Artificial Intelligence (AI) techniques have been applied in various teaching and/or learning platforms and will change teachers' teaching and students' learning behaviors. The AI-related techniques can track and analyze users' behavioral data and then provide personalized responses and feedback, such as individualized learning instructions. The customized educational content can enhance students’ learning experience and performance. In particular, deep learning AI techniques, Deep Neural Network (DNN), or Recurrent Neural Networks (RNN) can be used to analyze and assess students' weaknesses before providing customized learning materials. RNN can analyze students' exams and online discussion data to understand students’ learning needs. To give the students human-like interactions, AI-based Chatbots are widely adopted in the intelligent tutoring systems as well. The chatbot services can answer learners' questions instantly and give them personalized responses. As the services collect learners' data and interactions over time, they can provide a more meaningful learning guide.
Machine learning techniques can be used in educational data mining and predicting student's learning performance. These techniques can build predictive models and descriptive models to discover meaningful patterns and knowledge. For example, predictive models can predict students' scores, while descriptive models can discover new learning guides from big educational data. The use of these techniques allows Intelligent Tutoring Systems (ITS) to suggest individual studying strategies. ITS can be classified into three categories:
• Adaptive Sequence: Generating automatic learning sequences for learners based on their knowledge level and affective state;
• Adaptive Content: Creating personalized content according to learners' knowledge level, needs, and behaviors;
• Adaptive assessment: Providing customized exams for learners based on their previous assessment performance.
The use of AI-based systems will change teachers' teaching strategies and learners' behaviors. The users of the systems may face some challenges, such as learning new digital skills to use AI pedagogically or trusting in AI systems' suggestions. Therefore, there are many key issues in using AI-based systems that are being addressed, such as performance effectiveness, learning pedagogies, user experience, learning environment, and interactive content.
This Research Topic aims to bring together researchers, engineers, and practitioners from both academia and industry to report, review, and exchange the up-to-date progress of using artificial intelligence-related techniques in educational behaviors and settings, to explore future research directions, and to prompt better service provision in specific domains for a wider target audience from diverse fields.
Original research articles are welcomed, as well as theoretical studies, practical applications, new social technology, experimental prototypes, and case studies. Contributions may include, but are not limited to, the following topics:
? Augmented Reality (AR), Virtual Reality (VR), and eXtended Reality (XR) for learning;
? Interactive learning systems;
? Open and flexible learning;
? Experimental Learning;
? Learning analytics;
? Mobile learning;
? Open educational resources;
? Student advising and assessment;
? AI: Chatbots, virtual assistants, or intelligent tutoring systems;
? Robotics in the classroom to enhance student motivation;
? Innovative pedagogical approaches
Artificial Intelligence (AI) techniques have been applied in various teaching and/or learning platforms and will change teachers' teaching and students' learning behaviors. The AI-related techniques can track and analyze users' behavioral data and then provide personalized responses and feedback, such as individualized learning instructions. The customized educational content can enhance students’ learning experience and performance. In particular, deep learning AI techniques, Deep Neural Network (DNN), or Recurrent Neural Networks (RNN) can be used to analyze and assess students' weaknesses before providing customized learning materials. RNN can analyze students' exams and online discussion data to understand students’ learning needs. To give the students human-like interactions, AI-based Chatbots are widely adopted in the intelligent tutoring systems as well. The chatbot services can answer learners' questions instantly and give them personalized responses. As the services collect learners' data and interactions over time, they can provide a more meaningful learning guide.
Machine learning techniques can be used in educational data mining and predicting student's learning performance. These techniques can build predictive models and descriptive models to discover meaningful patterns and knowledge. For example, predictive models can predict students' scores, while descriptive models can discover new learning guides from big educational data. The use of these techniques allows Intelligent Tutoring Systems (ITS) to suggest individual studying strategies. ITS can be classified into three categories:
• Adaptive Sequence: Generating automatic learning sequences for learners based on their knowledge level and affective state;
• Adaptive Content: Creating personalized content according to learners' knowledge level, needs, and behaviors;
• Adaptive assessment: Providing customized exams for learners based on their previous assessment performance.
The use of AI-based systems will change teachers' teaching strategies and learners' behaviors. The users of the systems may face some challenges, such as learning new digital skills to use AI pedagogically or trusting in AI systems' suggestions. Therefore, there are many key issues in using AI-based systems that are being addressed, such as performance effectiveness, learning pedagogies, user experience, learning environment, and interactive content.
This Research Topic aims to bring together researchers, engineers, and practitioners from both academia and industry to report, review, and exchange the up-to-date progress of using artificial intelligence-related techniques in educational behaviors and settings, to explore future research directions, and to prompt better service provision in specific domains for a wider target audience from diverse fields.
Original research articles are welcomed, as well as theoretical studies, practical applications, new social technology, experimental prototypes, and case studies. Contributions may include, but are not limited to, the following topics:
? Augmented Reality (AR), Virtual Reality (VR), and eXtended Reality (XR) for learning;
? Interactive learning systems;
? Open and flexible learning;
? Experimental Learning;
? Learning analytics;
? Mobile learning;
? Open educational resources;
? Student advising and assessment;
? AI: Chatbots, virtual assistants, or intelligent tutoring systems;
? Robotics in the classroom to enhance student motivation;
? Innovative pedagogical approaches
