A Forward Chaining Expert System for Personalized Programming Language Selection

Choosing the right programming language is a critical decision for beginners in computer science because it greatly influences their learning experience and future opportunities. This paper presents an expert system designed to assist novices in selecting a programming language that aligns with their preferences and project requirements. The proposed system uses the forward chaining method, a robust approach to expert system development. It draws on comprehensive developer survey data from Stack Overflow, focusing on the top ten most popular programming languages. Our methodology involved formulating evaluative questions to create a decision-making framework supported by a rule-based table, which served as the foundation for the system's knowledge base. Implemented in Python, the expert system achieved a relevance score of 90 %, demonstrating its effectiveness in simplifying the programming language selection process for beginners.