The field of artificial intelligence (AI) in Pakistan is evolving rapidly and poised to grow significantly over the coming decade. AI is a technology that is creating new opportunities in fields like education. To help students learn simple concepts without a teacher, AI can be benefic
Question Generation through neural model
| The field of artificial intelligence (AI) in Pakistan is evolving rapidly and poised to grow significantly over the coming decade. AI is a technology that is creating new opportunities in fields like education. To help students learn simple concepts without a teacher, AI can be beneficial. Our software can generate questions using Deep Learning. The objective of Question Generation is to generate relevant and fluent questions, according to a given passage. This not only allows students to evaluate themselves but can also save costs by eliminating the need for assessment tutors. We have implemented an attention mechanism in this system to generate questions that are closest to what can be generated by a person. This software can be used by our educational dept to generate question paper as manual construction of questions is a complex process that requires training, experience, and resources. To reduce the expenses associated with manual construction of questions and to satisfy the need for a continuous supply of new questions. We aim to generate intelligent and relevant questions from the provided text using this system. Through this computerized application, we can reduce the task of an educator. Using this system, the student will be no longer dependent on tutor/mentor which save the cost and student can also self-assets themselves by using this system. |
The field of artificial intelligence (AI) in Pakistan is evolving rapidly and poised to grow significantly over the coming decade. AI is a technology that is creating new opportunities in fields like education. To help students learn simple concepts without a teacher, AI can be beneficial. Our software can generate questions using Deep Learning. The objective of Question Generation is to generate relevant and fluent questions, according to a given passage. This not only allows students to evaluate themselves but can also save costs by eliminating the need for assessment tutors. We have implemented an attention mechanism in this system to generate questions that are closest to what can be generated by a person. This software can be used by our educational dept to generate question paper as manual construction of questions is a complex process that requires training, experience, and resources. To reduce the expenses associated with manual construction of questions and to satisfy the need for a continuous supply of new questions.
We aim to generate intelligent and relevant questions from the provided text using this system. Through this computerized application, we can reduce the task of an educator. Using this system, the student will be no longer dependent on tutor/mentor which save the cost and student can also self-assets themselves by using this system.
The system will be designed in view to help the educational institution, students, and tutors.
Benefits for Educational institutions:
1.It can reduce the expenses of the education boards associated with the manual construction of questions by automating the question generation process eliminating the need for professional and trained professors.
2.It can be used for quizzes, assessments, or even FAQs in education institutions.
Benefits for Students and tutors:
3.The system can benefit the teachers by saving their time to make quizzes, test papers, or examination questions.
4.Student can also get benefit from this system by focusing more on the preparation of paper rather than making questions for self-assessments
5.Student will be no longer dependent on tutor/mentor which save the cost and student can also self-assets themselves by using this system.
Deliverables:
Website integrated with python backend. For the development of the website, we have used reactJS. We have integrated this website using API to the backend which is developed on python.
Technical Details
| Elapsed time in (days or weeks or month or quarter) since start of the project | Milestone | Deliverable |
|---|---|---|
| Month 1 | Requirement gathering | • Analyzing dataset to use. • Analyze System Objectives, Constraints, and Scope • Analyze Available Resources, Benefits, and Feasibility • Prepare and Submit Preliminary Project Proposal |
| Month 2 | Dataset Preprocessing | • Analyzing dataset • Preprocessing SQuAD dataset • Design block diagram of the complete Model • Review of Finalized ER Diagram |
| Month 3 | System development | • Implementing Encoder of Seq2Seq model • Implementing Decoder of Seq2Seq model • Designing frontend on react native • Training the model • Exporting the trained model • connecting the export model with API • Implementing API in frontend • Finalize the development part • Code Implementation, • Review the system |
| Month 4 | Integration | • Integrating the trained model with react frontend using API |
| Month 5 | Testing | • Unit Testing • Module Testing • Integration Testing • System Testing |
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