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AI and Digital Technologies in Sight-Singing and Aural Training: A Systematic Review of Innovations in Higher Music Education

Title: AI and Digital Technologies in Sight-Singing and Aural Training: A Systematic Review of Innovations in Higher Music Education
Language: English
Authors: Zhao-ling Chen (ORCID 0009-0000-2453-1605); He Lin
Source: European Journal of Education. 2026 61(1).
Availability: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Peer Reviewed: Y
Page Count: 16
Publication Date: 2026
Document Type: Journal Articles; Information Analyses; Reports - Research
Descriptors: Artificial Intelligence; Technology Uses in Education; Music Education; Music Reading; Singing; Technology Integration; Intelligent Tutoring Systems; Constructivism (Learning); Blended Learning; Barriers; Algorithms
DOI: 10.1111/ejed.70502
ISSN: 0141-8211; 1465-3435
Abstract: As artificial intelligence (AI) and digital technologies continue to advance, their integration into higher music education reshapes traditional pedagogical approaches, particularly in sight-singing and aural training, core components of musicianship. This systematic review examines empirical studies that explore the application, effectiveness and pedagogical integration of AI and digital tools to enhance these skills in higher education contexts. Following PRISMA guidelines, the review synthesises findings from diverse methodological approaches, ranging from quasi-experimental designs to case studies. It highlights both AI-powered platforms, such as intelligent tutoring systems and adaptive learning environments, and non-AI digital tools, such as EarMaster and MIDI-based systems. Results indicate that interactive and adaptive technologies significantly enhance pitch perception, rhythmic accuracy and melodic dictation when integrated within constructivist and blended learning models. However, critical limitations persist, including accessibility disparities, insufficient teacher training and the limited transparency of AI algorithms. The review identifies key gaps in longitudinal research, expressive skill development and cross-cultural applicability. Findings aim to inform future curriculum design, policy development and innovation in AI-assisted music pedagogy, supporting more inclusive, adaptive and pedagogically grounded approaches to aural and sight-singing instruction.
Abstractor: As Provided
Entry Date: 2026
Accession Number: EJ1497941
Database: ERIC