Work

Beyond One-Size-Fits-All Exercises: Personalizing Computer Science Worksheets with Large Language Models

Year: 2026

Type: conference-paper

Abstract: Motivation: Large Language Models (LLMs) have been widely applied to student-facing educational tools, this work explores their use in supporting instructors by presenting a practical adaptation of the Framework for Adaptive Content using Educational Technology (FACET) system to generate personalized instructional materials for an Introduction to Computer Programming (CS1) course.

Source: ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE)

DOI: 10.1145/3803400.3809330

Authors: Franco Ortiz, Runlong Ye, Michael Liut

Institution: University of Toronto

Topic: Intelligent Tutoring Systems and Adaptive Learning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: hybrid

Funders: University of Toronto, Natural Sciences and Engineering Research Council of Canada

Awards: G2705426481, G720524644