AI Adoption in Choral Music Education - TCDA 2026 Poster Session

AI Adoption in Choral Music Education

Current Landscape, Persistent Barriers, and a Two-Year Outlook

FINDINGS SYNTHESIS DOCUMENT · TCDA 2026 · Works in Progress

Matt Woodward | Co-Founder, MusEdLab.ai | Choral Director (18 years) | Anthropic AI Fluency & Foundations | Perplexity AI Business Fellow

Three Research Questions

RQ1 Where are we now in AI adoption within choral music education?

RQ2 What structural and dispositional barriers are slowing or preventing adoption?

RQ3 What should choral educators expect from AI over the next two years — and what determines the outcome?

Methodology & Verification Process

This study synthesizes 11 peer-reviewed, nationally representative, and practitioner sources (2023–2026), verified through a three-AI comparative extraction process using Claude, Gemini, and Perplexity. Each source was independently analyzed by all three models using a standardized extraction prompt aligned to the three research questions. Only findings with cross-model agreement were included in the verified findings bank. All 11 sources achieved zero conflicts across models — the highest possible confidence rating under this verification framework.

Two additional sources of original practitioner data are incorporated: field observations from the author's AI sessions at TMEA 2025 and 2026, and a discoverability audit revealing that practitioner-led AI content in music education does not surface through AI-assisted or Google-guided searches.

Scope note: No existing peer-reviewed source directly studies AI adoption among choral music educators. All literature is applied from adjacent domains — music education broadly, vocal education, K–12 generally, and educator adoption psychology. The identification of this choral-specific gap is itself a primary finding of this study.

RQ1 — WHERE ARE WE NOW?

The National K–12 Baseline — A Fast-Moving Target

As recently as fall 2023, only 18% of K–12 teachers reported using AI for teaching, with 66% categorized as non-users (Diliberti et al., 2024). By spring 2024, usage among ELA, math, and science teachers had risen modestly to 25%, though adoption appeared to be plateauing rather than accelerating, and music and arts teachers were explicitly excluded from the analysis (Kaufman et al., 2025). By early 2026, that picture had changed dramatically: six in ten K–12 teachers now report using AI for their professional work in some capacity, with three in ten using it at least weekly (Walton Family Foundation & Gallup, 2026, N=2,069).

That trajectory — 18% to 60% in roughly three years — represents one of the fastest technology adoption curves ever recorded in K–12 education. But depth matters. Of teachers who use AI, 64% use it for instructional planning only, and just 3–4% are daily power users who have integrated AI into core instructional workflows. Teachers who use AI tools weekly report saving an estimated 5.9 hours per week — approximately six weeks per school year — though this figure is based on self-report rather than observed workflow data (Walton Family Foundation & Gallup, 2025).

Source: Diliberti et al. (2024); Kaufman et al. (2025); Walton Family Foundation & Gallup (2025, 2026)

Where Music Education Stands

Within music education, AI has not yet crossed what O'Leary (2025) describes as 'the chasm' to mainstream adoption. Where technology adoption is occurring among music educators, it skews heavily toward administrative tasks — communication, grading, program management — rather than instructional integration. Arts and Humanities account for only 9.70% of AI-in-education research publications, compared to 71.52% from Computer Science (Mazlan et al., 2026). The tools being designed for music educators are overwhelmingly built by people outside music education, without domain expertise in repertoire, voicing, ensemble pedagogy, or choral culture.

This mirrors a pattern documented across previous technology cycles. Bauer (1999, cited in Price & Pan, 2002) found that music educators were 'not extensively using the Internet professionally' as late as 1999 and remained 'more comfortable with traditional methods of instruction' even as internet infrastructure reached 98% of U.S. schools by 2000. The parallel is direct: infrastructure availability and national adoption trends did not produce pedagogical adoption in music classrooms then, and awareness of AI will not produce adoption now without targeted, domain-specific intervention.

