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Navigating AI Literacy

An Open Educational Resource Exploring the Relationship between Generative AI, Writing, and Pedagogy

Welcome! This space is designed to capture concepts, thoughts, and ideas related to generative AI and multimodal pedagogies.

 

Multimodality is a popular term in higher education, though the promise of multimodality and reaching that promise are different things.

This digital space is designed to grapple with questions about generative AI and multimodality like:

How can people work with AI to meet the demands of the knowledge making situation?

This site intends to highlight important dimensions of scholarship on AI, education, literacy, and writing and provide a pathways to better, more intentional AI use that supports writing, communication, and learning.


If you have questions or want to talk generative AI and multimodal pedagogies, please email chris.eaton@utoronto.ca.

About

Special thanks is due to my Educational Developer colleagues Amanda Brijmohan, Rob Huang, Ann Gagne, and Dianne Ashbourne. Their insights on generative AI and pedagogy are always valued and have greatly informed this resource.
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How to use this Site

1. Links

This site will have links to other pages and documents that are helpful to supplement the text. 

2. Audio + Transcripts

Select sections and pages will have audio clips accompanying them to define key terms, expand on concepts, and enhance accessibility.

3. Visuals

Certain concepts or key terms will be visualized on the page to aid in the explanation and application of the knowledge learned.

Common Assumptions about AI

These assumptions guide the website’s design, its pages, and should guide its use.

The person consulting this space is using AI or is interested in using AI to support knowledge making.

Artificial intelligence is and will continue to influence knowledge making processes.

Human agency and oversight are integral to using AI effectively.

This space will be a starting point for thinking about the intersection of AI and knowledge making.

Pages

01.

Building Knowledge & AI

03.

Evaluating AI Output

05.

Reading with AI

02.

Human & Machine in the Loop

04.

Rhetoric & AI

06.

Accounting for Bias

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