Econ 100A: Intermediate Macroeconomic Theory

AI Tutor, Equitable learning, Improved Performance

Course Description

Catalog Description: Analysis of the determinants of the aggregate level of output, income, employment and prices.

Course Description: This course examines the economy as a whole, focusing on key issues such as unemployment, GDP, inflation, interest rates, and economic growth—topics frequently discussed in the media and central to public policy. This course explores the determinants of aggregate output, income, employment, and prices, as well as the role of monetary and fiscal policy in promoting economic stability. Students will develop an intermediate-level understanding of macroeconomic concepts and analytical tools, apply macroeconomic models to real-world issues, and critically evaluate policy decisions.

The course is structured as a lecture, has an enrollment capacity of 45 students, and serves as an upper-division core requirement for Economics majors.

Overview of the Redesign Project

Motivation and Redesign Details:
The motivation for my redesign project was to address persistent gaps in math skills among students, which previously hindered equitable learning outcomes and their ability to fully grasp macroeconomic theory. Students had often requested a TA or tutor due to the difficulty of the course, but funding limitations made this impossible. To bridge this gap, I adopted an AI tutor bot as a supplemental resource.

I carefully reviewed and screened all the tutoring content provided by the bot to ensure accuracy and appropriateness, deleting any unsuitable questions or hints. The AI tutor was designed not to give answers directly but to provide hints and guide students through problem-solving, encouraging active engagement and deeper learning. This approach ensured that students worked through the material themselves, which enhanced understanding and improved learning outcomes.

The primary goals of this redesign were to promote equity, increase accessibility to academic support, and improve student success without additional financial burden. This initiative aligns with departmental and campus priorities of leveraging technology to support diverse learners, fostering inclusive learning environments, and improving retention and achievement in challenging courses.

Assessment of the Redesign Project

To assess the impact of my redesign project on student learning, I compared performance data from the previous semester (without the AI tutor bot) to the redesigned course (with the AI tutor bot). Key findings include:

Homework Performance: The class average homework score improved by 8%, indicating stronger mastery of quantitative concepts.
Midterm Performance: Midterm scores also improved, with the mean increasing from 64.04 (Spring 2025) to 68.99 (Fall 2025), showing better understanding of macroeconomic theory and problem-solving skills.
Qualitative Feedback: Students reported that the AI tutor’s guided hints helped them work through problems independently, improving confidence and comprehension.

This table shows three ways I assessed my course redesign.
An Example of AI tutor's Response	Homework Performance	Midterm Performance
An example of AI tutor's response.	Homework performance improved by 8%.	Midterm performance also improved.

Reflection

My course redesign successfully integrated an AI tutor bot to address the wide variation in students’ math skills, a long-standing challenge in Econ 100A. This upper-division core course has traditionally been perceived as difficult, and students have consistently expressed a strong need for TA support. Due to limited funding, hiring qualified TAs was not feasible, but the AI tutor bot effectively filled this gap by providing on-demand assistance and personalized guidance. Students reported that the bot made the material more approachable and reduced frustration, leading to greater confidence and engagement with the course content. Their feedback highlighted the convenience and responsiveness of the tool, and performance on homework and exams improved significantly.

The main challenge was the extensive time required to review and refine the bot’s responses during the initial setup. However, now that the system is optimized, it is expected to function as a reliable virtual TA moving forward. Next steps include continuing to use the AI tutor bot in Econ 100A and exploring its adoption in other courses to further enhance student engagement and support individualized learning.

Faculty Biography

This table includes my faculty biography.
	Dr. Yan Zhou is a Professor of Economics. She teaches Intermediate Macroeconomic Theory, Introduction to Macroeconomics, International Finance, International Trade, and Research Methods of Economics.

**Program Sponsors**
	This work was supported by: Sacramento State Academic Affairs - Center for Teaching and Learning