Method for providing an ai content community platform that curates ai educational content for ai-based users

Abstract

The present invention relates to a method for providing an artificial intelligence content community platform that curates artificial intelligence educational content for artificial intelligence-based users.

Description

Technology Field

[0001] The present invention relates to a method for providing an artificial intelligence content community platform that curates artificial intelligence educational content for artificial intelligence-based users. Background Technology

[0002] Most existing online education platforms rely on a video-centric, single-content delivery method, which has structural limitations as it fails to consider users' skill levels or analyze the relevance to learners' career goals or corporate projects. Furthermore, recruitment platforms are predominantly based on resume-based search and manual filtering, lacking the functionality to quantitatively analyze detailed corporate project requirements and automatically match suitable candidates.

[0003] On the other hand, while artificial intelligence can quantitatively analyze complex data such as text, video, and portfolios by utilizing Natural Language Processing (NLP), Vector Embedding, Reinforcement Learning, and Predictive Models, existing platforms fail to provide an advanced system that integrates learning, project execution, competency verification, and hiring by applying these technologies to the entire flow of education and recruitment.

[0004] The present invention aims to resolve these inefficiencies and provide a technology that implements a closed innovation cycle leading from educational content recommendation to practical project execution, talent verification, and recruitment. The problem to be solved

[0005] The problem that the present invention aims to solve is to provide a method for providing an AI content community platform that curates AI educational content for AI-based users.

[0006] The problems that the present invention aims to solve are not limited to those mentioned above, and other unmentioned problems will be clearly understood by a person skilled in the art from the description below. means of solving the problem

[0007] The system of the present invention for solving the aforementioned problem is an educational system that curates AI-based educational content to learner users, and may be characterized by comprising: an AI analysis engine that generates a content vector by analyzing the educational content of an instructor user, generates a skill vector by analyzing the skill level, learning history, and project experience of a learner user, generates a required competency vector by analyzing project requirements entered by a company, and then curates educational content and automatically matches projects through similarity analysis of the content vector, skill vector, and required competency vector; and a practical training and verification system that provides practical training based on project execution results and collects and evaluates performance data so that a company can verify the learner's competency.

[0008] The above AI analysis engine may be characterized by including a function to calculate a matching score by combining the similarity between a content vector and a skill vector and the similarity between a skill vector and a required competency vector based on weights, and to provide content to learners or determine the possibility of participating in a project based on the matching score.

[0009] The above-mentioned practical training system may be characterized by including a function that analyzes code history, submission quality, and collaboration logs generated during the project execution process in real time to provide practical, hands-on feedback to learners and reflects the analyzed data in skill vectors to continuously update the learners' competencies.

[0010] The above talent evaluation and verification system may be characterized by including an AI-based performance evaluation model that predicts a learner's performance and growth potential based on evaluation results analyzing the completeness of project deliverables, problem-solving ability, and collaboration indicators.

[0011] A corporate customized education operation system may be characterized by including a function that automatically generates job models based on the company's job requirements and automatically designs the company's unique training courses and recruitment criteria by linking the generated job models with project performance evaluation data. Effects of the invention

[0012] This invention maximizes educational efficiency by having artificial intelligence simultaneously analyze educational content and learner capabilities to suggest the most suitable learning path for each user, and significantly improves the accuracy and efficiency of recruitment for companies by verifying talent based on project-based practical data.

[0013] Learners are provided with the experience of simultaneously acquiring theoretical knowledge and practical experience, with the results immediately reflected in hiring, while instructors benefit from automatic content quality evaluation and continuous exposure to suitable users.

[0014] In addition, the platform cyclically learns from all data, resulting in the effect of naturally enhancing curation accuracy and matching performance over time.

[0015] The effects of the present invention are not limited to those mentioned above, and other unmentioned effects will be clearly understood by a person skilled in the art from the description below. Specific details for implementing the invention

[0016] The advantages and features of the present invention and the methods for achieving them will become clear by referring to the embodiments described below in detail together with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below but may be implemented in various different forms. These embodiments are provided merely to ensure that the disclosure of the present invention is complete and to fully inform those skilled in the art of the scope of the present invention, and the present invention is defined only by the scope of the claims.

