AI-Powered and Mobile-Integrated Assessment Models Using Random Forest: Redefining Examinations and Grading

Abstract

The proposed system applies a completely different method for examinations and grading by using the supervisor learning technique, namely Random type Forest algorithms. Leveraging the power of artificial intelligence (AI), the process of evaluation is becoming automatic, thereby increasing the efficiency and accuracy in the students’ grading? this breakthrough technique is characterized by its hybrid supervised learning setup that exploits both labeled and unlabeled data to come up with a model that is extremely adaptive to unseen examination data. This not only substantially reduces the necessity of human intervention but also dramatically improves the model’s ability to perform reliable predictions based on the prevalent patterns. This AI-driven assessment model can be further integrated into a mobile platform to enable real-time student engagement. The integration can benefit the students as the interactive mobile applications can enable the students to enhance their performance by providing instant outcome, and flexibility to take assessments. The mobile applications can contribute to skill enhancement by providing student assessment data related to quizzes, formative assessments or project-based learning assessments, for the random forest (RF) model. Interactive mobile applications can also assist faculty in tracking student performance and analyzing their progress along with improving the accessibility of data. The system, through a comprehensive evaluation of student responses, introduces a more customized and equitable grading system. That is, fundamentally, the traditional assessment methods are being reimagined, and at the same time, they ensure that educational environments globally are both scalable and fair.