Chatbot-based learning of logical fallacies in EFL writing: perceived effectiveness in improving target knowledge and learner motivation

ABSTRACT

Chatbots have been increasingly applied for EFL education and demonstrated overall usefulness in improving knowledge and motivation, while this technology has yet to be used for learning logical fallacies (i.e. errors in reasoning) in EFL writing. However, knowledge of logical fallacies is essential, with which learners can avoid fallacies in EFL writing and have enhanced writing quality. To fill in the gap, this study investigated the perceived effectiveness of chatbots in developing knowledge of logical fallacy in EFL writing and enhancing learner motivation. Features of this learning method were also explored based on the comparison against website-based learning. Two groups of 15 Chinese EFL learners engaged in five-week autonomous, out-of-class, out-of-class learning of logical fallacies in EFL writing using a chatbot or a website. Semi-structured interviews, pre-post tests of fallacy knowledge and pre-post motivation questionnaires were conducted. The results showed that the chatbot was perceived as slightly less effective than the website in developing target knowledge but more effective in improving motivation. Compared to the website, chatbots were advantageous in high-quality human-computer interactions, study plan making, and high accessibility. Based on the research results, we discussed how this technology might influence fallacy learning based on the self-regulated learning framework.

KEYWORDS:

• Chatbot
• English writing
• logical fallacy
• second language writing
• technology-enhanced language learning

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Ruofei Zhang

Ruofei Zhang is a is a PhD candidate at the Education University of Hong Kong. Her research interests include technology-enhanced language learning, game-based language learning, and self-regulated language learning

Di Zou

Di Zou is the corresponding author of this paper. She is an Assistant Professor at the Education University of Hong Kong. Her research interests include technology-enhanced language learning, game-based language learning, and AI in English language education.

Gary Cheng

Gary Cheng is an Associate Professor at the Department of Mathematics and Information Technology at The Education University of Hong Kong. His research interests include but are not limited to: Information Technology Supported L2 Learning, ePortfolio-mediated Learning, Computer Programming Education, Online Learning Management System, e-Assessment, and Learning Analytics.