Using AI-generative tools in tertiary education: Reflections on their effectiveness in improving tertiary students’ English writing abilities
DOI:
https://doi.org/10.24059/olj.v28i3.4632Keywords:
artificial intelligence, shadow education, English writing, university education, computer-human interactionAbstract
Artificial intelligence (AI) tools have become a popular topic in the education field. Most of the schools in Hong Kong focus on how to properly use AI software to help students’ learning experience. As this is still a relatively new device, the stance for most of the schools in Hong Kong is skeptical. This study aims to find out whether AL-Generative tools, such as Chatgpt, can help to improve students’ English writing skills in university. Interviews were used to find out students and teachers’ opinions towards using AI in writing. The results indicate that students find AI tools convenient for learning writing skills, but teachers are concerned that the feedback and examples provided by AI are too general and ambiguous. This study provides some interesting opinions from students and teachers about their experience in using AI in learning and writing, and this helps us to understand more about how to use AI effectively in the education sector.
References
Akintayo, O. T., Eden, C. A., Ayeni, O. O., & Onyebuchi, N. C. (2024). Integrating AI with
emotional and social learning in primary education: Developing a holistic adaptive learning ecosystem. Computer Science & IT Research Journal, 5(5), 1076–1089.
Alam, A. (2021, December). Should robots replace teachers? Mobilisation of AI and learning
analytics in education. Proceedings of 2021 International Conference on Advances in Computing, Communication, and Control, 1–12. IEEE.
Alawamleh, M., Al-Twait, L. M., & Al-Saht. G. R. (2020). The effect of online learning on
communication between instructors and students during Covid-19 pandemic. Asian Education and Development Studies, 1–21
Al Ghamdi, A., Samarji, A., & Watt, A. (2016). Essential considerations in distance education in
KSA: Teacher immediacy in a virtual teaching and learning environment. International Journal of Information and Education Technology, 6, 17–22. https://doi.org/10.7763/IJIET.2016.V6.651
Andreenkova, A. (2018). How to choose interview language in different countries.
Advances in comparative survey methods: Multinational, multiregional, and multicultural contexts (3MC), 293–324.
Braun, V., &Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in
Psychology, 3(2), 77–101.
Bray, M. (2013). Benefits and tensions of shadow education: Comparative
perspectives on the roles and impact of private supplementary tutoring in the lives of Hong Kong students. Journal of International and Comparative Education (JICE), 18–30.
Bryman, A., & Bell, E. (2011). Business research methods (3rd ed.). Oxford University Press.
Chen, M., Chai, C. S., Jong, M. S. Y., & Jiang, M. Y. C. (2021). Teachers’ conceptions of
teaching Chinese descriptive composition with interactive spherical video-based virtual reality. Frontiers in Psychology, 12, 591708. https://doi.org/10.3389/fpsyg.2021.591708
Cheng, C. H. (2021). A need or a force? Shadow education in Hong Kong from
secondary school parent’s perspective. Curriculum and Teaching, 36(1), 37–48.
Cheng, C. H. R. (2024). A brighter path forward: How Hong Kong's government and
authorities are paving the way for Shadow Education's Future. International Journal of Education and Research, 12(3), 91–104.
Chinese University of Hong Kong (CUHK). (2023, March). Use of artificial intelligence
tools in teaching, learning and assessments: A guide for students. Retrieved from: https://www.aqs.cuhk.edu.hk/documents/A-guide-for-students_use-of-AI-tools.pdf
Chiu, T. K. F., Jong, M. S. Y., & Mok, I. A. C. (2020). Does learner expertise matter
when designing emotional multimedia for learners of primary school
mathematics? Educational Technology Research and Development, 68, 2305–
2320. https://doi.org/10.1007/s11423-020-09775-4
Chiu, T. K. F., Meng, H., Chai, C. S., King, I., Wong, S., & Yeung Y. (2022). Creation
and evaluation of a pre-tertiary Artificial Intelligence (AI) curriculum. IEEE
Transactions on Education, 65(1), 30–39.
http://dx.doi.org/10.1109/TE.2021.3085878
Chiu, T. K., Moorhouse, B. L., Chai, C. S., & Ismailov, M. (2023). Teacher support and student
motivation to learn with an Artificial Intelligence (AI) based chatbot. Interactive Learning Environments, 1–17.
