SECTION B: CASE STUDY$(30$ MARKS$)$

Case study: Intelligent Tutoring System for Mathematics

An educational technology company is developing an Intelligent Tutoring System $($ITS$)$ for mathematics to assist students in improving their problem$-$solving skills and understanding of mathematical concepts. The ITS will utilize AI techniques such as knowledge representation, forward chaining, backward chaining, and fuzzy logic to provide personalized learning experiences to students.

The ITS will represent various elements of mathematical knowledge using symbolic notation and procedural representations. Student profiles will be created to track learning preferences and areas of difficulty. Learning objectives will be structured hierarchically to guide the student's progression.

Applications of Al Techniques

Forward Chaining: The ITS will recommend learning activities based on the student's current knowledge and learning objectives, ensuring a gradual progression in difficulty.

Backward Chaining: When students request help for specific problems, the ITS will identify the necessary steps to reach the solution, providing targeted assistance.

Fuzzy Logic: Fuzzy logic will be used to assess students' understanding, providing nuanced feedback based on their performance and progress.

Scenario

For example, if a student struggles with geometry problems involving angles, the ITS will employ backward chaining to identify relevant geometric principles and provide targeted explanations. Additionally, fuzzy logic will assess the student's understanding, considering factors such as the number of correct responses and response time to provide personalized feedback.

Read the case study above and answer the following questions:

$2\mathrm{.}1$ What is knowledge representation in Al$?$ Explain its importance and describe the various elements of knowledge that are represented to an Al agent $(10$ Marks$)$