Artificial intelligence enables increasingly complex interactions between humans and machines. This technology, currently popularized by ChatGPT, provides a potentially huge opportunity for business professionals, business students, and business educators. However, these same technologies pose equally daunting challenges for teachers trying to illustrate, explain, and apply these ideas to students. How can teachers intentionally and openly incorporate AI into our classrooms while reducing the risk of students substituting the AI’s output for their own (aka “cheating”)?
The term “metaverse” was first popularized in 2003 by Neal Stephenson in a science fiction novel titled “Snow Crash.” It is no longer fiction. The idea gained momentum for online multiplayer games like World of Warcraft. Now it has evolved to allow all kinds of human interactions, including commerce, in two and three dimensions. Perhaps the “person” you are talking to is not really a person but a machine. When combined with artificial intelligence (AI), blockchain, and decentralized autonomous organizations (meaning, without humans), collectively known as Web3, we are on the next frontier for our students to find business opportunities.
This is also a new frontier for education, enhancing the online and hybrid experiences that have been employed during the pandemic to reach students in remote areas with immersive, hands-on experiences. This could be the beginning of the “death of distance”, where learning can happen anyway with internet access.
As a professor at Harvard, Stanford, and Hult, I ask graduate students studying innovation to use AI in their core tasks. Here is the method of my madness. First, I ask student teams to envision new businesses that employ multi-sided platform marketplaces or generate revenue in the purely virtual world of Decentraland. They record these presentations on video. Second, I randomly assign students to critique these team presentations. In the template I provide, I ask a number of different questions: For example, how did the team design their network effects, and how did they mitigate some of the headwinds that undermine the network effect? The last question in the series requires students to ask ChatGPT to write their own critique of the team’s idea. This requires students to iteratively improve the query so that AI provides an optimally useful answer. This is a skill, asking the right question, that the next generation of business leaders must learn.
I also require students to independently verify the accuracy of the ChatGPT answer. Borrowing an expectation from Wharton associate professor Ethan Mollick, I state that students are responsible for the final conclusions they draw from AI. They must find sources to reinforce or refute AI’s response. As with any external tool or resource, students must include an accurate citation of their use of ChatGPT. Since I don’t know the ideal formal format for such a quote, I suggest asking ChatGPT how to quote ChatGPT.
There are several objectives that underlie this part of the task. First, students must learn to harness AI to enhance the breadth, depth, and articulation of their knowledge. The AI is out of the box. We can’t put it back. Instead, as with fire, iron, light, and other mind-bending innovations, professionals must learn how to better implement technology to solve problems for clients and society. Universally accessible AI will also change the skills business graduates are expected to implement soon after graduation. Instead of collecting and manipulating data to support business decisions, professionals must master the art of asking the right question so that Web3 scans and organizes all the existing public knowledge to produce a useful answer. Instead of building a sustainable competitive advantage on intellectual property protected by patents or trademarks, the professionals of the future will profit through the speed of adaptation.
Second, the AI is not infallible. It doesn’t just fail because of the data it bases its answers on. It is also biased by the people asking the question and how they do it. The only way to improve your conclusions, and thus the data that the future AI machine will consider, is to make humans responsible for the accuracy of the AI’s conclusions. This is the same method that academia used with the proliferation of books, with the development of statistical methods, and with trust in the Internet: authors are responsible for their conclusions.
Third, this task forces students to test their own wisdom against that of the AI so that they can personally and directly compare their own experiences and logic with the AI’s responses. This reinforces the importance of human creativity and intuition as a driver of innovation.
AL can be good news for education. There is also a dark side: Web3 will pose challenges for both the teacher and the student. The anonymity and vastness of virtual worlds, which could boost equity and diversity, can lead to distractions and inappropriate interactions. DecentraLand, for example, is accessible in the two-dimensional browser to anyone without the need for identity verification. An avatar, a fake name, and poor impulse control could lead students down unproductive paths.
AI is now fully capable of building a student’s homework. I asked ChatGPT: “How can a teacher tell if a student wrote an essay themselves or used ChatGPT?” Answered:
There are several ways a teacher can tell if a student wrote an essay on their own or used a tool like ChatGPT to generate the text. One approach is to use plagiarism detection software, which can identify whether the essay matches any existing text on the Internet. This can be a reliable way to determine if a student used ChatGPT or another text generation tool, since such tools often produce output similar to existing text.
Another approach is to ask the student directly. A teacher might ask the student to explain her thought process and reasoning behind the essay, and compare her response to the essay itself. If there are significant discrepancies between the student’s explanation and the essay, it could be a sign that the student used a tool like ChatGPT to generate the text.
In addition, a teacher could also look for telltale signs of text generation in the essay itself. For example, the essay may contain repetitive or nonsensical sentences, lack coherence or logical structure, or use language or terminology that is not typical for a student at the appropriate level. All of these could be indicators that the essay was generated by a tool like ChatGPT.
This answer itself is almost impossible to distinguish from one written by a real person! Note that he even inserted gender-neutral language and transition words. Even after a decade of reading student submissions, I can’t tell this AI response from that of a human MBA.
A new tool, GPTZero, endeavors to use AI to determine whether an essay was written by AI by calculating its “perplexity” score, which measures consistency (from AI) or inconsistency (from humans). of sentence structure and word choice. A perplexed essay by a human being has many inconsistencies. The obvious hole in this tool is asking ChatGPT to build an essay with a lot of perplexity.
It’s an annoying axiom that technology advances faster than culture. The launch of ChatGPT a few months ago has already changed the landscape of education. My purpose as a servant of learning is not to fawn over academic tradition, but to discover new ways to prepare MBAs for the professional world they will soon enter. This includes ChatGPT. More to come.