Zhang, Hanlin; Feng, Yifan; Du, Yishan; Rode, Jennifer A; (2025) Gendering Prompts: The Role of Gender in AI-assisted Creative Programming. In: (Proceedings) The Sixth Decennial Aarhus Conference (AARHUS’25): The End of Programming (as we know it) Workshop. ACM (In press).

Text AIProgrammer_Decennial_HCI_25_v2.pdf - Accepted Version Access restricted to UCL open access staff until 2 January 2026. Download (399kB)

Abstract

Recent research highlights the growing integration of code-generation AI in both traditional computing and Creative Computing—an emerging field that blends computation with creativity and actively promotes gender inclusivity in STEM. However, it remains unclear whether such AI tools support or undermine the inclusivity and equity goals central to Creative Computing. Thus, this position paper addresses this gap by examining how AI coding assistance may interact with and potentially influence the gender gap. We synthesize literature on AI-assisted programming and gender bias in AI, and propose a research agenda to explore how AI interaction patterns, such as prompting strategies and critical evaluation of AI-generated solutions, may impact users’ self-efficacy and overall experiences. We argue that without an intersectional approach, AI coding assistance risks perpetuating existing gender inequities-particularly by undermining underrepresented gender minority groups' confidence and participation in creative computing.

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