Abstract
Training to effectively use Generative AI or large language models (LLMs) based applications to solve business problems is necessary to enhance university students’ employability. Employers are actively implementing these tools in order to leverage the utility of the newly available technology implementations in workplaces. Therefore, universities need to integrate the use of LLMs into the curriculum. The study discusses two different Gen AI-based assignments that were developed and tested at a large public university in the United States. The assignments were designed to address two tasks that differed in terms of their structure and characteristics. One task involved summarizing a set of text documents, while the second task consisted of an image classification project. Both assignments required using multiple publicly available LLM tools to complete the activities. Based on the Task Technology Fit model (Goodhue and Thompson, 1995), the students compared the experience of completing each task on three dimensions. The three dimensions adopted by Gu and Wang (2015) were task complexity, satisfaction, and perceived task-technology fit between the LLM used to perform the two tasks. The presentation provides a description of the two assignments and student reactions to the three tasks. References: Goodhue, D.L. and Thompson, D. L. (1995) “Task-Technology Fit and Individual Performance,” MIS Quarterly, Vol. 19, No. 2 (Jun., 1995), pp. 213-236. Gu, L. and Wang, J. (2015) “A Task Technology Fit Model on e-Learning,” Issues in Information Systems, Vol. 16, Issue I, pp. 163-169.
Presenters
Lutfus SayeedProfessor and Faculty Director, Lam Family College of Business, San Francisco State University, United States
Details
Presentation Type
Theme
KEYWORDS
Generative AI, Large Language Models, Task Technology Fit, Pedagogy
