A syllabus is a roadmap for instructors and students to successfully navigate and complete a course. A well-developed intelligent system for checking syllabus coherence and completeness can significantly enhance curriculum design and overall educational quality. Numerous educational research emphasize that a proper syllabus should contain several key components, including instructor contacts, course information, course description, course objectives, course outline or schedule, course requirements, alignment with program outcomes, course evaluation methods, prerequisites, required materials, grading scale, institutional policies, and additional course materials. However, despite the importance of these elements, many syllabi in practice suffer from inconsistencies, incompleteness, or lack of alignment with institutional and accreditation standards. These shortcomings can diminish the overall quality of education and lead to disengagement among students. Based on our literature review and to the best of our knowledge, there is currently no standardized automated tool available to evaluate whether a specific course syllabus meets these essential criteria. In this study, we propose a Natural Language Processing (NLP) technique that extracts and interprets the textual content of a course syllabus, identifies key components, and compares them against standardized syllabus templates. The system calculates a completeness score for the syllabus and highlights components that are either incomplete or missing. The system is currently in development and has shown initial success in detecting structural components within sample syllabi. The goal is to establish an automated, intelligent tool that benefits educators and academic institutions by supporting optimized curriculum structuring that aligns with educational standards and learning objectives.

Authors:
Md Nour Hossain, University at Albany, United States
Nabila Ayman, University at Albany, United States

About the Presenter(s)
Nabila Ayman is a Ph.D. student in information science at University at Albnay. Her research interest lies in development of education technologies with the integration AI and ML.

Connect on Linkedin
https://www.linkedin.com/nisat.07

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