Generative AI in Political Economy Teaching: A Classroom Practice Study

DOI

10.2991/978-94-6239-733-0_4How to use a DOI?

Keywords

Generative AI; Political Economy; Large-Class Instruction; Student Engagement

Abstract

The Political Economy course is a theoretical economics course characterized by high levels of abstraction and complex logical chains of reasoning. In large-class teaching environments, students often struggle to maintain effective engagement throughout the high-density, continuous theoretical lectures. Based on this, this paper focuses on undergraduate political economy courses to explore suitable methods for integrating generative artificial intelligence into classroom teaching and its pedagogical effectiveness. In instructional design, generative AI is not merely used as a simple content-generation tool but rather as a scaffolding aid in the teaching process. It is mainly reflected in generating numerical case studies, guiding theoretical reasoning through structured questions, and providing real-time exercises and mind-map summaries. During the explanation of key concepts, guiding questions and simplified or biased interpretations are introduced to help students identify logical gaps and refine the derivation process, thereby achieving a shift from passive reception to active construction.

This study selected two parallel classes with essentially identical teaching conditions for comparative analysis. One class served as the experimental group, in which generative AI was integrated as a teaching aid, while the other served as the control group and followed conventional lecture-based instruction. The results showed that students in the experimental group demonstrated higher engagement in classroom discussions, along with improved completion rates and accuracy in chapter exercises. Furthermore, the grade distribution in the experimental group was more concentrated, with a lower proportion of students receiving low grades and no instances of failing the course.

Overall, when generative AI aligns with the intrinsic logic of theoretical instruction and is embedded within a structured instructional design, it can serve as a cognitive scaffold to promote student engagement and reinforce the depth and stability of their understanding of theoretical logic, thereby playing a positive role in large-class theoretical course instruction.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Na Qu
AU  - Qinghui Wang
AU  - Fang Cui
PY  - 2026
DA  - 2026/07/17
TI  - Generative AI in Political Economy Teaching: A Classroom Practice Study
BT  - Proceedings of the 6th International Conference on Internet, Education and Information Technology (IEIT 2026)
PB  - Atlantis Press
SP  - 18
EP  - 27
SN  - 2667-128X
UR  - https://doi.org/10.2991/978-94-6239-733-0_4
DO  - 10.2991/978-94-6239-733-0_4
ID  - Qu2026
ER  -