Generative AI and Teachers -- For Us or Against Us? A Case Study
Not on the map. Why?
Abstract (arXiv)
We present insightful results of a survey on the adoption of generative artificial intelligence (GenAI) by university teachers in their teaching activities. The transformation of education by GenAI, particularly large language models (LLMs), has been presenting both opportunities and challenges, including cheating by students. We prepared the online survey according to best practices and the questions were created by the authors, who have pedagogy experience. The survey contained 12 questions and a pilot study was first conducted. The survey was then sent to all teachers in multiple departments across different campuses of the university of interest in Sweden: Luleaa University of Technology. The survey was available in both Swedish and English. The results show that 35 teachers (more than half) use GenAI out of 67 respondents. Preparation is the teaching activity with the most frequency that GenAI is used for and ChatGPT is the most commonly used GenAI. 59% say it has impacted their teaching, however, 55% say there should be legislation around the use of GenAI, especially as inaccuracies and cheating are the biggest concerns.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 5 of its 64 references that are in the map. First posted on arXiv 4 Apr 2024.
Builds on
5 of 64 references.
References · 5
- 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
- 2023Students' Voices on Generative AI: Perceptions, Benefits, and Challenges in Higher Educationcited by 41
- 2023The AI generation gap: Are Gen Z students more interested in adopting generative AI such as ChatGPT in teaching and learning than their Gen X and Millennial Generation teachers?cited by 12
- 2024Aurora-M: Open Source Continual Pre-training for Multilingual Language and Codecited by 10
- 2023ProCoT: Stimulating Critical Thinking and Writing of Students through Engagement with Large Language Models (LLMs)cited by 4
Built on by
Citing papers per year: 2024: 2; 2025: 4.
6 papers.
- 2024Fairness and Bias in Multimodal AI: A Surveycited by 20
- 2024On the Limitations of Large Language Models (LLMs): False Attributioncited by 13
- 2025Findings of MEGA: Maths Explanation with LLMs using the Socratic Method for Active Learningcited by 3
- 2025AI Must not be Fully Autonomouscited by 3
- 2025In Generative AI We (Dis)Trust? Computational Analysis of Trust and Distrust in Reddit Discussionscited by 0
- 2025From the Rock Floor to the Cloud: A Systematic Survey of State-of-the-Art NLP in Battery Life Cyclecited by 0
- 2025From the Rock Floor to the Cloud: A Systematic Survey of State-of-the-Art NLP in Battery Life Cycle31 Oct 2025
- 2025In Generative AI We (Dis)Trust? Computational Analysis of Trust and Distrust in Reddit Discussions17 Oct 2025
- 2025AI Must not be Fully Autonomous31 Jul 2025
- 2025Findings of MEGA: Maths Explanation with LLMs using the Socratic Method for Active Learning16 Jul 2025
- 2024Fairness and Bias in Multimodal AI: A Survey27 Jun 2024
- 2024On the Limitations of Large Language Models (LLMs): False Attribution6 Apr 2024