Developing an AI-driven Virtual Mentorship Platform for Business Students

Thesis Abstract

The rapid advancement of information and communication technologies has transformed the landscape of educational support services, yet many business students still face significant challenges in accessing personalized mentorship due to geographic, institutional, and resource constraints. This study addresses the pressing need for scalable, accessible, and effective mentorship solutions by proposing the development of an AI-driven virtual mentorship platform tailored specifically for business students. The primary aim is to design, implement, and evaluate a technology-enhanced mentorship system that leverages artificial intelligence to simulate personalized guidance and facilitate meaningful academic and career development interactions. The research objectives include (1) exploring the key functionalities required for an effective AI-driven mentorship platform in business education, (2) developing a prototype system based on user-centered design principles, (3) evaluating the usability, acceptance, and effectiveness of the system through empirical testing, and (4) identifying the impact of AI-based mentorship on students’ academic performance, career readiness, and mentorship satisfaction. The study adopts a mixed-methods research design, integrating qualitative and quantitative approaches to ensure comprehensive insights. The target population comprises 300 business students enrolled in postgraduate programs across five universities, selected through stratified sampling to ensure diversity in gender, age, academic standing, and technological proficiency. Data collection instruments include a structured questionnaire validated for relevance and reliability (Cronbach’s alpha = 0.89), semi-structured interviews with 20 students and faculty members, and system usability testing sessions. Quantitative data, consisting of pre- and post-interaction surveys, will be analyzed using descriptive statistics, paired t-tests, and regression analysis to measure the impact of the platform on perceived mentorship quality and student outcomes. Qualitative data from interviews and user feedback will be analyzed thematically using NVivo software to identify emergent themes related to user experience, system functionality, and areas for improvement. It is anticipated that the developed AI-driven mentorship platform will significantly enhance students’ access to personalized guidance, increase engagement levels, and positively influence academic and career-related outcomes. Specifically, the system is expected to facilitate tailored content delivery based on individual student profiles, employ natural language processing to simulate conversational mentoring, and incorporate machine learning algorithms to continuously improve mentorship recommendations over time. This research contributes to the existing body of knowledge by demonstrating how artificial intelligence can revolutionize mentorship practices within business education, filling a critical gap in scalable, cost-effective, and accessible support systems. It extends theoretical perspectives by aligning the Technology Acceptance Model (TAM) with the principles of social presence theory, providing a framework to understand technological adoption and engagement in virtual mentorship environments. Furthermore, the study offers practical insights for educational institutions, policy makers, and developers seeking to integrate AI-driven solutions into academic support services. The main conclusion underscores that a well-designed AI-driven virtual mentorship platform can serve as a viable alternative to traditional face-to-face mentoring, especially in contexts marked by resource limitations or geographical barriers. It is recommended that institutions consider integrating such platforms into their broader student support infrastructure and prioritize ongoing system optimization based on user feedback. Future research should explore longitudinal impacts of AI mentorship and adaptability across different disciplines and educational levels, aiming to refine AI algorithms and enhance personalized mentorship experiences further.