Artificial intelligence-based employee training assessment method, device and equipment

Abstract

The invention relates to the technical field of employee training, and discloses an artificial intelligence-based employee training assessment method, device and equipment, and the method comprises the steps: obtaining the basic data, work behavior data and historical training data of an employee, carrying out the fusion of all data, and constructing a multi-dimensional employee portrait; based on the performance goals, determining training goals of the employees, and generating personalized training contents; a knowledge system is constructed according to personalized training content, real-time knowledge recommendation is performed in actual work, and a practice result is recorded; generating personalized assessment content for intelligent assessment to obtain an assessment result; and evaluating the employee training effect according to the practice result and the assessment result. The employee portrait is constructed through multi-source data fusion, accurate mapping from performance goals to personalized training is achieved, the matching degree of training content and employee ability short boards is improved, the knowledge conversion efficiency is enhanced, closed-loop management is formed in combination with real-time knowledge recommendation and scene practice, and the talent training efficiency is improved.

Description

Technical Field

[0001] This invention relates to the field of employee training technology, and specifically to an employee training and assessment method, apparatus, and equipment based on artificial intelligence. Background Technology

[0002] In the process of enterprise development, employee training is a key link to improve the quality and business capabilities of employees. However, the traditional employee training model has many serious drawbacks. (1) The traditional daily uniform training method cannot fully consider the individual differences of employees, and it is difficult to guarantee the initiative of employees to learn. Whether they are seriously engaged in learning during the training process and whether they have truly mastered the training content after the training is not effectively monitored by the enterprise. At the same time, the uniform training content is often "one-size-fits-all". For some employees who have already mastered the relevant knowledge, participating in the training is a waste of time and reduces the utilization efficiency of training resources. (2) In terms of training assessment, since most enterprises lack a strict assessment and supervision environment, especially in departments with high mobility and complex work scenarios such as sales teams, the training assessment results cannot truly reflect the employees' knowledge mastery and ability improvement. The assessment becomes a formality and cannot provide the enterprise with an accurate basis for employee ability assessment, nor can it effectively guide employees to improve their own quality and ability. (3) After the training, employees often face the dilemma of not being able to review some knowledge points that they are interested in. Either there is a lack of convenient review channels, or the cost of review is too high, such as requiring the repurchase of training materials or spending a lot of work time to attend training again. As a result, employees cannot effectively consolidate and deepen their understanding of the valuable knowledge acquired in the training, which seriously affects the sustainability of the training effect and the practical application and transformation of knowledge. Summary of the Invention

[0003] In view of this, the present invention provides an artificial intelligence-based employee training and assessment method, apparatus and equipment to solve the problems of neglecting individual differences, lack of strict assessment and supervision environment and convenient review channels in traditional training.

[0004] In a first aspect, the present invention provides an employee training and assessment method based on artificial intelligence, the method comprising:

[0005] Acquire basic employee data, work behavior data, and historical training data, and integrate these data to construct a multi-dimensional employee profile.

[0006] Employee training objectives are determined based on performance targets, and personalized training content is generated based on employee profiles and corresponding training objectives.

[0007] After employees complete personalized training content, a knowledge system is built based on the personalized training content, and real-time knowledge recommendations are made in combination with the employees' real-time work scenarios, and the employees' practical results are recorded.

[0008] Generate personalized assessment content, and conduct intelligent assessment based on the personalized assessment content to obtain assessment results;

[0009] The effectiveness of employee training is evaluated based on practical results and assessment results.

[0010] The AI-based employee training and assessment method provided by this invention constructs employee profiles through multi-source data fusion, achieving precise mapping from performance targets to personalized training. This improves the matching degree between training content and employee skill gaps, enhances knowledge conversion efficiency, and, combined with real-time knowledge recommendation and scenario-based practice, forms a closed-loop management system of "data-driven - precise training - practical verification - assessment optimization." By focusing on employees to identify training weaknesses, it supports continuous iteration and optimization of training programs, helping enterprises reduce training costs and improve talent development efficiency.

[0011] In one optional implementation, basic employee data, work behavior data, and historical training data are acquired, and these data are integrated to construct a multi-dimensional employee profile, including:

[0012] The system collects basic employee data, historical training data, and real-time employee work behavior data through enterprise software service systems.

