The linguistic leap: Understanding, evaluating, and integrating AI in language education

Shai Cohen; Ludovic Mompelat; April Mann; Logan Connors

doi:10.54475/jlt.2024.012

Authors

Shai Cohen University of Miami https://orcid.org/0000-0002-1367-6718
Ludovic Mompelat University of Miami
April Mann University of Miami
Logan Connors University of Miami

DOI:

https://doi.org/10.54475/jlt.2024.012

Keywords:

artificial intelligence, generative-AI, language learning, educational technology, pedagogy, ethical considerations

Abstract

The landscape of language education is undergoing a pivotal transformation, spurred by the integration of Generative Artificial Intelligence (Gen-AI) into every facet of traditional and new methodologies and practices. Given the rapid societal adoption of AI, we believe that all language instructors – from the most technologically savvy to the most tech-averse – must engage critically and ethically with AI. To ensure that AI tools are brought into language education in pedagogically appropriate and ethical ways, we have developed two large projects at our university: 1) an AI Working Group in our Modern Languages Department and 2) a chatbot that all instructors can incorporate into their classroom practices. In this article, we describe the rationale for these projects and the steps we took to implement them. We hope that this work can help other departments come together to address the challenges and achieve a balance between technological advancement and the intrinsically human facets of language education.

Author Biographies

Shai Cohen, University of Miami

Shai Cohen teaches in the Michele Bowman Underwood Department of Modern Languages and Literatures at the University of Miami (FL). He is a specialist of Sephardic Studies and seventeenth-century Spanish political satire. His recent monograph, El poder de la palabra: la sátira política contra el conde duque de Olivares (CSIC, 2019), explores the relation between political satire and government in 17th century Spain. Currently, he works on few articles and leading a project focusing on the impact of Sephardi migration and the formation of new identities in the Americas. Shai is teaching languages, literatures, Sephardic Studies and is the coordinator of the Internship program. Since February 2023, he is spearheading the AI integration initiative into teaching and research in the Department.
Email: shaicohen@miami.edu
Ludovic Mompelat, University of Miami

Ludovic Vetea Mompelat is a Research Assistant Professor of French Linguistics, Creolistics and Computational Linguistics. His research lies at the intersection of Natural Language Processing (NLP), Machine Learning, Corpus Linguistics, Syntax, Semantics, and Sociolinguistics. His training in formal Linguistics with a focus on French and French-based Creoles, as well as in Computational Linguistics allows him to use a mixed-method cross-disciplinary research approach in his work. Some of his publications such as “How to Parse a Creole: When Martinican Creole Meets French” (2023) and “To Infinitive and Beyond, or Revisiting Finiteness in Creoles: A Contrastive Study of the Complementation Systems of Martinican Creole and Haitian Creole” (2023) are articulated around two complementary axes: first, the linguistic study and formal development of Creole languages, in comparison to one another and their lexifier language, and second, the creation of NLP solutions for under-represented languages as well as their promotion in the computational linguistics world.
Email: lvm861@miami.edu
April Mann, University of Miami

April Mann is the Director of the Writing Center and a senior lecturer in the Department of Writing Studies at the University of Miami (FL). She is currently co-editing a multi-authored volume documenting and addressing inequities in data and research methodologies for educational and social science researchers (Information Age Publishing, 2024). She is also co-facilitating Faculty Working Groups on using AI in the classroom.
Email: a.mann@miami.edu
Logan Connors, University of Miami

Logan J. Connors is Professor and Chair of the Michele Bowman Underwood Department of Modern Languages and Literatures at the University of Miami (FL). A specialist of eighteenth-century French theatre and cultural history, his most recent monograph is Theatre, War, and Revolution in Eighteenth-Century France and Its Empire (Cambridge University Press, 2024). He is currently co-editing a multi-authored volume on comparative performance cultures in times of revolution as well as conducting research for a new book on comedy and the French Terror.
Email: logan.connors@miami.edu

References

Burdick, A., et al. (2012) Humanities to digital humanities. MIT Press. https://doi.org/10.7551/mitpress/9248.001.0001 DOI: https://doi.org/10.7551/mitpress/9248.001.0001

Burstein, J., Tetreault, J., & Madnani, N. (2013). The e-rater® automated essay scoring system. In M. D. Shermis & J. Burstein (Eds.), Handbook of automated essay evaluation: Current applications and new directions. Routledge/Taylor & Francis Group. 55-67. https://psycnet.apa.org/record/2013-15323-004

Celik, I., Dindar, M., Muukkonen, H., & Järvelä, S. (2022). The promises and challenges of artificial intelligence for teachers: A systematic review of research. TechTrends, 66(4), 616-630. https://doi.org/10.1007/s11528-022-00715-y DOI: https://doi.org/10.1007/s11528-022-00715-y

Cuban, L. (2001). Oversold and underused: Computers in the classroom. Harvard University Press. DOI: https://doi.org/10.4159/9780674030107

Firat, M. (2023). What ChatGPT means for universities: Perceptions of scholars and students. Journal of Applied Learning and Teaching, 6(1), 57-63. https://10.37074/jalt.2023.6.1.22 DOI: https://doi.org/10.37074/jalt.2023.6.1.22

Gaudioso, E., Montero, M., & Hernandez-del-Olmo, F. (2012). Supporting teachers in adaptive educational systems through predictive models: A proof of concept. Expert Systems with Applications, 39(1), 621-625. https://doi.org/10.1016/j.eswa.2011.07.052 DOI: https://doi.org/10.1016/j.eswa.2011.07.052

Godwin-Jones, R. (2019). Telecollaboration as an approach to developing intercultural communication competence. In Emerging technologies: Artificial intelligence and language learning. Language Learning & Technology, 23(3), 8-28.

