Generative AI-Powered Educational Alignment: A Framework for Matching Syllabus Course Topics with Web Description
Year: 2024
Type: conference-paper
Abstract: The application of generative artificial intelligence (GAI) in the educational sector is increasingly gaining attention from researchers. This study explores the congruence between online course descriptions and actual course syllabi to improve course preparation and consistency for instructors. Alignment between course catalog descriptions and actual course content as detailed in the syllabus can lead to learning improvements, student satisfaction, and academic alignment in a program. Our research introduces a novel framework utilizing GAI to systematically evaluates and identifies mismatches and suggests content to close the gap between online course descriptions and syllabus content. We used OpenAI’s ChatGPT to extract key topics from course syllabi and assessed the congruence between results and course description content with embedding methods such as BERT, GPT-2, RoBERTa, and DistilBERT, coupled with cosine similarity metrics. Our framework also integrates an outlier detection algorithm to identify courses with significant misalignments and use GAI applications to refine and enhance course catalog descriptions. This approach helps higher education institutions update course offerings with cutting-edge technology and contributes to curriculum development, helping improve student learning efficiency and course design.
Institution: Saint Joseph's University
Language: English
Subfield: Education
Field: Social Sciences
Domain: Social Sciences
SDG: Quality education
Open Access status: hybrid
OpenAlex ID: W4401408996