Abstract
This longitudinal mixed-methods study investigates the effectiveness of AI-driven flipped writing workshops in business education by enhancing analytical writing skills, professional communication abilities, and self-regulated learning (SRL) strategies over a 14-week intervention. Grounded in socio-cognitive learning theory, the research compares an experimental group (n = 113), which utilized AI-driven feedback scaffolds in flipped workshops, with a control group (n = 81) that received traditional lecture-based instruction. Triangulated data - comprising pre/post writing assessments, AI-generated feedback logs, and semi-structured interviews - indicated that the AI-flipped model: (a) improved analytical writing skills (MEx = 14.53, SDEx = .787 vs Mcont = 10.59, SDcont = .907; p < .01, Cohen’s d = 4.66), with notable gains in argumentation rigor and evidence-based synthesis; (b) enhanced professional communication, particularly in audience adaptation and clarity, with qualitative feedback highlighting AI’s role in replicating real-world business contexts; and (c) promoted self-regulated learning behaviors, evidenced by increased revision cycles (4.2 compared to 1.8 in the control group) and greater goal-setting precision, supported by AI-facilitated progress monitoring. Thematic analysis revealed AI’s dual function as both a personalized writing tutorby providing adaptive feedback, and a metacognitive motivator which encouraged reflective practice. These findings contribute to the field of business education research by: (1) demonstrating a scalable model of AI-enhanced writing instruction; (2) proposing a framework for integrating AI into self-regulated learning in professional training; and (3) addressing key gaps in longitudinal, technology-enhanced education. This study offers practical insights for curriculum designers seeking to harness AI’s transformative potential, while also emphasizing the ethical implications of human–AI collaboration in higher education.
Keywords
Get full access to this article
View all access options for this article.