[0017] The terms used in this specification are for describing embodiments and are not intended to limit the invention. In this specification, the singular form includes the plural form unless specifically stated otherwise in the text. The terms "comprises" and / or "comprising" used in this specification do not exclude the presence or addition of one or more other components in addition to the components mentioned. Throughout the specification, the same reference numerals refer to the same components, and "and / or" includes each of the mentioned components and all combinations of one or more. Although terms such as "first," "second," etc., are used to describe various components, these components are not limited by these terms. These terms are used merely to distinguish one component from another. Therefore, the first component mentioned below may be the second component within the technical scope of the invention.

[0018] Unless otherwise defined, all terms used herein (including technical and scientific terms) may be used in a meaning commonly understood by those skilled in the art to which the present invention pertains. Additionally, terms defined in commonly used dictionaries are not to be interpreted ideally or excessively unless explicitly and specifically defined otherwise.

[0020] The present invention aims to provide a technology that precisely analyzes educational content based on artificial intelligence to automatically provide content suitable for learners, immediately matches suitable talent with projects required by companies, and quantitatively evaluates individual capabilities based on learning data generated during the execution of practical projects to reflect them in recruitment in real time.

[0021] The present invention relates to an integrated education and recruitment platform operating around a server-based artificial intelligence analysis engine. It is configured to collect all educational content, such as text, videos, documents, and code uploaded by instructor users, as well as learning history, practical project participation records, submitted deliverables, collaboration logs, technical self-assessment information generated by learner users on the platform, and project requirements entered by companies, as digital data. The AI ​​sequentially analyzes this data to convert it into content vectors, skill vectors, and required competency vectors, and then quantitatively evaluates the relationships between these vectors to simultaneously perform educational content curation, project matching, and talent verification.

[0022] First, during the content analysis process, the server performs a preprocessing step to extract metadata for each educational content uploaded by instructor users. Text-based materials such as lecture summaries, textbooks, and worksheets undergo processes like morphological analysis, part-of-speech tagging, and stop word removal via a natural language processing module, generating sentence- or paragraph-level embeddings. For video content, a speech recognition module is used to generate text scripts at the subtitle level; if necessary, key frames are extracted to supplement topic, difficulty, and domain information through an image analysis model. Content containing code examples is converted into an abstract syntax tree form using programming language parsers, after which features such as library usage patterns, algorithm types, and complexity are extracted. These feature values ​​extracted from each modality—text, images, and code—are input into an integrated multimodal embedding model to be combined into a single high-dimensional vector, which is then stored as the content vector. Content vectors are encoded with meta-attributes such as technology stack information, difficulty level, target learning area, practical applicability, and whether assessment quizzes are included; based on this information, the server also learns the relative relationships between various contents on the same topic that differ in difficulty or field of application.

[0023] In the profiling process related to learner users, the server prioritizes the collection of static information entered by the user upon signing up for the platform, such as their self-introduction, skill stack, interests, and years of experience. Subsequently, it dynamically tracks learning and project execution records occurring within the platform. Learning records include a list of courses taken, viewing rates for each piece of content, quiz accuracy rates, assignment submissions, review frequency, and study time. Project records include the types of projects participated in, assigned roles, lines of code contributed, commit patterns in version control systems, review requests and feedback incorporation, and team communication logs. This diverse behavioral data is stored as a sequence of events arranged in chronological order and utilized by sequence or graph-based models to estimate the user's skill growth trends and learning styles. The server generates a skill vector by reflecting both static and dynamic information; this vector is trained to represent not only the user's current skill level but also their growth rate for specific skills, preference for collaboration, and problem-solving tendencies.

[0024] Since project requirements registered by companies on the platform typically take the form of natural language descriptions, the server converts these descriptions into structured required competencies through a project analysis engine. Project descriptions may include elements such as the purpose of execution, the required technology stack, estimated difficulty, deadlines, number of collaborators, and essential and preferred competencies. The natural language processing module extracts technology keywords from the sentences and normalizes each keyword into a standard token of the corresponding domain by referencing a predefined technology ontology or vector dictionary. Subsequently, sentence embeddings are generated for the entire project description to obtain the project's core topic vector, while attributes such as the required technology stack, difficulty, job type, and collaboration method are represented as separate attribute vectors. This information is integrated and stored as a required competency vector; as the required competency vector is trained to map to the same embedding space as the skill vector, it can be utilized for immediate matching whenever a company registers a new project, without the need for separate rule definitions.

