2026 ASEE Annual Conference & Exposition

Gen AI is transforming the programming education landscape and has the potential to enhance the student experience when the perception, guidance, and attitudes towards AI align towards the notion of AI for supporting learning as opposed to AI as a tool to make it easier to do assignments and other assessments. We are also seeing that advanced AI models are increasingly accessible and offer significant support, influencing how students engage with learning computer science content, particularly in learning programming concepts and tasks. At the same time, educators are rethinking what and how to teach programming concepts in the age of AI. In this empirical research paper, we investigate how the intersectional identities of students influence their interaction with and perceived effectiveness of AI tools in learning to programming. Specifically, we ask: (1) How does a student’s identity affect their access to, perception of, and attitudes toward AI-assisted programming experiences? (2) Does applying an intersectional identity lens reveal different insights from the same dataset compared to analyses that do not account for identity? To explore these questions, we conducted a study with 50 participants from diverse intersectional backgrounds, recruited from an AI literacy course. The study methodology included a pre-task assessment, a programming task with access to Gen AI (OpenAI GPT 3.5), followed by a reflective survey on students’ experiences with the AI tool. By triangulating data from performance metrics, pre- and post-assessments, and reflection responses, we uncover associations between intersectional identities and’ AI tool usage, task outcomes, and their AI perceptions and attitudes. With a focus on gender and race, our findings suggest that intersectional identities influence both their interactions with and their perceptions of the AI tools. By employing a mixed-methods approach, incorporating both qualitative and quantitative data, this study offers a more comprehensive understanding of the participants’ experiences, attitudes, and perceptions when using AI-assisted programming tools. The study design contributes to the research community by providing a replicable methodology to assess the effectiveness of AI tools in educational settings. Initial findings suggest that identity plays a significant role in shaping students’ experiences with AI tools. The emerging themes offer insight into the potential benefits and limitations of integrating AI tools in programming education. By examining the competency building and cognitive experiences of students using AI tools during programming tasks with an intersectional lens, we can better understand the impact AI tools may play in shaping students’ learning outcomes and attitudes towards programming and AI technologies. We posit that other identity factors, beyond gender and race, are also relevant in considering how students perceive and interact with Gen AI in an educational context. While statistical significance in intersectional analyses remains a challenge due to sample size and variable complexity, the study contributes a robust methodology and valuable insights into how identity influences AI-assisted learning.