Source: O'Leary (2025); Mazlan et al. (2026); Bauer (1999); NCES (1994–2000)

Vocal Educator Data — The Closest Available Proxy

Donahue (2025) provides the only available empirical data from a music-adjacent population. In a mixed-methods study of 34 vocal educators — 32.35% of whom identified as choir directors — Likert means on AI attitudes ranged from 2.59 to 3.44 on a 5-point scale, reflecting cautious optimism rather than adoption. The highest-scoring item was forward-looking: 'I can see positive uses for AI in vocal education' (x̄ = 3.44, mode = 4). Notably, fear of job displacement was not the dominant concern. Fifteen of 34 participants emphasized that AI cannot replicate the multi-sensory, relational nature of vocal and choral instruction.

He & Ren (2025) add a striking counterpoint from pre-service music teachers: 96.4% hold positive attitudes toward technology — yet simultaneously report actual classroom use is 'still insufficient.' Positive attitude does not equal adoption. The gap between what music educators think about AI and what they actually do with it is the central behavioral puzzle this study addresses.

Source: Donahue (2025); He & Ren (2025)

Practitioner Field Observation — TMEA 2025–2026

The author presented AI-focused sessions at the Texas Music Educators Association (TMEA) convention in both 2025 and 2026. In 2025, the author's session was the only AI-focused offering at the conference, with approximately 80 attendees. By 2026, six AI sessions appeared on the TMEA program — indicating both growing practitioner confidence in presenting on this topic and formal recognition by TMEA and TIME of AI's relevance to the profession. Estimated combined attendance across all six 2026 sessions was approximately 500 educators, at a conference with 30,000+ total attendees.

In one year: a 6x increase in sessions. A 6x increase in estimated attendance. At a conference of 30,000+, demand is real and it is growing. When new technology arrives in music education, we hand educators a machete and point them toward the jungle. AI is following the same pattern. The trail doesn't exist yet. We are cutting it as we go.

Cross-source finding: Attitude toward AI in music education is broadly positive, but positive attitude does not equal adoption. The gap between what music educators think about AI and what they actually do with it is the central challenge this study addresses.

RQ2 — WHAT ARE THE CURRENT BLOCKERS?

The barriers to AI adoption in choral music education are not independent — they compound. Removing one layer does not produce adoption. A director who is curious about AI (positive attitude) but lacks time, domain-specific tools, district support, privacy guidance, peer models, and institutional clarity faces a six-layer barrier stack. Each layer is documented below.

1. Guidance Exists But Hasn't Reached the Classroom

In July 2025, NAfME published Guiding Principles, Frameworks, and Applications for AI in Music Education — the field's most authoritative response to date. MusicFirst Academy launched a paid AI for Music Educators course in 2026. These are meaningful steps. But awareness among working choral directors remains low, access is uneven due to cost and platform discoverability, and no published guidance addresses the choral classroom specifically.

The national picture confirms this at scale: only 18% of teachers report receiving any formal administrative guidance on AI use. Forty-eight percent rely entirely on informal peer guidance. Thirty-four percent receive no guidance at all (Walton Family Foundation & Gallup, 2026). This guidance vacuum has direct consequences beyond adoption lag — teachers without clear AI expectations from leadership report significantly higher burnout and lower intention to remain in teaching, connecting the AI guidance gap directly to the broader teacher retention crisis (Walton Family Foundation & Gallup, 2026).

Discoverability audit: During research for this study, AI-assisted searches — including Perplexity, Claude, and Google — did not surface the NAfME guidance document, MusicFirst Academy's AI course, or either Choralosophy or Music Ed Tech Talk podcast episodes addressing AI in music education. All were located through direct professional network knowledge. The content exists. It is not reaching educators through the channels they use most.

