![]() ![]() In their study, the conversational agent facilitated an immersive environment that enabled learners to simulate various daily conversations in English in order to reduce their anxiety and increase their self-confidence. used a semantic approach to demonstrate the positive impact of using a conversational agent on the willingness to communicate (WTC) in the context of English as a Foreign Language (EFL) by providing users with various daily conversation contexts. ![]() ![]() The authors found some notable differences such as that people communicated with the chatbot for longer durations (but with shorter messages) than they did with another human, and that human–chatbot communication lacked much of the richness of vocabulary found in conversations among people, which is consistent with later works related with the use of chatbots among children. compared 100 instant messaging conversations to 100 exchanges with the chatbot named Cleverbot along seven different dimensions: words per message, words per conversation, messages per conversation, word uniqueness and use of profanity, shorthand and emoticons. The findings also unveiled some gender-related differences regarding participants’ satisfaction with chatbot design and topics of interaction. The analysis yielded positive results regarding perceptions concerning the integration of conversational agents in language learning, particularly in relation to perceived ease of use (PeU) and attitudes (AT), but the scores for behavioral intention (BI) were more moderate. Quantitative and qualitative data were gathered through a pre-post-survey based on the CHISM and the TAM2 (technology acceptance) models and a template analysis (TA), and analyzed through IBM SPSS 22 and QDA Miner software. A learning module about Artificial Intelligence and language learning was specifically designed for this research, including an ad hoc model named the Chatbot–Human Interaction Satisfaction Model (CHISM), which was used by teacher candidates to evaluate different linguistic and technological features of the three conversational agents. In this mixed method research based on convenience sampling, 176 undergraduates from two educational settings, Spain ( n = 115) and Poland ( n = 61), interacted autonomously with three conversational agents (Replika, Kuki, Wysa) over a four-week period. This study aims to examine the knowledge, level of satisfaction and perceptions concerning the integration of conversational AI in language learning among future educators. Research published to date has mostly focused on chatbot accuracy and chatbot–human communication from students’ or in-service teachers’ perspectives. Recent advances in Artificial Intelligence (AI) and machine learning have paved the way for the increasing adoption of chatbots in language learning. ![]()
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