Hashem, R., Ali, N., El Zein, F., Fidalgo, P., & Khurma, O. A. (2024). AI to the rescue: Exploring the potential of ChatGPT as a teacher ally for workload relief and burnout prevention. Research & Practice in Technology Enhanced Learning, 19, 23. https://doi.org/10.58459/rptel.2024.19023 DOI: https://doi.org/10.58459/rptel.2024.19023

Heffernan, N. T., & Heffernan, C. L. (2014). The ASSISTments ecosystem: Building a platform that brings scientists and teachers together for minimally invasive research on human learning and teaching. International Journal of Artificial Intelligence in Education, 24, 470-497. https://doi.org/10.1007/s40593-014-0024-x DOI: https://doi.org/10.1007/s40593-014-0024-x

Heilman, M., Collins-Thompson, K., Callan, J., & Eskenazi, M. (2006). Classroom success of an intelligent tutoring system for lexical practice and reading comprehension. In Proceedings of the Ninth International Conference on Spoken Language Processing. https://doi.org/10.21437/Interspeech.2006-282 DOI: https://doi.org/10.21437/Interspeech.2006-282

Holstein, K., McLaren, B. M., & Aleven, V. (2019). Co-designing a real-time classroom orchestration tool to support teacher-AI complementarity. Journal of Learning Analytics, 6(2), 27-52. DOI: https://doi.org/10.18608/jla.2019.62.3

Ifelebuegu, A. (2023). Rethinking online assessment strategies: Authenticity versus AI chatbot intervention. Journal of Applied Learning and Teaching, 6(2), 1-8. https://doi.org/10.37074/jalt.2023.6.2.2 DOI: https://doi.org/10.37074/jalt.2023.6.2.2

Ifelebuegu, A. O., Kulume, P., & Cherukut, P. (2023). Chatbots and AI in Education (AIEd) tools: The good, the bad, and the ugly. Journal of Applied Learning and Teaching, 6(2), 1-14. https://doi.org/10.37074/jalt.2023.6.2.29 DOI: https://doi.org/10.37074/jalt.2023.6.2.29

Kelleher, J. (2019). Deep learning. The MIT Press Essential Knowledge series. DOI: https://doi.org/10.7551/mitpress/11171.001.0001

Kojima, T., Gu, S., Reid, M., Matsuo, Y., & Iwasawa, Y. (2023). Large language models are zero-shot reasoners. In 36th Conference on Neural Information Processing Systems (NeurIPS 2022).

Kolb, D.A. (1984). Experiential learning: experience as the source of learning and development. Prentice Hall.

Kuhail, M. A., Alturki, N., Alramlawi, S., & Alhejori, K. (2023). Interacting with educational chatbots: A systematic review. Education and Information Technologies, 28(1), 973-1018. https://doi.org/10.1007/s10639-022-11177-3 DOI: https://doi.org/10.1007/s10639-022-11177-3

Lewin, K., (1951). Field theory in social sciences. Harper & Row.

Luckin, R. (2018). Machine learning and human intelligence: The future of education for the 21st century. UCL IOE Press.

Mollick, E. (2023, July 1). The homework apocalypse: Fall is going to look very different this year. One Useful Thing. https://www.oneusefulthing.org/p/the-homework-apocalypse

MLA-CCCC Joint Task Force on Writing and AI. (2023). MLA-CCCC joint task force on Writing and AI working paper: Overview of the issues, statement of principles, and recommendations. Conference on College Composition and Communication. Modern Language Association. https://hcommons.org/app/uploads/sites/1003160/2023/07/MLA-CCCC-Joint-Task-Force-on-Writing-and-AI-Working-Paper-1.pdf

Pinker, S. (1994). The language instinct: How the mind creates language. William Morrow and Company. DOI: https://doi.org/10.1037/e412952005-009

Roll, I., & Wylie, R. (2016). Evolution and revolution in artificial intelligence in education. International Journal of Artificial Intelligence in Education, 26, 582-599. https://doi.org/10.1007/s40593-016-0110-3 DOI: https://doi.org/10.1007/s40593-016-0110-3

Selwyn, N. (2016). Is technology good for education?. Polity Press.

Shneiderman, B. (2020). Human-centered artificial intelligence: Three fresh ideas. AI Magazine, 41(4), 28-36. https://doi.org/10.17705/1thci.00131 DOI: https://doi.org/10.17705/1thci.00131

Siemens, L., & Burr, E. (2013). A Trip around the World: Accommodating Geographical, Linguistic and Cultural Diversity in Academic Research Teams. Literary and Linguistic Computing, 28(2), 331-343. https://doi.org/10.1093/llc/fqs018 DOI: https://doi.org/10.1093/llc/fqs018

Siemens, G. (2005). Connectivism: A learning theory for the digital age. International Journal of Instructional Technology and Distance Learning, 2(1), 1-8.

Swiecki, Z., Ruis, A. R., Gautam, D., Rus, V., & Williamson Shaffer, D. (2019). Understanding when students are active‐in‐thinking through modeling‐in‐context. British journal of educational technology, 50(5), 2346-2364. https://doi.org/10.1111/bjet.12869 DOI: https://doi.org/10.1111/bjet.12869

Terry, O. K. (2023, May 26). I’m a student. You have no idea how much we’re using ChatGPT. Chronicle of Higher Education. https://www.chronicle.com/article/im-a-student-you-have-no-idea-how-much-were-using-chatgpt

Tlili, A., Padilla-Zea, N., Garzón, J., Wang, Y., Kinshuk, K., & Burgos, D. (2023). The changing landscape of mobile learning pedagogy: A systematic literature review. Interactive Learning Environments, 31(10), 6462-6479. https://doi.org/10.1080/10494820.2022.2039948 DOI: https://doi.org/10.1080/10494820.2022.2039948