Applied Sciences · 2022 · 267 citations · 30 references
EducationComputer ScienceEducational TechnologyArtificial IntelligenceLanguage EducationTechnology-based Language TeachingArtificial Intelligence In Language LearningConversational InterfacesConversational User InterfacesDialogue SystemsDialogue Systems For LearningEducational ChatbotsChatbot AdoptionChatbot InteractionChatbot-based Language LearningChatbotsConversational AgentsLanguage Learning ChatbotsQualitative Data CollectionTechnology Acceptance
Recent AI advances have spurred the use of chatbots in language learning, yet most studies have examined only accuracy and student or teacher communication. This study investigates future educators’ knowledge, satisfaction, and perceptions of integrating conversational AI into language learning. A mixed‑method convenience sample of 176 Spanish and Polish undergraduates interacted with three chatbots over four weeks, and their responses were evaluated with the CHISM and TAM2 models via pre‑post surveys and template analysis, analyzed in SPSS and QDA Miner. Participants reported positive perceptions of ease of use and attitudes toward chatbots, moderate behavioral intention, and gender‑specific differences in satisfaction with design and interaction topics.
Recent advances in Artificial Intelligence (AI) and machine learning have paved the way for the increasing adoption of chatbots in language learning. Research published to date has mostly focused on chatbot accuracy and chatbot–human communication from students’ or in-service teachers’ perspectives. This study aims to examine the knowledge, level of satisfaction and perceptions concerning the integration of conversational AI in language learning among future educators. 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. 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. 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. 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. The findings also unveiled some gender-related differences regarding participants’ satisfaction with chatbot design and topics of interaction.
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Becky Inkster, Shubhankar Sarda, Vinod Subramanian · JMIR mhealth and uhealth · 2018 · 971 citations · Full text