Developing an AI-Powered Chatbot for Enhancing Career Guidance Services

Thesis Abstract

The increasing demand for accessible, personalized, and efficient career guidance services has underscored the necessity of integrating advanced technological solutions within the guidance domain, particularly through the development of artificial intelligence (AI)-driven tools. Existing career counselling frameworks, though valuable, often face limitations related to scalability, immediacy of support, and adaptability to individual needs, especially in contexts where professional counsellors are scarce. This study aims to develop and evaluate an AI-powered chatbot designed to enhance the delivery of career guidance services by providing immediate, personalized, and contextually relevant advice to users. The specific objectives are to (1) design an AI chatbot prototype aligned with career counselling principles; (2) assess the chatbot's usability and user satisfaction; (3) examine the impact of chatbot interaction on users' career decision-making confidence; and (4) identify factors influencing user engagement with AI-based guidance tools. Employing a mixed-methods research design, the study integrates quantitative surveys and qualitative interviews to holistically evaluate the chatbot's efficacy and user experience. The target population comprises recent secondary school graduates and university undergraduates within a metropolitan area, with a total population of approximately 10,000 students. A stratified random sampling technique selected a sample size of 400 participants for the quantitative component, with 40 participants purposively sampled for in-depth interviews based on diverse demographics and technological engagement levels. Data collection instruments include a standardized Career Decision-Making Self-Efficacy Scale, a User Satisfaction Questionnaire, and semi-structured interview protocols. Prior to deployment, the chatbot system underwent validation through expert reviews and pilot testing to establish content validity, and reliability analysis was conducted using Cronbach’s alpha for the quantitative tools. Data analysis involves descriptive statistics and inferential tests such as multiple regression analysis to investigate predictors of user satisfaction and decision-making confidence, as well as thematic analysis of qualitative interview data to explore user perceptions and engagement factors. The theoretical foundation integrates the Social Cognitive Career Theory (SCCT), which emphasizes self-efficacy and outcome expectations in career decision-making, and the Technology Acceptance Model (TAM), which explains user acceptance and usage behaviors of new technological tools. It is anticipated that the developed chatbot will demonstrate high usability scores, increased user confidence in career decision-making, and positive perceptions regarding its assistance. The findings are expected to reveal significant correlations between user engagement levels, perceived usefulness, and satisfaction, thereby confirming the chatbot’s potential as a supplementary career guidance resource. The study will contribute to knowledge by providing empirical evidence on the effectiveness of AI chatbots in career counselling, offering insights into factors that influence user engagement, and proposing a scalable model for integrating AI tools into existing guidance frameworks. In conclusion, the research advocates for the adoption of AI-powered chatbots as an innovative means of expanding access to personalized career guidance, particularly in resource-constrained settings. It recommends further refinement of chatbot algorithms based on user feedback and ongoing technological advancements, as well as integration of multimodal features such as multimedia content to enhance user interaction. Future research should explore longitudinal impacts and the adaptation of such tools across diverse socio-cultural contexts, thereby fostering more inclusive and innovative approaches to career counselling in the digital era.