[0013] After cleaning and deduplication of basic data, historical training data, and work behavior data, the data is standardized and a mapping relationship between the various data is established.

[0014] Based on the mapping of relationships between various data, and according to the evaluation index system, quantitative scores and visual employee profiles are generated for employees.

[0015] The AI-based employee training and assessment method provided by this invention ensures comprehensiveness of the profile by collecting multi-dimensional data covering basic employee information, historical training, and real-time work behavior. It improves data quality through data cleaning, deduplication, and standardization, and enhances the accuracy of the profile by constructing a data association network through relationship mapping. Based on evaluation indicators, it generates quantitative scores and visual profiles, providing enterprises with intuitive basis for talent assessment.

[0016] In one optional implementation, employee training objectives are determined based on performance targets, and personalized training content is generated according to employee profiles and corresponding training objectives, including:

[0017] Determine employee skill requirements based on performance targets, and determine employee training objectives based on employee profiles and skill requirements.

[0018] Based on the priority of training objectives and learning preference data in employee profiles, matching course modules are selected from the corporate training resource library. Knowledge graph technology is used to associate course knowledge points with learning preference data to dynamically generate personalized training content.

[0019] The AI-based employee training and assessment method provided by this invention breaks down performance targets into competency requirements, accurately anchors employee training directions, and ensures that training is in sync with corporate strategic development. By combining employee profiles and learning preferences to select course modules, it not only meets employees' personalized learning needs but also enhances their learning enthusiasm and initiative. The application of knowledge graph technology achieves a deep correlation between knowledge points and employee preferences, making training content more targeted and adaptable, and effectively improving training efficiency and knowledge conversion rate.

[0020] In one alternative implementation, after employees complete personalized training content, a knowledge system is built based on the personalized training content, including:

[0021] Collect learning records and practical feedback data generated by employees during personalized training;

[0022] Natural language processing technology is used to perform semantic analysis on textual materials and case descriptions in personalized training content to extract key knowledge points;

[0023] By combining employees' learning records and practical feedback data during training, key knowledge points are classified and integrated through clustering algorithms. A knowledge graph is constructed using graph database technology. With knowledge points as nodes and logical relationships, difficulty levels, and application scenarios between knowledge points as edges, a multi-dimensional knowledge system is formed that includes theoretical knowledge, practical experience, and industry-leading dynamics.

[0024] The AI-based employee training and assessment method provided by this invention collects learning records and practical feedback to deeply explore training effectiveness, making the knowledge system more aligned with employees' actual needs. It uses natural language processing technology to accurately extract key knowledge points, ensuring the professionalism and accuracy of the knowledge system content. The combination of clustering algorithms and graph database technology effectively organizes knowledge threads and constructs a logically clear and hierarchically distinct multi-dimensional knowledge graph, which helps employees systematically consolidate training results and achieve efficient internalization and flexible application of knowledge.

[0025] In one optional implementation, intelligent assessment is conducted based on personalized assessment content, including:

[0026] A multi-factor authentication system is constructed based on facial recognition, voiceprint recognition, and behavioral feature analysis, and the multi-factor authentication system is used to authenticate the identity of employees.

[0027] During the assessment process, employee behavior is monitored in real time, and abnormal behavior is identified based on this monitoring.

[0028] By combining the content of the standard answers with semantic understanding models, employee answers are analyzed to determine performance evaluation scores.

[0029] The artificial intelligence-based employee training and assessment method provided by this invention establishes a scientific and authentic training and assessment mechanism by constructing a multi-factor authentication system, promptly identifying abnormal behaviors by acquiring employee behavior in real time, maintaining the fairness of the assessment, and using artificial intelligence technology to ensure that the assessment results accurately reflect employee capabilities, thus providing enterprises with a reliable basis for employee evaluation.

[0030] In one alternative implementation, the effectiveness of employee training is evaluated based on practical results and assessment results, including:

[0031] Establish an evaluation index system for training effectiveness, which includes knowledge mastery, skill improvement, work performance improvement rate, and practical task completion quality score.

[0032] By comparing employee performance evaluations, completion of practical case studies, and job performance data before and after training, the weight of each evaluation indicator was determined using the analytic hierarchy process (AHP), and a weighted average algorithm was used to calculate the overall score of employee training effectiveness.

