Students’ Writing Ability By Using AI Generative Tools; Diffit, Brisk, Mendeley

Yelliza; Siska; M. Khairi Ikhsan; Willy Satria

doi:10.36057/jilp.v8i1.692

Authors

Yelliza Universitas PGRI Sumatera Barat
Siska Universitas PGRI Sumatera Barat
M. Khairi Ikhsan Universitas PGRI Sumatera Barat
Willy Satria Universitas PGRI Sumatera Barat

DOI:

https://doi.org/10.36057/jilp.v8i1.692

Keywords:

AI Generative, Writing ability, Diffit, Brisk, Mendeley

Abstract

This study examines the impact of three AI generative tools—Diffit, Brisk, and Mendeley—on university students’ writing perceptions and motivation. A mixed-methods approach combined quantitative pretest and posttest assessments with qualitative insights from interviews and classroom observations. The participants, 27 students from Universitas Ekasakti Padang, were purposively sampled and randomly assigned to groups based on the AI tool used. Quantitative results revealed that the Diffit group achieved self-efficacy improvements from 80% (initial) to 90% (post-intervention), with a task value of 60%. The Brisk group showed consistently high self-efficacy (90%) and task value (90%), achieving an 80% writing proficiency score. In contrast, the Mendeley group recorded self-efficacy scores of 100% initially, dropping to 60%, but maintained a writing proficiency score of 80% due to a task value of 80%. Findings highlight self-efficacy as the most critical factor influencing writing improvement, supported by task value, intrinsic motivation, and perceived usefulness. Recommendations include integrating AI tools to build students’ confidence, aligning tasks with personal goals, and fostering intrinsic motivation. This study demonstrates the effectiveness of AI tools in enhancing writing proficiency by addressing students' cognitive, emotional, and motivational needs.

Downloads

Download data is not yet available.

References

Aboutalebi, H., Mao, D., Fan, R., Xu, C., He, C., & Wong, A. (2023). DeepfakeArt Challenge: A Benchmark Dataset for Generative AI Art Forgery and Data Poisoning Detection. https://arxiv.org/abs/2306.01272v3

Barrett, A., & Pack, A. (2023). Not quite eye to A.I.: student and teacher perspectives on the use of generative artificial intelligence in the writing process. International Journal of Educational Technology in Higher Education, 20(1), 59. https://doi.org/10.1186/s41239-023-00427-0

Bayly-Castaneda, K., Ramirez-Montoya, M.-S., & Morita-Alexander, A. (2024). Crafting personalized learning paths with AI for lifelong learning: A systematic literature review. Frontiers in Education. https://doi.org/10.3389/feduc.2024.1424386

Bolton, K., Hill, C., Bacon-Shone, J., & Peyronnin, K. (2023). EMI (English-medium instruction) in Indonesian higher education. World Englishes, 42(3), 424–446. https://doi.org/https://doi.org/10.1111/weng.12622

Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa

Braun, V., Clarke, V., & Terry, G. (2015). Thematic Analysis. In Qualitative research in clinical and health psychology (P. Rohlede, pp. 95–113). Palgrave Macmillan. https://doi.org/LK - https://worldcat.org/title/884665236

Chan, C. K. Y., & Hu, W. (2023). Students’ voices on generative AI: perceptions, benefits, and challenges in higher education. International Journal of Educational Technology in Higher Education, 20(1). https://doi.org/10.1186/s41239-023-00411-8

Chiu, T. K. F. (2023). The impact of Generative AI (GenAI) on practices, policies and research direction in education: a case of ChatGPT and Midjourney. Interactive Learning Environments, 1–17. https://doi.org/10.1080/10494820.2023.2253861

Chiu, T. K. F. (2024). Future research recommendations for transforming higher education with generative AI. Computers and Education: Artificial Intelligence, 6, 100197. https://doi.org/10.1016/j.caeai.2023.100197

Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2024). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International, 61(2), 228–239. https://doi.org/10.1080/14703297.2023.2190148

Dodds, P., & Fletcher, J. D. (2004). Opportunities for New “Smart” Learning Environments Enabled by Next-Generation Web Capabilities. Journal of Educational Multimedia and Hypermedia, 13(4), 391–404.

