Bring the Artificial Intelligence to Tutoring Robots in Education: A Systematic Literature Review
Xinyue Zhang, Fangqing Zhu, Kun Wang, Guitao Cao, Yaofeng Xue, Mingzhuo Liu
- Year
- 2024
- Citations
- 7
Abstract
Advances in artificial intelligence technologies have given rise to traditional robotics. Intelligent tutoring robots, supported by artificial intelligence technologies and hardware design, have sparked a new revolution in all walks of traditional education. Despite the growing number of research papers published on intelligent tutoring robots, and a few limited reviews focusing on specific technologies or theoretical aspects, there is still no wide-ranging review work that systematically summarizes the studies from the perspectives of theoretical level and practical implications concluded in experiments. To address this challenge and promote a common understanding of the concept of intelligent tutoring robot, we conducted a systematic literature review of papers published between 2016 and 2023. We investigated the construction of intelligent tutoring robots, the advanced artificial intelligence technologies employed, and intelligent tutoring robots' applications in educational contexts. From a total of 1751 publications, we selected and analyzed 105 main studies to identify and address three research questions: 1) How does the construction of intelligent tutoring robots affect education? 2) What artificial intelligence technologies enable intelligent tutoring robots to be “intelligent”? 3) How are intelligent tutoring robots applied in educational contexts? Additionally, we discuss the challenges of current research and provide an outlook on future development trends to provide guidelines for participators and researchers in the fields of intelligent tutoring robots.
Keywords
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