Exploring the Implications of ChatGPT for Language Learning in Higher Education

Risang Baskara; Mukarto Mukarto

doi:10.21093/ijeltal.v7i2.1387

Exploring the Implications of ChatGPT for Language Learning in Higher Education

Risang Baskara, Mukarto Mukarto

Abstract

Recent developments in natural language processing have led to the creation of large language models, such as ChatGPT, which could generate human-like text. In this paper, we explore the potential implications of ChatGPT for language learning in higher education. We first provide an overview of ChatGPT and discuss its capabilities and limitations. For instance, ChatGPT can generate coherent and fluent text on various topics but may have difficulties comprehending more complex or abstract ideas. We then consider how ChatGPT could be integrated into language courses and programs in higher education and the potential benefits and challenges of doing so. For example, ChatGPT could provide personalised language instruction or generate authentic language material for learners to engage with. However, using ChatGPT in language learning may also raise concerns about the potential substitution of human language teachers and the ethical implications of using a machine learning system to generate text. Finally, we offer suggestions for future research on using ChatGPT in language learning in higher education, such as studying the effectiveness of ChatGPT-assisted language instruction and exploring the pedagogical implications of using large language models in the language classroom.

Keywords

ChatGPT, Natural language processing, Language learning, Higher education, Machine learning

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References

Albiladi, W. S., & Alshareef, K. K. (2019). Blended learning in English teaching and learning: A review of the current literature. Journal of Language Teaching and Research, 10(2), 232–238. https://doi.org/10.17507/jltr.1002.03

Aljawarneh, S. A. (2020). Reviewing and exploring innovative ubiquitous learning tools in higher education. Journal of Computing in Higher Education, 32, 57–73. https://doi.org/10.1007/s12528-019-09207-0

Andujar, A., Salaberri-Ramiro, M. S., & Martínez, M. S. C. (2020). Integrating flipped foreign language learning through mobile devices: Technology acceptance and flipped learning experience. Sustainability, 12(3), 1110. https://doi.org/10.3390/su12031110

Atlas, S. (2023). ChatGPT for Higher Education and Professional Development: A Guide to Conversational AI.

Baidoo-Anu, D., & Owusu Ansah, L. (2023). Education in the Era of Generative Artificial Intelligence (AI): Understanding the Potential Benefits of ChatGPT in Promoting Teaching and Learning. Available at SSRN 4337484.

Benbya, H., Davenport, T. H., & Pachidi, S. (2020). Artificial intelligence in organisations: Current state and future opportunities. MIS Quarterly Executive, 19(4).

Blanco-Gonzalez, A., Cabezon, A., Seco-Gonzalez, A., Conde-Torres, D., Antelo-Riveiro, P., Pineiro, A., & Garcia-Fandino, R. (2022). The Role of AI in Drug Discovery: Challenges, Opportunities, and Strategies. ArXiv Preprint ArXiv:2212.08104.

Borji, A. (2023). A Categorical Archive of ChatGPT Failures. ArXiv Preprint ArXiv:2302.03494.

Brown, H., Lee, K., Mireshghallah, F., Shokri, R., & Tramèr, F. (2022). What Does it Mean for a Language Model to Preserve Privacy? 2022 ACM Conference on Fairness, Accountability, and Transparency, 2280–2292. https://doi.org/10.1145/3531146.3534642

Cambria, E., & White, B. (2014). Jumping NLP curves: A review of natural language processing research. IEEE Computational Intelligence Magazine, 9(2), 48–57. https://doi.org/10.1109/MCI.2014.2307227

Chen, X., Zou, D., Xie, H., & Cheng, G. (2021). Twenty years of personalised language learning. Educational Technology & Society, 24(1), 205–222.

Chong, S. W., & Reinders, H. (2020). Technology-mediated task-based language teaching: A qualitative research synthesis. Language Learning and Technology.

Chuah, K.-M., & Kabilan, M. (2021). Teachers’ Views on the Use of Chatbots to Support English Language Teaching in a Mobile Environment. International Journal of Emerging Technologies in Learning (IJET), 16(20), 223–237. https://doi.org/10.3991/ijet.v16i20.24917

Citron, J. L. (1995). Can cross-cultural understanding aid second language acquisition? Toward a theory of ethno-lingual relativity. Hispania, 105–113. https://doi.org/10.2307/345230

Conteh, J. (2007). Opening doors to success in multilingual classrooms: Bilingualism, codeswitching and the professional identities of ethnic minority primary teachers. Language and Education, 21(6), 457–472. https://doi.org/10.2167/le711.0

Cotton, D. R., Cotton, P. A., & Shipway, J. R. (2023). Chatting and Cheating. Ensuring academic integrity in the era of ChatGPT. https://doi.org/10.35542/osf.io/mrz8h

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. ArXiv Preprint ArXiv:1810.04805.

