AI-Enhanced Adaptive Learning for Business Education Curriculum Design

Thesis Abstract

The rapid digitization of higher education and the growing diversity of business curricula demand pedagogical approaches that tailor learning experiences to individual student needs while maintaining curricular integrity. This study addresses the gap between static, one-size-fits-all business education and the dynamic requirements of contemporary workplaces by examining AI-enhanced adaptive learning (AI-AL) as a mechanism to design and deliver curriculum content that adapts to learner profiles, competencies, and contextual business scenarios. The aim is to investigate how AI-AL systems influence curriculum design decisions, learner engagement, knowledge retention, and skill acquisition in core business disciplines. Specific objectives are (1) to evaluate the impact of AI-AL on student engagement and time-to-competence in core MBA and business analytics modules; (2) to assess how adaptive sequencing, personalized feedback, and intelligent remediation affect mastery of strategic management, operations, and data-driven decision-making; (3) to examine instructors’ perspectives on curriculum-level design changes enabled by AI-AL, including alignment with learning outcomes and assessment integrity; and (4) to develop a framework for integrating AI-AL into business education curricula with considerations for ethics, fairness, and scalability. The study adopts a mixed-methods embedded design, combining a quasi-experimental component with qualitative inquiry. The population comprises 520 master’s students enrolled across three business schools offering MBA and MSc programs in Strategy, Operations, and Business Analytics. A purposive sample of 320 students will participate in the quasi-experimental phase, with 160 assigned to an AI-AL-enabled curriculum condition and 160 to a conventional fixed-sequence curriculum. Data collection instruments include (a) validated engagement scales and time-on-task metrics captured by the AI-AL platform, (b) pre-test and post-test instruments measuring domain-specific knowledge and strategic thinking, (c) system log analytics for adaptability events (e.g., recommended pathways, remediation instances), and (d) instructor interviews and curriculum mapping rubrics. Validity and reliability will be ensured through pilot testing, Cronbach’s alpha analyses (targeting ? ? .80 for scales), and triangulation of quantitative and qualitative data. Data analysis will employ (i) multilevel regression to examine effects of AI-AL on learning outcomes while controlling for prior achievement, (ii) MANOVA to compare multiple dependent variables across conditions, (iii) path analysis to model relationships among adaptive features, engagement, and mastery, and (iv) thematic analysis of instructor interviews to extract curriculum-design implications. Descriptive statistics and effect sizes (Cohen’s d) will accompany inferential tests. A conceptual model drawing on Constructivist Learning Theory and Self-Determination Theory will guide interpretation, with research hypotheses tested regarding the positive impact of adaptive sequencing, feedback granularity, and remediation on outcomes. Expected findings include (a) statistically significant improvements in post-test mastery and strategic thinking scores for AI-AL participants relative to controls; (b) higher engagement and reduced time-to-competence, particularly in data-driven decision-making and operations management modules; (c) evidence that adaptive pathways improve differentiation for diverse learner profiles without compromising curriculum coherence and alignment with program-level learning outcomes; and (d) nuanced insights from instructors on how AI-AL facilitates scalable curriculum redesign, including governance, assessment integrity, and faculty development needs. The study anticipates identifying moderators such as prior domain knowledge, self-regulated learning capacity, and course complexity that influence AI-AL effectiveness. Contributions to knowledge include (a) empirical evidence on the effectiveness of AI-driven adaptive learning in refining business education curricula; (b) a validated framework for integrating AI-AL into curriculum design that balances personalization with standardized outcomes; (c) methodological guidance for evaluating adaptive learning interventions in professional-master programs; and (d) policy-level considerations for ethics, fairness, and data governance in AI-enhanced curriculum design. The main conclusion is that AI-AL can meaningfully enhance business education by delivering personalized, competency-aligned learning experiences at scale, provided that curriculum design is deliberately aligned with assessment, faculty professional development, and ethical safeguards. Recommendations emphasize iterative curriculum mapping with stakeholder involvement, robust data governance, transparent transparency about adaptation logic, and ongoing research into long-term impacts on employability and professional practice.