[0033] The AI-based employee training assessment method provided by this invention comprehensively considers multiple indicators such as knowledge mastery and skill improvement, avoiding the one-sidedness of single-dimensional evaluation. It uses the analytic hierarchy process to determine weights, and can flexibly adjust the importance of each indicator according to the actual needs of the enterprise, making the assessment more aligned with business objectives. The weighted average algorithm integrates data to generate a quantitative comprehensive score, achieving objective and accurate quantification of training effectiveness.

[0034] In one alternative implementation, the method further includes:

[0035] The employee training effectiveness level is determined based on the comprehensive score, and whether the training effectiveness has achieved the expected results is determined based on the employee training effectiveness level.

[0036] If the training results do not meet expectations, then based on the skill gaps and learning preference data presented in the employee profile, combined with the distribution of weak knowledge points in the knowledge system, the training course combination will be re-planned, the training duration and learning schedule will be adjusted, and practical projects that are more in line with the needs of employees will be introduced to generate a new training optimization plan.

[0037] The new training optimization plan is linked and updated with employee profiles and knowledge systems to form a closed-loop employee training management system.

[0038] The AI-based employee training assessment method provided by this invention concretizes abstract evaluation results by classifying training effectiveness levels, making it easier for enterprises to quickly judge the effectiveness of training. For situations where expectations are not met, precise measures can be taken based on employee profiles, learning preferences, and knowledge gaps. This involves replanning courses, adjusting schedules, and optimizing practical projects to make training programs more aligned with employees' individual needs. This achieves a dynamic cycle of training effectiveness evaluation, optimization, and updating, effectively improving the relevance and effectiveness of training.

[0039] Secondly, the present invention provides an employee training and assessment device based on artificial intelligence, the device comprising:

[0040] The profile building module is used to acquire basic employee data, work behavior data, and historical training data, and integrate these data to build a multi-dimensional employee profile.

[0041] The training customization module is used to determine employee training objectives based on performance targets and generate personalized training content based on employee profiles and corresponding training objectives.

[0042] The practice record module is used to build a knowledge system based on the personalized training content after employees complete the personalized training content, and to make real-time knowledge recommendations based on the employees' real-time work scenarios, and record the employees' practice results.

[0043] The assessment module is used to generate personalized assessment content and perform intelligent assessments based on that content to obtain assessment results.

[0044] The effectiveness evaluation module is used to evaluate the effectiveness of employee training based on practical results and assessment results.

[0045] Thirdly, the present invention provides a computer device, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to perform the method described in the first aspect or any corresponding embodiment thereof.

[0046] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to perform the method described in the first aspect or any corresponding embodiment thereof. Attached Figure Description

[0047] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0048] Figure 1 This is a flowchart illustrating an artificial intelligence-based employee training and assessment method according to an embodiment of the present invention.

[0049] Figure 2 This is a flowchart illustrating another artificial intelligence-based employee training and assessment method according to an embodiment of the present invention.

[0050] Figure 3 This is a structural block diagram of an artificial intelligence-based employee training and assessment device according to an embodiment of the present invention;

[0051] Figure 4 This is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. Detailed Implementation

[0052] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0053] The existing technologies for employee training mainly have the following problems:

[0054] (1) Single data collection dimension: Existing technologies mainly rely on initial assessment information and learning process data, lacking real-time and multi-dimensional collection of employee behavior data in actual work scenarios. For example, employee capabilities are mainly assessed based on initial assessment scores and stage learning information, failing to effectively integrate key data such as employee performance in actual work and customer interaction quality, resulting in a disconnect between training content and actual work needs.

[0055] (2) Insufficient model adaptability: Most existing AI training models are statically constructed and lack dynamic perception and adaptation mechanisms for changes in employee performance goals and skill enhancement. For example, although course selection based on job path is provided, the training focus and difficulty are not dynamically adjusted according to the real-time performance and skill development of employees, resulting in a low correlation between training effectiveness and performance improvement.

[0056] (3) Weak assessment and supervision mechanisms: Existing technologies lack an effective intelligent anti-cheating supervision system, which cannot ensure the authenticity and reliability of assessment results. Existing technologies focus more on the personalized generation of assessment content and result evaluation, but neglect the authenticity guarantee during the assessment process, especially for remote assessment scenarios, where there is a lack of effective identity verification and behavior monitoring mechanisms.