Dida, H. A., Chakravarthy, D. S. K., & Rabbi, F. (2023). ChatGPT and Big Data: Enhancing Text-to-Speech Conversion. Mesopotamian Journal of Big Data, 2023, 33–37. https://doi.org/10.58496/MJBD/2023/005

Ferruz, N., & Höcker, B. (2022). Controllable protein design with language models. Nature Machine Intelligence, 4(6), 521–532. https://doi.org/10.1038/s42256-022-00499-z

García Botero, G., Questier, F., & Zhu, C. (2019). Self-directed language learning in a mobile-assisted, out-of-class context: Do students walk the talk? Computer Assisted Language Learning, 32(1–2), 71–97. https://doi.org/10.1080/09588221.2018.1485707

George, A. S., & George, A. H. (2023). A Review of ChatGPT AI’s Impact on Several Business Sectors. Partners Universal International Innovation Journal, 1(1), 9–23.

Hacker, P., Engel, A., & Mauer, M. (2023). Regulating ChatGPT and other Large Generative AI Models. ArXiv Preprint ArXiv:2302.02337.

Hirschberg, J., & Manning, C. D. (2015). Advances in natural language processing. Science, 349(6245), 261–266. https://doi.org/10.1126/science.aaa8685

Hoi, V. N. (2020). Understanding higher education learners’ acceptance and use of mobile devices for language learning: A Rasch-based path modeling approach. Computers & Education, 146, 103761. https://doi.org/10.1016/j.compedu.2019.103761

Huang, W., Hew, K. F., & Fryer, L. K. (2022). Chatbots for language learning—Are they really useful? A systematic review of chatbot‐supported language learning. Journal of Computer Assisted Learning, 38(1), 237–257. https://doi.org/10.1111/jcal.12610

Jeon, J. (2021). Exploring AI chatbot affordances in the EFL classroom: Young learners’ experiences and perspectives. Computer Assisted Language Learning, 1–26. https://doi.org/10.1080/09588221.2021.1987272

Jiao, W., Wang, W., Huang, J., Wang, X., & Tu, Z. (2023). Is ChatGPT a good translator? A preliminary study. ArXiv Preprint ArXiv:2301.08745.

Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., & Hüllermeier, E. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. https://doi.org/10.35542/osf.io/5er8f

Khalil, M., & Er, E. (2023). Will ChatGPT get you caught? Rethinking of Plagiarism Detection. ArXiv Preprint ArXiv:2302.04335.

Kim, N.-Y., Cha, Y., & Kim, H.-S. (2019). Future English learning: Chatbots and artificial intelligence. Multimedia-Assisted Language Learning, 22(3), 32–53.

Kirkwood, A., & Price, L. (2014). Technology-enhanced learning and teaching in higher education: What is ‘enhanced’and how do we know? A critical literature review. Learning, Media and Technology, 39(1), 6–36. https://doi.org/10.1080/17439884.2013.770404

Lund, B. D., & Wang, T. (2023). Chatting about ChatGPT: how may AI and GPT impact academia and libraries? Library Hi Tech News. https://doi.org/10.1108/LHTN-01-2023-0009

Maddigan, P., & Susnjak, T. (2023). Chat2vis: Generating data visualisations via natural language using chatgpt, codex and gpt-3 large language models. ArXiv Preprint ArXiv:2302.02094.

Maharaja, G. (2018). The Impact of Study Abroad on College Students’ Intercultural Competence and Personal Development. International Research and Review, 7(2), 18–41.

Mathew, L., & Bindu, V. R. (2020). A review of natural language processing techniques for sentiment analysis using pre-trained models. 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC), 340–345.

Mhlanga, D. (2023). Open AI in Education, the Responsible and Ethical Use of ChatGPT Towards Lifelong Learning. Education, the Responsible and Ethical Use of ChatGPT Towards Lifelong Learning (February 11, 2023).

Min, B., Ross, H., Sulem, E., Veyseh, A. P. B., Nguyen, T. H., Sainz, O., Agirre, E., Heinz, I., & Roth, D. (2021). Recent advances in natural language processing via large pre-trained language models: A survey. ArXiv Preprint ArXiv:2111.01243.

Mitits, L. (2018). Multilingual Students in Greek Schools: Teachers’ Views and Teaching Practices. Journal of Education and E-Learning Research, 5(1), 28–36. https://doi.org/10.20448/journal.509.2018.51.28.36

Nguyen, H. T., Fehring, H., & Warren, W. (2015). EFL Teaching and Learning at a Vietnamese University: What Do Teachers Say?. English Language Teaching, 8(1), 31–43.

OguzhanTopsakal, E. (2022). Framework for A Foreign Language Teaching Software for Children Utilizing AR, Voicebots and ChatGPT (Large Language Models). Journal, 7(2), 33–38.

