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Generative AI in Business

Here you'll find a collection of the resources we've been sharing at UVA Darden School of Business to learn more about generative AI (GenAI) and its impact on our MBA curriculum and instruction.

Learning Technologies
Updated December 2024
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Joanne Meier
Sr. Assistant Dean, Digital and Instructional Initiatives
Darden School of Business
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01

Ideas from Business School Faculty for Incorporating GenAI

Poets & Quants

In this article, three business school academics at the University of Cambridge share how they are thinking about generative AI and incorporating it into their classes.

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Joanne Meier

I find it helpful to hear ideas from faculty about how ChatGPT may affect their teaching. I like this article because I get to hear perspectives from faculty in a variety of business sub-disciplines: marketing, organization theory, and information systems.

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If there is a single topic that has captivated business school academics in this past year, it is Generative AI. The popular use of Chat-GPT has caused business school faculty all over the world to consider not only how it might change the world of work and play, but also how to teach MBA and other business students how to use it. While best practice examples are still emerging, there is one clear consensus: It cannot be banned nor ignored. It is a technology that needs to be embraced given the already widespread impact AI is having on every aspect of the business world.
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02

What Questions Help us Understand the Impact of GenAI?

Darden Ideas to Action

This article suggests a framework to understand whether GenAI represents a true paradigm shift. Author and UVA Darden faculty member Mike Lenox and Tom Schaumburg poses three key questions to help understand the impact of GenAI in business. The questions create a framework for business educators as well.

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Joanne Meier

I found this article helpful because it frames GenAI technologies as something to consider more broadly as a way to understand their true impact. Considering a few key questions may help us position GenAI the right way.

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Executives and investors need to address three key questions when considering the latest technology trend. The answers to those questions are likely to depend on the specific sector. First, what kind of change does GenAI promise to the business sector? Second, what is the pattern of this change--real or false? And third, how does a business best position themselves in light of this change?

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03

Using GenAI Tools to Elevate Case-Method Teaching and Learning

Harvard Business Publishing

In this webinar, Professor Mitchell Weiss (Harvard Business School) describes using GenAI in case teaching and how students may use it for case prep.

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Joanne Meier

Professor Weiss's presentation emphasizes that GenAI tools can be used to elevate case-method teaching and learning; faculty have decisions to make about how to use cases to make that so. Proficiency is the first step, and I find this webinar helpful because Professor Weiss demonstrates advanced prompt engineering that results in highly useful results.

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04

Explainer Posts & Research to Practice

Ethan Mollick

In his Substack, Professor Ethan Mollick (University of Pennsylvania Wharton School of Business) translates academic research into usable information for the higher-education classroom. Many of his recent posts are about GenAI, and he’s written on a range of topics including creativity and boredom with GenAI.

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Joanne Meier

I appreciate this Substack because Ethan posts new articles every week or so, and each one tackles a topic instructors are probably thinking about. Mollick writes, "While being aware of the threats [of GenAI], I have tried to embrace the opportunities."

I found one recent post in particular very helpful for considering ways to incorporate GenAI into class assignments. You can also view this article in the “Leveraging AI in Assignments” collection on Teaching Hub.

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05

Visualizations and Animations that Explain How LLMs Work

Jay Alammar

This blog post by Jay Alammar explains how GPT3—a large language model used by ChatGPT—is trained and how it works.

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Joanne Meier

This webpage was recommended to me by a Darden faculty member as one that does a great job of explaining how ChatGPT works using simple animations and visualizations. The visuals help explain how these large language models (LLMs) get trained on enormous amounts of information. It’s a great resource for those new to AI. [Note that the visualizations are based on GPT3. The current free version is 3.5.]

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The tech world is abuzz with GPT3 hype. Massive language models (like GPT3) are starting to surprise us with their abilities. While not yet completely reliable for most businesses to put in front of their customers, these models are showing sparks of cleverness that are sure to accelerate the march of automation and the possibilities of intelligent computer systems. Let’s remove the aura of mystery around GPT3 and learn how it’s trained and how it works.
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06

Using GenAI in Academic Research

Anton Korinek

Dr. Anton Korinek (UVA Darden Professor and David M. Rubenstein Fellow at the Brookings Institution) describes dozens of use cases for large language models (LLMs) along six domains in economics research: ideation and feedback, writing, background research, data analysis, coding, and mathematical derivations. The paper also describes the value of prompt engineering, emergent capabilities, and limitations. Korinek posits that researchers can significantly increase their productivity by incorporating LLMs into their workflow.

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Joanne Meier

It's important to explore possibilities of LLMs in higher education research—this article provides some great examples.

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Generative AI, in particular large language models (LLMs) such as ChatGPT, has the potential to revolutionize research in economics and other disciplines. I describe dozens of use cases along six domains in which LLMs are starting to become useful as both research assistants and tutors: ideation and feedback, writing, background research, data analysis, coding, and mathematical derivations. I provide general instructions and demonstrate specific examples of how to take advantage of each of these, classifying the LLM capabilities from experimental to highly useful. I argue that economists can reap significant productivity gains by taking advantage of LLMs to automate micro tasks. Moreover, these gains will grow as the performance of LLMs across all of these domains will continue to improve. I also speculate on the longer-term implications of cognitive automation via LLMs for economic research. The online resources associated with this paper provide regular updates on the latest capabilities of LLM that are useful for economists.

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