Learning System Question Templates for Accurate Remote 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 current 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.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If artificial intelligence models are used to answer students' questions in remote teaching systems, then the system can provide automated responses, but the models cannot correctly identify colloquial questions, leading to incorrect information being provided
Solution Approach 1:
The patent introduces an intermediary component (question template matching module) between the colloquial question input and the AI model. This intermediary converts colloquial questions into standardized question templates that the AI model can accurately process, thereby maintaining automated response capability while improving question identification accuracy.
Solution Approach 2:
The patent transforms the question representation from colloquial language to structured question templates by changing the parameters of question formulation. This parameter transformation enables the AI model to accurately understand and process questions while preserving the automated response function.
2Loss of time
If remote teaching systems use AI models to answer questions, then student guidance can be provided without teacher intervention, but the systems cannot provide timely and accurate answers due to colloquial question formatting
Solution Approach 1:
The patent performs preliminary action by pre-defining question templates and structures before the actual question-answering process. By preparing the question framework in advance, the system can quickly match incoming colloquial questions to appropriate templates, reducing response time while ensuring answer accuracy through the structured template approach.
3Device complexity
If the system directly processes colloquial questions with AI models, then the processing flow is simple, but the models answer incorrect information due to inability to correctly identify questions
Solution Approach 1:
The patent segments the question processing flow into distinct stages: colloquial question input, template matching, and AI model processing. This segmentation adds a moderate complexity step (template matching) that significantly improves question understanding accuracy while keeping the overall processing flow manageable and structured.
Data Source
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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.