# From "Let's Google" to "Let's ChatGPT": Student and Instructor Perspectives on the influence of LLMs on Undergraduate Engineering Education

**Authors:** Ishika Joshi, Ritvik Budhiraja, Pranav Deepak Tanna, Lovenya Jain, Mihika Deshpande, Arjun Srivastava, Srinivas Rallapalli, Harshal D Akolekar, Jagat Sesh Challa, Dhruv Kumar  
**arXiv:** [2309.10694](https://arxiv.org/abs/2309.10694)  
**Date:** 2023-09-19  
**Summary page:** https://www.summarizepaper.com/en/paper/2309.10694v1/

## Key points

- The paper explores the intersection of Large Language Models (LLMs), Human-Computer Interaction (HCI), and Education through surveys and interviews at undergraduate engineering universities in India.
- Specifically focuses on the academic usage of ChatGPT among students and instructors.
- Research includes 1306 survey responses, 112 student interviews, and 27 instructor interviews to provide insights into current usage patterns, benefits, threats, and challenges of LLMs in educational settings.
- Findings offer recommendations for enhancing adoption of LLMs among students and instructors.
- Literature review covers topics such as prompt engineering with LLMs, AI in Education, LLMs in Education, and Student and Instructor Perspectives on LLMs.
- Discussions include studies on prompt quality's influence on LLM responses, adaptability of large language models for mobile interfaces, challenges faced by users in understanding model syntax and capabilities.
- Positive experiences with interactive applications like chatbots for public health interventions are highlighted.
- Studies examining the impact of LLM-powered writing assistants on users' opinions and writings are explored to emphasize responsible use.

## Summary

The paper delves into the intersection of Large Language Models (LLMs), Human-Computer Interaction (HCI), and Education by conducting surveys and interviews at undergraduate engineering universities in India. The study specifically focuses on the academic usage of ChatGPT, a popular LLM, among students and instructors. Through 1306 survey responses, 112 student interviews, and 27 instructor interviews, the research provides valuable insights into current usage patterns as well as perceived benefits, threats, and challenges of LLMs in educational settings. These findings offer recommendations for enhancing the adoption of LLMs among students and instructors. In the related work section, a literature review is presented on topics such as prompt engineering with LLMs, AI in Education, LLMs in Education, and Student and Instructor Perspectives on LLMs. This includes discussions on various studies that highlight the influence of prompt quality on LLM responses, adaptability of large language models for mobile interfaces, challenges faced by users in understanding model syntax and capabilities. Additionally, positive experiences with interactive applications like chatbots for public health interventions are also discussed. Furthermore, studies examining the impact of LLM-powered writing assistants on users' opinions and writings are explored. These studies emphasize the importance of carefully monitoring and engineering opinions embedded in LLMs to ensure their responsible use. Challenges identified include issues related to communication style preferences when using code assistants in computational notebooks. Overall,and beyond.