Artificial Intelligence Enhanced Course Content Labelling: Conceptual Model and Testing with University Teachers

12 pages•Published: January 13, 2025

Ville Kivimaki, Sami Hautakangas, Heli Järvelä and Patrik Maltusch

Abstract

This study presents a novel approach by investigating the efficacy of an artificial intelligence (AI) -aided tool, Annif, in generating course-specific keywords for university courses. With the increasing number of university courses, there is a need for an effective method to assist students in navigating and selecting courses based on their interests and job market compatibility. The traditional manual keyword approach, while accurate, can be laborious and time-consuming. AI has the potential to automate this process, but it is crucial for teachers to validate the results to ensure accuracy. This study seeks to explore the potential of AI in this context, addressing two key questions: the ability of AI-generated keywords to establish course connections and the reactions of teachers to the use of AI in keyword generation. The results reveal that the AI tool can provide accurate keywords for about 64% of the courses. While teachers found this approach useful, the study highlights the need for teacher validation to ensure the accuracy and appropriateness of the AI-generated data. Therefore, while AI can significantly contribute to keyword generation, human intervention is still indispensable to maintain its quality in the academic context.

Keyphrases: analytics, artificial intelligence, automation, curriculum mapping, indexing, keyword, metadata generation, semi automated

In: Raimund Vogl, Laurence Desnos, Jean-François Desnos, Spiros Bolis, Lazaros Merakos, Gill Ferrell, Effie Tsili and Manos Roumeliotis (editors). Proceedings of EUNIS 2024 annual congress in Athens, vol 105, pages 314-325.

Links:	https://easychair.org/publications/paper/vGfT
	https://doi.org/10.29007/j4q6

BibTeX entry

@inproceedings{EUNIS2024:Artificial_Intelligence_Enhanced_Course,
  author    = {Ville Kivimaki and Sami Hautakangas and Heli Järvelä and Patrik Maltusch},
  title     = {Artificial Intelligence Enhanced Course Content Labelling: Conceptual Model and Testing with University Teachers},
  booktitle = {Proceedings of EUNIS 2024 annual congress in Athens},
  editor    = {Raimund Vogl and Laurence Desnos and Jean-François Desnos and Spiros Bolis and Lazaros Merakos and Gill Ferrell and Effie Tsili and Manos Roumeliotis},
  series    = {EPiC Series in Computing},
  volume    = {105},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {/publications/paper/vGfT},
  doi       = {10.29007/j4q6},
  pages     = {314-325},
  year      = {2025}}

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