Potential biases in the training data for Khanmigo, Khan Academy’s AI tool, include inaccuracies and systemic biases inherent in large language models. To overcome these, Khan Academy employs several strategies. First, they conduct rigorous testing known as red teaming with OpenAI, where they intentionally induce biases to identify and correct them. They also use specific instructional prompts to guide the AI in producing unbiased, high-quality outputs. Additionally, the AI links to Khan Academy’s extensive library of verified human-generated content, ensuring accuracy. By continuously refining the AI based on teacher feedback and using supplementary tools for precise tasks like math calculations, Khan Academy significantly mitigates potential biases in Khanmigo’s responses.