Junior high school student perspectives on the use of ChatGPT in music education

Synthesis: Weng and Chiang asked 508 Taiwanese junior high school students what shaped their attitude toward using ChatGPT for lyric learning, and answered by welding experiential marketing (XM) — Sense, Feel, Think, Act and Relate — onto the Technology Acceptance Model. Structural equation modeling shows the integrated model explaining 53% of the variance in attitude toward use and 63.4% in perceived enjoyment, with XM → PEOU the strongest path (Std. β = 0.630, p < 0.001). Enjoyment mediated the link from experience to attitude (PE → ATU, Std. β = 0.369, p < 0.001), and intention to use followed attitude (b = 0.716, p < 0.001). The paper argues that rational acceptance constructs under-explain emotion-driven coursework, so emotional involvement and prompt design are instructional variables rather than backdrop. It is cross-sectional and single-region, and never names the ChatGPT model version students used, so the emotional mechanism is well-evidenced while causal and technical claims stay modest.

Key Findings

The integrated XM–TAM model explains 53% of the variance in students' attitudes toward adopting ChatGPT for lyric learning, with XM → PEOU the strongest path (Std. β = 0.630, p < 0.001).
XM and PEOU jointly explain 63.4% of the variance in perceived enjoyment, and PE predicts attitude toward use (Std. β = 0.369, p < 0.001).
PU, jointly explained by XM and PEOU with R² = 0.384, significantly affects ATU (b = 0.318, p < 0.001); ATU then predicts intention to use (b = 0.716, p < 0.001).
The intervention was four procedures over 150 min — prompt demonstration, generation and comparison, rewriting and personalization, sharing and feedback — mapping onto Feel, Think, Sense/Act and Relate.
Data came from 508 valid responses to 550 questionnaires (92.36%); female students were 59.06% of the sample and most respondents were 15 years old (322, 63.39%).
Most respondents used the free version of ChatGPT (414, 81.5%) while 94 (18.50%) used the paid version; the highest item mean was 4.42 and the lowest 3.58.

Acceptance as an affective process

The paper's move is to argue that the Technology Acceptance Model is a rational account of adoption, emphasizing perceived usefulness and ease of use, while music coursework is grounded in emotional resonance. Experiential marketing supplies the complement: Sense, Feel, Think, Act and Relate operationalize "learning experience" as something measurable, grounded in Kolb's experiential learning cycle. The argument rests on mediation, not a direct effect. The study reports the direct XM → ATU path as non-significant (p = 0.191), concluding that experiential appeal works through enjoyment and usefulness. One inconsistency deserves note: the paper also reports ATU as significantly affected by XM (b = 0.125, p = 0.046), so mediation is the headline but the coefficients do not fully agree with it.

Designing the lyric-learning session

For replication, the intervention was four procedures over 150 min. Step 1 modeled structured prompts for lyric generation, targeting emotional engagement (Feel). Step 2 had students produce three distinct lyric versions under specified instructions, prompting comparative analysis (Think). Step 3 had students revise lyrics to incorporate personal experience (Sense and Act). Step 4 had groups exchange brief feedback on emotional resonance and linguistic fluency (Relate). All constructs were assessed immediately after the session, keeping responses tied to authentic instruction and limiting memory-related bias. The single-session design means reported attitudes reflect one encounter with the tool, not a sustained course.

How the measurement holds up, and what it does not settle

Confirmatory factor analysis under maximum likelihood estimation, followed by a second-order CFA, produced composite reliability from 0.796 to 0.903 and average variance extracted from 0.557 to 0.691, with loadings of 0.616 to 0.943. Fit after the Bollen–Stine correction was strong: normed χ2 of 1.094, RMSEA of 0.014, CFI and TLI of 0.997. The instrument behaves well, which is why this page carries medium confidence; the ceiling comes from the design and from under-specified technology. No ChatGPT model version is named, 81.5% of students used the free version, the collection window is only "a three-month period", and the outcome is intention rather than behavior.

What this means for practice

Instructors. Design the emotional arc of the lesson before the technical demonstration: the strongest path (XM → PEOU, β = 0.630) links experiential appeal to ease of use.
Instructors. Run the session as staged work rather than open access, since the paper credits each stage with a distinct experiential dimension.
Instructors. Treat enjoyment as a learning signal, not a distraction: PE carried the strongest mediation to attitude.
Curriculum designers. Budget for access equity, because 81.5% of this sample was on the free tier.
Researchers. Record the model and version, session dates, and access tier, none recoverable here.

Limitations

The design is cross-sectional, which the authors say captures covariate relationships but does not allow precise causal inference among constructs.
The data are single-source self-reports, and the outcome is behavioral intention rather than actual usage, which the authors note may be constrained by contextual or policy factors.
The sample came mainly from junior high school students in a single country or region, which the authors say limits generalizability to other cultural contexts or educational systems.
The ChatGPT model version is never specified and the collection window is only "a three-month period"; with 81.5% of students on the free version, reported enjoyment depends on undated tool capability.

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Citation

Weng, S.-S., & Chiang, H.-C. (2026). Junior high school student perspectives on the use of ChatGPT in music education. Computers and Education Open, 11, 100387.

Metadata

Study design	Structural Equation Modeling, Survey
Discipline	Music Education
Level	K 12
Intended audience	Instructors
Learning and instruction	Motivation, Student Engagement, Experiential Learning
Technologies and techniques	Technology Adoption Models, Generative AI, Conversational AI
Assessment and measurement	Self-Report Measures
Research methods and evaluation	Quantitative Research
People	Student Experience
Foundations of AI in education	Theories and Frameworks

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