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Artificial Intelligence + ACRL Frameworks

This libguide explores the integration of generative AI with the Framework for Information Literacy. It examines authority, information creation, value, research inquiry, and scholarship as conversation in relation to generative AI.

Research as Inquiry

Research is iterative and depends upon asking increasingly complex or new questions whose answers in turn develop additional questions or lines of inquiry in any field.

—Framework for Information Literacy for Higher Education, Page 18

Research as inquiry: Comparison

To what does scholarly inquiry lead in each system?​

To what does scholarly inquiry lead in each system?​

Scholarly

Questions may lead to original contributions to the field.​

Generative AI

Questions lead to an expanded understanding of a given topic.​

How does the system break down complex problems?​

How does the system break down complex problems?​

Scholarly

The cognitive load is mostly on the researcher to break the inquiry down into components.​

Generative AI

Generative AI can recognize and point out distinct patterns in compositions on the topic.​

How does each system narrow down the scope of inquiry?​

Scholarly

The cognitive load is mostly on the researcher to identify research opportunities in the field. This is typically done through lit review.​

Generative AI

Generative AI can create concept maps that may suggest new lines of inquiry.​

What is the role of critical analysis in evaluating new lines of inquiry?​

What is the role of critical analysis in evaluating new lines of inquiry?​

Scholarly

Critical reviews deliver contextual information and may expose bias in research processes and the interpretation of data.​

Generative AI

Limited access to context and biased data may limit the critical value of the responses generated by AI.​