CodeMentor-AI-Powered Learning Assistant

Example

A user learning Python starts with simple data types like integers and lists, progressing to more advanced topics like list comprehensions and lambda functions as they show improvement.

Scenario

A beginner Python user selects Python as their desired language and is presented with topics starting from variables, followed by conditionals, loops, and eventually more complex data structures. CodeMentor adapts the difficulty based on the user's grasp of these topics and suggests the next logical step in the learning process.

Example

After learning about Python dictionaries, the user is prompted with an exercise to create a dictionary that maps students’ names to their grades and to find the student with the highest grade.

Scenario

An intermediate user practicing data structures gets an exercise to build a complex dictionary in Python. CodeMentor provides feedback on the user's solution, pointing out areas where the code can be optimized without giving away the complete solution unless explicitly requested.

Example

A user completes several exercises on object-oriented programming in Java. Based on their performance, CodeMentor increases the complexity of the next set of exercises, introducing concepts like polymorphism and interfaces.

Scenario

A Java user progresses through various object-oriented programming tasks, and CodeMentor analyzes their performance, identifying areas of strength (e.g., inheritance) and weakness (e.g., interfaces). It then adjusts future exercises to focus more on weak areas, ensuring the user builds a well-rounded understanding.