Research Article
Sumie Tsz Sum Chan, Noble Po Kan Lo, Alan Man Him Wong
CONT ED TECHNOLOGY, Volume 16, Issue 4, Article No: ep541
ABSTRACT
This paper investigates the effects of large language model (LLM) based feedback on the essay writing proficiency of university students in Hong Kong. It focuses on exploring the potential improvements that generative artificial intelligence (AI) can bring to student essay revisions, its effect on student engagement with writing tasks, and the emotions students experience while undergoing the process of revising written work. Utilizing a randomized controlled trial, it draws comparisons between the experiences and performance of 918 language students at a Hong Kong university, some of whom received generated feedback (GPT-3.5-turbo LLM) and some of whom did not. The impact of AI-generated feedback is assessed not only through quantifiable metrics, entailing statistical analysis of the impact of AI feedback on essay grading, but also through subjective indices, student surveys that captured motivational levels and emotional states, as well as thematic analysis of interviews with participating students. The incorporation of AI-generated feedback into the revision process demonstrated significant improvements in the caliber of students’ essays. The quantitative data suggests notable effect sizes of statistical significance, while qualitative feedback from students highlights increases in engagement and motivation as well as a mixed emotional experience during revision among those who received AI feedback.
Keywords: LLMs, feedback, student engagement, student motivation, generative AI
Research Article
Yong Jik Lee, Robert O. Davis
CONT ED TECHNOLOGY, Volume 16, Issue 4, Article No: ep533
ABSTRACT
This research investigated the effects of generative AI on affective factors (motivation, interest, and confidence) of English as a foreign language (EFL) learners enrolled in Korean university-level general English courses. During the Spring 2024 semester, this study involved 89 participants exposed to a generative AI-based instruction model. Compared to traditional methodologies, these results highlight the potential effectiveness of generative AI-based English instruction for writing and speaking in supporting linguistic proficiency and positive affective attitudes toward language learning among EFL learners.
Keywords: general English courses, English writing and speaking, generative AI, university students, EFL