Here’s a quick breakdown of how the ACRL Framework can be used to frame discussions around AI tools like ChatGPT:
1. Authority Is Constructed and Contextual
- AI Lens: GPTs generate responses based on patterns in data, not on expertise or credentials. This challenges traditional notions of authority.
- Teaching Point: Encourage users to question the authority of AI-generated content and compare it with scholarly sources.
2. Information Creation as a Process
- AI Lens: GPTs create information dynamically, not through peer review or editorial processes.
- Teaching Point: Highlight the differences between AI-generated content, scholarly articles, and crowdsourced platforms like Wikipedia.
3. Information Has Value
- AI Lens: GPTs are trained on vast datasets, often without compensation to original creators. There are also ethical and economic implications in how AI-generated content is used.
- Teaching Point: Discuss intellectual property, data privacy, and the monetization of AI tools.
4. Research as Inquiry
- AI Lens: GPTs can support inquiry by helping brainstorm, summarize, or reframe questions—but they don’t replace critical thinking or deep research.
- Teaching Point: Use GPTs as a starting point, not an endpoint, in the research process.
5. Scholarship as Conversation
- AI Lens: GPTs can simulate conversation but don’t participate in scholarly discourse. They can, however, help users engage with it more effectively.
- Teaching Point: Emphasize the importance of engaging with real scholarly voices and using AI to support—not replace—dialogue.
6. Searching as Strategic Exploration
- AI Lens: GPTs can help refine search strategies, suggest keywords, or summarize sources, but they don’t access real-time databases or peer-reviewed journals.
- Teaching Point: Teach students to use AI tools alongside library databases and search engines for a more comprehensive research strategy.