[0025] In the matching phase, the AI ​​analysis engine calculates the similarity between the content vector, the skill vector, and the required competency vector to simultaneously perform educational content curation and project matching. For example, let U be the skill vector for a specific learner, C be the content vector corresponding to a single educational content, and P be the required competency vector corresponding to a corporate project. The server can evaluate content suitability and project suitability using a cosine similarity function. In this case, content suitability can be defined as shown in Equation 1.

[0026] [Mathematical Formula 1]

[0027]

[0028] In addition, project fit can be defined as in Equation 2.

[0029] [Mathematical Formula 2]

[0030]

[0031] When recommending content or matching projects, the server does not simply use similarity values, but utilizes a comprehensive scoring model that reflects the learner's past performance, project completion experience, weights assigned by the company, and job preferences. For example, the total matching score can be defined as shown in Equation 3.

[0032] [Mathematical Formula 3]

[0033]

[0034] Here, Alpha, Beta, and Gamma are weights that adjust the importance of content fit, project fit, and historical performance indicators, while H is a performance history score reflecting the learner's project completion rate, average evaluation score, and collaboration indicators. The server performs matching using initial weights set by the platform operator and has a structure that automatically improves matching accuracy over time by utilizing actual project results and corporate satisfaction feedback as training data to automatically adjust Alpha, Beta, and Gamma through reinforcement learning or meta-learning techniques.

[0035] Based on these matching results, the practical training system of the present invention collects all activity logs generated while learners participate in actual projects and reprocesses them into learning data. For example, in the case of a software development project, the server integrates with a version control system to collect each learner's commit records in chronological order and calculates code quality indicators such as complexity, code duplication rate, style compliance, and test coverage by linking with a static code analysis tool. Additionally, by integrating with an issue management system to collect issues created and resolved by learners, as well as review requests and response content, it quantitatively evaluates problem definition ability, feedback acceptance ability, and contribution to collaboration. This data is then reflected in updating skill vectors, and the server can calculate growth vectors for specific skills by comparing skill vectors at the start and end of the project and provide visualized feedback to the learners.

[0036] The talent evaluation and verification system trains an AI-based performance evaluation model by integrating data collected from the practical training system with evaluation results directly entered by the company. This model is designed to estimate a learner's pure competency contribution after adjusting for factors such as project difficulty, team composition, and external environment, and calculates multiple indicators for each learner, including technical competency scores, problem-solving scores, collaboration scores, and growth potential scores. Companies can view these indicators in real-time on a recruitment dashboard, and the platform continuously improves the predictive accuracy of the performance evaluation model by utilizing additional training data, such as whether a company has finally hired a specific talent and performance feedback over a certain period after hiring.

[0037] Regarding enterprise-specific job modeling, the server automatically generates a unique job profile for the company by analyzing the types of projects the company repeatedly performs and the skill vectors of learners who received high evaluations in each project. This generated job profile includes minimum competency standards, combinations of core skills, collaboration styles, and areas requiring growth for specific roles. Subsequently, when the company registers new projects or opens job postings, the required competency vectors are adjusted based on the job profile to provide more consistent matching results. Furthermore, the platform offers a customized training course design function; it automatically recommends appropriate content lists and project scenarios for competencies identified as lacking in the job profile, allowing HR managers to quickly configure in-house training programs with only minor modifications.

[0038] All computational processes according to the present invention are performed by a program stored on a server, and said program may be implemented in the form of machine code executable on a general processor or code written in a high-level programming language. The program may be composed of functional modules such as content analysis, user profiling, project analysis, matching score calculation, practical data collection, performance evaluation, and job model generation; these modules may be executed within the same server or distributed across multiple servers according to a microservices architecture. Data is appropriately distributed and stored in relational databases, vector search databases, log repositories, etc., and each repository has an index structure and a cache system for high-speed querying.

