Special Issue on "From Conventional AI to Modern AI in education : Re-examining AI and Analytics Techniques for Teaching and Learning"

With the rapid development of artificial intelligence (AI) and big data in recent years, a huge number of applications have been employed in educational contexts. Specifically, adaptive/personalized learning has been facilitated by recommendation models based on deep neural networks; affective learning is further explored based on emotion detection techniques according to bio-signal data sources like eye-tracking and EEG signals; classroom management or instant feedback is supported by face recognition techniques. Meanwhile, the employment of big data from mobile devices and learning logs enables AI models to have an in-depth understanding of learning behaviors and patterns. The existing educational models are transformed by these emerging techniques. It is critical for academic communities to address a research issue: how to sustain the innovative educational models that employ AI and big data techniques. There are many alternative solutions: establishing communities of practice for AI and big data innovations; proposing easily employed educational models based on AI and big data; developing novel teacher training framework for introducing AI and big data skills, and so on. Therefore, this Special Issue aims to provide some potential directions and solutions for this research issue.