Source: NAfME (2025); MusicFirst Academy (2026); Walton Family Foundation & Gallup (2026); Kaufman et al. (2025)

2. Generic Professional Development Consistently Fails

Six of eleven sources converge on the same conclusion: technical-only, episodic, self-taught professional development does not produce meaningful classroom AI integration. Aravantinos et al. (2026), synthesizing 43 empirical studies across 7,500+ in-service teachers, found that 'technical training alone is not sufficient' and that successful integration requires pedagogical knowledge, positive attitudes, organizational support, and ongoing practice. Tripathi et al. (2025) found that AI literacy among K–12 teachers is almost entirely self-taught, acquired informally without institutional guidance or structured training. The Walton Family Foundation & Gallup (2026) confirm that 52% of AI-using teachers taught themselves, with only 31% receiving district-provided training.

McGehee's (2024) meta-analysis of 46 studies identifies the mechanism: self-efficacy — not information exposure — is the single strongest predictor of AI adoption, supported by 11 AI-specific studies. Teachers need to feel capable of using AI, not merely informed about it. Information campaigns and one-time workshops do not build self-efficacy. Domain-specific, hands-on, sustained professional development does — as Bauer, Reese, and McAllister (2003) demonstrated for music technology integration over two decades ago.

Source: Aravantinos et al. (2026); Tripathi et al. (2025); Walton Family Foundation & Gallup (2026); McGehee (2024); He & Ren (2025); Cheng (2025); Bauer et al. (2003)

3. AI Threatens Choral Professional Identity ★

This blocker is identified through practitioner field observation and is not captured by any existing K–12 adoption literature. Choral directors' professional identity is grounded in relational, embodied, creative teaching — the ensemble experience, the human voice, the conductor-ensemble relationship, the irreplaceable presence of a director who hears, sees, and feels the room simultaneously. Generative AI is widely perceived within creative and artistic communities as antithetical to originality and artistic authenticity.

Mazlan et al. (2026) document AI's 'lack of cultural awareness and emotional sensitivity' as a technical limitation, and Donahue (2025) notes that vocal educators consistently emphasize that 'teaching vocals includes not only the ears, it includes the eyes, it includes the person.' McGehee (2024) adds nuance worth noting: some educator resistance to AI may reflect legitimate, valid concern rather than something to be simply overcome — what Sutton & Rao call 'constructive friction.' The choral-specific dimensions of this concern — its relationship to repertoire identity, creative authority, ensemble culture, and the fundamental human transaction of making music together — remain entirely unstudied in the research literature.

Source: Cheng (2025); Mazlan et al. (2026); Donahue (2025); McGehee (2024); Practitioner field observation [Gap in literature]

4. Data Privacy Concerns Block Content Creation

Perceived Risk — encompassing concerns about student privacy, information security, data safety, and FERPA compliance — is the second strongest predictor of actual AI usage behavior, with a normalized importance of 74.11%, behind only behavioral intention itself (He & Ren, 2025). This finding has particular relevance for K–12 choral directors working with minors, where student data protections create additional friction around AI-generated content involving student information, performance data, or recordings.

Diliberti et al. (2024) confirm that data privacy ranks as a top barrier for both AI users (36%) and non-users (36%) alike — meaning even educators who have adopted AI remain constrained by privacy concerns in how deeply they integrate it. The absence of clear district policy compounds this: when guidance is absent, educators default to risk avoidance.

Source: He & Ren (2025); Diliberti et al. (2024); Kaufman et al. (2025)

5. AI Is Not Domain-Specific

Choral directors report that AI does not know what they need it to know. Repertoire selection requires expertise in voicing, difficulty level, publisher licensing, SSAA vs. SATB instrumentation, thematic programming, and cultural appropriateness for specific ensemble demographics. Score analysis requires music theory expertise. Lesson planning requires understanding of the specific ensemble, the season in the choral calendar, the developmental level of singers, and the contextual demands of an upcoming performance.

Without domain-specific training, tools, or knowledge bases, AI output quality is insufficient for direct professional use — creating an additional time cost as directors must heavily edit, verify, or discard AI-generated content. O'Leary (2025) identifies a related structural problem: AI training data is scraped largely from unvetted curriculum marketplaces and edu-influencer content, meaning the quality and accuracy of AI-generated music education material is fundamentally constrained by the quality of what was fed into the model.

