Paper Summary
Source: arXiv (0 citations)
Authors: Rama K. Malladi
Published Date: 2023-08-15
Podcast Transcript
Hello, and welcome to paper-to-podcast. Today, we'll be diving headfirst into the wild world of AI taking finance exams. Yes, you heard that correctly. Our subject matter today is a fascinating paper by Rama K. Malladi titled "Emerging Frontiers: Exploring the Impact of Generative AI Platforms on University Quantitative Finance Examinations."
Here's how it went down. Our brave AI contenders—ChatGPT, BARD, and Bing AI—were thrust into the academic gauntlet, faced with answering 20 questions from a simulated undergraduate finance test. Now, did they pass with flying colors? Nope. Actually, it was more of a faceplant. ChatGPT led the group with a score of 30%, Bing AI managed a soft 20%, and BARD, poor BARD, was left trailing in the dust at 15%.
The AI models did show some academic prowess, understanding the context of the questions and even picking the right formulas most of the time. But when it came to doing the actual math—exponents, logs, that sort of thing—they stumbled. So, they may not be acing finance exams anytime soon, but they could make decent study buddies.
Now, the methods used in this study were quite intriguing. Imagine an episode of "AI's Got Talent," where the AI platforms, powered by large language models, had to answer these finance questions without any previous answers to cheat from. A reality show where the contestants can't sing covers; they have to perform original songs.
The strengths of this study lie in its application of AI technologies to real-world academic scenarios, specifically undergraduate finance exams. The researchers pulled off a commendable job in designing an exam with questions of varied difficulty levels, much like actual finance exams. They also ensured that the questions were entirely original to prevent AI platforms from recalling solutions from previous training.
However, every study has limitations, and this one is no exception. The chatbots were tested on a specific undergraduate finance exam. So, the results might not generalize to other subjects or difficulty levels. The bots were tested on their ability to compute answers, but not on their ability to explain these computations. The study also doesn't address how the chatbots handle qualitative questions, a significant part of finance exams.
Despite these limitations, the potential applications of this study are fascinating. The research could inspire the development of more sophisticated AI tutoring systems in the future. It also highlights the need for academic institutions to update their examination and academic integrity policies, considering AI's increasing capabilities.
In short, while our AI pals here might not be ready to ace your finance exam, they're making strides in the right direction. So, next time you're stuck on a pesky finance problem, you might want to call on your AI study buddy for some help. Just remember, they aren't great at doing the math... yet.
Thank you for tuning in to paper-to-podcast. We hope you enjoyed this episode as much as we did. You can find this paper and more on the paper2podcast.com website.
Supporting Analysis
This research paper put three Artificial Intelligence (AI) language models through a finance exam to test their abilities! The AI models—ChatGPT, BARD, and Bing AI—were asked to answer 20 questions from an undergraduate finance test. The results? Well, let's just say they wouldn't have passed the class. ChatGPT led the pack with a score of 30%, while Bing AI trailed behind at 20%, and BARD was left eating dust with a 15% score. The AIs didn't exactly ace the exam, but they didn't totally bomb it either. They managed to understand the context of the questions and even picked the right formulas to use most of the time. But, when it came to actually doing the math—like calculating exponents and logs—they stumbled. So, while these AI models might not be ready to take your finance exam for you, they could still be useful study buddies. Just don't expect them to do all the heavy lifting!
This research is like an exciting episode of "AI's Got Talent"! In the spotlight are three artificial intelligence (AI) platforms: ChatGPT, BARD, and Bing AI. They're all powered by large language models (LLMs), which are like the big brains of AI, trained on tons of text and computer code. The challenge? To answer 20 quantitative questions from a made-for-this-study undergraduate finance exam. The questions range from "very easy" to "very hard" and cover topics typically found in finance textbooks. The AI platforms get points if their answers are within 99% to 101% of the expected answer. No credit is given for answers outside this range. The researchers then analyzed each AI's performance based on their scores. Importantly, the exam was specially designed for this research, so the AI platforms couldn't cheat by finding answers from previous students online. This is like a reality show where the contestants can't sing covers; they have to perform original songs. With this awesome setup, the researchers really put these AI platforms to the test!
The most compelling aspect of this research is the application of emerging AI technologies to real-world academic scenarios, specifically in the context of undergraduate finance exams. The researchers have done a commendable job in designing an assessment with varied difficulty levels, which mirrors the structure of actual finance exams. They have also ensured that the questions are entirely original to prevent AI platforms from recalling solutions from previous training. The researchers demonstrated best practices by providing a comprehensive and transparent account of their methodology. Their decision to disclose the specific scoring range for answers, and their thorough explanation of the AI platforms' capabilities and limitations, contribute significantly to the study's credibility. Furthermore, their exploration of the ethical implications of AI in academic settings is an essential and responsible consideration in such research. They've also meticulously documented their findings, making it easier for others to replicate the study and verify their results. This paper is a brilliant example of how to conduct and present AI research in a clear, comprehensive, and responsible manner.
This study has some limitations that could impact the findings. One key issue is that the AI chatbots were tested on a specific undergraduate quantitative finance exam, so the results might not generalize to other subjects or difficulty levels. Also, the AI chatbots were tested on their ability to compute answers to questions, but not on their ability to explain these computations in a way that a student could understand and learn from. The study also doesn't address how the chatbots handle qualitative questions, which are often a significant part of finance exams. Finally, the chatbots' performance was assessed based on a single exam attempt, which might not fully capture their capabilities or potential for improvement over time.
The research on generative AI platforms and their performance in academic environments has important implications for both education and AI development. The study presents a unique application of AI in tutoring or self-learning situations, particularly in subject areas like finance. It could potentially inspire the development of more sophisticated AI tutoring systems in the future. The findings also highlight the need for academic institutions to adapt and update their examination and academic integrity policies, considering the increasing capabilities of AI. Moreover, the examination of AI's performance in the academic context could guide the development of more advanced AI models that can accurately handle complex computations and formula selection. Lastly, the research may also encourage further cross-disciplinary studies to determine the effectiveness of AI chatbots in various academic fields beyond finance.