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In this session, we’ll discuss what LLMs (large language models) are, how they are trained, and what they can reveal about the ways we use language, especially in writing. We’ll also overview some AI platforms used in professional contexts (ChatGPT Plus, Perplexity AI, Popai, Rytr AI). We’ll then discuss critical AI literacy: accountability and awareness of the social, linguistic, ethical, environmental, and economic perils and possibilities of generative AI

Crafting Transparent and Learning-Focused Policies for Generative AI

In this session, we will look at syllabus policies for generative AI that are transparent, specific, and clearly tied to the learning goals of the course, whether instructors permit no use, limited use, or liberal use. We will look at samples from various disciplines and institutions, as well as current guidance for documentation and acknowledgement of generative AI in various style guides (MLA, APA, etc.).

Designing Writing Assignments with Generative AI in Mind

In this session, we will look at how principles of TILT (Transparency in Teaching and Learning) as well as the concept of “use-case” can foster student engagement in writing assignments and discourage inappropriate reliance on generative AI. We’ll provide sample assignments for discussion, and time permitting, workshop assignments with participants.

Responding to Suspicious Writing

In this final session, we will explore response strategies when students submit writing that appears to have been generated by AI (against course policies). We will look at the current state of AI detection tools and consider learning-centered responses to student work we find suspicious.