Developing an AI-Powered Adaptive Learning System for Business Education Enhancement

Thesis Abstract

The rapid evolution of digital technologies has transformed business education, necessitating innovative instructional approaches to enhance learner engagement, retention, and practical skills development. Despite the proliferation of online learning platforms, conventional instructional methods often fail to address the diverse learning needs and pace of individual students, leading to suboptimal educational outcomes. This study aims to develop a robust, AI-powered adaptive learning system specifically tailored for business education, designed to personalize learning experiences, optimize content delivery, and improve academic performance among tertiary students. The primary objectives include identifying core pedagogical and technological requirements for adaptive systems in business contexts, designing and implementing a prototype, and evaluating its effectiveness relative to traditional learning methods. The research adopts a mixed-methods design, integrating qualitative and quantitative approaches to ensure comprehensive system development and evaluation. Qualitatively, the study involves a thematic analysis of interviews conducted with 20 business educators and digital learning specialists to identify pedagogical strategies and technical features necessary for effective adaptation. Quantitatively, the study surveys 300 business students across three universities to assess baseline learning preferences, engagement levels, and performance metrics prior to and after system implementation. The development phase employs an agile iterative process, leveraging machine learning techniques, particularly reinforcement learning algorithms, to enable real-time personalization of content and feedback. Data collection instruments encompass semi-structured interview guides, standardized questionnaires on learning preferences and engagement, system usability scales, and academic performance records. Validity and reliability of quantitative instruments are established through pilot testing and Cronbach’s alpha coefficients exceeding 0.80. For data analysis, thematic analysis is employed for qualitative insights, while statistical techniques such as paired t-tests, multiple regression analysis, and ANOVA are utilized to evaluate the impact of the adaptive system on students’ engagement and academic achievement. The analytic framework incorporates established theories, notably Vygotsky’s Zone of Proximal Development to inform pedagogical adaptation, and the Technology Acceptance Model (TAM) to gauge user acceptance and system usability. Expected findings indicate that the AI-driven adaptive learning system significantly enhances learner engagement, motivation, and academic performance compared to conventional methods. Insights are anticipated to reveal critical design features, including personalized content pathways, dynamic assessment mechanisms, and real-time feedback systems. Additionally, the study intends to demonstrate that technological acceptance correlates positively with increased utilization and satisfaction, thereby affirming TAM’s applicability within business education contexts. The findings are expected to contribute to existing knowledge by providing empirical evidence on the efficacy of AI-enabled personalization in business curricula and establishing a framework for scalable system deployment. The study concludes that integrating artificial intelligence with adaptive learning frameworks offers substantial potential to revolutionize business education by fostering individualized learning experiences and improving educational outcomes. Recommendations include adopting the developed system across diverse higher education institutions, investing in instructor training for AI integration, and further research into long-term impacts and scalability. Ultimately, this research provides a foundational model for leveraging cutting-edge AI technologies to meet evolving educational demands, promoting learner-centric pedagogies, and enhancing the quality of business education in digital environments.