Learning System Question Standardization for Accurate Answers
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Solution Overview
Problem
Remote teaching systems struggle to provide timely and accurate answers to students' colloquial questions due to the limitations of existing artificial intelligence models, leading to incorrect information being provided.
Innovation Solution
A learning system that includes a host and optional cloud server, utilizing machine learning models to convert colloquial questions into templated questions and answers, with natural language processing and user feedback mechanisms to improve accuracy and relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If artificial intelligence models are used to answer students' questions in remote teaching systems, then timely guidance can be provided to students, but the models cannot correctly identify colloquial questions, leading to incorrect information being provided
Solution Approach 1:
The patent introduces an intermediary component (question processing module) that mediates between the colloquial question input and the AI model. This intermediary processes and standardizes the colloquial questions before they reach the AI model, enabling accurate identification while maintaining ease of use for students asking informal questions.
Solution Approach 2:
The system changes the parameters of the question representation by transforming colloquial questions into standardized formats through natural language processing. This parameter transformation allows the AI model to accurately identify questions while students continue to input questions in their natural, colloquial language.
2Reliability
If remote teaching systems provide detailed guidance to students, then learning quality improves, but students cannot receive timely responses due to lack of immediate teacher availability
Solution Approach 1:
The system enables self-service by allowing students to input their questions in colloquial language and receive automated responses from the AI model. This eliminates the need for students to wait for teacher availability while maintaining high-quality guidance, thus resolving the contradiction between response time and guidance quality.
Solution Approach 2:
The system performs preliminary processing of student questions through natural language processing and question standardization modules before routing them to the AI model. This preliminary action ensures that questions are ready for immediate accurate response, reducing response time while maintaining guidance quality.
Data Source
AI summary
A learning system and an execution method thereof are provided. The execution method includes: displaying an option object through a graphical user interface of a display device, wherein the option object includes at least one option, and the at least one option is associated with course information; in response to one of the at least one option being selected, the graphical user interface displaying a user image and a virtual object; in response to the virtual object being triggered, the graphical user interface displaying a function window, and the function window at least partially overlapping with the user image, wherein the function window has at least one learning option; in response to one of the at least one learning option being triggered, the graphical user interface displaying a question-and-answer window; the question-and-answer window of the graphical user interface displaying a templated answer corresponding to the input data.