| Autor: |
Jennifer Meyer, Thorben Jansen, Ronja Schiller, Lucas W. Liebenow, Marlene Steinbach, Andrea Horbach, Johanna Fleckenstein |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Computers and Education: Artificial Intelligence, Vol 6, Iss , Pp 100199- (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2666-920X |
| DOI: |
10.1016/j.caeai.2023.100199 |
| Popis: |
Writing proficiency is an essential skill for upper secondary students that can be enhanced through effective feedback. Creating feedback on writing tasks, however, is time-intensive and presents a challenge for educators, often resulting in students receiving insufficient or no feedback. The advent of text-generating large language models (LLMs) offers a promising solution, namely, automated evidence-based feedback generation. Yet, empirical evidence from randomized controlled studies about the effectiveness of LLM-generated feedback is missing. To address this issue, the current study compared the effectiveness of LLM-generated feedback to no feedback. A sample of N = 459 upper secondary students of English as a foreign language wrote an argumentative essay. Students in the experimental group were asked to revise their text according to feedback that was generated using the LLM GPT-3.5-turbo. The control group revised their essays without receiving feedback. We assessed improvement in the revision using automated essay scoring. The results showed that LLM-generated feedback increased revision performance (d = .19) and task motivation (d = 0.36). Moreover, it increased positive emotions (d = 0.34) compared to revising without feedback. The findings highlight that using LLMs allows to create timely feedback that can positively relate to students’ cognitive and affective-motivational outcomes. Future perspectives and the implications for research and practice of using LLM-generated feedback in intelligent tutoring systems are discussed. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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