ETEC 500 #2: Compare Contradictory Research Studies
Upon browsing the “Most Read” category on the publisher’s website for the Music Educators Journal, I stumbled across two different articles on Artificial Intelligence; one being about implications and the other about practical application. Being intrigued, I then searched “artificial intelligence” directly in the Music Educators Journal with the filters “research articles” and “2022–2025.” The second result was: M. Guarriello’s “Idea Bank: Exploring Applications of Artificial Intelligence in Music Education: A Focus on Creativity and Composition” (2024). I felt that the optimistic and pro-technology stance offered in this study would make for an interesting comparison to that of another article.
I found my second article by going to Google Scholar and searching “challenges AI Music Education,” through which I found L. Cheng’s “The Impact of Generative AI on School Music Education: Challenges and Recommendations” (2025) as the third search result. I thought that these two articles and their differing attitudes towards implementing artificial intelligence in the context of music education may provide an insightful contrast of opinion and research for the task of a comparative analysis.
Where you find the research matters
Right off the bat, it is evident through reading both articles that there seems to be inherently different pre-conceived outlooks and objectives both researchers take towards exploring this topic of AI being utilized in music education. One could say that the location in which these journals appear is somewhat indicative of the nature of the findings and the questions the research poses. For instance, the focus of Guarriello’s research takes an experimental but practical approach to the usage of this technology as she may intend to share her findings with a particular audience (ie. music educators via the Music Educators Journal). Comparatively, the focus of Cheng’s work, found in the Arts Education Policy Review journal, may appear to be catered towards music curriculum designers, or policy-makers seeking to understand how to approach AI through an equitable and ethical lens.
Through each of their respective findings, it became clear to me that both researchers imbued different philosophies and mindsets towards the research conducted and their intended outcomes. Guarriello, who went on to compare music creativity and generative AI to that of entrepreneurship in her research (p. 12), seems to root her pedagogical perspective towards creative output, whereas Cheng’s research around AI technologies in music education appears more cautionary and primed towards cultural complexities and content creation.
Creativity or Standardization
One perspective Guarriello takes towards using AI for creative fodder was that it “spits out not just one idea but rather several for students to browse through and think about. The hope is that AI sparks ideas and wonderments…” (Guarriello, 2024, p. 14), thereby helping students overcome creative obstacles that students often face at the start of projects. In her words, “getting a little push about what to do next is really helpful. . . and overcomes those barriers. . . ” (Guarriello, 2024, p. 11). As an example of this, she shares a prompt to generate “10 ideas for [solo violin] pieces I can write” (Guarriello, 2024, p. 12), from the AI in order to evaluate its capacity to provide creative inspiration as if the technology was a collaborative compositional partner.
In opposition, Cheng cites that using generative AI may “often overwhelm users with frustrating, non-deterministic outputs” (Cheng, 2025, p. 3), potentially leading to a lack of creative confidence in students or superficial engagement. As “many training datasets for generative AI models . . . are predominantly based on Western sources. . . . [the] content is heavily biased toward Western music (Barenboim et al., 2024), resulting in an over-representation of Western cultures and values (Karpouzis, 2024) . . . [thus] leading to a reduction in the collective diversity of novel content (Epstein & Hertzmann, 2023)” (as cited by Cheng, 2025, p. 2).
While the findings of both journals were not directly contrasting in regard to the conceived potential and functionality of generative AI technology, I found the differing tone of both researchers’ positions interesting to compare, as they both illuminated the importance of contextualizing research findings. It seems that in the end, both interpretations provided unique considerations that ultimately were “suggesting that educators embrace and navigate a collaborative relationship in which human creativity intertwines with digital capabilities” (Guarriello, 2024, p. 14), and “that the integration of generative AI should not be viewed as a threat but an open-ended opportunity for music education” (Cheng, 2025, p. 6).
