# Generative AI in Education: A Study of Educators' Awareness, Sentiments, and Influencing Factors

arXiv: https://arxiv.org/abs/2403.15586 · HTML: https://mlsanity.com/paper/2403.15586/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 45 references that are in the map.
- First posted on arXiv 22 Mar 2024.

## Builds on (most cited first, 14 of 14)

- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Automatic Generation of Programming Exercises and Code Explanations using Large Language Models](https://mlsanity.com/paper/2206.11861/) (2022)
- [Programming Is Hard -- Or at Least It Used to Be: Educational Opportunities And Challenges of AI Code Generation](https://mlsanity.com/paper/2212.01020/) (2022)
- [The Robots are Here: Navigating the Generative AI Revolution in Computing Education](https://mlsanity.com/paper/2310.00658/) (2023)
- [Experiences from Using Code Explanations Generated by Large Language Models in a Web Software Development E-Book](https://mlsanity.com/paper/2211.02265/) (2022)
- [Conversing with Copilot: Exploring Prompt Engineering for Solving CS1 Problems Using Natural Language](https://mlsanity.com/paper/2210.15157/) (2022)
- [Using Large Language Models to Enhance Programming Error Messages](https://mlsanity.com/paper/2210.11630/) (2022)
- ["It's Weird That it Knows What I Want": Usability and Interactions with Copilot for Novice Programmers](https://mlsanity.com/paper/2304.02491/) (2023)
- [Computing Education in the Era of Generative AI](https://mlsanity.com/paper/2306.02608/) (2023)
- [Large Language Models (GPT) Struggle to Answer Multiple-Choice Questions about Code](https://mlsanity.com/paper/2303.08033/) (2023)
- [Prompt Problems: A New Programming Exercise for the Generative AI Era](https://mlsanity.com/paper/2311.05943/) (2023)
- [AI-driven Development Is Here: Should You Worry?](https://mlsanity.com/paper/2204.07560/) (2022)
- [Robosourcing Educational Resources -- Leveraging Large Language Models for Learnersourcing](https://mlsanity.com/paper/2211.04715/) (2022)
- [A Case Study in Engineering a Conversational Programming Assistant's Persona](https://mlsanity.com/paper/2301.10016/) (2023)

## Built on by (most cited first, 10 of 10)

- [Graph Retrieval-Augmented Generation: A Survey](https://mlsanity.com/paper/2408.08921/) (2024)
- [PersonaMem-v2: Towards Personalized Intelligence via Learning Implicit User Personas and Agentic Memory](https://mlsanity.com/paper/2512.06688/) (2025)
- [Graph-based Approaches and Functionalities in Retrieval-Augmented Generation: A Comprehensive Survey](https://mlsanity.com/paper/2504.10499/) (2025)
- [Epistemic Alignment: A Mediating Framework for User-LLM Knowledge Delivery](https://mlsanity.com/paper/2504.01205/) (2025)
- [ChatGPT in Data Visualization Education: A Student Perspective](https://mlsanity.com/paper/2405.00748/) (2024)
- [Generative AI Adoption in Classroom in Context of Technology Acceptance Model (TAM) and the Innovation Diffusion Theory (IDT)](https://mlsanity.com/paper/2406.15360/) (2024)
- [Understanding Student and Academic Staff Perceptions of AI Use in Assessment and Feedback](https://mlsanity.com/paper/2406.15808/) (2024)
- [TERAG: Token-Efficient Graph-Based Retrieval-Augmented Generation](https://mlsanity.com/paper/2509.18667/) (2025)
- [Bridging Text and Video Generation: A Survey](https://mlsanity.com/paper/2510.04999/) (2025)
- [Analyzing the Impact of AI Tools on Student Study Habits and Academic Performance](https://mlsanity.com/paper/2412.02166/) (2024)

## Abstract (arXiv)

The rapid advancement of artificial intelligence (AI) and the expanding integration of large language models (LLMs) have ignited a debate about their application in education. This study delves into university instructors' experiences and attitudes toward AI language models, filling a gap in the literature by analyzing educators' perspectives on AI's role in the classroom and its potential impacts on teaching and learning. The objective of this research is to investigate the level of awareness, overall sentiment towardsadoption, and the factors influencing these attitudes for LLMs and generative AI-based tools in higher education. Data was collected through a survey using a Likert scale, which was complemented by follow-up interviews to gain a more nuanced understanding of the instructors' viewpoints. The collected data was processed using statistical and thematic analysis techniques. Our findings reveal that educators are increasingly aware of and generally positive towards these tools. We find no correlation between teaching style and attitude toward generative AI. Finally, while CS educators show far more confidence in their technical understanding of generative AI tools and more positivity towards them than educators in other fields, they show no more confidence in their ability to detect AI-generated work.