Exploring Pre-Service Teachers' Difficulties of ChatGPT as a Tool for Planning The Learning Process

Dimas Dimas; Alvindi Alvindi; Tengku Khoirunnisa; Rahma Yani; Pardamean Pardamean; Khoirunnisa Simanjuntak

doi:10.31004/innovative.v5i1.17801

Authors

Dimas Dimas Universitas Islam Negeri Sumatra Utara
Alvindi Alvindi Universitas Islam Negeri Sumatera Utara
Tengku Khoirunnisa Universitas Islam Negeri Sumatera Utara
Rahma Yani Universitas Islam Negeri Sumatera Utara
Pardamean Pardamean Universitas Islam Negeri Sumatera Utara
Khoirunnisa Simanjuntak Universitas Islam Negeri Sumatera Utara

DOI:

https://doi.org/10.31004/innovative.v5i1.17801

Keywords:

ChatGPT, Pre-Service Teachers, Learning Process, Teacher Education, Artificial Intelligence

Abstract

This study aims to investigate the challenges faced by pre-service teachers in using ChatGPT as a tool for planning the learning process. Despite the potential of ChatGPT to enhance efficiency, creativity, and innovation in educational contexts, its implementation among pre-service teachers remains limited due to a range of difficulties. These include challenges in formulating learning objectives, selecting appropriate teaching methods and tools, and assessing the relevance of AI-generated content to curriculum standards. Using a quantitative research approach, a survey was conducted with 30 pre-service teachers in North Sumatra, Indonesia, to explore the barriers encountered across the planning, implementation, and evaluation phases of the learning process. Findings indicate that while ChatGPT offers valuable support, pre-service teachers experience moderate to high levels of difficulty in critical areas such as identifying relevant resources, structuring learning activities, and facilitating effective student engagement. These challenges underscore the need for targeted training and pedagogical support to optimize the integration of AI tools in teacher education programs. The study provides insights into the complexities of adopting AI in education and proposes strategies for overcoming these barriers, contributing to the discourse on technology-enhanced learning in developing educational contexts.

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

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

Bryman, A. (2016). Social research methods. Oxford university press.

Bruner, J. (1966). Constructivist theory. Retrieved from http://www.instructionaldesign.org/theories/constructivist/

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

Darling-Hammond, L. (2017). Teacher education around the world: What can we learn from international practice?. European journal of teacher education, 40(3), 291-309.

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

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

Holmes, W., Bialik, M., & Fadel, C. (2021). Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Center for Curriculum Redesign.

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

Ingersoll, R. M., & Strong, M. (2011). The impact of induction and mentoring programs for beginning teachers: A critical review of the research. Review of educational research, 81(2), 201-233.

Jamal, A. (2023). The Role Of Artificial Intelligence (AI) In Teacher Education: Opportunities & Challenges. International Journal of Research and Analytical Reviews (IJRAR). 10 (1). 139-146.

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

Jorgensen, M. E. (2019). Moving Forward on Outcomes Assessment: Seven Recommendations for Administrators. Assessment Update, 31(6).

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.

Knight, J. (2009). What can we do about teacher resistance?. Phi Delta Kappan, 90(7), 508-513.

Likert, R. (1932). A Technique for the Measurement of Attitudes. Archives of Psychology, 22, 5-52.

Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2020). Intelligence Unleashed: An Argument for AI in Education. Pearson Education.

Magnaye, M. (2023). Pupils' Perceptions on Varied Closure Activities in English Class. Scholarum: Journal of Education, 3(1).

Metin, M. (2013). Teachers' Difficulties in Preparation and Implementation of Performance Task. Educational Sciences: Theory and Practice, 13(3), 1664-1673.

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

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

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

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

Szulanski, G., & Amin, K. (2001). Learning to make strategy: balancing discipline and imagination. Long Range Planning, 34(5), 537-556.

Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education: The state of play. International Journal of Educational Technology in Higher Education, 16(1), 39.