ABSTRACT

• An AI-based Question Answer Generation and Evaluation System employs state-of-the-art natural language processing and machine learning techniques to automate the creation and assessment of questions and answers.
• This system is capable of generating diverse, contextually relevant questions from a wide range of textual content, facilitating comprehensive testing and personalized learning experiences.
• The evaluation component uses sophisticated algorithms to accurately assess the correctness and quality of answers, providing detailed feedback and scoring.
• This technology enhances educational and training environments by ensuring consistent, efficient, and scalable assessment methods, ultimately improving the overall learning experience and outcomes.

EXISTING SYSTEM

Rule-Based Approaches:

Early systems relied on predefined templates and rule-based algorithms to generate questions from text. These methods, while straightforward, often lacked flexibility and adaptability.

Automated Grading Systems:

These systems use NLP techniques to evaluate the correctness of answers by comparing them to a set of predefined correct answers or using similarity measures. Techniques include keyword matching, syntactic analysis, and semantic similarity.

DISADVANTAGES

• Accuracy and Reliability: Ensuring high accuracy in question generation and answer evaluation remains a challenge, especially with complex and ambiguous inputs.
• Bias and Fairness: Mitigating biases in the training data and models is crucial to ensure fair and equitable assessment.
• Adaptability: Adapting systems to different domains, languages, and educational levels requires extensive customization and training.

PROPOSED SYSTEM

• The proposed system for AI-based question answer generation and evaluation aims to leverage advanced artificial intelligence technologies to create a more efficient, accurate, and scalable solution for educational and training purposes.
• The system comprises two main components: Question Generation and Answer Evaluation.
• The methodology for developing an AI-based question-answer generation and evaluation system involves several key stages: data collection, preprocessing, model training, question generation, answer evaluation, and system integration.
• Each stage is critical to ensuring the system’s accuracy, efficiency, and scalability.
• For the question generation component, an RNN model is employed due to its ability to handle sequential data effectively.
• The RNN is trained on the preprocessed dataset to understand the context and generate relevant questions.
• The model is fine-tuned using techniques like teacher forcing and beam search to improve the quality and diversity of the generated questions.
• In the answer evaluation component, a separate RNN model is trained to assess the correctness and quality of the answers.
• This model processes the sequential nature of the answers and compares them to ideal responses using semantic similarity measures. It assigns scores based on predefined rubrics and provides detailed feedback.

PROJECT VIDEO

Software Requirements:

• Front End – Anaconda IDE
• Backend – SQL
• Language – Python 3.8

Hardware Requirements

• •Hard Disk: Greater than 500 GB
• •RAM: Greater than 4 GB
• •Processor: I3 and Above

Including Packages

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• * Base Paper
• * Complete Source Code
• * Complete Documentation
• * Complete Presentation Slides
• * Flow Diagram
• * Database File
• * Screenshots
• * Execution Procedure
• * Readme File
• * Addons
• * Video Tutorials
• * Supporting Softwares

Specialization =======================

• * 24/7 Support * Ticketing System
• * Voice Conference
• * Video On Demand 
• * Remote Connectivity
• * Code Customization
• * Document Customization 
• * Live Chat Support