Parmaxi, A., & Demetriou, A. A. (2020). Augmented reality in language learning: A state‐of‐the‐art review of 2014–2019. Journal of Computer Assisted Learning, 36(6), 861–875. https://doi.org/10.1111/jcal.12486

Pavlik, J. V. (2023). Collaborating With ChatGPT: Considering the Implications of Generative Artificial Intelligence for Journalism and Media Education. Journalism & Mass Communication Educator, 10776958221149576. https://doi.org/10.1177/10776958221149577

Perkins, M. (2023). Academic Integrity considerations of AI Large Language Models in the post-pandemic era: ChatGPT and beyond. Journal of University Teaching & Learning Practice, 20(2), 07. https://doi.org/10.53761/1.20.02.07

Qadir, J. (2022). Engineering Education in the Era of ChatGPT: Promise and Pitfalls of Generative AI for Education.

Randall, N. (2019). A survey of robot-assisted language learning (RALL). J. Hum.-Robot Interact., 9(1). https://doi.org/10.1145/3345506

Reis, E. S. D., Costa, C. A. D., Silveira, D. E. D., Bavaresco, R. S., Righi, R. D. R., Barbosa, J. L. V., Antunes, R. S., Gomes, M. M., & Federizzi, G. (2021). Transformers aftermath: Current research and rising trends. Communications of the ACM, 64(4), 154–163. https://doi.org/10.1145/3430937

RudolphA, J. (2022). Journal of Applied Learning & Teaching. Journal of Applied Learning & Teaching, 5(1).

Sallam, M. (2023). The Utility of ChatGPT as an Example of Large Language Models in Healthcare Education, Research and Practice: Systematic Review on the Future Perspectives and Potential Limitations. MedRxiv, 2023.02. 19.23286155.

Schwitzgebel, E., Schwitzgebel, D., & Strasser, A. (2023). Creating a Large Language Model of a Philosopher. ArXiv Preprint ArXiv:2302.01339.

Shadiev, R., & Yang, M. (2020). Review of studies on technology-enhanced language learning and teaching. Sustainability, 12(2), 524. https://doi.org/10.3390/su12020524

Shahriar, S., & Hayawi, K. (2023). Let’s have a chat! A Conversation with ChatGPT: Technology, Applications, and Limitations. ArXiv Preprint ArXiv:2302.13817.

Shen, Y., Heacock, L., Elias, J., Hentel, K. D., Reig, B., Shih, G., & Moy, L. (2023). ChatGPT and Other Large Language Models Are Double-edged Swords. In Radiology (p. 230163). Radiological Society of North America.

Sun, Y., & Gao, F. (2020). An investigation of the influence of intrinsic motivation on students’ intention to use mobile devices in language learning. Educational Technology Research and Development, 68, 1181–1198. https://doi.org/10.1007/s11423-019-09733-9

Susnjak, T. (2022). ChatGPT: The End of Online Exam Integrity? ArXiv Preprint ArXiv:2212.09292.

Taecharungroj, V. (2023). “What Can ChatGPT Do?” Analysing Early Reactions to the Innovative AI Chatbot on Twitter. Big Data and Cognitive Computing, 7(1), 35. https://doi.org/10.3390/bdcc7010035

Tafazoli, D., Gomez Parra, M. E., & Huertas-Abril, C. A. (2018). Cross-cultural perspectives on technology-enhanced language learning. IGI Global.

Torfi, A., Shirvani, R. A., Keneshloo, Y., Tavaf, N., & Fox, E. A. (2020). Natural language processing advancements by deep learning: A survey. ArXiv Preprint ArXiv:2003.01200.

Truong, L. B., & Tran, L. T. (2014). Students’ intercultural development through language learning in Vietnamese tertiary education: A case study on the use of film as an innovative approach. Language and Intercultural Communication, 14(2), 207–225. https://doi.org/10.1080/14708477.2013.849717

Vieira, I., Lopes, A. P., & Soares, F. (2014). The potential benefits of using videos in higher education. EDULEARN14 Proceedings, 750–756.

Zhai, X. (2022). ChatGPT User Experience: Implications for Education. Available at SSRN 4312418.

Zhang, R., & Zou, D. (2020). Types, purposes, and effectiveness of state-of-the-art technologies for second and foreign language learning. Computer Assisted Language Learning, 1–47. https://doi.org/10.1080/09588221.2020.1744666

Zhang, S., Fan, R., Liu, Y., Chen, S., Liu, Q., & Zeng, W. (2023). Applications of Transformer-based Language Models in Bioinformatics: A Survey. Bioinformatics Advances. https://doi.org/10.1093/bioadv/vbad001

Zhou, C., Li, Q., Li, C., Yu, J., Liu, Y., Wang, G., Zhang, K., Ji, C., Yan, Q., & He, L. (2023). A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT. ArXiv Preprint ArXiv:2302.09419.

Zhuo, T. Y., Huang, Y., Chen, C., & Xing, Z. (2023). Exploring AI Ethics of ChatGPT: A Diagnostic Analysis. ArXiv Preprint ArXiv:2301.12867.

DOI: http://dx.doi.org/10.21093/ijeltal.v7i2.1387

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