The Integrated Development Path of Artificial Intelligence and Open Learning

With the rapid development of artificial intelligence, problems such as fragmentation of learning content and unequal distribution of resources as well as difficulties in evaluating the learning effects emerge for open learning. This paper proposes hybrid learning through AI-based intelligent recommendation and adaptive learning techniques as a solution. Statistics from the students' learning history and behavioural data are used with deep learning for the prediction of students' learning needs accurate enough to recommend personalised resources. Students' progress in understanding is checked through natural language processing before further personalisation. The complexity of the learning content then changes dynamically in accordance to every individual. The tasks of collecting data, training the proposed model, optimising its algorithm, and validating the experiments therein are undertaken. The experiments find that the performance of student learning using the hybrid model improved by 9.3%, reduced learning time by 15.5%, and improved finding the right recommendation by about 23.8% compared to the non-intelligent platform. In conclusion, the study proves that Ai and open learning, in practice, can increase the efficiency of learning and enhance the learning experience, providing a way to bridge the equity in education with personalised education.