Comprehensive Summary
This article, titled ' Utilizing Big Data and Artificial Intelligence to Improve the Learning Experience: A Systematic Overview. ', presents a systematic overview of how big data and artificial intelligence (AI) technologies are being leveraged to enhance educational and learning experiences. Pang & Ma explore how current AI-driven systems can drive predictive analytics for student outcomes, real-time feedback mechanisms, and create an adaptive curriculum in educational platforms by using large scale data sets to identify patterns in student behavior. The paper provides a framework for the back-end architecture of AI systems in education (LTSM networks, adaptive molecules), and how these structures allow big data to be mined to tailor learning paths to the individual. Initial studies have demonstrated improved retention rates and interaction times among students. However, the integration of AI into global learning systems poses the risk of ensuring data privacy throughout the mining process. Additionally, integrating novel AI systems into existing educational workflows may prove as a barrier, particularly to educators less equipped to the shift in technology. Likewise, it is critical that both learners and teachers are given the necessary training to ensure a successful integration of AI into education systems.
Outcomes and Implications
The findings suggest that incorporating AI and big data driven analytics into learning systems has the potential to significantly increase student retention and engagement with classroom material. In one model, the authors reported a 98.5% higher retention rate after the implementation of AI models. To fully leverage the real-time feedback and adaptive personalization offered by AI, institutions may need to consider redesigning their curricula architectures to adapt to new workflows and modalities. On a more micro scale, educators themselves will need to understand how AI-driven insights are generated, how they can be interpreted, and how to apply them in pedagogy rather than solely relying on them. Ethically and operationally, it is important to consider issues such as student data security and algorithmic bias. Although AI algorithms are machine in nature, they can still be susceptible to bias. The growing implementation of AI into the classroom setting must not come at the cost of equity or inadvertent discrimination.