Understanding infuential factors for college instructors’ adoption of LLM‑based applications using analytic hierarchy process
Abstract
With the accessibility of advanced artificial intelligence (AI)-based tools, particularly large language models (LLMs) such as ChatGPT, integrating LLMs into higher education has been considered a transformative shift in educational paradigms. However, instructors have numerous objections against the adoption of LLM-based applications. To promote the proper adoption of LLM-based applications for Chinese college instructors, this study investigates and assesses factors that affect instructors’ adoption. Specifically, this study proposes a multi-criteria decision-making model drawing upon technology acceptance theories such as the value-based adoption model to determine four key influential factors and their sub-factors. After collecting expert data from 22 Chinese college instructors with experience in integrating AI applications into classrooms across seven provinces, an analytic hierarchy process is adopted to weigh and prioritize these factors. Results show that “Usefulness” is the most important factor for encouraging instructors’ use of LLM-based applications, while “Effort” is of less concern. Among the sub-factors, “Effectiveness” and “Efficiency” are of intermediate importance in LLM-based application adoption, while “Perceived fee” has the least influence. Based on the findings, the study provides insights into Chinese college instructors’ adoption experiences of LLM applications as well as suggestions for promoting LLMs’ integration into instruction.
Link to publication in Springer Nature
