TopicShare: An AI and ChatGPT-Based Data Sharing Framework on Teaching Content in Higher Education

Abstract

In higher education, information asymmetry significantly affects instructional efficacy and curriculum development. Syllabi are meant to clearly guide students and instructors, often confuse due to unclear course content. This leads to redundant material coverage and missed opportunities for new content, impacting learning and operational efficiency. Our study introduces a novel framework to improve syllabus data sharing among higher education stakeholders. By using the advanced capabilities of ChatGPT and AI, our framework named “Top-icShare” analyzes syllabi to extract essential teaching content, such as course topics, prerequisites, and textbooks. The extracted data is then presented through an intuitive data visualization interface, enhancing the comprehensibility and accessibility of syllabus content. We implemented our framework as a pilot within the Decision and System Sciences Department at a US-based university. This implementation served primarily to test the feasibility of the framework by creating a visualization platform and successfully extracting accurate information from syllabi. The pilot demonstrated that our framework significantly enhances the clarity and usability of syllabi, providing faculty with actionable insights that help refine their courses. These results affirm that our approach can help faculties share learning content, thereby promoting a more collaborative and efficient educational environment. The experiments' data visualization results can be found at: Data Visualization Page

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