Cognitive Load Management Through Adaptive AI learning System Implications for Student Focus and Retention

Abstract

The purpose of this study was to examine how adaptive Artificial intelligence (Artificial intelligence, AI) tools may affect the focus, cognitive load, retention, and academic performance of students during online learning. The important aim was to discover the way how AI adaptive features could help students to reduce mental load and reinforce learning outcomes. The research method used was quantitative and a sample size of 250 university students were used in the research using structured questionnaire. The analysis involved correlation, regressions and ANOVA. By the findings, the relationship between cognitive load and the focus, i.e. the greater the mental pressure was on the students, the less they were able to focus, was very high negative. The regression analysis revealed that adaptive AI features made a significant positive change in knowledge retention that enables concluding the personalized AI-aid positively impacted the learning memory of the students. ANOVA indicated that the performance of the students who used adaptive learning AI tools was high in comparison to the learners who studied under the traditional method of learning techniques. Demographically; the sample was determined to be uniform as pertaining to gender, age, and level of education and hence gave the results better generalization. In conclusion, adaptive AI may as well come in handy in preventing cognitive overloads and achievement in academics among the students. The suggestion is to start the investments into an adaptive AI system and welcome it to be applied in teaching facilities to enhance the learning process and the performance. The paper also proposes future research studies that can be carried out on emotional and motivational impacts of AI at different learning sites.