Answer: Chain-of-Thought Prompting

## Explanation **Chain-of-Thought Prompting (Option C)** is the most appropriate technique for this scenario because: - **Step-by-step reasoning**: Chain-of-Thought prompting explicitly encourages the AI model to break down its reasoning process into sequential steps, which aligns perfectly with the requirement to explain "how it arrived at its answer step-by-step" - **Transparency**: By forcing the model to articulate its intermediate reasoning steps, this technique provides visibility into the decision-making process, making the AI tutor's responses more transparent and interpretable - **Educational value**: For an AI tutor, showing the step-by-step process helps students understand not just the final answer but also the methodology and logical progression behind it **Why other options are less suitable:** - **Zero-shot Prompting (A)**: The model generates responses without any examples, but doesn't inherently provide step-by-step explanations - **Negative Prompting (B)**: Focuses on telling the model what NOT to do, which doesn't address the need for transparent reasoning - **Few-shot Prompting (D)**: Provides examples to guide responses but doesn't specifically enforce step-by-step reasoning transparency Chain-of-Thought prompting is specifically designed for complex reasoning tasks where showing the intermediate steps is crucial for understanding and verification.

Author: Ritesh Yadav