Farrelly, T., & Baker, N. (2023). Generative Artificial Intelligence: Implications and Considerations for Higher Education Practice. Education Sciences, 13(11). https://doi.org/10.3390/educsci13111109

Fathi, J., Rahimi, M., & Derakhshan, A. (2024). Improving EFL learners’ speaking skills and willingness to communicate via artificial intelligence-mediated interactions. System, 121, 103254. https://doi.org/https://doi.org/10.1016/j.system.2024.103254

Glasersfeld, E. von. (2020). A Constructivist Approach to Teaching. In Constructivism in Education (pp. 21–34). Routledge. https://doi.org/10.4324/9780203052600-5

Hansen, K., & Świderska, A. (2023). Integrating open- and closed-ended questions on attitudes towards outgroups with different methods of text analysis. Behavior Research Methods, 0123456789. https://doi.org/10.3758/s13428-023-02218-x

Huff, C. (2024, October 1). The promise and perils of using AI for research and writing. APA. https://www.apa.org/topics/artificial-intelligence

Liu, F., Asim, R., Battu, B., Benabderrahmane, S., Alhafni, B., Adnan, W., Alhanai, T., AlShebli, B., Baghdadi, R., Bélanger, J. J., Beretta, E., Celik, K., Chaqfeh, M., Daqaq, M. F., Bernoussi, Z. El, Fougnie, D., de Soto, B. G., Gandolfi, A., … Zaki, Y. (2023). Perception, performance, and detectability of conversational artificial intelligence across 32 university courses. Scientific Reports, 13(1). https://doi.org/10.1038/s41598-023-38964-3

Ivankova, N. V, Creswell, J. W., & Stick, S. L. (2006). Using Mixed-Methods Sequential Explanatory Design: From Theory to Practice. Field Methods, 18(1), 3–20. https://doi.org/10.1177/1525822X05282260

Wells, R., Shanks, E., Boey, T., & Parsons, B. (2024). Student perspectives on the use of generative artificial intelligence technologies in higher education. International Journal for Educational Integrity, 20. https://doi.org/10.1007/s40979-024-00149-4

Grotewold, K., Hartwick, P., & Papin, K. (2023). Generative AI and Teachers’ Perspectives on Its Implementation in Education. Journal of Interactive Learning Research, 34(2), 313–338. https://www.learntechlib.org/p/222363

Keser, H. (2024). The impact of AI tools on writing proficiency and student engagement. Educational Research Review, 29, 67-82. https://doi.org/10.1016/j.edurev.2024.02.003

Konstantinova, L. V., Vorozhikhin, V. V., Petrov, A. M., Titova, E. S., & Shtykhno, D. A. (2023). Generative Artificial Intelligence in Education: Discussions and Forecasts. Open Education, 27(2), 36–48. https://doi.org/10.21686/1818-4243-2023-2-36-48

Lee, Y.-J., Davis, R. O., & Lee, S. O. (2024). University students’ perceptions of artificial intelligence-based tools for English writing courses. Online Journal of Communication and Media Technologies, 14(1), e202412. https://doi.org/10.30935/ojcmt/14195

Liao, H., Xiao, H., & Hu, B. (2023). Revolutionizing ESL Teaching with Generative Artificial Intelligence—Take ChatGPT as an Example. International Journal of New Developments in Education, 5(20), 39–46. https://doi.org/10.25236/IJNDE.2023.052008

Longoni, C., Tully, S., & Shariff, A. (2023). Plagiarizing AI-generated Content Is Seen As Less Unethical and More Permissible. 1–23. https://doi.org/10.31234/osf.io/na3wb

Luckin, R., Cukurova, M., Kent, C., & Du Boulay, B. (2022). Empowering educators to be AI-ready. Computers and Education: Artificial Intelligence, 3, 100076. https://doi.org/10.1016/j.caeai.2022.10007.