Research on the Cost Benefit of AI Technology and Teacher Innovation Development in Economic
Abstract
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The foundation of education lies in responding to the times. In the wave of digital transformation in education, AI technology has moved from "pilot exploration" to "large-scale promotion" in the field of economic education. However, it still faces the dilemma of "high cost" and "scattered benefits" - with only intelligent tools, if the teacher's educational core is not adapted to technology, it will be difficult to demonstrate the effectiveness of education. This article is based on the cost-benefit framework of economic education, combined with domestic practices such as the "Smart Education Demonstration Zone" and "AI+Education" special projects. With a scholarly attitude of "studying the world and understanding the changes of the past and present", through literature research, policy analysis, and case analysis, it explores the triple costs (explicit hardware, implicit adaptation, and continuous operation and maintenance) and three-dimensional benefits (efficiency improvement, personalized supply, and resource balance) of AI education technology. The focus is on analyzing the bottlenecks of teacher innovation and development: technological capability gaps, lack of incentive mechanisms, and insufficient collaboration between teaching and technology. Research has found that the benefits of AI education are not solely related to investment, but rather to the linkage of "cost optimization benefit transformation teacher empowerment"; Only by building regional resource sharing to reduce costs, creating a "technology+teaching" integrated training, and improving innovation evaluation, can we achieve two-way empowerment - not only to warm up the landing of technology, but also to enable teachers to uphold their original intention of educating people in the process of change. This article provides a practical path for the application of AI in economics and education, as well as a theoretical framework for teachers to adapt to the digital ecosystem.
Record information
- Authors
- Jiani Tan
- Year
- 2025
- DOI
- https://doi.org/10.54097/7kv5wn23
- OpenAlex ID
- https://openalex.org/W7117354198
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Jiani Tan
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