Artificial Intelligence Adoption in Higher Education: Economic Benefits and Strategic Management for Sustainable Learning

Abstract

The future of higher education is defined by a rapidly evolving landscape and AI is playing a transformative role in the learning experience, providing personalized, adaptive and sustainable learning solutions that boost student outcomes, educational management and sustainability initiatives. In existing intelligent learning systems, it is challenging to integrate 3 concepts, semantic knowledge representation, sequential learning behavior analysis and adaptive recommendation optimization, which result in certain level of personalization, low learning engagement and sub-optimal learning decision making.To tackle the problems mentioned above, an intelligent learning recommendation system named Adaptive Sustainable Learning Intelligence Network (ASLIN) is proposed for higher education to recommend learning in this research.The main goals are to provide tailored, sustainable and affordable learning trajectories and for strategic management and to boost educational results.First, the educational information such as students' scores, learning style and examination scores and sustainable education course information are normalized, encoded and integrated. Finally, the Learning Behavior Modeling of Temporal Networks BiLSTM-MHA is used to model the learning behavior of temporal networks to extract key learning interactions and the personalized learning recommendations are optimized using Reinforcement Learning (RL) with Soft Actor-Critic (SAC) algorithm over time.Through the experimental evaluation, it can be seen that the proposed framework has high Prediction Accuracy of 97.84%, which proves the high accuracy of personalized recommendation performance.Personalization in Higher (HE) Education, strategic management of HE and lifelong learning improvement and enhancement are all visionary, scalable and sustainable goals addressed by ASLIN.