Artificial Intelligence in Solfeggio Teaching and Learning: Current Status, Needs, And Lecturer-Guided Priorities
DOI:
https://doi.org/10.65150/EP-jsshrs/V2E11/2026-08Keywords:
artificial intelligence, solfeggio, sight-singing, aural skills, higher music education, formative feedback, personalized practice.Abstract
Background: Solfeggio requires repeated practice in listening, pitch production, rhythmic accuracy, and sight-singing, yet students may receive limited feedback during independent study. Objective: This study investigated teaching and self-practice difficulties, artificial intelligence (AI) readiness, and preferred support functions at Hong Duc University, Vietnam. Methods: A descriptive mixed-methods design used 12 valid lecturer questionnaires, 72 retained student questionnaires from 80 screened, and six semi-structured lecturer interviews collected during 1-7 September 2026. Frequencies, percentages, item-level means on five-point scales, and thematic summaries were examined without inferential testing. Results: Lecturers prioritized AI-assisted pitch practice and recognition (4.75/5) and listening, recording, and error identification (4.67/5). Students gave the strongest priority to exercises matched to individual errors (4.63/5) and recording with basic error detection (4.61/5). Readiness to try AI tools was 4.08/5 among lecturers; students’ readiness to use AI with lecturer guidance was 4.50/5. Concerns about feedback accuracy were substantial in both groups. Conclusion: The findings support a limited, lecturer-supervised role for AI in self-practice and formative feedback. They do not establish the effectiveness of any particular AI system.
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Copyright (c) 2026 Pham Ngoc Dinh (Author)
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