Construction and Implementation of a "Learner-Centered" Business Education Model Based on Artificial Intelligence Technology_Vol. 1 No. 3 (JBM 2024)_Journal of Business and Marketing (ISSN: 3005-5717)

Home > Journal of Business and Marketing (ISSN: 3005-5717) > Vol. 1 No. 3 (JBM 2024) >

Construction and Implementation of a "Learner-Centered" Business Education Model Based on Artificial Intelligence Technology

Download PDF

DOI: https://doi.org/10.62517/jbm.202409306

Author(s)

Tian Zhao

Affiliation(s)

Modern College of Northwest University, Xi’an, Shaanxi, China

Abstract

With the rapid development of artificial intelligence (AI) technology, its application in education has attracted increasing attention. This paper aims to explore how to construct a "learner-centered" business education model based on AI technology and analyze its practicality from a theoretical perspective. The study first elaborates on the concept of "learner-centered" education through literature review and analyzes the existing problems in business education. Then, drawing on theories from education and information technology fields, this paper proposes a framework for a business education model that integrates AI technology. The study adopts logical reasoning and model construction methods to analyze the application of AI in personalized learning path design, learning process management, and learning effectiveness evaluation systematically. The paper emphasizes the role of AI in assisting teachers in understanding student needs, optimizing teaching content and methods, and improving the interactivity of teaching activities and the accuracy of learning effectiveness measurement. The conclusion suggests that an AI-based business education model can enhance personalization and adaptability in education, improve student engagement and satisfaction, while highlighting the need to address ethical and privacy concerns in AI applications. This research has important theoretical significance and practical value for guiding future innovations in business education models.

Keywords

Artificial Intelligence; Business Education; Learner-centered; Teaching Model; Personalized Learning

References

[1] Wang Qianjuan. Exploring AI-assisted teaching [J]. Primary School Science: Teachers, 2021(10):2. [2] LAN Guoshuai, Guo Qian, Wei Jiacai, et al. 5G+ intelligent technology: Building a new intelligent education ecosystem in the era of "intelligent +" [J]. Distance education journal, 2019, 37(3):14. [3] Li Ziliang, Zhuang Xiaoyun, Guo Tingguang, et al. Research on Construction and Practice of Smart Classroom teaching Model based on Core Accomplishment ─ A case study of Overseas Chinese Middle School in Guangdong Province [J]. Information Technology in Education, 2023(7):25-29. [4] Wu Bo, He Guokun, Chen Ruihao. Deep into the education of the whole teaching system of education informatization construction and practice [J]. World education information, 2019, 32(23):4. [5] Lu Dongyu. Exploration and practice of integrated teaching based on BYOD: A case study of Public Computer Foundation Course [J]. China Science and Technology Journal Database Research, 2022(8):4. [6] Wan Xin. Analysis of enterprises' Needs and Suggestions for training marketing professionals in colleges and universities under the background of "New Business" [J]. Commercial economy, 2019(3):3. [7] Liu Jing, Su Changqing. Under the background of the new business engineering ability training to explore innovative undertaking [J]. Journal of service science and management, 2023, 12(6):5. [8] Yin Xuefei. An analysis on the ability quality and training path of new business teachers in Higher vocational colleges [J]. Science and Education Guide (Electronic Edition), 2021, 000(001):65-66. [9] Zhou Zhiwei, Zhao Wenwen, Yu Wenliu. Construction of intelligent outpatient pharmacy based on Internet of Things and Artificial Intelligence technology [J]. China Medical Equipment, 2024(002):039. [10] Mi Weiwen. Exploring the construction of learner-centered foreign language digital teaching resources [J]. Education Guide, 2019, 657(05):77-81. [11] Feng Xiongbo, Huang Yuxin, Lai Hua, et al. Reinforcement learning based on low resources across language method research [J]. Computer engineering, 2024, 50(2):68-77. [12] Zhang Hewen. Interaction and Shaping: the impact of generative Artificial Intelligence on content and risk regulation in publishing industry [J]. Chinese media technology, 2024(3):14-17.