RECORD
Beyond Traditional Teaching: The Potential of Large Language Models and Chatbots in Graduate Engineering Education
Computers and Society · Submitted 9 Sep 2023
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim The use of LLMs and chatbots in graduate education can be greatly beneficial but requires ongoing evaluation and adaptation to ensure ethical and efficient use.
The compiled record
The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.
Open the compiled record
The abstract, sentence by sentence
In the rapidly evolving landscape of education, digital technologies have repeatedly disrupted traditional pedagogical methods. This paper explores the latest of these disruptions: the potential integration of large language models (LLMs) and chatbots into graduate engineering education. We begin by tracing historical and technological disruptions to provide context and then introduce key terms such as machine learning and deep learning and the underlying mechanisms of recent advancements, namely attention/transformer models and graphics processing units. The heart of our investigation lies in the application of an LLM-based chatbot in a graduate fluid mechanics course. We developed a question bank from the course material and assessed the chatbot's ability to provide accurate, insightful responses. The results are encouraging, demonstrating not only the bot's ability to effectively answer complex questions but also the potential advantages of chatbot usage in the classroom, such as the promotion of self-paced learning, the provision of instantaneous feedback, and the reduction of instructors' workload. The study also examines the transformative effect of intelligent prompting on enhancing the chatbot's performance. Furthermore, we demonstrate how powerful plugins like Wolfram Alpha for mathematical problem-solving and code interpretation can significantly extend the chatbot's capabilities, transforming it into a comprehensive educational tool. While acknowledging the challenges and ethical implications surrounding the use of such AI models in education, we advocate for a balanced approach. The use of LLMs and chatbots in graduate education can be greatly beneficial but requires ongoing evaluation and adaptation to ensure ethical and efficient use.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | In the rapidly evolving landscape of education, digital technologies have repeatedly disrupted traditional pedagogical methods. | background 1.00 | 0.04 | 0.01 | asserted 1.00 |
| s1 | This paper explores the latest of these disruptions: the potential integration of large language models (LLMs) and chatbots into graduate engineering education. | contribution 0.85 | 0.21 | 0.04 | hedged possibility 0.61 |
| s2 | We begin by tracing historical and technological disruptions to provide context and then introduce key terms such as machine learning and deep learning and the underlying mechanisms of recent advancements, namely attention/transformer models and graphics processing units. | background 0.92 | 0.58 | 0.02 | asserted 0.87 |
| s3 | The heart of our investigation lies in the application of an LLM-based chatbot in a graduate fluid mechanics course. | contribution 0.90 | 0.54 | 0.03 | asserted 0.95 |
| s4 | We developed a question bank from the course material and assessed the chatbot's ability to provide accurate, insightful responses. | method 0.98 | 0.97 | 0.03 | asserted 0.98 |
| s5 | The results are encouraging, demonstrating not only the bot's ability to effectively answer complex questions but also the potential advantages of chatbot usage in the classroom, such as the promotion of self-paced learning, the provision of instantaneous feedback, and the reduction of instructors' workload. | result 0.61 | 0.94 | 0.50 | asserted 0.98 |
| s6 | The study also examines the transformative effect of intelligent prompting on enhancing the chatbot's performance. | contribution 0.82 | 0.73 | 0.04 | asserted 0.89 |
| s7 | Furthermore, we demonstrate how powerful plugins like Wolfram Alpha for mathematical problem-solving and code interpretation can significantly extend the chatbot's capabilities, transforming it into a comprehensive educational tool. | result 0.52 | 0.90 | 0.06 | asserted 1.00 |
| s8 | While acknowledging the challenges and ethical implications surrounding the use of such AI models in education, we advocate for a balanced approach. | limitation 0.82 | 0.25 | 0.13 | asserted 0.86 |
| s9 | The use of LLMs and chatbots in graduate education can be greatly beneficial but requires ongoing evaluation and adaptation to ensure ethical and efficient use. | future work 0.70 | 0.14 | 0.81 | hedged possibility 0.69 |
What was examined, and what was not
- arXiv listing abstract v2
- full text
- figures and tables
- references and citing sentences
- relations to other papers
- P abstract
- S sections
- T body sentences
- C citations
- R cross-paper relations
The paper
Original abstract
In the rapidly evolving landscape of education, digital technologies have repeatedly disrupted traditional pedagogical methods. This paper explores the latest of these disruptions: the potential integration of large language models (LLMs) and chatbots into graduate engineering education. We begin by tracing historical and technological disruptions to provide context and then introduce key terms such as machine learning and deep learning and the underlying mechanisms of recent advancements, namely attention/transformer models and graphics processing units. The heart of our investigation lies in the application of an LLM-based chatbot in a graduate fluid mechanics course. We developed a question bank from the course material and assessed the chatbot's ability to provide accurate, insightful responses. The results are encouraging, demonstrating not only the bot's ability to effectively answer complex questions but also the potential advantages of chatbot usage in the classroom, such as the promotion of self-paced learning, the provision of instantaneous feedback, and the reduction of instructors' workload. The study also examines the transformative effect of intelligent prompting on enhancing the chatbot's performance. Furthermore, we demonstrate how powerful plugins like Wolfram Alpha for mathematical problem-solving and code interpretation can significantly extend the chatbot's capabilities, transforming it into a comprehensive educational tool. While acknowledging the challenges and ethical implications surrounding the use of such AI models in education, we advocate for a balanced approach. The use of LLMs and chatbots in graduate education can be greatly beneficial but requires ongoing evaluation and adaptation to ensure ethical and efficient use.
Human review
How the literature uses it
2 arXiv papers cite this in 3 sentences
Papers citing it · 2 sentences without a confident label
Cites it as support 1 paper
-
These models excel at addressing common beginner mistakes and help students grasp complex engineering concepts by offering tailored explanations and hints, rather than direct answers, thereby encouraging independent problem-solving [ref] .
Large Language Models for Manufacturing · LLMs Impact on Future Education · 0.96
Discussion
Know something about this paper? Leave a comment, or highlight any passage above to comment on it.