#inquiry-based-learning

Honoria: I am trying to help my college students work with AI as collaborators, especially in brainstorming & ideation phases of creative work. Do you advise students to routinely ask the AI about the reasoning systems it is basing advice and ideas upon?

Gemini: That's a fantastic and forward-thinking approach to integrating AI into creative education! Yes, I absolutely advise students to routinely ask the AI about the reasoning systems it is basing its advice and ideas upon. This practice fosters several crucial skills and a deeper understanding of AI as a tool:

Here's why it's so important:

Demystifying the "Black Box": AI can often feel like a magical entity dispensing wisdom. By asking about its reasoning, students begin to understand that AI operates based on algorithms, data, and patterns. This demystification is crucial for moving beyond simply accepting AI output and towards critical engagement.  

Understanding Limitations and Biases: Knowing the underlying systems can reveal potential limitations and biases in the AI's suggestions. For example, if an AI is trained primarily on Western art history, its brainstorming might be skewed. Understanding this helps students contextualize the AI's input and avoid blindly adopting potentially narrow perspectives.

Developing Critical Thinking Skills: Asking "why?" encourages students to apply critical thinking to the AI's contributions. They learn to evaluate the rationale behind suggestions, identify assumptions, and determine the relevance and usefulness of the AI's input for their specific creative goals.  

Fostering a Collaborative Mindset: By understanding the AI's "thinking," students can engage in a more informed dialogue. Instead of just receiving ideas, they can challenge assumptions, ask for alternative perspectives based on different reasoning, and guide the AI towards more fruitful avenues of exploration. This truly transforms the interaction into a collaboration.

Learning About AI as a Tool: This practice provides students with valuable insights into how AI works, its strengths, and its weaknesses. This knowledge is increasingly important for anyone working in creative fields, as AI tools become more prevalent. It empowers them to be informed users and even potential future developers of such technologies.

Encouraging Experimentation: Understanding the reasoning can inspire students to experiment with different prompts and parameters to see how they influence the AI's output. They can learn to "steer" the AI more effectively by understanding its underlying logic.

How students can ask about reasoning:

Directly ask: "Could you explain the reasoning behind these brainstorming ideas?" or "What data or principles are you drawing upon to suggest this approach?"

Request alternatives based on different reasoning: "Can you suggest some alternative ideas based on a different aesthetic principle or a different cultural context?"

Challenge assumptions: "You suggested this color palette. What are the underlying associations or theories that inform this suggestion? Are there other possibilities based on different associations?"

Inquire about the training data (if the AI can provide general information): "Can you tell me generally about the types of data you were trained on that might influence your suggestions in this area?"