IEFInternational Education Forum3083-49022981-8605Bio-Byword Scientific Publishing Pty. Ltd.10.26689/ief.v4i3.14396ArticleRedesigning the Business Intelligence and AI Course: A Four-Dimensional Teaching Modelhttps://artdesignp.com/journal/IEF/4/3/10.26689/ief.v4i3.14396BaoShiqi2026432026-03-31Amid the rapid evolution of the digital economy, the convergence of business intelligence (BI) and artificial intelligence (AI) has become a key driver of enterprise digital transformation, intensifying the demand for interdisciplinary professionals who possess both business acumen and technical expertise in intelligent systems. As an emerging interdisciplinary course that integrates computer science, management science, and statistics, Business Intelligence and Artificial Intelligence faces several pedagogical challenges. These include a disconnect between technical instruction and real-world business contexts, superficial integration of AI content into the curriculum, and a misalignment between practical training and current industry needs. Drawing on existing teaching reform literature and emphasizing the integrated nature of BI and AI education, this paper proposes a comprehensive teaching framework centered on content, methodology, practice, and assessment. Specifically, it outlines innovative pathways across four dimensions: redesigning curriculum content, transforming instructional approaches, constructing a multi-tiered practical training system, and refining evaluation mechanisms. Each pathway is discussed in terms of implementation strategies and necessary resource support, with a focus on applied instruction that embeds AI technologies within authentic business scenarios. The proposed model aims to enhance teaching quality and cultivate applied, interdisciplinary talent equipped to meet evolving industry demands.

Xiao WL, Wang ZH, Chen H, et al., 2020, Analysis on Research Dynamics and Development Trends of Domestic and Foreign Business Intelligence and Big Data Analytics. Science & Technology and Economy, 33(6): 66–73.

Zhang Q, 2019, Discussion on the Teaching Reform of Business Intelligence Course in Applied Undergraduate Universities. Modern Computer, 2019(6): 79–82.

Cheng X, Liu M, Li HF, 2022, Research on Blended Teaching Mode of Business Intelligence under the Background of Big Data. Computer Knowledge and Technology, 18(23): 123–124.

Lou C, Yang X, Liu TT, 2022, Exploration of Business Course Reform Based on CDIO Concept and Personalized Learning: Taking Business Intelligence as an Example. Journal of Qiannan Normal University for Nationalities, 42(4): 45–53.

Zhu WP, Chen JL, 2017, Research on the Teaching Reform of Business Intelligence and Data Mining Course in the Era of Big Data. Computer Education, 2017(10): 33–36.

Li JF, Hou JY, 2022, Exploration on the Teaching Reform of Business Intelligence Course under the BOPPPS Teaching Mode. Education Teaching Forum, 2022(36): 129–132.

Presthus W, Bygstad B, 2012, Business Intelligence in College: A Teaching Case with Real Life Puzzles. Journal of Information Technology Education: Innovations in Practice, 2012(11): 121–137.

Zhao WD, Wu R, Yang L, 2013, Experimental Design of Business Intelligence Course Oriented to Enterprise Needs. Computer Education, 2013(18): 106–109.

Ministry of Education of the People’s Republic of China, 2020, Action Plan for Artificial Intelligence Innovation in Higher Education Institutions (2020–2025). http://www.moe.gov.cn/srcsite/A16/s3342/202006/t20200617_462479.html

Ministry of Education of the People’s Republic of China, 2018, Notice on Issuing the Action Plan for Educational Informatization 2.0. http://www.moe.gov.cn/srcsite/A16/s3342/201804/t20180425_334188.html

Kang Y, Lin Y, Zhu YP, et al., 2020, Teaching Reform of Data Mining Course Based on SE-CDIO. Journal of Yunnan University (Natural Sciences Edition), 2020(S1): 54–57.

Qu B, Zhang ZS, 2017, Research on the Construction of Business Intelligence Curriculum System under the Environment of Big Data. Industry and Information Technology Education, 2017(9): 41–45.

Chen Y, Chen CM, Liu ZY, et al., 2015, The Methodological Functions of CiteSpace Knowledge Mapping. Studies in Science of Science, 33(2): 242–253.

Ji WL, Wang HL, Su GB, et al., 2020, A Survey of Recommendation Methods Based on Association Rule Algorithms. Computer Engineering and Applications, 2020(22): 33–41.

Teaching Steering Committee for Business Administration Majors in Higher Education Institutions of the Ministry of Education, 2021, Guidelines for the Practice of New Business Disciplines Construction. Higher Education Press, Beijing, 45–52.