Analysis of the User Perception of Chatbots in Education Using A Partial
  Least Squares Structural Equation Modeling Approach
Md Rabiul Hasan Nahian Ismail Chowdhury Md Hadisur Rahman Md Asif Bin Syed JuHyeong Ryu
The integration of Artificial Intelligence (AI) into education is a recent
development, with chatbots emerging as a noteworthy addition to this
transformative landscape. As online learning platforms rapidly advance,
students need to adapt swiftly to excel in this dynamic environment.
Consequently, understanding the acceptance of chatbots, particularly those
employing Large Language Model (LLM) such as Chat Generative Pretrained
Transformer (ChatGPT), Google Bard, and other interactive AI technologies, is
of paramount importance. However, existing research on chatbots in education
has overlooked key behavior-related aspects, such as Optimism, Innovativeness,
Discomfort, Insecurity, Transparency, Ethics, Interaction, Engagement, and
Accuracy, creating a significant literature gap. To address this gap, this
study employs Partial Least Squares Structural Equation Modeling (PLS-SEM) to
investigate the determinant of chatbots adoption in education among students,
considering the Technology Readiness Index (TRI) and Technology Acceptance
Model (TAM). Utilizing a five-point Likert scale for data collection, we
gathered a total of 185 responses, which were analyzed using R-Studio software.
We established 12 hypotheses to achieve its objectives. The results showed that
Optimism and Innovativeness are positively associated with Perceived Ease of
Use (PEOU) and Perceived Usefulness (PU). Conversely, Discomfort and Insecurity
negatively impact PEOU, with only Insecurity negatively affecting PU. These
findings provide insights for future technology designers, elucidating critical
user behavior factors influencing chatbots adoption and utilization in
educational contexts.