Development of an AI-Based Chatbot for Academic and Career Guidance Support

Thesis Abstract

The escalating demand for accessible and personalized academic and career guidance services among university students underscores the necessity for innovative technological solutions capable of overcoming limitations related to resource constraints and demographic disparities. This study aims to develop and evaluate an artificial intelligence (AI)-based chatbot designed to provide comprehensive academic and career counseling support to higher education students. The specific objectives include designing a conversational AI system tailored to deliver contextually relevant guidance, assessing the system’s usability and effectiveness, and exploring its impact on students’ decision-making processes and academic performance. The research adopts a mixed-methods approach, integrating quantitative and qualitative data to ensure a comprehensive understanding of the chatbot’s functionality and influence. The quantitative phase employs a quasi-experimental design involving a sample of 400 undergraduate students from a large university, randomly assigned into experimental and control groups. The experimental group interacts with the AI chatbot over a semester, while the control group receives traditional manual counseling on an as-needed basis. Data collection instruments include structured questionnaires measuring user satisfaction, perceived usefulness, and behavioral intentions, alongside academic records for measuring performance outcomes. Qualitative data are gathered through focus group discussions to explore user experiences and perceptions in depth. The validity and reliability of quantitative instruments are established through pilot testing, Cronbach’s alpha coefficient, and expert review, ensuring robust measurement; qualitative data are analyzed via thematic analysis to identify common themes and insights. Quantitative data are analyzed using descriptive statistics, t-tests, and multiple regression analysis to examine the relationship between chatbot usage and academic or behavioral outcomes. Theoretical grounding is provided by the Technology Acceptance Model (TAM) and the Social Cognitive Theory, guiding the design and evaluation of the intervention. Expected findings indicate that students in the intervention group will report higher satisfaction levels, perceive the chatbot as a useful tool aligning with their needs, and demonstrate improvements in academic decisions and performance metrics relative to the control group. Additionally, qualitative insights are anticipated to reveal themes related to ease of use, perceived credibility of AI responses, and the chatbot’s role in reducing counseling access barriers. These findings are expected to contribute significantly to the body of knowledge on digital guidance systems, especially in the context of Higher Education, by demonstrating how AI-driven conversational agents can augment traditional counseling services. Furthermore, the study will offer evidence-based recommendations for integrating AI chatbots into institutional support frameworks, emphasizing the importance of user-centered design and ongoing system refinement. The research concludes that AI chatbots are viable, scalable, and effective tools for enhancing academic and career guidance, particularly in environments where counseling resources are limited or unevenly distributed. Policymakers, university administrators, and counseling practitioners are advised to consider adopting such technologies to foster inclusive and responsive student support systems. Future research directions include longitudinal studies to assess long-term impacts, exploring integration with other digital learning environments, and enhancements incorporating multilingual capabilities to serve diverse student populations. Ultimately, this study advances understanding of how AI technologies can transform traditional guidance paradigms, promoting more accessible, personalized, and efficient support mechanisms in higher education contexts.