Fostering Language Learner Autonomy Through Adaptive Conversation Tutors

CALL can be a route to learner autonomy, allowing students to use PC-based software to learn individually, without need of class or teacher. However, language is above all a medium for communication, implying a dialogue of two or more participants: although CALL can provide exercises and lessons in grammar, vocabulary, writing skills etc, it might seem that conversation practice still calls for a teacher, or at least fellow students. A chatbot is a program which can chat in natural language, on a topic built into the chatbot’s internal linguistic knowledge model. Many chatbots exist, with different “knowledge” programmed by the chatbot builder. A chatbot may appear to be a suitable partner for conversation practice; for example, the speak2me.net website includes Lucy, who “chats” in the language and style of a nice, polite British young lady. However, language teachers will know that conversation practice is normally on a specific topic, to learn topic-specific vocabulary and language. Lucy is nice to chat with initially, but doesn’t adapt to different topics or lessons. Furthermore, Lucy helps autonomous learners of English, but not other languages. We have developed algorithms for adapting or retraining a chatbot with a corpus, to chat in the language and topic of the training corpus. An attraction of the corpus-training approach is that in principle any corpus, in any language and on any topic, can be used; so we have gone on to test this principle, by using domain-specific corpora to train chatbots to chat on specific topics such as the Qu’ran , Computing Frequently Asked Questions, and non-English language corpora such as the Corpus of Spoken Afrikaans. Language learners and teachers have given evaluation feedback, indicating that these adaptive chatbots offer a useful autonomous alternative to traditional classroom-based conversation practice.