C hat B ack: Investigating Methods of Providing Grammatical Error Feedback in a GUI -based Language Learning Chatbot (2023)

Abstract: The increasing use of AI chatbots as conversation partners for second-language learners highlights the importance of providing effective feedback. To ensure a successful learning experience, it is essential for researchers and practitioners to understand the optimal timing, methods of delivery, and types of feedback that are most beneficial to learners. Synchronous grammar corrective feedback (CF) has been shown to be more effective than asynchronous methods in online writing tasks. Additionally, self-correction by language learners has proven more beneficial than teacher-provided correction, particularly for spoken language skills and non-novice learners. However, existing language-learning AI chatbots often lack synchronous CF and self-correction capabilities. To address this, we propose a synchronous conversational corrective feedback (CCF) method, which allows self-correction and provides metalinguistic explanations (ME). Our study suggests that in chatbot-driven language-learning tools, corrective feedback is more effectively delivered through means other than the social chatbot, such as a GUI interface. Furthermore, we found that guided self-correction offers a superior learning experience compared to providing explicit corrections, particularly for learners with high learning motivation or lower linguistic ability.

## introduction the growing prevalence of ai chatbots as conversational partners for second-language learners emphasizes the vital role of delivering effective feedback to enhance the overall learning experience. as researchers and practitioners work to optimize computer-based conversational language learning, it is essential to determine the optimal timing, methods of delivery, and feedback types that contribute to the most successful outcomes. prior research has shown that synchronous corrective feedback (cf) for grammatical errors is more effective than asynchronous methods in online writing tasks @xcite . however, the best form of synchronous cf in ai chatbot systems has yet to be determined. furthermore, self-correction by language learners has proven to be more beneficial than teacher-provided correction @xcite , especially for spoken language skills and for learners with more than limited l2 proficiency. despite this evidence, numerous current languagelearning ai chatbots lack diverse synchronous cf and self-correction features. and while past research has shown that learners' proficiency levels significantly influence their preferences @xcite @xcite , the optimization of feedback strategies to adapt to users with varying proficiencies and motivations in language-learning chatbots remains unexplored. to address this limitation, we propose a ai chatbot for language learning with synchronous conversational corrective feedback (ccf), and investigate the effect of the feedback form and selfcorrection with metalinguistic explanations (me). specifically, we explore the following two research questions: rq1: how do the forms of cf delivery, specifically, feedback from the conversational partner (i.e., the chatbot) and a separate role (i.e., a gui), impact the learning experience, including conversational enjoyment, negative emotions, self-efficacy, perceived usefulness, and intention to use the system? we hypothesize that: h1: learners prefer receiving feedback from a separate role rather than from the conversation partner. rq2: how does the process of self-correction (compared to explicit feedback without selfcorrection) impact the learning experiences, including conversational enjoyment, negative emotions, self-efficacy, perceived usefulness, and intention to use the system? specifically, what are the effects on people with different linguistic ability and learning purposes? we hypothesize that: h2.1: learners with lower linguistic ability prefer receiving guided self-correction compared to those with higher proficiency. and h2.2: learners with serious learning purposes prefer receiving guided self-correction relative to those who report other learning motivation. ## conclusion results from this preliminary study provide evidence that learners may prefer getting corrective feedback from a separate role, instead of from the conversation partner to reduce frustration. in addition, guided self-correction may provide better learning experiences than the absence of selfcorrection, especially for learners with lower lexical competence or more serious learning motivation. these findings highlight the importance of considering users' individual differences when designing language-learning chatbots, and the need for personalized feedback mechanisms that cater to individual users' need.