Generative AI for Enhancing Active Learning in Education: A Comparative Study of GPT-3.5 and GPT-4 in Crafting Customized Test Questions
- URL: http://arxiv.org/abs/2406.13903v1
- Date: Thu, 20 Jun 2024 00:25:43 GMT
- Title: Generative AI for Enhancing Active Learning in Education: A Comparative Study of GPT-3.5 and GPT-4 in Crafting Customized Test Questions
- Authors: Hamdireza Rouzegar, Masoud Makrehchi,
- Abstract summary: This study investigates how LLMs, specifically GPT-3.5 and GPT-4, can develop tailored questions for Grade 9 math.
By utilizing an iterative method, these models adjust questions based on difficulty and content, responding to feedback from a simulated'student' model.
- Score: 2.0411082897313984
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This study investigates how LLMs, specifically GPT-3.5 and GPT-4, can develop tailored questions for Grade 9 math, aligning with active learning principles. By utilizing an iterative method, these models adjust questions based on difficulty and content, responding to feedback from a simulated 'student' model. A novel aspect of the research involved using GPT-4 as a 'teacher' to create complex questions, with GPT-3.5 as the 'student' responding to these challenges. This setup mirrors active learning, promoting deeper engagement. The findings demonstrate GPT-4's superior ability to generate precise, challenging questions and notable improvements in GPT-3.5's ability to handle more complex problems after receiving instruction from GPT-4. These results underscore the potential of LLMs to mimic and enhance active learning scenarios, offering a promising path for AI in customized education. This research contributes to understanding how AI can support personalized learning experiences, highlighting the need for further exploration in various educational contexts
Related papers
- LLMs Still Can't Avoid Instanceof: An Investigation Into GPT-3.5, GPT-4
and Bard's Capacity to Handle Object-Oriented Programming Assignments [0.0]
Large Language Models (LLMs) have emerged as promising tools to assist students while solving programming assignments.
In this study, we experimented with three prominent LLMs to solve real-world OOP exercises used in educational settings.
The findings revealed that while the models frequently achieved mostly working solutions to the exercises, they often overlooked the best practices of OOP.
arXiv Detail & Related papers (2024-03-10T16:40:05Z) - Benchmarking GPT-4 on Algorithmic Problems: A Systematic Evaluation of Prompting Strategies [47.129504708849446]
Large Language Models (LLMs) have revolutionized the field of Natural Language Processing.
LLMs lack systematic generalization, which allows to extrapolate the learned statistical regularities outside the training distribution.
In this work, we offer a systematic benchmarking of GPT-4, one of the most advanced LLMs available.
arXiv Detail & Related papers (2024-02-27T10:44:52Z) - Evaluating Large Language Models on the GMAT: Implications for the
Future of Business Education [0.13654846342364302]
This study introduces the first benchmark to assess the performance of seven major Large Language Models (LLMs)
Our analysis shows that most LLMs outperform human candidates, with GPT-4 Turbo not only outperforming the other models but also surpassing the average scores of graduate students at top business schools.
While AI's promise in education, assessment, and tutoring is clear, challenges remain.
arXiv Detail & Related papers (2024-01-02T03:54:50Z) - A Comprehensive Evaluation of GPT-4V on Knowledge-Intensive Visual
Question Answering [56.01977227584777]
multimodal large models (MLMs) has significantly advanced the field of visual understanding.
Yet, the true challenge lies in the domain of knowledge-intensive visual question answering (VQA) tasks.
This study provides an in-depth evaluation of the newly introduced GPT-4V.
arXiv Detail & Related papers (2023-11-13T18:22:32Z) - Prompt Engineering or Fine Tuning: An Empirical Assessment of Large
Language Models in Automated Software Engineering Tasks [8.223311621898983]
GPT-4 with conversational prompts showed drastic improvement compared to GPT-4 with automatic prompting strategies.
fully automated prompt engineering with no human in the loop requires more study and improvement.
arXiv Detail & Related papers (2023-10-11T00:21:00Z) - The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision) [121.42924593374127]
We analyze the latest model, GPT-4V, to deepen the understanding of LMMs.
GPT-4V's unprecedented ability in processing arbitrarily interleaved multimodal inputs makes it a powerful multimodal generalist system.
GPT-4V's unique capability of understanding visual markers drawn on input images can give rise to new human-computer interaction methods.
arXiv Detail & Related papers (2023-09-29T17:34:51Z) - Sparks of Artificial General Intelligence: Early experiments with GPT-4 [66.1188263570629]
GPT-4, developed by OpenAI, was trained using an unprecedented scale of compute and data.
We demonstrate that GPT-4 can solve novel and difficult tasks that span mathematics, coding, vision, medicine, law, psychology and more.
We believe GPT-4 could reasonably be viewed as an early (yet still incomplete) version of an artificial general intelligence (AGI) system.
arXiv Detail & Related papers (2023-03-22T16:51:28Z) - GPT-4 Technical Report [116.90398195245983]
GPT-4 is a large-scale, multimodal model which can accept image and text inputs and produce text outputs.
It exhibits human-level performance on various professional and academic benchmarks, including passing a simulated bar exam with a score around the top 10% of test takers.
arXiv Detail & Related papers (2023-03-15T17:15:04Z) - Reframing Instructional Prompts to GPTk's Language [72.69833640335519]
We propose reframing techniques for model designers to create effective prompts for language models.
Our results show that reframing improves few-shot learning performance by 14% while reducing sample complexity.
The performance gains are particularly important on large language models, such as GPT3 where tuning models or prompts on large datasets is not feasible.
arXiv Detail & Related papers (2021-09-16T09:44:43Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.