Clarke, V., & Braun, V. (2017). Thematic analysis. The Journal of Positive Psychology,
12(3), 297–298.
Crawford, J., K., Butler-Henderson, J., Rudolph, B., … Lam, S. (2020). COVID-19: 20 countries’ higher education intra-period digital pedagogy responses. Journal of Applied Learning & Teaching, 3 (1), 1–20.
Creswell, J. W. (2004). Educational research: Planning, conducting, and evaluating
quantitative and qualitative research. Pearson.
Denzin, N. K., & Lincoln, Y. S. (2000). Introduction: The discipline and practice of
qualitative research. In N. K. Denzin & Y. S. Lincoln (Eds.), Handbook of qualitative
research (pp. 1–28). Sage.
Dizon, G., & Gayed, J. M. (2021). Examining the impact of Grammarly on the quality of mobile
L2 writing. JALT CALL Journal, 17(2), 74–92
Du‐Harpur, X., Watt, F. M., Luscombe, N. M., & Lynch, M. D. (2020). What is AI?
Applications of artificial intelligence to dermatology. British Journal of Dermatology, 183(3), 423–430.
Education Bureau. (2020). Hong Kong as a regional education hub.
Education Bureau. (2023). Primary and secondary education.
Edwards, A., & Edwards, C. (2017). The machines are coming: Future directions in instructional
communication research. Communication Education, 66(4), 487–488. https://doi.
org/10.1080/03634523.2017.1349915
Fitria, T. N. (2021). Grammarly as AI-powered English writing assistant: Students’ alternative
for writing English. Metathesis: Journal of English Language, Literature, and Teaching, 5(1), 65–78.
Fryer, L. K., Nakao, K., & Thompson, A. (2019). Chatbot learning partners: Connecting
learning experiences, interests and competence. Computers in Human Behavior,
93, 279–289. https://doi.org/10.1016/j.chb.2018.12.023
Gayed, J. M., Carlon, M. K. J., Oriola, A. M., & Cross, J. S. (2022). Exploring an AI-based
writing assistant's impact on English language learners. Computers and Education: Artificial Intelligence, 3, 100055.
GTPE Communication. (2016, November 10). Meet Jill Watson: Georgia Tech’s first AI
teaching assistant. https://pe.gatech.edu/blog/meet-jill-watson-georgia-techs-first-ai-teaching-assistant
Hamouma, C., & Menezla, N. (2019). The impact of digital literacy proficiency on EFL students'
academic writing performance: A case study of Algerian third year EFL students. International Journal of Digital Literacy and Digital Competence, 10(4), 40–55.
Hart-Davidson, W. (2018). Writing with robots and other curiosities of the age of machine
rhetorics. In J. Alexander & J. Rhodes (Eds.), The Routledge handbook of digital writing and rhetoric (pp. 248–255). Routledge.
Hill, J., Ford, W. R., & Farreras, I. G. (2015). Real conversations with artificial
intelligence: A comparison between human-human online conversations and
human–chatbot conversations. Computers in Human Behavior, 49, 245–250.
https://doi.org/10.1016/j.chb.2015.02.026
Hong Kong Adventist College. (2024). Guidelines for the use of AI-generative tools. HKAC.
Hsieh, W. M., & Tsai, C. C. (2017). Taiwanese high school teachers’ conceptions of mobile
learning. Computers and Education, 115, 82–95. https://doi.org/10.1016/j.compedu.2017.07.013
Kaufmann, R., Sellnow, D. D., & Frisby, B. N. (2016). The development and validation of
the Online Learning Climate Scale (OLCS). Communication Education, 65, 307–321.
https://doi.org/10.1080/03634523.2015.1101778
Kim, J., Merrill Jr., K., Xu, K., & Sellnow, D. D. (2020). My teacher is a machine:
Understanding students’ perceptions of AI teaching assistants in online education. International Journal of Human–Computer Interaction, 36(20), 1902–1911. https://doi.org/10.1080/10447318.2020.1801227
Kim, J., Merrill Jr., K., Xu, K., & Sellnow, D. D. (2022). Embracing AI-based education:
Perceived social presence of human teachers and expectations about machine teachers in online education. Human-Machine Communication, 4, 169–184. https://doi.org/10.30658/hmc.4.9
Kolchenko, V. (2018). Can modern AI replace teachers? Not so fast! Artificial intelligence and
adaptive learning: Personalized education in the AI age. HAPS Educator, 22(3), 249–252.