Source: O'Leary (2025); Mazlan et al. (2026); Donahue (2025); Practitioner field observation

6. Environmental and Ethical Concerns ★

Music educators with strong environmental consciousness are aware of AI's documented energy and water consumption costs. This concern does not appear in any of the eleven sources reviewed — it represents a choral-educator-specific blocker identified entirely through practitioner field observation. Additionally, Western dataset bias in AI music generation tools further alienates directors committed to diverse and culturally-responsive repertoire programming. Cheng (2025) and Mazlan et al. (2026) both document the Western-centric training data problem, noting it limits global applicability and reduces diversity in AI-generated musical content.

Source: Cheng (2025); Mazlan et al. (2026); Practitioner field observation [Gap in literature]

★ Blockers 3 and 6 are identified through practitioner field observation and are not represented in any existing K–12 AI adoption literature. Their absence from the research base is itself a finding — and a call for choral-specific empirical study.

RQ3 — WHAT SHOULD WE EXPECT IN THE NEXT TWO YEARS?

Scenario A: Without Targeted Intervention — Slow Adoption Mirroring Historical Curves

The internet adoption parallel is instructive and directly applicable. Between 1994 and 2000, internet access in U.S. schools grew from 35% to 98% — a six-year infrastructure buildout. Yet as late as 1999, only half of teachers used the internet for instruction and only one-third felt prepared to do so, even after infrastructure was fully in place (NCES, 1994–2000). In music education specifically, the gap persisted even longer: Bauer (1999, cited in Price & Pan, 2002) found music educators remained more comfortable with traditional methods years after schools were connected.

AI is following the same curve with additional friction. K–12 baseline adoption has accelerated to 60% broadly, but depth of integration — especially instructional integration — remains shallow, and music education almost certainly lags even the general K–12 figure. O'Leary (2025) finds no evidence of AI crossing the chasm to mainstream adoption in music education. Without domain-specific guidance, choral-specific tools, and targeted professional development, this study projects that meaningful AI integration in choral music education will lag the K–12 baseline by at least 3–5 additional years.

Source: NCES (1994–2000); Bauer (1999); O'Leary (2025); Walton Family Foundation & Gallup (2026)

Scenario B: With Practitioner-Led Action — Accelerated Peer Adoption

He & Ren (2025) provide the single most actionable finding in this dataset: Social Influence is the #1 predictor of behavioral intention to adopt generative AI among music teachers, with a normalized importance of 100% in ANN analysis. Peer behavior, institutional advocacy, and observable examples from working practitioners drive willingness to adopt more than any other measured factor — more than attitude, more than perceived usefulness, more than technical skill.

This means that every choral director who presents an AI session, posts a video, writes a paper, or demonstrates a workflow to a colleague is performing the most empirically-supported adoption intervention available. The TMEA evidence supports this in practice: one AI session in 2025 became six in 2026, with attendance growing from approximately 80 to 500 estimated — a direct effect of practitioner-led momentum at a single conference.

Bauer, Reese, and McAllister (2003) established that intensive, domain-specific professional development workshops can significantly improve music teachers' technology knowledge, comfort, and frequency of use simultaneously. That study remains, more than two decades later, one of the only empirical examinations of PD effectiveness for music teachers and technology. The model works. It requires practitioners to build it.

Source: He & Ren (2025); Donahue (2025); McGehee (2024); Bauer et al. (2003); Practitioner field observation

Practitioners Already Leading — The Dissemination Gap

Practitioner-led dissemination is already underway. Chris Munce's Choralosophy podcast addressed AI in choral music education as early as May 2023 (Ep. 147) and returned to the topic in June 2026 (Ep. 291), featuring choral conductor and AI strategist Beth Philemon in a nuanced conversation about copyright, environmental ethics, and the human-in-the-loop argument — exactly the tensions this study identifies as blockers. Robby Burns' Music Ed Tech Talk dedicated a full segment in December 2025 (Ep. 91) specifically to the NAfME AI guidance document and its practical classroom implications.

