Rapid development of AI technologies with breakthroughs in algorithmic machine learning and autonomous decision making have generated unprecedented opportunities for technological innovation. There is consensus from policy report that in the next years AI could revolutionise the way teaching and learning is designed and delivered to students leading to seamless intelligent services that are more tailored to student’s needs and interests. Current impetus on AI-based research in education has mainly focused on a knowledge-based approach inherently prevalent in Intelligent Tutoring Systems employed in specific domains. The aim of this paper is to proliferate a compendious framework that classifies teaching practice with a spectrum of AIED applications and tools. The framework acts as a point of departure for teachers that envisage to use AI for enhancing the way learning and teaching is manifested. It may also serve as a blueprint for AIED developers to design AIED systems that focus on specific teaching and learning instances.
|Name||2022 IEEE Global Engineering Education Conference (EDUCON)|
|Conference||2022 IEEE Education Engineering |
|Period||28/03/22 → 31/03/22|
- Artificial intelligence
- technology-enhanced learning