European research on art education increasingly emphasises creativity, aesthetic experience, inclusion, and digital transformation as central dimensions of higher education (Bamford, 2006; Winner, Goldstein, & Vincent-Lancrin, 2013). In parallel, artificial intelligence (AI) has emerged as a powerful yet contested force in education, raising critical questions regarding creativity, authorship, and the role of technology in artistic and pedagogical practices (Holmes et al., 2019). Despite growing interest in AI in education, its potential as an arts-based pedagogical mediator remains underexplored within art education research.

Music occupies a significant position within European traditions of art education due to its embodied, emotional, and multimodal nature (Bresler, 2007). In foreign language education, songs have been shown to support vocabulary acquisition, listening comprehension, pronunciation, and affective engagement (Ludke, Ferreira, & Overy, 2014). From a sociocultural perspective, musical activities can lower affective filters and promote meaningful participation in language learning (Krashen, 1985). However, instructors in multilingual higher education contexts frequently encounter limitations when attempting to integrate music into language curricula, as existing songs rarely align with CEFR-based learning objectives or specific linguistic content.

Recent advances in generative AI offer new opportunities to address these challenges. AI tools such as ChatGPT, Suno, and Udio enable the creation of original songs tailored to pedagogical goals, learner levels, and curricular requirements. From an art education perspective, such tools invite reconsideration of creative agency and human–AI collaboration within post-digital learning environments (Knochel & Patton, 2015).

Although the empirical study was conducted in Kazakhstan, it is conceptually framed within European educational and art education discourses. Kazakhstan represents a multilingual higher education context comparable to many European systems in terms of language diversity, digitalisation, and competency-based curricula. The study therefore addresses shared European challenges and contributes transferable insights to research on arts-based pedagogy in higher education.

The study employed a mixed-methods, arts-informed research design consistent with methodological traditions in European art education research (Leavy, 2015). An AI-supported curriculum integrating AI-generated songs was implemented over one academic semester in undergraduate foreign language courses. For each instructional unit, original song lyrics were generated using ChatGPT, embedding target vocabulary, grammatical structures, and discourse patterns aligned with CEFR-based learning outcomes. These lyrics were subsequently transformed into complete musical compositions using AI music-generation platforms such as Suno and Udio. The resulting songs were conceptualised as artistic artefacts rather than supplementary teaching materials. Pedagogical activities reflected arts-based education principles and included guided listening, collective singing, rhythmic and phonetic analysis, interpretive dialogue, and reflective tasks addressing emotional and aesthetic responses. Students were encouraged to engage critically with the songs as creative products, discussing their artistic qualities, linguistic form, and the role of AI in creative production. Such practices align with experiential and interpretive approaches central to art education (Eisner, 2002). The programme was implemented during the 2024/25 academic year with approximately 90 undergraduate students from diverse academic disciplines. Experimental groups engaged with AI-generated songs, while control groups followed conventional language instruction. Quantitative data were collected through pre- and post-tests assessing vocabulary acquisition, listening comprehension, and grammatical accuracy. Qualitative data included questionnaires, reflective journals, classroom observations, and focus group interviews. Data analysis combined statistical methods with thematic analysis, enabling triangulation of cognitive and aesthetic learning outcomes.

Quantitative findings indicate that students in the experimental groups achieved significantly higher learning gains than those in the control groups. Improvements in vocabulary acquisition and listening comprehension were substantially greater in the AI-song condition, with statistically significant differences (p < .05). These results align with previous research demonstrating the effectiveness of music-based and multimodal learning in language education (Ludke et al., 2014; Paquette & Rieg, 2008). Qualitative data further highlight the aesthetic and affective dimensions of the learning experience. Students reported increased motivation, reduced language anxiety, and greater confidence in language use. The collective musical activities fostered inclusive participation, particularly benefiting students with lower initial proficiency levels. Such findings resonate with European educational priorities related to inclusion, learner well-being, and student-centred pedagogy (European Commission, 2020). In addition, students engaged critically with issues of creativity and authorship, reflecting on AI as a collaborator rather than a replacement for human creativity. These reflections align with contemporary European debates on post-digital art education and creative technologies (Knochel & Patton, 2015). Minor linguistic inaccuracies in AI-generated lyrics were identified but were addressed pedagogically, often serving as opportunities for critical language analysis and collaborative learning. Overall, both students and instructors evaluated the approach as innovative, artistically meaningful, and pedagogically effective, supporting its relevance for arts-based learning in higher education.

Bamford, A. (2006). The wow factor: Global research compendium on the impact of the arts in education. Waxmann. Bresler, L. (2007). International trends in music education. International Journal of Music Education, 25(1), 8–18. https://doi.org/10.1177/0255761407074891 Eisner, E. W. (2002). The arts and the creation of mind. Yale University Press. European Commission. (2020). Digital education action plan 2021–2027. https://education.ec.europa.eu Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign. Knochel, A. D., & Patton, R. M. (2015). If art education then critical digital making. Studies in Art Education, 56(3), 281–295. https://doi.org/10.1080/00393541.2015.11518974 Krashen, S. D. (1985). The input hypothesis: Issues and implications. Longman. Leavy, P. (2015). Method meets art: Arts-based research practice (2nd ed.). Guilford Press. Ludke, K. M., Ferreira, F., & Overy, K. (2014). Singing can facilitate foreign language learning. Memory & Cognition, 42(1), 41–52. https://doi.org/10.3758/s13421-013-0342-5 Paquette, K. R., & Rieg, S. A. (2008). Using music to support the literacy development of young English language learners. Early Childhood Education Journal, 36, 227–232. https://doi.org/10.1007/s10643-008-0277-1 Winner, E., Goldstein, T. R., & Vincent-Lancrin, S. (2013). Art for art’s sake? The impact of arts education. OECD Publishing.