| 英文摘要 |
As an interdisciplinary frontier research field, Conversation-based Intelligent Tutoring System (CBITS) has been highly anticipated because it is a possible path for the scale, automation, and humanization of deep learning. From the three domains (cognitive, intrapersonal, interpersonal) of deep learning perspectives, this study analyzed literature of the definition of CBITS, theoretical basis, architecture features, learning effects and then pointed out that CBITS can promote learners’ comprehensive analysis, qualitative reasoning as well as other deep learning abilities by imitating human’s one-on-one natural language tutoring. However, CBITS’s learning efficiency, insufficient deep learning comprehensive support, and the high cost of development still need to be improved. For better efficiency and effect, the future research of CBITS will mainly focus on multimodal interaction, multi-dimensional affective computing, and multi-agent team tutoring. At the same time, interdisciplinary collaboration, introducing the Generalized Intelligent Framework for Tutoring (GIFT) and the ethics of Artificial Intelligence should also be taken into future consideration.
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