


































Global Research in Higher Education 
ISSN 2576-196X (Print) ISSN 2576-1951 (Online) 

Vol. 8, No. 2, 2025 

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56 

 

 

Original Paper 

Research on the Construction and Practice of an AI-Enhanced 

Blended Teaching Model for Econometrics 

Fanjie Fu
1
 

1
 College of Finance and Economics, Sichuan International Studies University, Chongqing 400031, 

China 

 

Received: May 21, 2025         Accepted: May 28, 2025        Online Published: June 5, 2025 

doi:10.22158/grhe.v8n2p56                URL:http://dx.doi.org/10.22158/grhe.v8n2p56 

 

Abstract 

Artificial intelligence (AI) has emerged as a key driver of curriculum reform in higher education. For 

econometrics—a discipline that emphasizes both theoretical rigor and methodological application—the 

integration of AI into blended teaching models is a pressing issue in contemporary pedagogy. This 

study investigates the development of an AI-enhanced instructional framework for econometrics, 

focusing on students majoring in International Economics and Trade at Sichuan International Studies 

University. By incorporating tools such as ChatGPT and machine learning into a blended learning 

environment that combines online platforms with in-person instruction, the course adopts a 

―Technology Integration–Project-Based Learning–Practice Orientation‖ approach. In the practical 

process, the course focuses on strengthening students’ comprehensive abilities in data processing, 

empirical modeling, and economic problem analysis, promoting their transition from theoretical 

learning to research and practice. Research has shown that AI empowerment can effectively enhance 

the teaching efficiency of econometrics courses and improve students’ practical application skills, 

providing a viable pathway for curriculum reform. 

Keywords 

Artificial Intelligence(AI), Econometrics, Blended Learning, Teaching Reform, Empirical Practice 

 

1. Introduction 

In recent years, with the steady advancement of China’s New Liberal Arts initiative, curriculum 

reform in humanities and social sciences has entered a new phase characterized by integrated 

innovation. Emphasizing a balance between theoretical literacy and practical competence, this 

reform advocates the deep integration of technological empowerment with course content. Within 



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the discipline of economics, econometrics—serving as a methodological bridge between statistics, 

economic theory, and empirical analysis—has attracted growing attention in educational reform 

discourse. Scholars increasingly argue that econometrics instruction should break free from its 

traditional emphasis on theory over practice, instead prioritizing empirical skill development and 

pedagogical innovation (Bai & Chen, 2012; Li & Zhang, 2013; Li, 2019). A variety of reform 

strategies have since emerged, including case-based teaching, task-driven instruction, and 

interactive experimentation, all contributing to a broader shift toward application-oriented talent 

cultivation (Jin, 2016; Ye et al., 2018; Zeng, 2020). 

Meanwhile, the rapid development of artificial intelligence and big data technologies has injected 

new vitality into econometrics education. Under the influence of paradigms such as machine 

learning and transfer learning, traditional course content, teaching methods, and assessment 

frameworks are undergoing fundamental transformation. A growing body of research suggests that 

integrating AI tools into instruction not only enhances students’ capacity for handling 

high-dimensional data, but also fosters deeper problem-solving skills and greater technological 

fluency (Wang, 2021; Zheng, 2024; Cheng et al., 2024). Hong and Wang (2024) further argue that 

large language models such as ChatGPT are reshaping the research paradigm in economics, offering 

new opportunities for innovation in econometrics pedagogy. Nevertheless, these technologies still 

present challenges in areas such as interpretability and causal inference, which necessitate 

alignment with economic theory to ensure accurate content renewal and comprehensive skills 

development. 

Against this dual backdrop of digital transformation and New Liberal Arts reform, the integration of 

AI into econometrics curriculum design has emerged as a key focus in teaching research. This study, 

drawing on the practical context of Sichuan International Studies University, aims to construct and 

implement an AI-augmented instructional model for econometrics. The objective is to provide a 

feasible and adaptable reference for advancing curriculum reform in related fields and respo nding 

effectively to the evolving demands of talent cultivation in the era of digital economics.  

 

2. Challenges in Implementing AI-Enhanced Blended Teaching in Econometrics 

2.1 Insufficient Integration of AI Technology with Course Content 

Although AI technology holds great promise in educational applications, its deep integration with 

the content of econometrics courses remains a significant challenge. Econometrics involves 

complex mathematical modeling and advanced data analysis, which current AI-powered teaching 

tools may not fully accommodate. For example, intelligent question banks may struggle to generate 

exercises that meet the cognitive rigor of high-level econometric thinking, and AI algorithms often 

fall short in evaluating students’ logical consistency in constructing economic models. Moreover, 



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many instructors lack adequate technical training and institutional support when implementing 

AI-enhanced pedagogical models, leading to superficial adoption of AI tools that fails to 

substantively improve instructional quality. Therefore, how to meaningfully align AI technologies 

with the core content of econometrics remains an urgent issue.  

