Pedagogy and andragogy suggestion using NLP & Gen AI

Abstract

This invention presents an AI-driven system to help educators by suggesting tailored pedagogical strategies for specific subjects and topics. Using NLP, the system identifies the exact topic based on user input and recommends the top three pedagogies from previous records. Accepted suggestions trigger an AI-generated implementation guide, while rejected ones lead to dataset adjustments, refining future recommendations. Continuous feedback improves the system's accuracy, ensuring that the pedagogies evolve with user interactions, ultimately enhancing teaching methods and learning outcomes.

Specification

Description:FIELD OF INVENTION
[001] This invention falls under the field of educational technology, specifically AI-powered systems for personalized teaching. It focuses on tools that recommend instructional methods tailored to individual subjects and learning needs.
BACKGROUND AND PRIOR ART

[002] In traditional educational environments, teachers must rely on their experience and generalized instructional methods to deliver lessons. Prior systems have attempted to personalize education through fixed or limited digital resources, but none have dynamically adapted to feedback or individualized needs at scale. Existing technologies fail to offer a mechanism for real-time, AI-driven pedagogical suggestions based on both past data and continuous updates. This invention overcomes the shortcomings of prior systems by implementing a flexible, adaptive, and data-driven approach to pedagogy recommendation.
SUMMARY OF THE INVENTION
[003] The invention provides a sophisticated system that integrates NLP and machine learning to offer educators tailored pedagogical strategies. By analyzing user-inputted subjects and topics, the system can recommend instructional methods, which are then refined through continuous feedback. Accepted methods are paired with detailed implementation guides, whereas ineffective suggestions are downgraded in relevance. This system is designed to constantly improve, making it a versatile tool for educators seeking to enhance their teaching effectiveness through personalized learning.
BRIEF DESCRIPTIONS OF DRAWINGS:
[004] 1. Select academic year:
The process starts by selecting the relevant academic year. This could be important for contextualizing the curriculum and pedagogical approaches.
[005] 2. Enter the Subject and module name:
Input the specific subject and module name. This helps narrow down the scope and ensures relevance of the suggested pedagogy.
[006] 3. Using NLP figure out which topic the user is exactly referring to:
Natural Language Processing is employed to precisely understand the user's topic of interest. This step ensures accuracy in topic identification.
[007] 4. Suggest a pedagogy based on previous records:
The system then suggests a pedagogical approach based on historical data and previous successful implementations.
[008] 5. Suggested pedagogy decision point:
At this stage, the suggested pedagogy can be either accepted or rejected.
• If accepted:
o The system moves to generate a methodology for implementation.
o It also suggests the pedagogy for future reference.
• If rejected:
o The system decreases the relevance score of that pedagogy and updates the dataset, ensuring continuous improvement.
[009] 6. Generate the methodology for implementation:
If the pedagogy is accepted, the system (using Gen AI, specifically the Gemini API) generates a detailed methodology for implementing the pedagogy. This includes specifics like ICT (Information and Communication Technology) tools to be used.
[010] 7. Give the top 3 pedagogy that can be used:
The system provides three top pedagogical approaches that could be effective for the given subject and module.
[011] 8. Update the record and knowledge base:
After successful implementation, the system updates its records and knowledge base. It links the used pedagogy and module for future reference, creating a feedback loop for continuous improvement.
[012] 9. Dataset update:
After every successful suggestion, the dataset is updated to make it more effective and tailored for that particular user.
DETAILED DESCRIPTION OF THE INVENTION
[013] To implement this framework:
1. Develop a user interface that allows easy input of academic year, subject, and module.
2. Implement an NLP system to accurately interpret user queries.
3. Create a robust database of pedagogical approaches linked to subjects and modules.
4. Develop an AI system (using Gemini API as suggested) to generate implementation methodologies.
5. Implement a feedback system for accepting or rejecting suggestions.
6. Create a mechanism for updating the dataset based on user interactions and successful implementations.
7. Develop a system to track and update the relevance scores of different pedagogical approaches.
8. Implement a reporting feature to provide top pedagogical suggestions.
9. Ensure the entire system is interconnected to allow for continuous learning and improvement based on user interactions and outcomes.
This framework aims to create a dynamic, AI-driven system for suggesting and implementing effective pedagogical approaches in education, with built-in mechanisms for continuous improvement and personalization. , C , Claims:Claim 1: A system for recommending pedagogical methods, comprising an NLP engine for processing educational inputs, suggesting top pedagogies, and a feedback system for refining future suggestions based on user interaction.
Claim 2: The system of claim 1, wherein the NLP engine identifies the subject and specific module of interest from user input.
Claim 3: The system of claim 1, further comprising a mechanism for providing detailed implementation guides for accepted pedagogies.
Claim 4: The system of claim 1, wherein rejected pedagogies are deprioritized in future recommendations via dataset updates.
Claim 5: A method for updating a dataset based on user feedback to continuously refine the pedagogical suggestions.

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