Innovation and Evaluation of AI-Driven Economics Experimental Course Teaching Model
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Abstract
To address the shortcomings of current outcome-oriented static evaluation mechanisms in economics experimental teaching, which are lagging behind and unable to accurately measure the evolution of students’ higher-order economic decision-making thinking, this paper proposes an AI (Artificial Intelligence)-driven innovation and dynamic evaluation system for economics experimental teaching, integrating a large language model and a time-series tracking algorithm. First, in terms of teaching model innovation, an LLM (Large Language Model) agent with dynamic risk preferences and memory mechanisms is introduced as a virtual market participant to reconstruct a highly realistic and adaptive economic game scenario. Second, a seamless multimodal data acquisition engine is constructed, which, in addition to extracting clickstream and latency data, utilizes an NLP (Natural Language Processing) model to deeply analyze the textual sentiment of negotiation dialogues; combined with economic theory, the fragmented interaction logs are transformed into quantitative economic features representing the degree of risk probing and information processing resistance. Finally, in terms of effect evaluation and feedback design, a deep tracking model based on the attention mechanism is constructed to accurately map the feature matrix to students’ economic cognitive state, and then calculate a dynamic decision rationality index. Simultaneously, by focusing on the extraction of key price adjustment decision nodes using the model’s attention weights, high theoretical interpretability of the process evaluation is achieved, and this is used as a trigger condition to provide students with LLM-driven real-time personalized intervention feedback. The deployment results of this system in the course “Two-Way Auctions and Oligopoly Markets” showed that the decision rationality index of the experimental group students significantly increased by 0.278 compared to the traditional group, and the dynamic comprehensive evaluation score output by the system highly matched the students’ final professional ability performance (Pearson correlation coefficient r=0.88). This study effectively addresses the interpretability concerns of the traditional teaching evaluation’s ‘algorithm black box’ by establishing a differentiable mapping between model attention weights and specific economic decision-making indicators, thereby providing a new path for the reconstruction of the economics experimental teaching paradigm in the digital age.
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References
L. Mihai, L.-G. Manescu, L. Vasilescu, et al., “A systematic analysis of new approaches to digital economic education based on the use of AI technologies,” Amfiteatru Economic, vol. 26, no. 65, pp. 201-219, 2024, doi: 10.24818/EA/2024/65/201.
O. Glazunova, T. Saiapina, V. Korolchuk, et al., “Formation Digital Intelligence of a Modern Economist: A Competence Approach,” INSTICC, vol. 1, no. 1, pp. 432-447, 2023, doi: 10.5220/0012065100003431.
M. Kesse, “Designing a digital interactive simulation for teaching business analytics, strategy, and economics,” The Journal of Applied Business and Economics, vol. 26, no. 5, pp. 122-138, 2024, doi: 10.33423/jabe.v26i5.7339.
J. Collins A and S. Etemadidavan, “Interactive agent-based simulation for experimentation: a case study with cooperative game theory,” Modelling, vol. 2, no. 4, pp. 425-447, 2021, doi: 10.3390/modelling2040023.
A. Calinescu, “The Grading Process in System 1 and System 2 of Thinking: A Behavioral Economic Approach to Evaluation,” Integrated Journal for Research in Arts and Humanities, vol. 3, no. 6, pp. 105-122, 2023, doi: 10.55544/ijrah.3.6.12.
J. Liu and Y. Wang, “Static and dynamic evaluations of college teaching quality,” International Journal of Emerging Technologies in Learning (iJET), vol. 17, no. 2, pp. 114-127, 2022, doi: 10.3991/ijet.v17i02.29005.
M. G. Hahn, S. M. B. Navarro, F. Valentin L. D. L., et al., “A systematic review of the effects of automatic scoring and automatic feedback in educational settings,” IEEE Access, vol. 9, no. 1, pp. 108190-108198, 2021, doi: 10.1109/ACCESS.2021.3100890.
D. Danz, L. Vesterlund, and J. Wilson A, “Evaluating behavioral incentive compatibility: Insights from experiments,” Journal of Economic Perspectives, vol. 38, no. 4, pp. 131-154, 2024, doi: 10.1257/jep.38.4.131.
M. Yang and D. Li, “Personalized Intelligent Recommendation Model for Educational Games Based on Data Mining,” Information Technology and Control, vol. 54, no. 1, pp. 64-83, 2025, doi: 10.5755/j01.itc.54.1.37088.
M. Liu, C. Li, Z. Pan, et al., “Mining big data to help make informed decisions for designing effective digital educational games,” Interactive Learning Environments, vol. 31, no. 5, pp. 2562-2582, 2023, doi: 10.1080/10494820.2019.1639061.
M. Wang, D. Zhang, J. Zhu, et al., “Effects of incorporating a large language model-based adaptive mechanism into contextual games on students’ academic performance, flow experience, cognitive load and behavioral patterns,” Journal of Educational Computing Research, vol. 63, no. 3, pp. 662-694, 2025, doi: 10.1177/07356331251321719.