2.2 Balancing Student Autonomy and Dependence on AI Technologies 

In AI-supported blended learning environments, students benefit from personalized learning 

pathways, intelligent tutoring, and real-time feedback. However, these advantages may 

inadvertently foster overreliance on technological support, thereby hindering the development of 

self-directed learning competencies. For instance, students may depend too heavily on AI-generated 

answers or solutions, lacking motivation for independent inquiry or critical reflection. Others may 

focus narrowly on AI systems’ instant feedback, at the expense of cultivating systemat ic knowledge 

and long-term conceptual understanding. Furthermore, students unfamiliar with AI interfaces or 

skeptical of their reliability may experience reduced learning efficacy. Hence, striking a balance 

between leveraging AI for efficiency and fostering student autonomy and critical thinking is a core 

pedagogical challenge in AI-enhanced instruction. 

2.3 Challenges in Redefining the Teacher’s Role and Enhancing Professional Competence 

The AI-enhanced blended teaching model requires educators to transition from being traditional 

knowledge transmitters to learning facilitators and curriculum designers—a shift that imposes 

higher demands on their professional capabilities. First, instructors must acquire foundational 

knowledge of AI principles and applications to effectively integrate these tools into their teaching 

practice. Yet, many educators lack technical backgrounds and may struggle to adapt quickly to this 

new paradigm. Second, teachers must be able to incorporate ideological and value-based education 

(“curriculum ideology”) into AI-enhanced course design, a task that requires both pedagogical 

creativity and socio-political awareness. Lastly, instructors need strong data literacy skills to 

interpret AI-generated learning analytics and make evidence-based adjustments to instructional 

strategies. Facilitating this professional transition is essential for the successful implementation of 

AI-empowered teaching models in higher education. 

 

3. Construction of an AI-Enhanced Blended Teaching Model for Econometrics 

The construction of an AI-enhanced blended teaching model for econometrics aims to promote deep 

integration between emerging technologies and disciplinary knowledge, breaking through the 

limitations of traditional instructional approaches and enhancing students’ data literacy, modeling 

skills, and comprehensive application abilities. Supported by artificial intelligence, this model 

advances students’ practical competencies and research potential through three key components: 

restructuring course content, innovating teaching methods, and strengthening practical training. The 



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detailed structure of the model is illustrated in Figure 1.  

 

 

Figure 1. Construction Framework of an AI-Enhanced Blended Teaching Model for 

Econometrics 

 

3.1 Restructuring Course Content: Integrating Intelligent Technologies with Disciplinary Knowledge 

Integrating AI technologies into the teaching of econometrics has become a key direction in 

contemporary curriculum reform. First, it is essential to incorporate  AI-related modules—such as 

machine learning models, natural language processing, and data mining—into the curriculum to 

strengthen students’ abilities in data processing and model construction in the context of big data. 

For instance, alongside traditional topics like OLS regression and cointegration testing, the course 

should introduce modern algorithms such as random forests and Lasso regression to broaden 

students’ methodological horizons. Second, training in mainstream programming languages such as 

Python and R should be emphasized, enabling students to proficiently apply AI libraries such as 

Scikit-learn and XGBoost for tasks including data cleaning, modeling, and prediction. This 

technical integration not only enhances the practical relevance of the course but also aligns it more 

closely with current industry and research trends. In particular, AI algorithms can compensate for 

the limitations of traditional econometric models in areas like economic forecasting and market 

analysis by improving model accuracy and explanatory power. Furthermore, the course should be 

grounded in real-world economic cases, such as “Predicting Consumer Spending Behavior Using 

Machine Learning” or “Analyzing China’s Export Data with ChatGPT,” to help students understand 



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how AI tools are applied in economic decision-making. By deeply integrating disciplinary 

knowledge with AI technologies, students are empowered to construct economically interpretable 

models in real-world contexts, thereby enhancing their cross-disciplinary application skills. 