[0057] (4) Fragmented knowledge management: Existing technologies are relatively simple in terms of training content processing and knowledge management, mostly relying on text classification or tagging storage, lacking the ability for deep semantic understanding and knowledge structuring. This makes it difficult for employees to easily retrieve, review, and apply key knowledge points from training, affecting the sustainability and practicality of training effectiveness.

[0058] (5) Lack of closed-loop performance feedback mechanism: Although existing technologies can assess the learning effect after training, they fail to establish a correlation analysis and feedback optimization mechanism between training content and actual performance. They cannot scientifically verify the actual contribution of training content to performance improvement, nor can they continuously optimize training content based on performance.

[0059] This invention provides an artificial intelligence-based employee training and assessment method. By combining employee profiles with performance targets to develop personalized training content, a closed-loop management system of "data-driven - precise training - practical verification - assessment optimization" is formed to improve the effectiveness of talent development.

[0060] According to an embodiment of the present invention, an embodiment of an employee training and assessment method based on artificial intelligence is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0061] This embodiment provides an artificial intelligence-based employee training and assessment method, which can be used in the aforementioned computer system. Figure 1 This is a flowchart of an artificial intelligence-based employee training and assessment method according to an embodiment of the present invention, such as... Figure 1 As shown, the process includes the following steps:

[0062] Step S101: Obtain basic employee data, work behavior data, and historical training data, and integrate the basic data, work behavior data, and historical training data to construct a multi-dimensional employee profile.

[0063] Specifically, sales personnel face a wide variety of customer types and market environments, requiring them to be flexible and adaptable. Sales skills (such as industry-specific sales pitches) are highly experience-based and need to be continuously refined and updated. Therefore, this embodiment uses sales personnel as the target employees for explanation.

[0064] The company acquires basic employee data and historical training data through its Human Resources System (HRS). Basic data includes, but is not limited to, personal information such as age, education, job position, and date of employment, as well as performance data such as sales volume, sales target completion rate, and number of clients developed. Historical training data includes, but is not limited to, training courses attended, training results, and training feedback. Employee work behavior data is collected through the Application Programming Interface (API) and Customer Relationship Management (CRM) APIs of the Software as a Service (SaaS) system. This includes, but is not limited to, daily behavior data, audio and text recordings of real-time conversations with clients, product demonstration quality, client feedback ratings, client visit records, and transaction data. Work behavior data may also include specific behavioral performances recorded using modular Internet of Things (IoT) sensors, such as demonstration proficiency, communication frequency, and problem-solving speed. Heterogeneous data fusion algorithms (such as multi-dimensional heterogeneous data fusion networks) are used to integrate employee basic data, real-time work behavior data, and training content data to construct a unified data representation model. Based on this model, a comprehensive data profile of sales personnel is built.

[0065] Step S102: Determine employee training objectives based on performance targets, and generate personalized training content based on employee profiles and corresponding training objectives.

[0066] Specifically, the goal decomposition tree algorithm is used to decompose the company's total performance goals into partial goals, team goals, and individual goals. Then, through the capability-performance correlation matrix, the performance goals are mapped to specific capability improvement requirements, which serve as employee training objectives.

[0067] Based on deep reinforcement learning algorithms (such as Deep Q-Network), employee profiles, training objectives, and training content are used as environmental state variables. Through continuous trial and feedback, the training content recommendation strategy is optimized to achieve real-time adaptive recommendation of training content. A reward function is defined, including: learning progress indicators (e.g., weight 0.3), knowledge mastery indicators (e.g., weight 0.4), and performance improvement indicators (e.g., weight 0.3), to comprehensively evaluate the training recommendation effectiveness.

[0068] Based on employee profiles and corresponding training objectives, personalized training content is adaptively generated using a trained deep reinforcement learning model.

[0069] Step S103: After employees complete personalized training content, a knowledge system is built based on the personalized training content, and real-time knowledge recommendations are made in combination with the employees' real-time work scenarios, and the employees' practice results are recorded.