This is the Social Influence mechanism in action: practitioners modeling engagement with difficult questions for the broader profession. But here is the critical finding: none of this content was surfaced by AI-assisted or Google-guided searches during the research process for this study. It was located only through direct professional network knowledge. The content exists and is growing. It is not reaching educators through the channels they use most. The dissemination problem is not a content problem — it is an infrastructure and discoverability problem.

Source: Munce (2023, 2026); Burns (2025); He & Ren (2025)

The Equity Risk

Adoption gains are not distributed equally. Only 13% of principals in the highest-poverty schools report any district AI guidance, compared to 25% in the lowest-poverty schools — and that gap is projected to widen (Kaufman et al., 2025). The Walton Family Foundation & Gallup (2026) corroborate this: teachers in higher-income schools are somewhat more likely to receive AI guidance of any kind. Critically, teachers in schools without clear AI expectations from leadership report significantly higher burnout and lower intention to remain in teaching — connecting the guidance gap directly to the teacher retention crisis already disproportionately affecting under-resourced schools.

Choral programs in under-resourced schools face compounded barriers: less time, less technology infrastructure, less administrative support, and less access to professional development. Without intentional, equity-centered AI professional development, AI adoption advantages will accrue primarily to directors in well-funded programs — deepening existing disparities in choral program quality and teacher retention.

Source: Kaufman et al. (2025); Walton Family Foundation & Gallup (2026); Aravantinos et al. (2026)

The Research Agenda

He & Ren (2025) project that as AI becomes more accepted in music education, research will shift from technology acceptance toward examining how AI-integrated teaching provides personalized, contextual feedback and affects student creativity. Mazlan et al. (2026) call for longitudinal studies examining sustained impact on student motivation, creative self-efficacy, and artistic voice. Aravantinos et al. (2026) recommend research differentiated by subject area — noting that arts educators have distinct PD needs that no existing study has addressed.

This study adds a choral-specific research agenda: adoption rate studies among working choral directors, efficacy studies on AI-assisted repertoire selection and sight-reading instruction, professional development intervention studies modeled on Bauer et al. (2003), and investigation of the choral-specific blockers identified here — artistic identity threat, environmental concern, and domain specificity failure — none of which currently exist in the literature.

"Critical literacies will need to be cultivated intentionally and not occur simply through the use of, and exposure to, AI tools." — O'Leary (2025). The same is true of choral-specific AI adoption. It will not happen on its own.

ORIGINAL CONTRIBUTION — WHAT THIS STUDY ADDS

The literature provides a robust foundation in adjacent domains. This study contributes the choral-specific synthesis that does not yet exist anywhere in the published research base.

What the Existing Literature Provides

→ K–12 AI adoption baselines in U.S. general education (Diliberti, 2024; Kaufman, 2025; Walton Family Foundation & Gallup, 2025, 2026)

→ Music education AI landscape — technical tools and publication trends (Mazlan et al., 2026; Cheng, 2025)

→ General K–12 educator PD needs for AI integration (Aravantinos et al., 2026)

→ Music teacher and vocal educator attitude and acceptance data (He & Ren, 2025; Donahue, 2025)

→ Critical AI literacy frameworks specific to music education (O'Leary, 2025)

→ General educator adoption psychology meta-analysis (McGehee, 2024)

→ Qualitative K–12 AI use patterns in classroom settings (Tripathi et al., 2025)

→ NAfME guiding principles for AI in music education (NAfME, 2025)

What This Study Contributes

★ First synthesis specifically framed for choral music educators — not music education broadly

★ Identification of choral-specific blockers absent from all existing literature: artistic identity threat, environmental and ethical concerns, domain specificity failure

★ Historical parallel using NCES internet adoption data and Bauer (1999) music educator internet data — providing an evidence-based adoption timeline comparison

★ Practitioner field observation data from TMEA 2025–2026 quantifying the demand-guidance gap in real time (1 session → 6 sessions; ~80 → ~500 estimated attendees; 30,000+ conference)

★ Discoverability audit revealing that practitioner-led AI content in music education does not surface through AI-assisted or Google-guided search

★ Connection between AI guidance vacuum and teacher burnout and attrition — raising stakes beyond adoption rates

★ A practitioner-led acceleration model grounded in the Social Influence finding (He & Ren, 2025, normalized importance 100%)

★ A call for a choral-specific AI research agenda — the first formal articulation of what empirical studies need to be conducted

CALL TO ACTION

In the absence of institutional guidance, practitioner-led dissemination is the most powerful available lever.

