Research

The advancement of artificial intelligence (AI) has drawn educators' attention to its educational potential. However, not all learners fully benefit from AI, and not every learner-AI interaction is equally effective. The efficacy of AI for learning depends largely on how learners interact with it. Despite the significance of learner-AI interaction, research on interaction patterns remains limited. This study investigates behavioral patterns in the learner-AI interaction during an English as a Foreign Language (EFL) writing task. Through an experiment involving 29 EFL undergraduates, three distinct interaction patterns emerged, exhibiting significant differences in their engagement with the AI tool. Findings suggest that even with the same AI tool, different learners engage in diverse ways to complete tasks. To foster productive learner-AI interactions, instructors should teach how to collaboratively participate in co-constructing knowledge with AI. Data-driven adaptive systems should be developed to enable monitoring of students' behavioral patterns, allowing instructors to provide personalized support. 

The advancement of artificial intelligence (AI) has drawn educators' attention to its educational potential. However, the efficacy of AI depends on learners' effective interaction, avoiding either neglect or excessive dependence. Despite the significance of learner-AI interaction, research on interaction patterns remains limited. This study investigates how EFL (English as a foreign language) learners interact with an AI tool for English writing and explores the effects of different interaction patterns on their writing performance. Through an experiment involving 29 EFL undergraduates, three distinct interaction patterns emerged, exhibiting significant differences in their engagement with the AI tool. The comparisons of the three clusters indicate that different AI interaction patterns lead to varied interaction approaches, and not all learners equally benefit from AI's potential. In order to promote productive learner-AI interaction in educational environments, instructors should provide personalized support and feedback.

The rapid development of artificial intelligence (AI) has transformed how people teach and learn, fostering individualized and personalized learning. However, AI systems are not always successful, and their effectiveness varies according to how learners interact with the systems. This study aims to develop the conceptual model of learner-AI collaboration, which explains why some learners are better than others in carrying out a task with AI. A total of 30 students in a university, South Korea, were interviewed after conducting drawing and writing tasks with AI systems. The interviews were analyzed to develop the conceptual model of learner-AI collaboration that consisted of four components: learners, tasks, AI, and contexts. The conceptual model would provide meaningful insights to learners, educators, and AI developers. 

Recently, many researchers and educators are getting interested in employing a flipped classroom approach to innovate their teaching practices. However, not every flipped classroom is successful because of a lack of instructional strategies. The purpose of the study was to investigate factors that influence the successful implementation of flipped classrooms from the students’ perspectives in a university. A total of 350 students participated in this study. The results showed that design for the flipped classroom, instructor effort, and student effort significantly explain students’ successful learning experiences, including perceived learning, course satisfaction and attitudes towards a flipped classroom approach. Further discussion and implications will be provided.