[0070] Specifically, after employee training, assessments are conducted on both practical and theoretical aspects. Deep learning algorithms are used to semantically analyze personalized training content, extracting key concepts and integrating them into the employee's existing knowledge base to create a more aligned knowledge system with the training objectives. During employee-customer communication, real-time knowledge recommendations are made based on real-time work scenarios, and practical results are recorded based on customer feedback. For example, personalized training for sales personnel at an electronics company might focus on explaining the selling points of high-end smartphones and handling customer objections. After training, the system extracts core knowledge points, such as "durability parameters of foldable screen phones" and "response techniques for customers who complain about the price," constructing a knowledge system. When a salesperson in a store encounters a customer who doubts the durability of a foldable screen phone, the system, based on the real-time work scenario, pushes "100,000 folding test data for foldable screens" and successful case studies to the mobile device in real time. The recommended knowledge is used to persuade the customer and close the deal. The system automatically records the practical results for subsequent training effectiveness evaluation and knowledge system optimization. This is just one example, but not the only possible approach.

[0071] Step S104: Generate personalized assessment content and conduct intelligent assessment based on the personalized assessment content to obtain the assessment results.

[0072] Specifically, for theoretical assessments, personalized assessment content is automatically generated based on the individualized training content. Employees complete the assessment using the intelligent assessment system, which has a complete authentication and monitoring module to ensure that the assessment is completed by the employees themselves and that their behavior meets the requirements during the assessment, thus enabling monitoring of the entire assessment process.

[0073] Step S105: Evaluate the effectiveness of employee training based on the results of practice and assessment.

[0074] Specifically, a comprehensive evaluation indicator system should be constructed, including evaluation indicators for practical results such as work performance improvement rate and practical task completion quality scores, as well as evaluation indicators for assessment results such as knowledge mastery. Weights can be assigned to each evaluation indicator based on actual circumstances, and the values ​​of each indicator can be determined based on employee performance after training, thereby comprehensively evaluating the effectiveness of employee training.

[0075] The AI-based employee training and assessment method provided in this embodiment constructs employee profiles through multi-source data fusion, achieving precise mapping from performance targets to personalized training. This improves the matching degree between training content and employee skill gaps, enhances knowledge conversion efficiency, and, combined with real-time knowledge recommendation and scenario-based practice, forms a closed-loop management system of "data-driven - precise training - practical verification - assessment optimization." By focusing on employees to identify training weaknesses, it supports continuous iteration and optimization of training programs, helping companies reduce training costs and improve talent development efficiency.

[0076] In some alternative implementations, the method further includes:

[0077] Step S106: Determine the employee training effectiveness level based on the comprehensive score, and determine whether the training effectiveness has achieved the expected results based on the employee training effectiveness level.

[0078] Specifically, training effectiveness is graded based on overall scores (e.g., excellent, satisfactory, needs improvement) to determine whether expectations have been met. Different standards can be set for different levels of employees to meet expectations. For example, management staff may need to achieve an excellent rating to meet expectations, while entry-level staff may only need to achieve a satisfactory rating.

[0079] Step S107: If the training effect does not meet expectations, then based on the skill gaps and learning preference data presented in the employee profile, combined with the distribution of weak knowledge points in the knowledge system, the training course combination is re-planned, the training duration and learning progress are adjusted, and practical projects that are more in line with the needs of employees are introduced to generate a new training optimization plan.

[0080] Specifically, for employees who fail to meet the standards, the problems are precisely identified by combining their employee profiles and knowledge gaps (e.g., an employee has high performance evaluation scores but low practical application rate, exposing a disconnect between theory and practice). This provides a basis for optimizing subsequent training, forming a closed-loop management system of "assessment-diagnosis-improvement." For example, employee profile analysis reveals that an employee has poor communication skills and prefers fragmented learning, lacking mastery of business communication techniques. Based on this, the training program is redesigned, reducing redundant theory in the course mix and supplementing courses on weak knowledge points; the learning schedule is adjusted to match the employee's preferred fragmented learning pace; and targeted simulation tasks are designed for practical projects, such as communication scenario drills. This is just an example, but not a limitation.

[0081] Step S108 involves updating the new training optimization plan in conjunction with employee profiles and knowledge systems to form a closed-loop employee training management system.

[0082] Specifically, a new plan is ultimately generated, linked to employee profiles and knowledge systems, and its effectiveness is continuously tracked to form a closed loop for training optimization.

