Integrating Artificial Intelligence into A Bi-Modal Approach for Competency-Based Accounting Instruction in Public Universities

Vollständiger Abstract

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Competency-Based Accounting Education [CBAE] requires instructional models that bridge theory and practice while adapting to diverse learner needs. This study examined the integration of Artificial Intelligence [AI] into a bi-modal approach for CBAE instruction in Nigerian public universities. Using a quasi-experimental design, 246 accounting undergraduates from two public universities were assigned to experimental and control groups for one academic semester. The experimental group received AI-supported bi-modal instruction combining face-to-face and Learning Management System delivery with adaptive feedback, automated assessment, and chatbots. Data were collected via the Competency-Based Accounting Achievement Test [CBAAT], Student Engagement Scale [SES], and semi-structured interviews. Findings revealed significant improvement in technical competencies t = 11.73, p <.001, d = 1.49 and higher engagement levels for the AI bi-modal group compared to the traditional group. Qualitative data indicated improved self-paced learning, instant feedback, and simulation of real-world accounting scenarios as key benefits. Challenges included digital literacy gaps and infrastructure constraints. The study concludes that AI-integrated bi-modal instruction strengthens CBAE outcomes but requires institutional investment in training and infrastructure. The article identifies gaps in African contexts and recommends a framework for scalable adoption.[244]