Social Influence is the single strongest predictor of AI adoption intention in music educators — normalized importance of 100% (He & Ren, 2025). That means every session presented, every video posted, every paper shared, every conversation had by a choral director using AI moves the field forward. NAfME has published guidance. MusicFirst has built a course. Practitioners are podcasting and presenting. The infrastructure is being built. What is missing is the bridge between what is being created and what is reaching the director standing in front of a choir on Monday morning. We are that bridge.

The agenda:

1. Find the tools. Learn them through domain-specific experimentation, not generic training.
2. Share what works. Sessions, videos, posts, conversations, papers — all of it counts and all of it moves the needle.
3. Document the outcomes. The field needs efficacy data. Your classroom is the research site.
4. Build the community. Professional learning networks are second only to peer modeling as a PD mechanism (Aravantinos et al., 2026).
5. Demand equity. Advocate for AI professional development that reaches directors in under-resourced programs, not just those in well-funded districts.
6. Name the gap. Every time you present, write, or speak about AI in choral music education, you are filling a gap that no publisher, tech company, or institution has filled yet.

"If we want it to become more widely used, it has to be people like us beating the drum for it, creating programs for it, writing the papers about it, presenting those papers to a wider audience. It has to be widely disseminated." — Practitioner voice, Donahue (2025)

VERIFIED SOURCE INVENTORY — ALL 11 SOURCES

All sources verified through three-AI comparative extraction (Claude, Gemini, Perplexity). Zero cross-model conflicts across all 11 sources.

Aravantinos, S., Lavidas, K., Komis, V., Karalis, T., & Papadakis, S. (2026). Artificial intelligence in K-12 education: A systematic review of teachers' professional development needs for AI integration. Computers, 15(1), 49. https://doi.org/10.3390/computers15010049

Type: Systematic review — 43 studies, 7,500+ K–12 teachers Primary RQ: RQ2, RQ3

Cheng, L. (2025). The impact of generative AI on school music education: Challenges and recommendations. Arts Education Policy Review, 126(4), 255–262. https://doi.org/10.1080/10632913.2025.2451373

Type: Policy review — music education specific Primary RQ: RQ1, RQ2

Diliberti, M. K., et al. (2024). Using artificial intelligence tools in K–12 classrooms. RAND Corporation (RR-A956-21). N=1,020 teachers, 231 districts.

Type: Nationally representative survey — U.S. K–12, fall 2023 Primary RQ: RQ1, RQ2

Donahue, J. (2025). Breaking barriers: A mixed-methods study of vocal educators' perspectives on the use of AI in vocal education. Dibon Journal of Education, 1(3), 277–306.

Type: Mixed methods — N=34 vocal educators, 32.35% choir directors Primary RQ: RQ1, RQ2

He, S., & Ren, Y. (2025). Exploring pre-service music teachers' acceptance of generative AI: A PLS-SEM-ANN approach. Frontiers in Psychology, 16, 1571279. https://doi.org/10.3389/fpsyg.2025.1571279

Type: Quantitative (PLS-SEM/ANN) — N=301 pre-service music teachers Primary RQ: RQ2, RQ3

Kaufman, E., et al. (2025). Uneven adoption of artificial intelligence tools among U.S. teachers and principals in the 2023–2024 school year. RAND Corporation (RR-A134-25). N=9,126 teachers, 3,631 principals.

