The findings from this small-scale study suggest that AI-assisted coding, or “vibe coding,” cannot be assumed to be neither universally positive nor negative towards learning. Instead, its pedagogical impact is shaped by when and how is introduced on an individual basis. The contrasting experiences of the two participants highlight a key issue for Computational Arts education: how the uneven distribution of prior coding competence greatly affects the experience of using powerful AI tools for the first time.
Participant A’s experience points to a potential social justice issue for students without a privileged background knowledge of coding. Encountering vibe coding at the very start of their studies, without foundational coding knowledge or structured guidance, led to confusion and a loss of confidence. AI-generated code appeared opaque and difficult to claim ownership over, raising ethical and creative concerns about authorship and understanding. In this context, vibe coding risked accelerating towards finished assignments without supporting comprehension, producing what felt like progress without learning. A crude analogy is that of a beginner driver being told that they can just take taxis as it will get them to their destination faster. Beginners learning alongside more competent coders could feel further discouraged and lacking confidence if they felt like they needed the AI crutch to fulfil the same assignments.
Participant B’s experience, by contrast, shows how vibe coding can function as an accelerator once a baseline level of competence is established. Having learned to code prior to the availability of AI assistants, Participant B was able to use conversational coding tools to extend their practice, overcome creative plateaus, and shift focus from overcoming technical limitations towards conceptual intent. Here, AI supported learning rather than replacing it.
These findings suggest a clear pedagogical implication: vibe coding should not be treated as neutral nor left for students to discover for themselves. Doing so risks reproducing existing inequalities, where students with prior experience benefit disproportionately, while novices experience anxiety, dependency, or disengagement. An inclusive approach requires designing teaching according to different levels of competence.
One potential intervention is the introduction of early diagnostic activities that assess students’ confidence, prior exposure, and understanding of coding concepts to inform individual support. Alongside this, vibe coding could be introduced in a structured and reflective way. A new priority would be to bring it in first as a learning tool that supports understanding, before introducing more advanced functions that autocomplete assignments, foregrounding understanding over output. From a pedagogical perspective, the teaching team may need to conceptually separate coding as a technical skill, and coding as a creative medium, to not always run ahead with the latter.
Such an approach also responds to wider critiques of AI in education, including those articulated in Current Affairs (Purser, 2025). The article warns of “cognitive debt,” where reliance on AI produces an illusion of engagement while eroding underlying skills. While this critique is persuasive it risks flattening all uses of AI into a single systemic failure and issue for HE. The findings of this study suggest a more nuanced position: the danger lies not in AI itself, but in pedagogical models that either turn a blind eye to AI and leave students discovering it for themselves without support, or models that place too much focus on final outputs and assessment criteria and that are fundamentally ill-equipped to monitor and support developmental trajectories.
In Computational Arts, where learning is already exploratory, non-linear, and practice-based, the challenge is not to ban vibe coding, but to bring about reflection, intentionality, and also to explore the benefits of coding coding without AI support as part of the learning experience. Schoolteacher Carl Haefemeyer makes a useful analogy saying that “[learning] is like weightlifting. You wouldn’t bring a forklift to the gym. The goal isn’t to get the bar up in the air, the goal is to build your muscle by lifting the bar.” (MPR News, 2025).
Ultimately, a more inclusive pedagogy that helps computational artists grapple with vibe coding requires shifting away from its image as a shortcut to that of a software tool to be dissected in class with support and critical reflection built into the curriculum. The field of Software Studies may provide a useful theoretical framework, with authors such as Matthew Fuller (2008) and Lev Manovich (2013) equipping us with the conceptual tools for unpacking complexities of software applications. Additionally, some pedagogical and pastoral attention needs to be placed on preexisting inequalities within student cohorts, as these are only amplified by AI tools, rather than resolved by them.
References
Fuller, M. ed. (2008). Software studies: A lexicon. Mit Press.
Manovich, L. (2013). Software takes command (p. 376). Bloomsbury Academic.
Purser, R. (2025). AI is Destroying the University and Learning Itself. [online] Currentaffairs.org. Available at: https://www.currentaffairs.org/news/ai-is-destroying-the-university-and-learning-itself. [Accessed 8 Dec 2025]
MPR News, (2025). AI in schools: St. Paul teacher says it’s ‘like bringing a forklift to the gym’. [online] MPR News. Available at: https://www.mprnews.org/episode/2025/09/09/ai-in-schools-st-paul-teacher-says-its-like-bringing-a-forklift-to-the-gym# [Accessed 3 Feb. 2026].