They can be used to forecast the grades of students in the class. Based on certain parameters collected, if the model generates that the student will have a poor CGPA then the model can generate a warning to the instructor indicating that the student will have to work harder to reach the desired mark.
The teacher can know where the student lacks and implement study plans for him accordingly.
During interviews of the students to schools and Universities, the judging panel can model the student’s performance at the entry test to know if he/she has the potential since these entry tests and CGPAs would have a very high correlation.
They may also record absentee rates and study how that affects the performance of all these students using models.
A lot of students in different parts of the world drop out of schools and colleges because of various reasons.
Predictive models help in evaluating the risks of student dropouts using data analysis and in turn help in taking precautionary measures against it.
There is also an AI-based evaluation virtual interview platform that mimics an actual face to face interview.
It can be used to automatically evaluate the body language of the candidate. The same technique can be used in classrooms to examine who in the classroom is paying proper attention, who is not and who is pretending.
Teachers may leverage the use of cloud technology to have maximum access to study material to students.
The technology can also encourage independent learning. Students can take ownership of their learning and learn from even hours outside of school.
The University of Michigan uses a tool called E2Coach automatically sends its students personalized course performance messages based on a continually updated algorithm.