# Leveraging LLM Tutoring Systems for Non-Native English Speakers in Introductory CS Courses

arXiv: https://arxiv.org/abs/2411.02725 · HTML: https://mlsanity.com/paper/2411.02725/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Nov 2024.

## Built on by (most cited first, 8 of 8)

- [CoPL: Collaborative Preference Learning for Personalizing LLMs](https://mlsanity.com/paper/2503.01658/) (2025)
- [LLMs as Educational Analysts: Transforming Multimodal Data Traces into Actionable Reading Assessment Reports](https://mlsanity.com/paper/2503.02099/) (2025)
- [Breaking the Programming Language Barrier: Multilingual Prompting to Empower Non-Native English Learners](https://mlsanity.com/paper/2412.12800/) (2024)
- [GuideAI: A Real-time Personalized Learning Solution with Adaptive Interventions](https://mlsanity.com/paper/2601.20402/) (2026)
- [Neural-Symbolic Knowledge Tracing: Injecting Educational Knowledge into Deep Learning for Responsible Learner Modelling](https://mlsanity.com/paper/2604.08263/) (2026)
- [Designing Gaze Analytics for ELA Instruction: A User-Centered Dashboard with Conversational AI Support](https://mlsanity.com/paper/2509.03741/) (2025)
- [Bringing Multi-Modal Multi-Task Federated Foundation Models to Education Domain: Prospects and Challenges](https://mlsanity.com/paper/2509.07946/) (2025)
- [Large Models for Small Devices: Recent Advances and Empirical Analysis of Edge AI Deployment](https://mlsanity.com/paper/2608.15693/) (2026)

## Abstract (arXiv)

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.