My Experiments with a Groundbreaking New AI Writing System (Article 1/2)
This article represents my honest perspective based on first-hand experience testing a GenAI tool, not a paid or sponsored promotion.
Written by Sai Gattupalli
Disclaimer:
I recently got to test drive an exciting new AI writing tool that is simply truly next-level, and unlike anything I've experienced before, such as ChatGPT, Bard, Claude, or other GenAI systems. Although I cannot name the tool due to obvious (NDA, sort of) reasons, the interface and capabilities of this tool are truly groundbreaking. It was developed by a leading AI research institute, that has previously developed many computer vision, and visualization tools, among many others, towards humanity's intellectual progress. This tool is not available to the public yet.
I decided to give it a spin using a couple of my own published academic papers to see what it could come up with. The posts below were generated entirely by this cutting-edge AI tool summarizing key points from my research.
While it did an amazing job extracting and explaining core concepts from my papers, the unique perspectives and opinions expressed are the AI's own. I only made minor edits for clarity and flow.
I'm sharing these AI-written articles as an experiment to showcase this innovative new technology. The future is here! Let me know your thoughts on these machine-generated takes on my work in the comments.
The original paper supplied is located here.
Below is the summary, which came into being after spending slightly over 1 hour interacting with the tool.
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Exploring the Efficacy of Large Language Models in Generating Math Hints: A Comparative Study with Human Teachers in K12 Education
Purpose of hints in math education:
Hints in math education in K12 serve as guidance to help students understand complex problems. They stimulate critical thinking, promote problem-solving skills, and prevent students from getting stuck or frustrated. Hints provide "scaffolding", encourage independent learning by nudging students towards the correct solution without directly providing the answer.
Challenge: (This section was generated completely out of prompts, and no scholar work was supplied. Yet, it is true to fact.)
Lack of Experienced Teachers: Experienced math teachers who have a deep understanding of the subject and the ability to simplify complex concepts are hard to find. They are also better equipped to provide effective hints and strategies to students. The lack of such teachers makes the production of quality hints even more challenging. Human resources are always hard to find, especially when in financial crunch, such as lack of research funds/grants.
Utility of LLM for hint generation:
Large language models, like OpenAI's GPT4, have been trained on a diverse range of internet text. They can generate creative, coherent, and contextually relevant text based on the input they receive. This makes them highly useful in a variety of applications, including generating math hints.
Mathematical models can be incredibly beneficial as they offer detailed guidance to solve problems, simplify complex concepts, and even create new practice problems. They are especially valuable in educational environments. Here, they can offer personalized help to students, aiding them in understanding and solving math problems at a pace that suits them. Additionally, these models make learning more engaging by being conversational, interactive, and dynamic.
However, it's important to note that while these models are powerful, they are not perfect. They do not truly understand the content they are generating and can sometimes produce incorrect or nonsensical responses. They should be used as a tool to aid learning, not as a sole source of information. In our study, we collected perceptions, opinions, and feedback from pre-service teachers, enrolled at a US public research university, to assess their views on the math hints generated by Large Language Models (LLMs) versus those crafted by experienced human teachers.
What did we find out?
In this study involving five math word problems from a math intelligent tutoring system, pre-service teachers provided feedback on their prefered choice of hints (i.e. human crafted vs. LLM-generated). Two of the problems were favored when they were crafted by humans, primarily due to the inclusion of a visual hint, a feature that our chosen LLM was unable to generate at the time. Conversely, two other problems were preferred when generated by the LLM, as these included simple step-by-step guides, interconnected hints, and more accessible language. The final problem elicited mixed responses from the participants.
What are the implications?
An implication of this study could be that Large Language Models (LLMs) like OpenAI's GPT4 can be effectively used as a supplementary tool in math education, particularly in situations where there is a lack of experienced teachers. The LLMs can generate hints that are simple, interconnected, and use accessible language, which can aid students in understanding and solving math problems. However, the study also suggests that LLMs may not be able to replace human teachers entirely, as, at the time, they lacked the ability to provide visual hints and may sometimes produce incorrect or nonsensical responses (aka hallucinations). Therefore, a combination of human teachers and LLMs may provide the most effective math education, which parallels the concept of "human-in-the-loop". This study could also imply the need for further research and development in improving the capabilities of LLMs in math education, such as enabling them to generate visual hints.
Limitations: (Also generated using prompting; little to no scholar work was supplied. Impressive output!)
Quality Control:
As the study mentions, LLMs can sometimes produce incorrect or nonsensical responses. This could lead to misinformation and confusion among students, posing a challenge to the study's effectiveness. Hence, having real human teachers validate and verify LLM-generated hints is important.
Lack of Personal Touch:
While LLMs can generate hints, they lack the personal touch, emotion, and understanding that a human teacher can provide. This could affect the learning experience of the students and pose a challenge to the study.
Resistance to Change:
The introduction of LLMs in math education could face resistance from teachers, students, or parents who prefer "traditional" teaching methods. This could pose a challenge to the implementation of the study's findings.
Future Research: (Also generated using prompting; little to no scholar work was supplied. Impressive output!)
Exploring the Potential of Large Language Models in Generating Hints for Different Subjects: A Comparative Study in Science, History, and Math Education.
Conceptualizing an AI Chatbot for K12 Math Education: A Theoretical Framework
The Effect of Large Language Models on the Quality of Math Education in Underprivileged Schools: A Case Study.
The Role of Large Language Models in Addressing the Shortage of Experienced Math Teachers: A Cost-Benefit Analysis.
The Impact of Large Language Models on Reducing Math Anxiety among K12 Students: An Experimental Study.
TL; DR: (I wanted to try a new approach for readers who spend less time reading; and hence the TLDR. Again, impressive output!)
Hints in math education are crucial for promoting critical thinking, problem-solving skills, and independent learning among K12 students.
There is a shortage of experienced math teachers who can effectively provide quality hints and strategies to students.
Large Language Models (LLMs) like OpenAI's GPT4 can be used to generate math hints, offering detailed guidance and simplifying complex concepts.
LLMs can make learning more engaging and personalized, but they are not perfect and can sometimes produce incorrect or nonsensical responses.
The study found that pre-service teachers preferred human-crafted hints for some problems, particularly those that included a visual hint, while they preferred LLM-generated hints for other problems due to their simplicity and accessible language.
LLMs can be effectively used as a supplementary tool in math education, especially in situations where there is a lack of experienced teachers.
However, LLMs may not be able to replace human teachers entirely, suggesting a combination of human teachers and LLMs may provide the most effective math education.
The study implies the need for further research and development in improving the capabilities of LLMs in math education, such as enabling them to generate visual hints.
The study's limitations include the potential for LLMs to produce incorrect responses, the lack of a personal touch in LLM-generated hints, and possible resistance to the introduction of LLMs in math education.
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After seeing the AI-generated posts, I'm thoroughly impressed with the capabilities of this new research tool. It did an amazing job of extracting key information and themes from my academic papers and transforming them into compelling blog-style articles. The summaries are accurate, coherent, and easy to understand even for non-experts. This technology represents a real leap forward in context-aware, custom AI writing assistants. I'm excited to see how tools like this might aid researchers and science communicators in the future.
We will end with a motivational quote:
Source: r/motivation
Until next time.