3.2 Innovating Teaching Methods: Enhancing Interactivity and Practice Orientation 

To enhance instructional effectiveness, the blended teaching model combines online platforms with 

face-to-face classroom activities. In the online component, platforms such as Xuexitong and Rain 

Classroom are used to release theoretical content, programming tutorials, and AI model 

demonstrations in advance, enabling students to engage in knowledge preheating and personalized 

learning. The in-person sessions focus on problem-based learning and case discussions, where 

instructors guide students through economic data analysis, model construction, and the use of AI 

tools for empirical modeling and data visualization. Project-based learning serves as a core 

instructional strategy in this model. The course is designed around practical, economically relevant 

project topics such as “Using AI Models to Predict Stock Market Volatility” or “An Empirical 

Study on the Impact of Macroeconomic Variables on Household Consumption.” Students work in 

groups to complete the full analytical process, including data collection, model specification, 

selection of appropriate AI tools, and interpretation of results. This approach not only develops 

students’ teamwork and communication skills but also deepens their understanding of how AI 

techniques can be integrated with econometric modeling. In addition, a flipped classroom 

mechanism encourages students to use AI assistants such as ChatGPT during pre-class preparation 

for previewing concepts and self-testing. Class time is then dedicated to problem-solving, Q&A 

sessions, and hands-on case analysis. This model significantly increases student engagement and 

transitions instruction from “knowledge delivery” to “skill development” and “cognitive 

cultivation.” 

3.3 Strengthening Practical Training: Enhancing Applied Competence 

A critical component of AI-enhanced courses is the development of a structured and robust practical 

training system. First, a semester-long data lab module should be established, in which students use 

AI tools to conduct modeling and analysis on real-world economic datasets, such as those from the 

National Bureau of Statistics or the World Bank. This module should encompass four key stages: 

data preprocessing, model construction, result validation, and economic interpretation—forming a 

complete learning cycle that links tools, methods, and real-world economic problems. Second, the 

course can be integrated with enterprise-based projects through collaborations with fintech firms, 

data analytics companies, or policy research institutions. These “AI + Economic Analysis” project s 

are designed to simulate actual business scenarios and enhance students’ ability to address practical 

challenges. Example topics include predicting customer churn rates for banks or forecasting 

regional GDP growth. Such tasks encourage students to apply econometric models and AI 



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algorithms in complex, dynamic environments. In addition, students should be encouraged to 

participate in AI modeling competitions and academic writing initiatives, both within and beyond 

the university. These opportunities help students develop scientific communication skills and 

improve their ability to present technical results. The accumulation of competition experience and 

project-based outcomes not only reinforces core competencies but also provides a portfolio of 

demonstrable achievements that supports future academic advancement and career development.  

 

4. Implementation and Impact of the Teaching Model Reform 

To evaluate the effectiveness of the AI-enhanced blended teaching model in econometrics, this 

study conducted a teaching reform experiment with undergraduate students majoring in Finance 

(Undergraduate students of 2023) at Sichuan International Studies University. The experimental 

group adopted an innovative model combining AI tools, project-based learning, and blended 

instruction, while the control group followed a traditional lecture-based approach. The results 

demonstrated that students in the experimental group significantly outperformed those in the control 

group in terms of average course grades, project quality, and classroom engagement. Specifically, 

the average score of the experimental group was 5.5 points higher than that of the control group, 

and over 80% of students in the experimental group effectively utilized AI tools such as Python and 

ChatGPT throughout the course. A post-course survey revealed that more than 90% of students in 

the experimental group expressed high satisfaction with improvements in learning interest, practical 

skill development, and the effectiveness of AI integration. The model also notably enhanced 

students’ ability to construct models and address real-world economic problems. However, it 

simultaneously posed greater demands on instructional design and technical infrastructure. Overall, 

the AI-enhanced blended teaching model significantly improved teaching effectiveness and offered 

a viable pathway and practical reference for the transformation of econometrics education under the 

New Liberal Arts framework. 

 

5. Conclusion and Future Outlook 

The AI-enhanced blended teaching model for econometrics has demonstrated significant advantages 

in both theoretical design and practical implementation. By integrating artificial intelligence 

technologies with economic modeling skills, the course has successfully overcome the longstanding 

challenges of outdated content, rigid instructional methods, and insufficient practical engagement 

inherent in traditional teaching approaches. Empirical findings indicate that this model effectively 

improves students’ data analysis capabilities, learning motivation, and classroom participation, 

thereby contributing to the development of comprehensive academic competencies.  

 



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This study not only offers a practical case for the reform of econometrics education but also 

provides a replicable framework for exploring the pedagogical application of AI technologies in 

economics-related courses. Moving forward, further course optimization should focus on 

strengthening training in model interpretability, fostering students’ critical and reflective use of AI 

tools, and promoting the construction of integrated resource platforms and interdisciplinary 

collaboration. These efforts will be essential for expanding the depth and breadth of the model’s 

application and for supporting the cultivation of high-quality, interdisciplinary economic talent in 

the digital era. 

 

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