Wen Gu

Associate Professor in the Practice of Information Systems & Operations Management | Emory University, Goizueta Business School

Atlanta, GA, UNITED STATES

Integrating innovative technology and practices into business education. Helps business students communicate and make decisions with data.

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Biography

Prof. Wenjun (Wen) Gu joined Emory University’s Goizueta Business School in Fall 2019 after serving on the faculty of Georgia State University’s Robinson College of Business. A recognized leader in AI-powered pedagogical solutions, he focuses on integrating Large Language Models (LLMs) and emerging technology into business education to enhance both student engagement and learning outcomes.

Major Innovations in AI-Powered Education

- AI-Powered Virtual Teaching Assistant
* Python Toolkit (BUS 390A) and SQL Toolkit (BUS 390C): Real-time coding assistance, interactive tutorials, and practical data analytics exercises.
* ISOM 352: Applied Data Analytics and ISOM 550: Data and Decision Analytics: Customized coding challenges and instant error correction, ensuring mastery of critical analytical methods.
- LLM-augmented Autograder
Utilizes cutting-edge LLMs to design measurable rubrics, deliver rapid and accurate grading, and enable multi-stage validation and regrading. Seamlessly integrated with Canvas, it streamlines instruction and keeps gradebooks up-to-date in real time.
- Intelligent Academic Advisor
Leverages AI-driven insights to guide students on course selection, degree requirements, and academic policies. By aligning individual goals with targeted opportunities, it promotes informed decision-making and smooth academic progress.

Pedagogical Research and LLM Integration

Prof. Gu’s research explores how LLM-based Virtual TAs and AI-driven autograders shape student engagement, satisfaction, and knowledge retention. Through data-driven studies on real-time feedback and adaptive assessment, he refines educational strategies that optimize learning outcomes in both undergraduate and graduate business analytics courses.

Research Interests

- AI in Education: Virtual TAs, AI-based Autograding, and Intelligent Advising
- Decision Analysis & Data Analytics for Operations
- Green Product Design & Operations: Innovations in remanufacturing and environmental impact
- Pedagogical Innovations: Adaptive learning, AI integration, and technology-enhanced delivery

Before embracing AI-driven learning, Prof. Gu gained recognition for pioneering interactive content in online and hybrid classrooms. His paper, “Is Remanufacturing Necessarily Environmentally Friendly?”, received the Best Paper award at the 2012 Decision Science Institute Annual Conference.

Education (3)

University of Illinois at Urbana-Champaign: PhD, Operations Management 2012

Shanghai Jiao Tong University: MS, Management Science 2006

Shanghai Jiao Tong University: BE, Costal Engineering and Accounting 2003

Areas of Expertise (7)

Data Analytics

Articifical Intelligence

Artificial Intelligence and Education

Decision Analysis

PEdagogical Research in Higher Education

Green Product Design

Operations and Marketing Interface

Publications (2)

Effects of Remanufacturable Product Design on Market Segmentation and the Environmen

Decision Sciences Journal

Shi, T., Gu, W., Chhajed, D., and Petruzzi, N. C.

2016-01-19

Despite documented benefits of remanufacturing, many manufacturers have yet to embrace the idea of tapping into remanufactured-goods markets. In this article, we explore this dichotomy and analyze the effect of remanufacturable product design on market segmentation and product and trade-in prices by studying a two-stage profit-maximization problem in which a price-setting manufacturer can choose whether or not to open a remanufactured-goods market for its product. Our results suggest that it is optimal for a manufacturer to design a remanufacturable product when the value-added from remanufacturing is relatively high but product durability is relatively low and innovation is nominal. In addition, we find that entering a remanufactured-goods market in and of itself does not necessarily translate into environmental friendliness. On the one hand, the optimal trade-in program could result in low return and/or remanufacturing rates. On the other hand, a low price for remanufactured products could attract higher demand and thereby potentially result in more damage to the environment. Meanwhile, external restrictions imposed on total greenhouse gas emissions draw criticism in their own right because they risk stifling growth or reducing overall consumer welfare. Given these trade-offs, we therefore develop and compare several measures of environmental efficiency and conclude that emissions per revenue can serve as the best proxy for emissions as a metric for measuring overall environmental stewardship.

Quality design and environmental implications of green consumerism in remanufacturing

International Journal of Production Economics

Gu, W., Chhajed, D., Petruzzi, N. C., and Yalabik, B.

2015-04-15

We study quality design and the environmental consequences of green consumerism in a remanufacturing context. Specifically, a firm has the option to design a non-remanufacturable or a remanufacturable product and to specify a corresponding quality, and the design choices affect both the production costs and consumer valuations associated with the product. On the cost side, remanufacturable products cost more to produce originally, but less to remanufacture, than non-remanufacturable products cost to produce. Analogously, on the consumer side, remanufacturable products are valued more, but remanufactured products are valued less, than non-remanufacturable products are valued. Given this, we investigate the environmental consequences of designing for remanufacturability by first defining a measure of environmental impact that ultimately is a function of what is produced and how much is produced, and then applying that measure to assess the environmental impact associated with the firm׳s optimal strategy relative to the environmental impact associated with the firm׳s otherwise optimal strategy if a non-remanufacturable product were designed and produced.

Working Papers/Projects (1)

Evaluating the Efficacy of Virtual Teaching Assistants in a Business Data Analytics Course

Who is Using It, and Does It Work?

2025-03-05

The integration of Virtual Teaching Assistants (VTAs) powered by Large Language Models (LLMs) is transforming business education by offering real-time coding support, automated feedback, and adaptive learning pathways. This study investigates the adoption patterns, engagement levels, and learning outcomes associated with VTAs in data analytics courses. Using student interaction data from ISOM 352 and ISOM 550, we analyze who is using VTAs, how frequently they engage, and the impact on academic performance. By applying a mixed-methods approach—combining quantitative performance metrics, engagement analytics, and qualitative student feedback—we assess whether these AI-powered tools improve knowledge retention, problem-solving efficiency, and student satisfaction. Initial findings suggest that frequent VTA users demonstrate higher assignment completion rates and course satisfaction, compared to non-users. However, disparities in adoption highlight the need for personalized onboarding strategies to maximize accessibility and effectiveness. This research contributes to the growing body of AI in education, offering actionable insights on optimizing LLM-driven Virtual TAs for scalable, equitable, and effective learning in data analytics and beyond.