AWS Certified Cloud Practitioner

Explanation

Chain-of-Thought (CoT) prompting is the most appropriate technique for this scenario because:

1. Step-by-step reasoning: Chain-of-Thought prompting explicitly asks the AI model to break down its reasoning process into intermediate steps, which aligns perfectly with the requirement for transparency in explaining how the AI tutor arrived at its answer.

2. Transparency: By forcing the model to articulate its thought process, CoT prompting makes the AI's decision-making more interpretable and understandable to users.

3. Educational value: For an AI tutor, showing the step-by-step process helps students learn the methodology and problem-solving approach, not just the final answer.

Why other options are less appropriate:

• Zero-shot Prompting (A): The model is given no examples and must generate an answer directly, which doesn't inherently produce step-by-step explanations.

• Negative Prompting (B): This technique focuses on telling the model what NOT to do, which doesn't address the need for transparent step-by-step reasoning.

• Few-shot Prompting (D): While this provides examples, it doesn't specifically enforce step-by-step reasoning unless the examples themselves demonstrate such reasoning.

Chain-of-Thought prompting has been shown to improve performance on complex reasoning tasks and increase transparency in AI decision-making, making it ideal for educational applications where understanding the process is as important as getting the correct answer.