[0083] The AI-based employee training assessment method provided in this embodiment concretizes abstract evaluation results by classifying training effectiveness levels, making it easier for enterprises to quickly judge the effectiveness of training. For situations where expectations are not met, precise measures can be taken based on employee profiles, learning preferences, and knowledge gaps. Courses can be redesigned, schedules adjusted, and practical projects optimized to make training programs more aligned with employees' individual needs. This achieves a dynamic cycle of training effectiveness evaluation, optimization, and updating, effectively improving the relevance and effectiveness of training.

[0084] This embodiment provides an artificial intelligence-based employee training and assessment method, which can be used in the aforementioned computer system. Figure 2 This is a flowchart of an artificial intelligence-based employee training and assessment method according to an embodiment of the present invention, such as... Figure 2 As shown, the process includes the following steps:

[0085] Step S201: Obtain basic employee data, work behavior data, and historical training data, and integrate the basic data, work behavior data, and historical training data to construct a multi-dimensional employee profile.

[0086] Specifically, step S201 includes:

[0087] Step S2011: Collect basic employee data, historical training data, and real-time employee work behavior data through the enterprise software service system.

[0088] Specifically, a data collection period can be set, such as 7 days, and audio and video acquisition equipment can be used to collect employee work behavior data in real time. The specific content of each data item is detailed in step S101 of the previous embodiment, and will not be repeated here.

[0089] Step S2012 involves cleaning and deduplicating the basic data, historical training data, and work behavior data, then standardizing them and establishing a mapping between the various data sets.

[0090] Specifically, the system scans for errors, missing values, and outliers in the data. For example, if the age field in the employee's basic data contains negative numbers or values ​​exceeding a reasonable range, it needs to be corrected or marked. For missing educational information, if it cannot be supplemented, it is placed using a specific symbol (such as "NULL"). Duplicate records are identified based on unique identifiers (IDs). For example, if there are duplicate attendance records from the same time in the work behavior data, only one valid record is retained.

[0091] Convert data from different formats and units into a unified standard. For example, employee contact information in the basic data may be recorded as mobile phone numbers or landline numbers including area codes, requiring a standardized format. Utilize logical relationships between data to construct data networks. For instance, link job information in the basic data to project participation and task completion in the work behavior data using employee IDs to analyze differences in work behavior among employees in different positions; link course types in historical training data to performance in the work behavior data to determine which training courses have a significant impact on work outcomes. These are just examples and are not exhaustive.

[0092] Step S2013: Based on the mapping of relationships between various data, and according to the evaluation index system, generate quantitative scores and visualized employee profiles for employees.

[0093] Specifically, based on enterprise management needs, multi-dimensional employee indicators can be constructed. For example, for sales personnel, indicators such as sales performance achievement rate, customer satisfaction, and training course completion rate can be set, and weights can be assigned according to the importance of each indicator. Based on relationship mapping, data related to the indicators can be extracted from the integrated data. For example, order volume and customer complaint rate for sales personnel can be obtained from work behavior data, combined with job objectives in the basic data, to calculate the sales performance achievement rate. This is just an example, but not a limitation. Data visualization technology can be used to present quantitative scores and employee information intuitively. For example, radar charts can be used to display employee performance in various evaluation dimensions, intuitively comparing their strengths and weaknesses; bar charts can be used to present score changes over different periods, reflecting growth trends.

[0094] The AI-based employee training and assessment method provided in this embodiment ensures comprehensiveness of the profile by collecting multi-dimensional data covering basic employee information, historical training, and real-time work behavior. It improves data quality through data cleaning, deduplication, and standardization, and enhances the accuracy of the profile by constructing a data association network through relationship mapping. Based on evaluation indicators, it generates quantitative scores and visual profiles, providing enterprises with intuitive basis for talent assessment.

[0095] Step S202: Determine employee training objectives based on performance targets, and generate personalized training content based on employee profiles and corresponding training objectives.

[0096] Specifically, step S202 includes:

[0097] Step S2021: Determine employee competency requirements based on performance targets, and determine employee training objectives based on employee profiles and competency requirements.

[0098] Specifically, the company's overall performance goals are broken down layer by layer to each department and employee. Taking the marketing department as an example, to increase market share, it is necessary to strengthen capabilities such as competitor analysis and precise marketing planning; to improve customer renewal rates, it is necessary to enhance capabilities in understanding customer needs and designing after-sales value-added services. By analyzing the knowledge, skills, and qualities required to achieve each sub-goal, a list of job competency requirements is formed, clarifying the essential capabilities that employees must possess to achieve performance goals.

