AI-Enabled Microlearning for Practical Business Communication Skills

Thesis Abstract

This study addresses the persistent gap between theoretical business communication curricula and practical workplace exigencies by exploring AI-enabled microlearning as a scalable solution for enhancing practical communication competencies among business students and early-career professionals. The aim is to evaluate the effectiveness, usability, and sustainability of a personalized microlearning intervention delivered via an AI-driven mobile platform to improve practical communication skills in real-world business contexts. Specific objectives are (1) to design an adaptive microlearning curriculum targeting core competencies such as concise writing, persuasive messaging, cross-cultural communication, and professional presentation; (2) to assess learning gains using performance-based assessments and validated instruments; (3) to examine user engagement, perceived usefulness, and perceived ease of use; (4) to analyze the role of AI personalization in motivation and knowledge retention; and (5) to identify organizational and individual factors that influence adoption and transfer of learning to workplace performance. The study employs a mixed-methods design anchored in social constructivist and cognitive load theories, with the Technology Acceptance Model (TAM) and Situated Learning Theory guiding theoretical framing. A quasi-experimental, pretest–posttest control group design will be conducted with two cohorts (n = 120 in the intervention group and n = 120 in the control group) drawn from two business faculties at a leading multinational university. The intervention group will engage in a 10-week AI-enabled microlearning program, delivering 3–5 minute personalized modules each day, complemented by weekly practical tasks aligned to learning outcomes. Data collection will involve (a) performance-based assessments comprising a business email, a concise executive summary, and a short persuasive pitch, scored with rubrics adapted from the AACU communication rubric; (b) a validated scale for cognitive load and intrinsic motivation; (c) system usage logs capturing frequency, duration, and sequence of module interactions; and (d) semi-structured interviews (n = 20) with participants and focus groups (n = 4) with instructors for qualitative triangulation. Instrument validity will be established through expert panel reviews and pilot testing (N = 30). Reliability will be assessed using Cronbach’s alpha (? ? 0.80 for multi-item scales) and inter-rater reliability for performance rubrics (? ? 0.75). Quantitative data will be analyzed using ANCOVA to compare post-test outcomes while controlling for baseline performance, regression analysis to explore predictors of learning gains, and mediation analysis to examine the role of engagement as a mediator between AI personalization and skill improvement. Descriptive statistics and normality tests will accompany these analyses. Qualitative data will undergo thematic analysis following Braun and Clarke’s approach, supported by NVivo coding to extract patterns related to user experience, perceived transfer, and contextual barriers. A convergent mixed-methods design will integrate findings to provide a comprehensive view of effectiveness, usability, and transfer. Key expected findings include statistically significant improvements in practical business communication performance for the intervention group compared with the control group, particularly in concise written communication and persuasive oral pitch tasks. It is anticipated that higher engagement, driven by AI-driven content sequencing and adaptive feedback, will positively predict learning gains, with cognitive load remaining within optimal levels. The study also expects to identify contextual factors—such as organizational support, time constraints, and supervisor endorsement—that influence transfer of learning to workplace tasks. The contribution to knowledge lies in demonstrating the viability of AI-enabled microlearning as a scalable, evidence-based mechanism to bridge theory and practice in business communication education, extending literature on intelligent tutoring systems, microlearning efficacy, and skill transfer in professional settings. Practical implications include actionable guidelines for implementing AI-driven microlearning across business schools and corporate training programs, including design principles for module granularity, personalization algorithms, assessment rubrics, and integration with existing learning management systems. The study also offers a validated measurement framework for evaluating microlearning interventions in professional communication domains and informs policy decisions regarding resource allocation for digital upskilling initiatives. In conclusion, AI-enabled microlearning presents a potent approach to enhancing practical business communication skills, with recommendations emphasizing robust content curation, transparent AI heuristics, ongoing instructor facilitation, and organizational readiness to support sustained skill transfer.