Artificial intelligence (AI) technology is revolutionizing the way we live, learn, and work. In academic settings, AI tools such as chatbots, neural machine translation, plagiarism detectors, image generators and many more are becoming increasingly popular. However, as the use of AI is becoming more widespread, concerns about the impact it may have on learning, academic integrity, and the role of traditional teaching methods are growing.

As teachers of English for Specific Purposes (ESP) at Universidad Nacional de Salta, in 2022 we carried out research on the effectiveness of neural machine translation (NMT) for academic reading, and on students’ attitudes towards the use of the technological device available. Working with three groups assisted by the software and a control group that used only bilingual dictionaries, we found out that

1)  NMT output for academic texts is not perfect, but has few language mistakes, mostly due to inappropriate text organisation in the original version;

2) the experimental groups  got significantly higher scores than the control group, which used only bilingual dictionaries.

In November 2022, Chat GPT (Generative Pre-trained Transformer) was launched and it is becoming more and more popular among students at university settings. As this new technology can be tailored to specific academic fields and subject matters, it achieves better contextual understanding than NMT, resulting in higher quality language outputs. For this reason, we resumed our research addressing the use of AI in general, including both neural machine translation and GPT. This time, we aimed to explore the perspectives of university teachers on students' use of AI in higher education.

We conducted qualitative interviews covering various topics, including the acceptability of students' use of technology, AI's role in reading and writing, academic integrity, language development, and university policy. These interviews helped us assess teachers' knowledge of AI, their perceptions of its potential benefits and challenges, and its impact on their teaching practices. Eight participants belonging to both private and public universities took part in the study.

Now, our aim is to explore how AI can be used to design engaging learning activities that adapt to our students' needs in specialized fields like medicine, engineering or the humanities. As the second part of what we presented in the Conference last year, we will share some carefully designed activities that integrate AI-powered tools to meet the pedagogical goals and the unique requirements of our ESP programs in our university.

References

1. Bottiglieri, L., Irrazabal, M. & Ramallo, C. (2023) The use of machine translation for reading comprehension in a university context. Artesol ESP e-Journal. https://drive.google.com/file/d/1QvK5lcg5tBzeKc5NV_gfsgaumI4hNrZ9/view
2. Bowker, L. & Buitrago Ciro, J. (2019 ). Machine translation and global research: Towards Improved machine translation literacy in the scholarly community. London: Emerald Publishing.
3. Ferris, D., Liu, H., Sinha, A., & Senna, M. (2013). Written corrective feedback for individual L2 writers. Journal of Second Language Writing, 22(3), 307-329. http://www.sciencedirect.com/science/article/pii/S1060374312000811
4. García Peñalvo, F. (2023) The perception of Artificial Intelligence in educational contexts after the launch of ChatGPT: Disruption or Panic? Education in the Knowledge Society. DOI: 10.14201/eks.31279
5. Groves, M., & Mundt, K. (2015). Friend or foe? Google Translate in language for academic purposes. English for Specific Purposes, (37), 112-121.