Harnessing AI in Finance: Eric So’s Innovative Take on Teaching Value Investing

Eric So, Sloan Distinguished Professor of Global Economics and Management

Sloan Distinguished Professor of Global Economics and Management, Eric So’s innovative assignment at MIT Sloan School of Management invites students of his Alphanomics class to initiate such a dialogue. However, the goal isn’t simply to teach the AI to think like Buffett, but rather to prompt it to write an essay in the voice of the legendary investor, aimed at explaining the concept of value investing to high school students.    

So, what makes this AI-generated essay such an effective base material for learning? The answer lies in forcing students to break through the illusion of explanatory depth (IOED; The Decision Lab, n.d.). The IOED argues that we often harbor the illusion that our grasp of the world is more comprehensive than it truly is. It’s only when we’re called upon to articulate a concept that we confront the boundaries of our understanding. By having students coach and refine the machine’s output, the assignment serves as an engaging point of departure, offering students a synthesized understanding of complex investment principles framed in the recognizable language of one of the world’s most famous investors. 

Rather than being passive consumers of information, Professor So’s approach transforms students into teachers in their learning journey. Through the use of generative AI as a starting point for a more nuanced exploration of economic theories, this assignment enriches both individual learning experiences and the broader classroom dialogue. 

The Journey Beyond the Prompt: Critique & Refine

Following the creation of the initial AI essay, students are tasked with two further steps: critique and refine. By critically evaluating the AI’s essay, they engage in a hands-on exploration of value investing principles. This encourages them to identify gaps, question assumptions, and even catch any algorithmic ‘shortcuts’ or errors in logic. Students are then required to provide the AI with feedback to improve its understanding of value investing.

This iterative process is particularly effective for several reasons. First, it engages students in active learning. By taking on the role of both learner and educator, they develop a multi-dimensional understanding of the subject matter. Second, the assignment fosters critical thinking and problem-solving skills. Lastly, by having to express their thoughts clearly and logically during the critique and refinement steps, students enrich their learning experience in a more inclusive manner that accommodates diverse ways of demonstrating knowledge and skills (CAST, 2018).

AI-Enhanced Pedagogy: Advantages for Faculty

For faculty members contemplating the integration of generative AI into their pedagogy, So’s approach offers a compelling case study in effectiveness and efficiency:

• Elevated Classroom Dialogue: Students come to class having engaged in a dynamic process with the AI, bringing not just questions but also refined essays that are enriched with their own insights.
• Informed, Efficient Assessments: By examining the original AI essay, the student’s critique, and the final revised essay, faculty can gain a nuanced understanding of each student’s learning progression.
• Long-Term Sustainability: With a properly designed AI framework, faculty can reuse and adapt the assignment for different classes and evolving curricula.
• Personalized Learning Opportunities: The detailed insights gleaned from how each student interacts with and refines the AI-generated content can be leveraged to customize future teaching strategies (CAST, 2018).

A Paradigm Shift

Professor So’s assignment illuminates the rich potential of a self-reinforcing cycle of teaching and learning. In the role of both educators and learners, students deepen their understanding of value investing, while faculty benefit from a multi-dimensional platform for assessing and guiding student progress. It’s not just about adapting to the modern educational landscape; it’s about shaping its future.

Through this inventive blend of generative AI and pedagogy, Professor So demonstrates how technology can serve as a catalyst for enriching both teaching and learning in profoundly interactive and inclusive ways.

References

CAST. (2018). Universal design for learning guidelines. http://udlguidelines.cast.org

The Decision Lab. (n.d.). The illusion of explanatory depth. https://thedecisionlab.com/biases/the-illusion-of-explanatory-depth