Abstract
This study provided a content analysis of studies aiming to disclose how artificial intelligence (AI) has been applied to the education sector and explore the potential research trends and challenges of AI in education. A total of 100 papers including 63 empirical papers (74 studies) and 37 analytic papers were selected from the education and educational research category of Social Sciences Citation Index database from 2010 to 2020. The content analysis showed that the research questions could be classified into development layer (classification, matching, recommendation, and deep learning), application layer (feedback, reasoning, and adaptive learning), and integration layer (affection computing, role-playing, immersive learning, and gamification). Moreover, four research trends, including Internet of Things, swarm intelligence, deep learning, and neuroscience, as well as an assessment of AI in education, were suggested for further investigation. However, we also proposed the challenges in education may be caused by AI with regard to inappropriate use of AI techniques, changing roles of teachers and students, as well as social and ethical issues. The results provide insights into an overview of the AI used for education domain, which helps to strengthen the theoretical foundation of AI in education and provides a promising channel for educators and AI engineers to carry out further collaborative research.
Highlights
Xuesong Zhai,1 Xiaoyan Chu,1 Ching Sing Chai,2 Morris Siu Yung Jong,2 Andreja Istenic,3,4,5 Michael Spector,6 Jia-Bao Liu,7 Jing Yuan,8 and Yan Li 1
We proposed the challenges in education may be caused by artificial intelligence (AI) with regard to inappropriate use of AI techniques, changing roles of teachers and students, as well as social and ethical issues. e results provide insights into an overview of the AI used for education domain, which helps to strengthen the theoretical foundation of AI in education and provides a promising channel for educators and AI engineers to carry out further collaborative research
According to the above coding criteria and content analysis, the three dimensions of research questions are shown in Table 2 and the 72 studies from 63 empirical studies (5 papers have two studies and 2 papers have three studies) are further subclassified into 11 categories. ere are 23 studies in the dimension of development. e AI technique was utilized as a development tool for the construction of a smart learning environment, which can be subclassified as focusing on the development of algorithms including classification, matching, recommendation, and deep learning for teaching and learning purposes
Summary
According to the above coding criteria and content analysis, the three dimensions of research questions are shown in Table 2 and the 72 studies from 63 empirical studies (5 papers have two studies and 2 papers have three studies) are further subclassified into 11 categories. ere are 23 studies in the dimension of development. e AI technique was utilized as a development tool for the construction of a smart learning environment, which can be subclassified as focusing on the development of algorithms including classification, matching, recommendation, and deep learning for teaching and learning purposes. 35 reviewed studies were found in the dimension of extraction, which referred to the application of developed AI techniques, normally based on algorithms, to offer students feedback, reasoning, and adaptive learning. With the support of the natural language process, it could automatically create new themes, theories, and pedagogical contents as a response to learners’ feedback, to help teachers save time and effort [31] It constructed a human-computer interaction and widely used to generate real-time and intelligent feedback according to learners’ input, which has been regarded as a reliable feature in modern assessment system [32]. Only three empirical studies were identified in this review, some researchers were very positive to the future promotion of adaptive system in teaching and learning Technologies such as intelligent speech recognition and automated writing evaluation [44] have been tested with promising findings. Research on personalization in the context of the adaptive system is limited to the users’ characteristics related to domain knowledge. e deeper internal characters, such as human mental status and creativity, were barely noticed and studied [21]. is has vital research potential with the development of advanced AI techniques such as biofeedback techniques
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