Chatbot Adoption Model in Determining Student Career Path Development: Pilot Study

Mohamed Hassan Ahmed - Department of Computer Science, Faculty of Computing, SIMAD University, Somalia
Rusli Abdullah - Department of Information Systems, Faculty of Computer Science and Information Technology, University Putra Malaysia, Malaysia
Yusmadi Yah Jusoh - Department of Information Systems, Faculty of Computer Science and Information Technology, University Putra Malaysia, Malaysia
Masrah Azrifah Azmi Murad - Department of Information Systems, Faculty of Computer Science and Information Technology, University Putra Malaysia, Malaysia


Citation Format:



DOI: http://dx.doi.org/10.62527/joiv.9.1.3798

Abstract


A career decision is incredibly essential in one's life. It shapes one's future role in society, influences professional development, and can lead to success and fulfillment. Making a sound and consistent career decision based on skills and interests is critical for personal and professional development. Since generative AI is an emerging and revolutionizing technology industry in the market, which is very good in generating contents, providing consultancies and answering questions in humanly fashion, integrating AI chatbots into the career planning process can help students to get more accurate and personalized advice for their future career. This pilot study emphasized the student’s adoption of chatbot technology for career selecting processes utilizing the extended Unified Theory of Acceptance and Use of Technology (UTAUT2) model with four additional constructs which influence the student’s career selection, namely: Perceived Student’s External Factors (PEF), Perceived Student’s Interest (PSN), Perceived Career Opportunities (PCO) and Perceived Self-Efficacy (PSF). An online survey was conducted, and 37 responses were received and analyzed. The measurement model produced a promising result, and the discriminant validity, construct reliability and validity of the model were confirmed with a Cronbach’s alpha (α) above 0.70 threshold and AVE over 0.5 cut-off for most of the constructs including the four above mentioned latent variables. However, the Price Value (PPV) and Facilitating Conditions (PFC) UTAUT2 constructs produced alpha () of 0.680 and 0.611 respectively which is still adequate since their AVE is above the 0.5 threshold. Consequently, their interpretation and conclusions should be approached with caution.

Keywords


ChatGPT; Chatbots; determinants; large language models; structural equation modeling

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References


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