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Computer science has historically presented barriers for non-native English speaking (NNES) students, often due to language and terminology challenges. With the rise of large language models (LLMs), there is potential to leverage this technology to support NNES students more effectively. Recent implementations of LLMs as tutors in classrooms have shown promising results. In this study, we deployed an LLM tutor in an accelerated introductory computing course to evaluate its effectiveness specifically for NNES students. Key insights for LLM tutor use are as follows: NNES students signed up for the LLM tutor at a similar rate to native English speakers (NES); NNES students used the system at a lower rate than NES students---to a small effect; NNES students asked significantly more questions in languages other than English compared to NES students, with many of the questions being multilingual by incorporating English programming keywords. Results for views of the LLM tutor are as follows: both NNES and NES students appreciated the LLM tutor for its accessibility, conversational style, and the guardrails put in place to guide users to answers rather than directly providing solutions; NNES students highlighted its approachability as they did not need to communicate in perfect English; NNES students rated help-seeking preferences of online resources higher than NES students; Many NNES students were unfamiliar with computing terminology in their native languages. These results suggest that LLM tutors can be a valuable resource for NNES students in computing, providing tailored support that enhances their learning experience and overcomes language barriers. 

Over the past decades, computer science education (CSEd) research has studied the multitude of factors that may impact student success in introductory programming courses (CS0/CS1). The lack of foundational structure behind how these factors interrelate has made it difficult to gain a thorough understanding of this area of CSEd literature. Gaining a deeper understanding and applying structure to these factors would allow CSEd to adopt better teaching practices, study habits, learning environments, course materials, etc. and to better understand the student experience to better foster success among a broader population of students. Our systematic literature review used search criteria for factors that predicted student success in CS0/CS1, which yielded 311 research articles. We then mapped this body of work under the Biggs' 3P (Presage, Process, Product) educational model, which provides a comprehensive framework for how students engage with learning opportunities. We discovered that although many studies focused on the Presage and Product phases of the model, fewer studies mapped to the Process phase, which describes the students' active learning processes. Our study shows there is a potential gap in the literature and future studies should focus more specifically on how students choose to engage with learning opportunities and what factors may be hindering that engagement throughout a learning period.