T13 - AI Integration in College Education

Exploring College Students’ Errors in Derivatives to Construct an Ai-Based Support System for Calculus Courses

This project’s objective is to analyze students’ errors in calculus to train and program a calculus tutoring system. First-year CEGEP students’ (n=14) errors on derivative tasks were analyzed using Siyepu’s (2013) framework. The analysis revealed several errors regarding the application of derivative rules (e.g., power rule and chain rule). With these findings, the AI-based tutoring system will be programmed to analyze students’ answers, detect errors and offer strategies to students in problem solving.

Presenters: Jonathan Morcos and Neerusha Baurhoo Gokool

Transparency in AI Integration: Sharing AI-Prompts to Enhance Learning

The integration of artificial intelligence (AI) in teaching materials often remains unseen by students. This proposal suggests that educators should share the AI prompts with students to enhance transparency. A survey revealed that 78-85% of students are unaware of AI’s role in their course materials. Making the AI interaction process visible to students can significantly benefit their understanding of content origins and encourage critical thinking.

Presenter: Bobby Connolly

Learning Groups With Professionals in a Community of Practice to Apprehend the Emergence of AI: A Case Study of the Model at the Réseau Reptic

We delve into the process of establishing and managing learning groups within the Community of Practice (CoP) of Réseau REPTIC, at the Fédération des cégeps. Drawing from a real-world example—our first AI learning group—we explore each of the five steps in the annual cycle. Our discussion encompasses the context that led to the development of this framework, an analysis of its advantages and disadvantages, and an examination of its suitability for various participants.

Presenters: Marco Guilbault and Nathalie Bastien