The Role of AI in Reducing Cognitive Overload Complex Learning Environments

The current paper has examined the use of Artificial Intelligence (AI) in the complex learning environment, considering how AI-based learning features (e.g., Intelligent Tutoring System, adaptive sequencing, automated feedback, and learning analytics) can be utilized to assist students in coping with the enormous learning demands. The research was conducted through quantitative research approach, where a sample of 280 undergraduate and graduate students doing different disciplines like engineering, statistics, computer science, and other sciences were utilized. Data collection was conducted with structured questionnaires and cognitive load scales and analyzed with SPSS 26 through the tools of statistical analysis such as the linear regression, correlation analysis and ANOVA. The demographic study revealed that gender, age groups, education levels, and fields of study were well represented so as to be generalized. Findings of the regression supported Hypothesis 1 indicating that there was a significant negative correlation between AI-based tool and cognitive overload; that is, greater AI support was associated with lower amount of overload. Hypothesis 2 was further supported by correlation analysis with the findings being that AI support and overload were strongly negatively correlated. Results of ANOVA Hypothesis 3 revealed that groups had significant differences, and the effects of AI tools were different among students based on their educational background. The findings are consistent with earlier researches and provide new data in contexts of complex learning. This paper explored that AI tools are effective in reducing cognitive load and enhancing learning, and has implications on curriculum design, equity, and the further adoption of technology.