[0039] Storage media can take various forms, such as ROM, RAM, hard disk, flash memory, SSD, or virtual storage provided in a cloud environment, and programs can be recorded on these storage media and then updated via a network. Client terminals communicate with the server via a web browser or mobile application and are configured to use security protocols such as HTTPS to ensure that data, such as educational content, project information, and evaluation results, are securely transmitted and received. Accordingly, the present invention may be implemented in software alone, or as a hardware-software integrated system combined with dedicated acceleration hardware or a GPU cluster; regardless of the form in which it is implemented, it shares the same technical concept in that it performs AI-based curation, project matching, practical training, and talent verification functions based on the aforementioned content vector, skill vector, and required competency vector.

[0041] The method of operating the system of the present invention described above may be implemented as a program (or application) to be executed in combination with a server, which is hardware, and stored on a medium.

[0042] The aforementioned program may include code encoded in a computer language such as C, C++, JAVA, or machine language, which can be read by the computer's processor (CPU) through the computer's device interface, in order for the computer to read the program and execute the methods implemented in the program. Such code may include functional code related to functions that define the necessary functions for executing the methods, and may include control code related to execution procedures necessary for the computer's processor to execute the functions according to a predetermined procedure. Additionally, such code may further include memory reference code regarding where (address) additional information or media necessary for the computer's processor to execute the functions should be referenced in the computer's internal or external memory. In addition, if the processor of the computer needs to communicate with any other computer or server located remotely in order to execute the above functions, the code may further include communication-related code regarding how to communicate with any other computer or server located remotely using the communication module of the computer, and what information or media to transmit or receive during communication.

[0043] The above-mentioned storage medium refers to a medium that stores data semi-permanently and is readable by a device, rather than a medium that stores data for a short period of time, such as a register, cache, or memory. Specifically, examples of the above-mentioned storage medium include, but are not limited to, ROM, RAM, CD-ROM, magnetic tape, floppy disk, and optical data storage device. That is, the above-mentioned program may be stored on various recording media on various servers that the computer can access, or on various recording media on the user's computer. Additionally, the above-mentioned medium may be distributed across networked computer systems, and computer-readable code may be stored in a distributed manner.

[0044] The steps of the method or algorithm described in connection with embodiments of the present invention may be implemented directly in hardware, implemented as a software module executed by hardware, or implemented by a combination thereof. The software module may reside in RAM (Random Access Memory), ROM (Read Only Memory), EPROM (Erasable Programmable ROM), EEPROM (Electrically Erasable Programmable ROM), Flash Memory, a hard disk, a removable disk, a CD-ROM, or any form of computer-readable recording medium well known in the art to which the present invention belongs.

[0045] Although embodiments of the present invention have been described above with reference to the attached drawings, those skilled in the art will understand that the present invention may be implemented in other specific forms without altering its technical concept or essential features. Therefore, the embodiments described above should be understood as illustrative in all respects and not restrictive.

Claims

Claim 1 An AI-based project matching and practical education platform characterized by comprising: an AI analysis engine that generates content vectors by analyzing the educational content of instructor users, generates skill vectors by analyzing the skill level, learning history, and project experience of learner users, generates required competency vectors by analyzing project requirements entered by companies, and then curates educational content and automatically matches projects through similarity analysis of content vectors, skill vectors, and required competency vectors; and a practical education and verification system that provides practical education based on project execution results and collects and evaluates performance data to enable companies to verify the competencies of learners. Claim 2 The AI-based project matching and practical education platform according to claim 1, characterized in that the AI ​​analysis engine calculates a matching score by combining the similarity between a content vector and a skill vector and the similarity between a skill vector and a required competency vector based on weights, and provides content to a learner or determines the possibility of participating in a project based on the matching score. Claim 3 The AI-based project matching and practical training platform according to claim 1, characterized in that the practical training system includes a function that analyzes code details, submission quality, and collaboration logs generated during the project execution process in real time to provide practical-oriented feedback to learners and reflects the analyzed data in skill vectors to continuously update the learners' competencies. Claim 4 The AI-based project matching and practical education platform according to claim 1, wherein the talent evaluation and verification system includes an AI-based performance evaluation model that predicts a learner's performance and growth potential based on evaluation results analyzing the completeness of project deliverables, problem-solving ability, and collaboration indicators. Claim 5 The AI-based project matching and practical training platform according to claim 1, characterized in that the enterprise-customized education operating system includes a function that automatically generates a job model based on the enterprise's job requirements and links the generated job model with project performance evaluation data to automatically design the enterprise's unique training course and recruitment criteria.

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