Abstract
This study thoroughly analyses the incorporation of chatbots in English as a Foreign Language (EFL) education by examining 11 research studies carried out from 2020 to 2023. The review carefully combines findings, techniques, and consequences to offer a detailed overview of the complex field of chatbot-mediated language learning treatments. From quasi-experimental designs to qualitative explorations, the evaluated research uses a range of approaches to study various characteristics such as learning attainment, effectiveness, motivation, attitudes, perceptions, cognition, learning styles, and user experience. The results show how adaptable chatbot technology is in supporting task-oriented learning, vocabulary development, conversational practice, and pedagogical support, providing individualized and interesting language learning opportunities. Additionally, the contextual complexities present in chatbot interventions are emphasised, highlighting the significance of taking learner demographics, educational situations, and cultural backgrounds into account.
TopIntroduction
Artificial intelligence technologies are expected to have a substantial influence on teaching and learning methods in the next years (Can, Gelmez-Burakgazi, & Celik, 2019). Chatbots, which are intelligent conversational systems that can interact with users through voice or text, have potential for aiding in Second Language Acquisition (SLA) (Fryer & Nakao, 2009). AI has progressed, especially in voice technology, enabling chatbots to imitate human-like interactions with language learners (Fryer et al., 2020; Shadiev & Liu, 2023; Zhang et al., 2023). Studies have confirmed that speech-recognition technology-based chatbots have a beneficial effect on language learning such as motivation and speaking anxiety (Jeon, 2022; Tai & Chen, 2020).
Chatbots are virtual agents that engage with people using natural language. They have a historical origin in ELIZA, the first chatbot created in 1956 by Joseph Weizenbaum to mimic a psychiatrist. Chatbots such as ALICE, Claude, and HeX have developed throughout time, using various frameworks and features (Shawar & Atwell, 2015). Chatbots have been used as pedagogical agents in education for a long time. Recent advancements in technology have sparked greater interest in using them for teaching and learning (Laurillard, 2013). Chatbots in language learning have also shown various benefits such as reducing anxiety, offering repeated practice, focusing on students, providing realistic communication, and being widely available (Fryer et al., 2020; Kohnke et al., 2023; Zhang et al., 2023). The affordances, shown in several fields, enhance successful learning experiences with the use of chatbots (Zhang et al., 2023). Chatbots are being used into education due to the widespread use of instant messaging, showcasing its ability to enhance teaching and learning (Coniam, 2014). Some language chatbots have demonstrated enhanced grammatical skills, while others like Chatbot Ethnobot aid in gathering data for ethnographic studies (Tallyn et al., 2018). The increasing popularity of mobile instant messaging applications, such as WhatsApp, emphasises the favourable view and adoption of chatbots for educational use (Pimmer et al., 2019).
AI chatbots are gaining attention in language instruction, especially in English as a Foreign Language (EFL) settings. Recent studies have shown promising results regarding the use of AI chatbots as conversational partners in EFL settings, although the empirical evidence supporting the effectiveness of ChatGPT in English language learning is still developing. Research conducted in EFL settings generally centres on dialogue-based automated agents and applications, which allow language learners to practise English by engaging in verbal or textual exchanges with chatbots. Kim (2019) conducted an experimental study with Korean undergraduate EFL learners, demonstrating that the usage of AI chatbots notably enhanced students' English grammatical abilities in comparison to a human chat group. Ebadi and Amini (2022) investigated how a chatbot application in an Iranian EFL university setting affected students' confidence and motivation to study English. They discovered that the accuracy and human-like qualities of the chatbot had a favourable influence on these factors. Bibauw et al.'s (2022) meta-analysis found that involving language learners with AI-driven conversation systems could forecast vocabulary and morphosyntactic results, improving learners' overall competency and accuracy in language usage.
Key Terms in this Chapter
Data Synthesis: The results of separate investigations are combined using suitable techniques such narrative synthesis, meta-analysis (where relevant), or theme analysis. Data synthesis entails condensing important discoveries, investigating patterns and connections, and pinpointing overarching themes or trends found in several investigations.
Technology Integration: It refers to integrating technological tools, resources, or platforms into educational environments to improve teaching and learning results. Technology integration in language learning involves using digital resources, multimedia materials, and AI-driven applications such as chatbots.
ChatBot: A chatbot is an artificial intelligence programme created to mimic communication with human users, usually through text or voice interactions. Chatbots can offer language practice and assistance to learners in the realm of language acquisition.
Artificial intellectual (AI): It refers to the emulation of human intellectual processes by technology, especially computer systems. AI technologies empower robots to carry out activities that usually need human intellect, such learning, problem-solving, comprehending natural language, and making decisions.
Second Language Acquisition (SLA): It is the process by which individuals acquire a language that is not their native language. The subject covers vocabulary acquisition, grammatical growth, and communicative skill in the target language.
Systematic Review: It is a thorough and detailed approach to combining information from several studies on a specific research issue or topic. It entails methodically finding, choosing, evaluating, and combining pertinent material to offer a comprehensive summary of the current evidence.
Computer-Assisted Language Learning (CALL): It refers to the utilisation of technology to aid and improve language learning tasks. CALL in AI chatbots refers to using chatbot technology into language learning platforms or software to offer learners interactive language practice and feedback.
Negotiation for Meaning (NfM): It is the collaborative process when language learners work together to clarify and address communication failures during exchanges. NfM commonly happens in language learning situations when learners come across novel terminology or grammatical structures and ask for clarification from their conversation partners, whether they be human or AI-driven chatbots.