CodeHelp: Using Large Language Models with Guardrails for Scalable Support in Programming Classes (2308.06921v1)

Abstract: Computing educators face significant challenges in providing timely support to students, especially in large class settings. LLMs have emerged recently and show great promise for providing on-demand help at a large scale, but there are concerns that students may over-rely on the outputs produced by these models. In this paper, we introduce CodeHelp, a novel LLM-powered tool designed with guardrails to provide on-demand assistance to programming students without directly revealing solutions. We detail the design of the tool, which incorporates a number of useful features for instructors, and elaborate on the pipeline of prompting strategies we use to ensure generated outputs are suitable for students. To evaluate CodeHelp, we deployed it in a first-year computer and data science course with 52 students and collected student interactions over a 12-week period. We examine students' usage patterns and perceptions of the tool, and we report reflections from the course instructor and a series of recommendations for classroom use. Our findings suggest that CodeHelp is well-received by students who especially value its availability and help with resolving errors, and that for instructors it is easy to deploy and complements, rather than replaces, the support that they provide to students.

Overview of CodeHelp: Using LLMs with Guardrails for Scalable Support in Programming Classes

The paper "CodeHelp: Using LLMs with Guardrails for Scalable Support in Programming Classes" introduces a novel tool aimed at addressing the challenges faced by educators in providing timely, scalable support to students in large programming classes. With the increasing use of LLMs in educational settings, the authors present CodeHelp as a solution leveraging LLMs to offer on-demand assistance while incorporating "guardrails" to prevent over-reliance on the automated system by students.

Tool Design and Implementation

The design of CodeHelp integrates LLMs with specific strategies to ensure educational assistance without providing direct solutions. The tool is structured with the ability to intercept and mediate the LLM-generated outputs through a systematic pipeline of prompting strategies. The guardrails are a central feature of anonreview, as they attempt to address concerns around students relying heavily on LLMs by guiding them to develop their problem-solving skills rather than just furnishing them with complete answers.

The authors describe the deployment of CodeHelp in a first-year college computer and data science course. The deployment with 52 students allowed for practical evaluation over a 12-week period, focusing on usage patterns, student perceptions, and instructor feedback. The implementation capitalizes on LLMs' ability to generate resources dynamically, offering potential solutions that help students resolve errors and develop a deeper understanding without directly displaying solutions.

Evaluation and Findings

The paper reports on empirical findings from the deployment of CodeHelp and highlights students' positive reception due to its availability and error-resolving capabilities. A significant takeaway is the tool's adaptability and reliability, which facilitated an engaging learning environment while being straightforward for instructors to deploy. The tool complements traditional teaching methods rather than replacing them, effectively broadening student support mechanisms.

Implications and Future Work

The development and findings of CodeHelp hold both practical and theoretical implications. Practically, the tool showcases the potential of LLMs to transform educational support systems, especially in addressing large-scale instructional challenges. Theoretically, the research underscores the necessity of integrating checks (or guardrails) in LLM applications within educational contexts to ensure their responsible deployment.

Looking ahead, the future of AI in educational support is promising. Further research might involve refining the prompting strategies and guardrails to improve the accuracy and appropriateness of LLM-generated educational content across various contexts. Future developments could explore personalized adaptive LLM responses based on individual student needs, thereby enhancing the scope and impact of AI-driven educational tools.

In summary, the paper outlines a thoughtful approach to harnessing LLMs' potential in educational settings while addressing the risks associated with their use. CodeHelp exemplifies an innovative step towards integrating AI responsibly within computer science education, paving the way for further advancements in scalable and intelligent student support systems.