Sufficient and necessary conditions for ChatGPT adoption in medical education: a combined partial least square-structural equation modelling and necessary condition analysis

Abstract

Background ChatGPT has gained rapid adoption in education due to its capacity to generate human-like responses, support personalized learning, and assist with complex knowledge retrieval. Yet, concerns about misinformation, overreliance, privacy, and ethical risks continue to shape students’ acceptance of AI tools. While prior research on factors influencing ChatGPT adoption has relied on regression-based approaches using frameworks like the Technology Acceptance Model, these methods assess only the sufficiency but not the necessity of these factors. This study combined Partial Least Squares Structural Equation Modelling (PLS-SEM) with Necessary Condition Analysis (NCA) to determine both the sufficient and necessary factors influencing medical students’ intentions to use ChatGPT for learning.

Methods A cross-sectional survey was conducted among 146 pre-clinical Year 2 medical students at Universiti Malaysia Sarawak (UNIMAS). Seven variables were measured, i.e., perceived usefulness (PU), perceived ease of use (PEOU), social influence (SI), hedonic motivation (HM), perceived risk (PR), attitude (At), and behavioral intention (BI). Data were analyzed using SmartPLS 4.0, following a two-stage SEM approach, and NCA using the ceiling envelopment–full disposal hull (CE-FDH) technique.

Results PLS-SEM showed substantial explanatory power (R² = 0.68) with attitude (β = 0.600), social influence (β = 0.133), and perceived usefulness (β = 0.096) as significant factors influencing intention for ChatGPT adoption. NCA revealed that attitude (d = 0.384), social influence (d = 0.310), and perceived usefulness (d = 0.183) were both necessary and sufficient conditions for BI whereas, hedonic motivation (d = 0.235) and perceived risk (d = 0.234), although non-significant in SEM, were significant necessary conditions, indicating threshold requirements of these 2 factors for adoption.

Conclusion By combining PLS-SEM and NCA, this study enhanced the richness and granularity of understanding on the factors shaping ChatGPT adoption in medical education.