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
Carmen Köhler, Johannes Hartig
CONT ED TECHNOLOGY, Volume 16, Issue 4, Article No: ep528
ABSTRACT
Since ChatGPT-3.5 has been available to the public, the potentials and challenges regarding chatbot usage in education have been widely discussed. However, little evidence exists whether and for which purposes students even apply generative AI tools. The first main purpose of the present study was to develop and test scales that assess students’ (1) knowledge about ChatGPT, (2) actual ChatGPT usage and perceived value of use, and (3) attitude towards ChatGPT. Our second aim was to examine the intercorrelations between these scales, and to investigate differences (a) across five academic fields (i.e., human sciences, social sciences, teaching profession, health sciences, and law and economics) and (b) between stages of education (i.e., number of semesters). N = 693 students from various German universities participated in our online survey. Quality checks (Cronbach’s alpha, MacDonald’s omega, and confirmatory factor analyses) show satisfactory results for all scales. The scales all positively relate to each other, except for the knowledge and attitude scales. This means that more knowledge about ChatGPT is connected to a less favorable attitude regarding the generative AI tool. Lastly, MANOVA and subsequent Bonferroni corrected ANOVA tests show that ChatGPT is mostly used by law and economics students, and most frequently by students in the third year of higher education.
Keywords: ChatGPT in higher education, student knowledge, student use, student attitude, scale development, assessment, large language models (LLMs)