Lee, M., Liang, P., & Yang, Q. (2022). Designing a human-AI collaborative
writing dataset for exploring language model capabilities. Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems, USA, 388, 1–19. https://doi.org/10.1145/3491102.350203
Marcos, L. R., Alpert, M., Urcuyo, L., & Kesselman, M. (2006). The effect of interview language on the evaluation of psychopathology in Spanish-American schizophrenic patients. American Journal of Psychiatry, 130(5), 549–553.
McKee, H. A., & Porter, J. E. (2018). The impact of AI on writing and writing instruction.
DCR. https://www.digitalrhetoriccollaborative.org/2018/04/25/ai-on-writing/
Mollick, E. R., & Mollick, L. (2023). Using AI to implement effective teaching strategies in
classrooms: Five strategies, including prompts. SSRN Electronic Journal, 1–26. http://dx.doi.org/10.2139/ssrn.4391243
Moore, K. A., Rutherford, C., & Crawford, K. A. (2016). Supporting postsecondary English
language learners' writing proficiency using technological tools. Journal of International Students, 6(4), 857–872. http://dx.doi.org/10.32674/jis.v6i4.321
Muslimin, A. I., Mukminatien, N., & Ivone, F. M. (2024). Evaluating Cami AI across SAMR
stages: Students’ achievement and perceptions in EFL writing instruction. Online Learning, 28(2), 1–19. http://dx.doi.org/10.24059/olj.v28i2.4246
Nair, D. (2019). Saving face in diplomacy: A political sociology of face-to-face interactions in
the Association of Southeast Asian Nations. European Journal of International Relations, 25(3), 672–697.
Ou, A. W., Gu, M. M., & Lee, J. C. K. (2022). Learning and communication in online
international higher education in Hong Kong: ICT-mediated translanguaging competence and virtually translocal identity. Journal of Multilingual and Multicultural Development, 1–14.
Onwuegbuzie, A. J., & Leech, N. L. (2004). Enhancing the interpretation of significant findings:
The role of mixed methods research. The Qualitative Report, 9(4), 770–792.
Parker, C., Scott, S., & Geddes, A. (2019). Snowball sampling. Sage.
Punch, K., & Oancea, A. (2014). Introduction to research methods in education (2nd ed). Sage.
Raja, R., & Nagasubramani, P. C. (2018). Impact of modern technology in education. Journal
of Applied and Advanced Research, 3, 33–35.
Salhab, R. (2024). AI literacy across curriculum design: Investigating college instructors’
perspectives. Online Learning, 28(2), 22–47. https://doi.org/10.24059/olj.v28i2.4426
Savin-Baden, M., Gourlay, L., & Tombs, C. (2010). Researching in immersive spaces. In
M. Savin-Baden & C. H. Major (Eds.), New approaches to qualitative research: Wisdom and uncertainty (pp. 162–171). Routledge.
University of Hong Kong. (2023, February 17). About ChatGPT. Retrieved from
https://tl.hku.hk/2023/02/about-chatgpt/
Yin, J., Goh, T. T., Yang, B., & Xiaobin, Y. (2021). Conversation technology with
micro-learning: The impact of chatbot-based learning on students’ learning
motivation and performance. Journal of Educational Computing Research,
59(1), 154–177. https://doi.org/10.1177/0735633120952067
Yung, K. W. H. (2019). Learning, teaching, and researching in shadow education in Hong
Kong: An autobiographical narrative inquiry. ECNU Review of Education, 2(1), 64–76.
Yung, K. W. H., & Yuan, R. (2020). “The most popular star-tutor of English”: Discursive
construction of tutor identities in shadow education. Discourse: Studies in the Cultural Politics of Education, 41(1), 153–168.
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