E. Huber S, K. Kiili, S. Nebel, et al., “Leveraging the potential of large language models in education through playful and game-based learning,” Educational Psychology Review, vol. 36, no. 1, pp. 25-45, 2024, doi: 10.1007/s10648-024-09868-z.
Y. Zheng, Z. Xu, and A. Xiao, “Deep learning in economics: a systematic and critical review,” Artificial Intelligence Review, vol. 56, no. 9, pp. 9497-9539, 2023, doi: 10.1007/s10462-022-10272-8.
Y.-T. Shiao, C.-H. Chen, K.-F. Wu, et al., “Reducing dropout rate through a deep learning model for sustainable education: long-term tracking of learning outcomes of an undergraduate cohort from 2018 to 2021,” Smart Learning Environments, vol. 10, no. 1, pp. 55-71, 2023, doi: 10.1186/s40561-023-00274-6.
M. Fernandez J, A. Yetter E, and K. Holder, “What do economic education scholars study? Insights from machine learning,” The Journal of economic educaTion, vol. 52, no. 2, pp. 156-172, 2021, doi: 10.1080/00220485.2021.1887027.
M. Dell, “Deep learning for economists,” Journal of Economic Literature, vol. 63, no. 1, pp. 5-58, 2025, doi: 10.1257/jel.20241733.
M. Rivera, P. Lee, and A. Desai, “Real-Time Knowledge Tracing in Online Learning Environments Using Interpretable Transformer-Bayesian Models,” Frontiers in Interdisciplinary Educational Methodology, vol. 2, no. 3, pp. 165-178, 2025, doi: 10.71465/fiem360.
R. Bernsteiner, C. Ploder, Y. Su, et al., “Design principles of real time students’ performance monitoring in simulation-based business education,” Procedia Computer Science, vol. 266, no. 1, pp. 1147-1154, 2025, doi: 10.1016/j.procs.2025.08.142.
M. Rizinski and D. Trajanov, “AI Agents in Finance and Fintech: A Scientific Review of Agent-Based Systems, Applications, and Future Horizons,” Computers, Materials, & Continua, vol. 86, no. 1, pp. 1-35, 2026, doi: 10.32604/cmc.2025.069678.
Y. Yu, Z. Yao, H. Li, et al., “Fincon: A synthesized llm multi-agent system with conceptual verbal reinforcement for enhanced financial decision making,” Advances in Neural Information Processing Systems, vol. 37, no. 1, pp. 137010-137045, 2024, doi: 10.52202/079017-4354.
Y. Dong, F. Wu, K. Zhang, et al., “Large language model agents in finance: A survey bridging research, practice, and real-world deployment,” Findings of the Association for Computational Linguistics: EMNLP, vol. 2025, no. 1, pp. 17889-17907, 2025, doi: 10.18653/v1/2025.findings-emnlp.972.
S. Ellsworth and T. Kingsley, “Accelerating Financial Intelligence via High Throughput Distributed Systems for Large Language Model Augmented Time Series Forecasting,” International Journal of Artificial Intelligence Research, vol. 1, no. 1, pp. 1-10, 2026.
E. Ulitzsch, Q. He, V. Ulitzsch, et al., “Combining clickstream analyses and graph-modeled data clustering for identifying common re- sponse processes,” psychometrika, vol. 86, no. 1, pp. 190-214, 2021, doi: 10.1007/s11336-020-09743-0.
Z. Wang, J. Wei, A. Aiken, et al., “LogCIoud: Fast Search of Compressed Logs on Object Storage,” Proceedings of the VLDB Endowment, vol. 18, no. 8, pp. 2362-2370, 2025, doi: 10.14778/3742728.3742733.
W. Ke, Y. Zheng, Y. Li, et al., “Large language models in document intelligence: A comprehensive survey, recent advances, challenges, and future trends,” ACM Transactions on Information Systems, vol. 44, no. 1, pp. 1-64, 2025, doi: 10.1145/3768156.
M. Herrera, M. Sasidharan, J. Merino, et al., “Handling irregularly sampled IoT time series to inform infrastructure asset management,” IFAC-PapersOnLine, vol. 55, no. 19, pp. 241-245, 2022, doi: 10.1016/j.ifacol.2022.09.214.
C. F. Lui, Y. Liu, and M. Xie, “A supervised bidirectional long short-term memory network for data-driven dynamic soft sensor modeling,” IEEE Transactions on Instrumentation and Measurement, vol. 71, no. 1, pp. 1-13, 2022, doi: 10.1109/TIM.2022.3152856.
S. Mei, X. Li, X. Liu, et al., “Hyperspectral image classification using attention-based bidirectional long short-term memory network,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, no. 1, pp. 1-12, 2021, doi: 10.1109/TGRS.2021.3102034.
E. Rouskas, “Mergers, multiperiod Cournot competition, and Coasian dynamics,” Bulletin of Economic Research, vol. 75, no. 2, pp. 270-286, 2023, doi: 10.1111/boer.12348.
T. Willis and G. Punzo, “Multi-layer Cournot-congestion model,” IFAC-PapersOnLine, vol. 55, no. 40, pp. 61-66, 2022, doi: 10.1016/j.ifacol.2023.01.049.