The Two-Way Track of AI and HU in the Classroom

Abstract

More effective and efficient solutions may be possible with the help of AI's new possibilities for in-situ and real-time learning and teaching. Therefore, AI may have an effect on the ways in which humans and machines work together to learn and teach, as well as on the integration of AI with other forms of artificial intelligence. The conversation surrounding artificial intelligence (AI) in the classroom has evolved from a singular emphasis on AI-based automation to a broader goal of AI-enhanced human capacities in the areas of learning and instruction. Here, humans and AI may divide up work through reciprocal engagement. This means that AI systems can recognize patterns or structures in big datasets, even unstructured data, in real time, which can aid intelligent human decision making in learning and teaching scenarios. For instance, recommender systems exist to aid students in subjects where the volume of material outstrips their processing capacity by surfacing the most pertinent, engaging, or practical objects. Traditional approaches presume that students who have similar tastes will gravitate toward the same materials. This is shown by content-based recommender systems and collaborative filtering. Adaptation that works includes things like discovering relevant learning information, complete courses, or the best way to learn things. Given the ongoing difficulties in incorporating AI into educational settings, particularly for more complex activities, a potential solution might be a partnership between humans and AI, which would enhance the capabilities of both parties.