Abstract
In recent years, the use of artificial intelligence (AI) for writing feedback has become widespread in universities (Hung et al., 2023). Some potential benefits of AI tools are that they can positively affect revision (Link et al., 2022), improve engagement (Nazari, 2021), and free up teachers’ time so that they can focus on higher-level issues (Stevenson & Phakiti, 2019). However, there is also the risk that some students may depend on the feedback too much and not revise successfully. Additionally, students may not trust the feedback or have difficulties using the AI tools effectively (Zhai & Ma, 2021). There have also been concerns that the kinds of feedback produced by these tools may not always be appropriate for the context or needs of the students (Strobl, 2019).
Technological innovations are also presenting new challenges. Until recently, the main tools used were automatic writing evaluation (AWE) programs and intelligent tutoring systems (ITS) (Huang et al., 2023). However, since 2022, Generative Artificial Intelligence (GenAI) tools have also been used by students and teachers to generate writing feedback (Carlson et al., 2023). These tools produce different types of feedback than was previously possible but also come with disadvantages. For instance, GenAI responses may cause difficulties for students by being too verbose. Thus, methods for implementing these tools effectively need to be developed.
As AI writing feedback tools continue to improve, the possibilities for teaching and learning will likely continue to increase. In this rapidly changing environment, it is useful to examine how the trend has developed and consider how previous insights can be applied to future directions. This presentation will examine the use of A.I. writing feedback tools for university students. It will track the trend from 2019 to 2024 and will discuss benefits, challenges, and recommendations for the future.
References
Carlson, M., Pack, A., & Escalante, J. (2023). Utilizing OpenAI's GPT‐4 for written feedback. TESOL Journal, e759. https://doi.org/10.1002/tesj.759
Huang, X., Di, Z., Cheng, G., Xieling, C., & Haoran, X. (2023). Trends, research issues and applications of artificial intelligence in language education. Educational Technology and Society, 26(1). https://www.j-ets.net/collection/forthcoming-articles/26_1#h.p0l22f3fwnvt
Link, S., Mehrzad, M., & Rahimi, M. (2022). Impact of automated writing evaluation on teacher feedback, student revision, and writing improvement. Computer Assisted Language Learning, 35(4), 605-634. https://doi.org/10.1080/09588221.2020.1743323
Nazari, N., Shabbir, M. S., & Setiawan, R. (2021). Application of Artificial Intelligence powered digital writing assistant in higher education: randomized controlled trial. Heliyon, 7(5), e07014. https://dooi.org/10.1016/j.heliyon.2021.e07014
Stevenson, M., & Phakiti, A. (2019). Automated Feedback and Second Language Writing. In F. Hyland & K. Hyland (Eds.), Feedback in Second Language Writing: Contexts and Issues (2 ed., pp. 125-142). Cambridge University Press. https://doi.org/10.1017/9781108635547.009
Strobl, C., Ailhaud, E., Benetos, K., Devitt, A., Kruse, O., Proske, A., & Rapp, C. (2019). Digital support for academic writing: A review of technologies and pedagogies. Computers & Education, 131, 33-48. https://doi.org/https://doi.org/10.1016/j.compedu.2018.12.005
Zhai, N., & Ma, X. (2021). Automated writing evaluation (AWE) feedback: a systematic investigation of college students’ acceptance. Computer Assisted Language Learning, 1-26. https://doi.org/10.1080/09588221.2021.1897019
Technological innovations are also presenting new challenges. Until recently, the main tools used were automatic writing evaluation (AWE) programs and intelligent tutoring systems (ITS) (Huang et al., 2023). However, since 2022, Generative Artificial Intelligence (GenAI) tools have also been used by students and teachers to generate writing feedback (Carlson et al., 2023). These tools produce different types of feedback than was previously possible but also come with disadvantages. For instance, GenAI responses may cause difficulties for students by being too verbose. Thus, methods for implementing these tools effectively need to be developed.
As AI writing feedback tools continue to improve, the possibilities for teaching and learning will likely continue to increase. In this rapidly changing environment, it is useful to examine how the trend has developed and consider how previous insights can be applied to future directions. This presentation will examine the use of A.I. writing feedback tools for university students. It will track the trend from 2019 to 2024 and will discuss benefits, challenges, and recommendations for the future.
References
Carlson, M., Pack, A., & Escalante, J. (2023). Utilizing OpenAI's GPT‐4 for written feedback. TESOL Journal, e759. https://doi.org/10.1002/tesj.759
Huang, X., Di, Z., Cheng, G., Xieling, C., & Haoran, X. (2023). Trends, research issues and applications of artificial intelligence in language education. Educational Technology and Society, 26(1). https://www.j-ets.net/collection/forthcoming-articles/26_1#h.p0l22f3fwnvt
Link, S., Mehrzad, M., & Rahimi, M. (2022). Impact of automated writing evaluation on teacher feedback, student revision, and writing improvement. Computer Assisted Language Learning, 35(4), 605-634. https://doi.org/10.1080/09588221.2020.1743323
Nazari, N., Shabbir, M. S., & Setiawan, R. (2021). Application of Artificial Intelligence powered digital writing assistant in higher education: randomized controlled trial. Heliyon, 7(5), e07014. https://dooi.org/10.1016/j.heliyon.2021.e07014
Stevenson, M., & Phakiti, A. (2019). Automated Feedback and Second Language Writing. In F. Hyland & K. Hyland (Eds.), Feedback in Second Language Writing: Contexts and Issues (2 ed., pp. 125-142). Cambridge University Press. https://doi.org/10.1017/9781108635547.009
Strobl, C., Ailhaud, E., Benetos, K., Devitt, A., Kruse, O., Proske, A., & Rapp, C. (2019). Digital support for academic writing: A review of technologies and pedagogies. Computers & Education, 131, 33-48. https://doi.org/https://doi.org/10.1016/j.compedu.2018.12.005
Zhai, N., & Ma, X. (2021). Automated writing evaluation (AWE) feedback: a systematic investigation of college students’ acceptance. Computer Assisted Language Learning, 1-26. https://doi.org/10.1080/09588221.2021.1897019
| Original language | English |
|---|---|
| Publication status | Not published / presented only - 20 Jun 2024 |
| Event | International Conference on Emerging Technologies in English Language Education in the 21st Century - Duration: 19 Jun 2024 → 21 Jun 2024 |
Conference
| Conference | International Conference on Emerging Technologies in English Language Education in the 21st Century |
|---|---|
| Period | 19/06/24 → 21/06/24 |