Generative AI-Driven Feedback in Flipped Writing Workshops: Transforming Business Education Through Improved Writing, Communication, and Self-Regulation

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 (M Ex = 14.53, SD Ex = .787 vs M cont = 10.59, SD cont = .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.