Type: Nationally representative survey — U.S. K–12, spring 2024 (ELA/math/science only) Primary RQ: RQ1, RQ2

Mazlan, C. A. N., et al. (2026). Artificial intelligence applications and pedagogical challenges in music education. Discover Education, 5(1), 140. https://doi.org/10.1007/s44217-026-01127-3

Type: Systematic review / bibliometric — 165 publications, 463 authors, 2006–2025 Primary RQ: RQ1, RQ2

McGehee, R. (2024). Breaking barriers: A meta-analysis of educator acceptance of AI technology in education. Michigan Virtual. 46 studies (16 AI-specific, 30 general EdTech).

Type: Meta-analysis (TAM/UTAUT2-based) — general K–12 and higher ed Primary RQ: RQ2, RQ3

O'Leary, E. (2025). Considering the possibilities and problems of AI in music education: The need for critical literacies. Action, Criticism, and Theory for Music Education, 24(3), 138–164. https://doi.org/10.22176/act24.3.138

Type: Conceptual essay — music education specific, not empirical Primary RQ: RQ1, RQ2, RQ3

Tripathi, T., Sharma, S. R., Singh, V., Bhargava, P., & Raj, C. (2025). Teaching and learning with AI: A qualitative study on K-12 teachers' use and engagement with artificial intelligence. Frontiers in Education, 10, 1651217. https://doi.org/10.3389/feduc.2025.1651217

Type: Qualitative — N=20 K–12 teachers, Delhi private schools Primary RQ: RQ2

Walton Family Foundation & Gallup. (2025, 2026). Teaching for Tomorrow: Unlocking Six Weeks a Year With AI (2025); Teaching for Tomorrow: Closing the Expectations Gap (2026). Gallup. 2025: N=2,232; 2026: N=2,069. Both nationally representative via RAND American Teacher Panel.

Type: Nationally representative longitudinal survey — U.S. K–12 (all subjects) Primary RQ: RQ1, RQ2, RQ3

Practitioner and Policy Sources

Bauer, W. I. (1999). Music educators and the Internet. Cited in Price, H. E., & Pan, C. (2002). Journal of Technology in Music Learning.

Type: Historical — music educator internet adoption Primary RQ: RQ1, RQ3

Bauer, W. I., Reese, S., & McAllister, P. (2003). Transforming music teaching via technology: The role of professional development. Journal of Research in Music Education, 51(4), 289–301.

Type: Empirical PD intervention — music technology Primary RQ: RQ3

Burns, R. (Host). (2025, December 30). Episode 91 [AI in music education and NAfME guiding principles]. Music Ed Tech Talk. https://musicedtechtalk.com

Type: Practitioner podcast — music education Primary RQ: RQ3

MusicFirst Academy. (2026). AI for music educators. [Online course]. https://www.musicfirstacademy.com

Type: Domain-specific practitioner PD Primary RQ: RQ2

Munce, C. (Host). (2023, May 4). Leveraging artificial intelligence in music education with ChatGPT (Ep. 147). Choralosophy. https://choralosophy.com/2023/05/04/episode-147-leveraging-artificial-intelligence-in-music-education-with-chatgpt/

Type: Practitioner podcast — choral music education Primary RQ: RQ3

Munce, C. (Host). (2026, June 5). Wrestling with AI in the arts with Beth Philemon (Ep. 291). Choralosophy. https://choralosophy.com/2026/06/05/episode-291-wrestling-with-ai-in-the-arts-with-beth-philemon/

Type: Practitioner podcast — choral music education Primary RQ: RQ2, RQ3

NAfME. (2025, July 29). Guiding principles, frameworks, and applications for AI in music education. National Association for Music Education. https://nafme.org/resource/art-int-and-music-education/

Type: Professional organization policy document Primary RQ: RQ2

NCES. (1994–2000). Internet access in U.S. public schools and classrooms. National Center for Education Statistics.

Type: Federal longitudinal data — K–12 technology infrastructure Primary RQ: RQ1, RQ3

Woodward, M. (2026). AI Adoption in Choral Music Education: Current Landscape, Persistent Barriers, and a Two-Year Outlook. Poster session, TCDA Annual Convention, San Antonio, TX.