career expert system

Project Description

This project is a Career Expert System designed to provide intelligent and explainable guidance for individuals who want to start or switch their career in the tech industry.

The system is implemented using Prolog and simulates human expert decision-making by analyzing user inputs such as experience level, interests, and technical skills. Based on this information, it generates personalized career recommendations along with a clear explanation and action plan.

Unlike traditional recommendation systems, this expert system focuses on logical reasoning and transparency, allowing users to understand why a specific career path is suggested.

Tools & Technologies

Prolog (Logic Programming Language)

Rule-Based Expert System Design

Knowledge Base & Inference Engine

Console-based User Interface

Core Concepts

Knowledge Base (facts + rules)

Inference Engine (decision-making logic)

Forward & Backward Chaining

Explainable AI (transparent reasoning)

How It Works

The system asks the user about their status:

New to tech

Career switcher

The user selects a preferred direction:

Web Development

Data & AI

Cybersecurity

IoT & Embedded Systems

The system evaluates the user’s skills based on the chosen direction.

Using rule-based logic, the system:

Matches user inputs with predefined rules

Generates a suitable career recommendation

Provides a detailed explanation and action plan

Usage

Run the Prolog program.

Start the system using the start predicate.

Answer the questions about:

Your experience level

Your area of interest

Your technical skills

The system will output:

Recommended career path

Personalized learning roadmap

Explanation of the decision

Features

Intelligent career recommendations

Explainable decision-making (transparent logic)

Context-aware (beginner vs. career switcher)

Direction-based skill assessment

Personalized action plans

Use Cases

Students choosing a tech career path

Professionals switching into tech

Educational tools for learning AI & expert systems

Academic projects (AI / Knowledge-Based Systems)

Limitations

Rule-based (does not learn automatically)

Limited to predefined knowledge

Binary skill evaluation (yes/no only)

Future Improvements

Add skill proficiency levels

Integrate Machine Learning

Expand career paths (DevOps, Mobile, etc.)

Add probabilistic reasoning

Improve user interaction