Prompt-Engineering Strategies for Minimizing Bias in Large Language Model Outputs: Applications in Computing Education

As large language models (LLMs) increasingly permeate educational applications, concerns about the perpetuation of bias persist. We present our preliminary work on developing prompt-engineering strategies to mitigate bias in content generated by LLMs in computer science (CS) education. This work investigates both empirical insights into fairness-aware prompt formulation and actionable takeaways for educators. We focus on an initial list of prompting strategies for mitigating bias and explore their impact on educational content generation. Recent research has shown the efficacy of prompt-base debiasing [1] as well as the potential disadvantages of using prompts that have not been mitigated for bias, from user dissatisfaction [2] to unsafe outputs [5, 6]. Additionally, a growing body of empirical work points to the idea that certain properties of in-context examples such as flow [7], illustration [3], and order [4] could either improve or derail LLM performance. Our study leverages these findings in the context of generating educational content. The goal is to promote fairness-aware approaches which can be applied to the automated generation of learning materials and the development of LLM-based educational tools. This work also contributes practical insights on prompt-engineering to the evolving curriculum of Ethics in Artificial Intelligence (AI).