AI-Driven Mentor Service Advising Application for Timely Course Completion using RNN/LSTM

To be precise, time is a very sensitive factor that concerns course completion in colleges. MOOCs and other such sources of online learning have begun to make education accessible to everyone. One of the significant problems that exist in the course of studying is the fact that so many students flunk. The study recommends an AI-based mentor service thus integrating an advising app that will ensure that students complete their courses on time. The program applies the AI and ML to provide the academic planning of performance and direction, immediate assistance and constant feedback to students, resulting in higher scores and enhanced satisfaction levels. Recurrent Neural Networks (RNN s) and in particular the implementation of Long Short-Term Memory (LSTM) are applied to an innovative virtual mentor service which allows the generation of individual learning pathways and enhancement of completion time for online students. The LSTM network determines and categories how the students are transitioning throughout the course content and distinction between the zones of strength and areas that might require the students to be guided. Possible Challenges With the help of the technology, a student might indicate such problems as being lost at a topic or lagging behind Growth of learning patterns can be estimated. Proposing specific actions As regards barrier-related knowledge deficits, the system provides specific recommendations in terms of special approaches. Such interventions may comprise of suggesting new resources that include products, or skillful practice sessions, or even different learning resources. Accelerate Completion The technology to complete courses in time based on students' performance as well as recommendations. It was used for Comparing therapies and suggestion using experiments hence used as AlB Tool. The rationale of this approach is to enhance timely completion of courses and consequently the results of learning processes.