklists) revealed limited credibility in included studies due to small sample sizes, limited control groups, or unclear reporting of assessment reliability and validity. Methodological variability precluded definitive conclusions. Therefore, future reviews may benefit from focusing on studies with stronger experimental designs and validated assessment tools to improve the reliability of findings. Despite these limitations, this review offers valuable insights into the role of digital tools in music education and highlights key areas for further research and methodological improvement.

Conclusion

This study conducted a systematic review and analysis of digital tools’ usage and effectiveness in elementary music education, synthesizing evidence from 15 included studies. Several key findings emerged. First, digital tools are diverse in nature, and the integration of innovative tools with creative research designs highlights the vast potential of digital methods in education. Among these, software applications stand out due to their flexibility and multifunctionality, dominating many music intervention experiments, albeit with reliance on supporting hardware infrastructure.

Within these applications, core musical literacy and performance skills in elementary music education are prominently addressed. In terms of effectiveness, all studies reported positive impacts on students’ musical skill development, regardless of the scale or duration of the experiments. While limitations in representativeness and reliability were observed in some studies, their overall value remains undeniable. The significance of such studies lies in clarifying their specific objectives and contexts and in maximizing the effectiveness of their interventions.

Thus, this review not only provides an overview of the current application of digital tools in music education but also underscores the importance of rigorous research design. Furthermore, it offers direction for future studies to contribute effectively to the broader application and deeper exploration of digital tools in music education.

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