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Personalized College English Learning Based on Artificial Intelligence: Algorithm-Driven Adaptive Learning Method

    https://doi.org/10.1142/S0129156425401020Cited by:0 (Source: Crossref)

    Technological developments in the field of education have contributed to the current surge in popularity of online courses. Every day, there is an exponential increase in both the rate of development and the availability of learning information. The trend in education systems across the world is toward putting the student at the center of everything. Education systems throughout the world are shifting toward a model that is more tailored to each individual student. This allows current technology to adapt different qualities of humans. Finding an appropriate learning strategy for a course with a large body of material that may be quite a challenge. The recommendation of a learning route aids students in methodically completing their coursework and reaching their objectives. Smart robots and computers are now able to comprehend individual-specific demands, technology advancements like AI, Machine Learning, and Big Data have made this possible. This paper suggests an AI-based learning-teaching model (AI-L-TM) for recommending learning paths that centers on analyzing learning performance and acquiring new information. Educational analytics improves a plethora of English-language individualized learning experiences by evaluating supplied data to provide valuable learning results. Through the use of Internet of Things (IoT) devices, data mining methods, and classroom data gathering, this project seeks to enhance the English learning experiences of college and university students. Here, AI methods might be helpful for a number of reasons, such as creating a learning–teaching model that mimics human thinking and decision-making and reducing uncertainty to make the process more efficient. Using artificial intelligence techniques for adaptive educational systems within e-learning, this paper presents a range of topics related to the field. It discusses the pros and cons of these techniques, and how important they are for creating smarter and more adaptive environments for online learning.

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