This chapter discusses the theoretical frameworks for artificial intelligence (AI) teachers and how AI teachers have been applied to facilitate game-based literacy learning in existing empirical studies. While the application of artificial intelligence (AI) in education is a relatively emerging research area, it has received increasing attention in the scientific community. In the future, AI teachers are likely to be able to serve as powerful supplementary tools in classroom teaching in support of human teachers. The main goal here is to provide the readers with new insights on promoting game-based literacy learning from the perspectives of AI teachers. To this end, the authors introduce the readers to the key concepts of AI teachers, the merits and demerits of AI teachers in education, scientific research on AI teachers in literacy learning, and some highlighted examples of AI teachers in literacy classrooms for practical concerns.
TopIntroduction
The advent of modern technology brings a substantial impact on the whole world, including educational practice. Such advances enrich game-based learning with digital platforms. Artificial Intelligence (AI) technology stands out as one of the most profound techniques to customize the learning needs of different learners and teachers. AI could have many benefits in education, as suggested as early as in Papert’s (1980) theory of constructivism, which argues that robotics activities can engage children to learn and construct objects more effectively, especially in language learning. Among others, educational robots (e.g., AI teacher) have become very popular due to their positive effects on students' learning outcomes indicated by a meta-analysis of 49 empirical studies (Shan et al., 2019), and thus have been used to assist children in learning, especially in the K-12 learning environment (Mubin, Stevens et al., 2013).
To date, researchers have developed and applied AI teacher to facilitate literacy learning and teaching in many countries across continents. The present chapter aims to introduce professionals and scholars to AI teacher regarding the extent to which AI teacher works for literacy learning. First, the present chapter introduces the theoretical background of AI teacher in general, including their merits and the respective challenges to human teacher and students. Second, this chapter discusses empirical work on AI teacher in literacy learning, linking to game-based literacy learning. In this part, we also provide a detailed illustration of some representative, evidenced-based AI teacher for literacy learning for readers’ references. Lastly, we reflect on the current advancement of AI teacher in literacy learning and point out the future directions.
Background of AI Teacher in Education
The current application of AI technology mainly includes three aspects: adaptive learning, intelligent tutoring systems, and educational robots (Zhu & Ma, 2019). Previous studies indicate that educational robots have become very popular and have a positive effect on students' learning outcomes in different stages (Shan et al., 2019). Huang et al. (2017) classified educational robot products into 12 categories. Among them, classroom robot assistants and robot teachers are mainly used in teaching. Robot as Teacher is also known as AI teacher. AI teacher has the function of assisting teachers to complete classroom teaching and achieve certain teaching effects (Huang et al., 2017).
AI teacher is an educational service robot, which can directly carry out intelligent auxiliary service in the teaching process. Its application process is often classified into three basic roles, namely mentor, student companion, and supervisor. Many studies have tested the role of AI teacher in supporting educational classroom activities. Technically, oral dialogue technology systems or other scripted programming technology systems allow AI agents to take on more important tasks than accessibility tools. It reduces the burden on human teachers and improves the quality of student learning (Edwards et al., 2016).
In the course of the operation of AI teacher, AI can obtain subjective cognitive ability and creativity similar to teachers' learning, which solves the problem of an insufficient number of human teachers and uneven professional level to a certain extent. For instance, AI teachers such as the Korean white oval telepresence robot Eng Key, the Japanese humanoid robot Saya, and the Israeli humanoid robot Robo Thespian have learned to elaborate concepts, explain cases, assign homework, and announce answers to students (Chen et al., 2019).
Yu and Wang (2019) indicated that there are four steps in the basic workflow using deep learning for AI teacher. First, the machine-based perceptual intelligence collects and extracts learners’ and teachers’ problems and their operational behavior in the process of teaching and learning. Second, the computational intelligence processes the collected data, and then establishes learners’ and teachers’ problem behavior patterns. Third, cognitive intelligence calculates a large-scale knowledge base to obtain the adaptive reference for behavior patterns in the current situation. Lastly, AI teacher compares and analyzes the problem behavior patterns, identify the shortcomings of learners, and recommend resources and programs for improving learning.