Developing Fictive Dialogs for a Classroom Language Learning Conversational Interface
Matthew P. Aylett, Shiyi Tang, Xuanchen Li, Xinyang Liu, Chengcheng Liu, Ruiqing Li, Chenxi Meng, Zhengxian Qu, Sirui Wang, Zechen Yang · 2024
The use of Educational Conversational Agents (ECA) offers benefits for language learning but needs to overcome challenges such as student distraction and lack of engagement. Our aim is to enhance learning outcomes by designing an ECA to serve as both teacher and peer. Integrating RASA, a conversational agent from Rapport, and Automatic Speech Recognition (ASR) from Deepgram, the ECA engages in interactive sessions with a human confederate and student participant. During teaching, speech synthesis is used to read out an educational text, while interruptions from the confederate trigger semi-scripted dialogues, fostering students’ engagement and understanding. Evaluation includes conditions with and without dialogue elements, assessed through test questionnaires and Godspeed and NASA-TLX surveys to explore user satisfaction. Results show that more dialogues between the agent and the human confederate lead to better learning outcomes for students, but direct conversations between the agent and the student may lower the performance.