Author Information
Lauren Miller, The University of Queensland, AustraliaFelix Egger, The University of Queensland, Australia
Aneesha Bakharia, The University of Queensland, Australia
Abstract
Programming education across STEM disciplines faces significant institutional and pedagogical barriers, including student identity conflicts, syntax knowledge gaps, and limited faculty support for interdisciplinary work. This integrative literature review examines how generative artificial intelligence (GenAI) tools can address cross-disciplinary programming barriers while meeting diverse disciplinary learning needs. From experimental research (2018-2025), we identified key challenges that particularly affect non-CS students, including programming self-efficacy barriers and overwhelming syntax requirements. Our findings reveal that GenAI tools function as sophisticated low-code programming environments, significantly increasing programming interest in students by enabling natural language interactions and reducing debugging anxiety. However, concerns about critical thinking erosion and “one-shot prompting” behaviors highlight the need for scaffolded implementation approaches. Our teaching approach uses discipline-specific content generation, integrated focus on GenAI alongside coding skills, and structured prompting exercises that develop iterative refinement skills. Students begin viewing programming as an important tool rather than separate technical skill, with reduced debugging anxiety and improved computational thinking development. This research emphasizes that while GenAI tools can democratize programming access across disciplines, institutional support, staff collaboration and thoughtful pedagogical integration with metacognitive scaffolding is essential for maintaining learning quality and developing critical thinking alongside technical competencies.
Paper Information
Conference: PCE2025Stream: Design
This paper is part of the PCE2025 Conference Proceedings (View)
Full Paper
View / Download the full paper in a new tab/window
To cite this article:
Miller L., Egger F., & Bakharia A. (2025) AI Collaboration for Programming Education Beyond Computer Science ISSN: 2758-0962 The Paris Conference on Education 2025: Official Conference Proceedings (pp. 843-851) https://doi.org/10.22492/issn.2758-0962.2025.65
To link to this article: https://doi.org/10.22492/issn.2758-0962.2025.65
Comments
Powered by WP LinkPress