This study explored how AI-assisted coding (“vibe coding”) is experienced by students within Computational Arts pedagogy, with particular attention to learning, confidence, creativity, and inclusivity. Two contrasting participant accounts offer insight into how prior experience and timing of exposure shape these outcomes. Participant A is a current Year 1 student (2025–26), encountering vibe coding at the very start of their higher education. Participant B is a recent graduate who began their studies in 2021–22, prior to the widespread adoption of AI-assisted coding, and later integrated these tools into their practice.
A key difference between the participants concerns initial confidence and comprehension. Participant A, with no prior experience of writing code, described early use of ChatGPT as alienating: autogenerated code “didn’t really mean anything” and reduced rather than increased confidence. This sense of opacity prompted the student to move away from ChatGPT and use Google Gemini’s Guided Learning feature, which supported understanding and increased confidence when asked questions. As Participant A reflected, this reduced the anxiety of producing work they could not explain, highlighting a strong ethical orientation towards authorship and responsibility.
By contrast, Participant B reported a largely positive trajectory, underpinned by existing coding literacy developed before the emergence of vibe coding. Having already internalised foundational programming logic, they used ChatGPT conversationally to extend their practice, moving from “janky” single-script programs towards modular systems with functions, multiple files, and classes. Participant B described this as helping them overcome a learning plateau, increasing both confidence and ambition: they felt capable of developing more complex work than they would previously have attempted.
Both participants emphasised the importance of understanding code beyond AI output, though for different reasons. Participant A argued that creative ownership requires knowing “what I am writing and why,” likening coding to traditional artistic mastery. Participant B framed foundational knowledge as necessary for maintaining creative control, diagnosing problems, and preventing AI from imposing its own aesthetic or structural logic. In both cases, AI was positioned not as a replacement for learning, but as something that must be carefully integrated into the learning process.
In terms of creativity and accessibility, both accounts suggest that vibe coding lowers barriers to realising code-based ideas. Participant A described AI as a catalyst for ideation, enabling the recombination of “mediocre” suggestions into personally meaningful outcomes. Participant B described a shift from starting with technical feasibility (“what can I do?”) to starting with conceptual intent (“what do I want to make?”), which they identified as transformative for their artistic practice. However, Participant B also noted the risk of distraction, overproduction, and aesthetic homogenisation without clear creative guardrails.
Taken together, these findings suggest that vibe coding can enhance inclusivity and creative ambition, but that its pedagogical value is highly contingent on timing, prior experience, and framing. For novice students, unmediated AI-generated code risks undermining confidence and understanding, whereas guided, reflective use may support learning. For more experienced students, vibe coding appears to function as an accelerant rather than a shortcut. These contrasts underscore the need for intentional pedagogical strategies that treat AI-assisted coding not as neutral infrastructure, but as a situated learning tool requiring critical and ethical engagement.