In recent decades, the rapid development of artificial intelligence (AI) technology has led to the increasing use of AI speaking apps in foreign language learning. This research investigates the impact of social network-based interaction on students’ English speaking practice with the assistance of AI speaking apps in China. During the summer vacation, 70 students from different Chinese universities and majors were recruited for the experiment. They were required to practice speaking skills with AI apps for five weeks and were divided into two groups. Participants in the experimental group were encouraged to engage in various interactive activities when practicing speaking with AI apps, while those in the control group were asked to use AI speaking apps without interaction. Data were collected through questionnaires and semi-structured interviews as well as pre-and post-tests. The results indicated that students generally held positive attitudes towards interactive activities when using AI apps to practice their spoken English. The finding also showed that social network-based interaction can effectively improve learners’ speaking skills in the AI context. This study contributes to the research on the implementation and promotion of AI speaking apps with social networking and extends the previous studies on network-based interaction to the AI-assisted learning environment. An investigation of interactions based on Chinese social network-based platforms such as WeChat can be further applied to other social networking platforms such as Facebook or WhatsApp in different cultural contexts for AI-assisted speaking practice.

The development of artificial intelligence (AI) technology has enhanced the use of automated speech evaluation systems for language learners to practice speaking skills. This study investigated whether various automatic feedback offered by AI speech evaluation programs can help English as a foreign language (EFL) learners develop speaking skills. Forty EFL learners in China participated in this study. Data collection included qualitative and quantitative data. The results showed that the majority of participants believed they improved their speaking skills with the feedback offered by the AI speaking evaluation program. The findings also revealed that there were significant improvements in their mean scores of speaking skills in pre- and post-tests. Therefore, it is suggested that AI speaking evaluation systems could provide more varied textual feedback and practical suggestions to assist EFL learners in developing speaking skills.

Online teaching has been massively conducted during the novel coronavirus period all over the world. How to evaluate online teaching has been increasingly researched recently. This study looked at how English as a foreign language (EFL) teaching was delivered online by university teachers during the COVID-19 pandemic. We investigated university teachers and students’ perception of effective EFL online teaching and learning based on several evaluation modes in using technology in education. Data were collected using questionnaires and interviews from teachers and students in a variety of provinces in Mainland China. The results showed that various methods were used to deliver online EFL courses and these approaches are found to correlate with each other. Teachers and students provided positive comments on online teaching and were satisfied with their online teaching and learning. Participants also noted effective ways in online EFL teaching. The findings indicated that when teachers have more training, more skills, and more confidence, they could deliver more effective online teaching and learning.