[0099] By analyzing employee profiles and cross-referencing them with a competency requirements list, the gap between each employee's current capabilities and the required skills can be accurately identified. Based on these gaps and in line with the company's business priorities, personalized training goals can be developed for each employee. For example, if an employee's profile shows a lack of innovation in marketing strategies in past projects and no prior training in "big data user profiling analysis," indicating a weakness in precision marketing capabilities, a training plan could be developed for them to "master big data user profiling analysis tools and independently complete three precision marketing strategy proposals within three months." This is just one example and is not a limitation.

[0100] Step S2022: Based on the priority of training objectives and learning preference data in employee profiles, select matching course modules from the enterprise training resource library, and use knowledge graph technology to associate course knowledge points with learning preference data to dynamically generate personalized training content.

[0101] Specifically, training objectives are prioritized based on corporate goals and employee job value, and tiered screening ensures that training resources are directed towards enhancing key capabilities. Based on employee learning preference data (such as historical course completion rates, learning time distribution, and course type preferences), course selection is further optimized. For example, if an employee prefers short, fragmented learning in the form of short videos and has a historical completion rate of over 90% for case-based courses, the "10-Minute High-Value Sales Case Study Explanation" video series will be prioritized. Through knowledge graph technology, a relationship network is constructed between course knowledge points and employee learning preferences and skill gaps. This allows for dynamic adjustments to course combinations and knowledge point associations based on employee learning progress and feedback, achieving continuous optimization and precise adaptation of training content.

[0102] The AI-based employee training and assessment method provided in this embodiment breaks down performance targets into competency requirements, accurately anchors employee training directions, and ensures that training is in sync with corporate strategic development. By combining employee profiles and learning preferences to select course modules, it not only meets employees' personalized learning needs but also enhances their learning enthusiasm and initiative. The application of knowledge graph technology enables a deep correlation between knowledge points and employee preferences, making training content more targeted and adaptable, and effectively improving training efficiency and knowledge conversion rate.

[0103] In step S203, after the employee completes the personalized training content, a knowledge system is constructed based on the personalized training content, and real-time knowledge recommendation is carried out in combination with the employee's real-time work scenario, and the employee's practice results are recorded.

[0104] Specifically, the above step S203 includes:

[0105] Step S2031, collect the learning records and practical operation feedback data generated by the employee during the personalized training process.

[0106] Specifically, through multiple channels such as the enterprise training management system, online learning platform, and practical operation simulation system, the employee's learning data is collected in real time. For example, obtain the course viewing duration, chapter completion progress, and after-class test scores from the training platform; collect the task completion time, operation step records, error types and frequencies from the practical operation simulation system; through questionnaires or instant feedback tools, collect the employee's subjective evaluations of the course content, lecturer performance, and practical links.

[0107] The collected data is structurally processed and then stored in the database. For example, store the learning progress data in the format of "employee ID - course ID - chapter - completion time"; the practical operation feedback data is archived in the dimension of "task name - operation steps - error code - improvement suggestions", ensuring that the data is traceable and easy to analyze, providing the original basis for the subsequent construction of the knowledge system.

[0108] Step S2032, use natural language processing technology to perform semantic analysis on the text materials and case descriptions in the personalized training content, and extract key knowledge points.

[0109] Specifically, clean the text in the training content such as courseware documents, case reports, and lecturer manuscripts, remove punctuation marks and stop words (such as "de" and "le"), and convert unstructured text into structured data. Use natural language processing technologies such as named entity recognition, keyword extraction, and topic models to identify the core concepts, professional terms, and key operation steps in the text. For example, extract knowledge points such as "complaint type", "processing process", and "script template" from the "customer complaint handling case"; through sentiment analysis, dig out the lessons in the case (such as "it is necessary to appease the customer's emotions first"), ensuring that the extracted knowledge points cover multiple levels of theory and practice.

[0110] Step S2033, combine the learning records and practical operation feedback data of the employee during the training process, classify and integrate the key knowledge points through clustering algorithms, and use graph database technology to construct a knowledge graph. With knowledge points as nodes and the logical relationships, difficulty levels, and application scenarios between knowledge points as edges, a multi-dimensional knowledge system including theoretical knowledge, practical experience, and industry frontiers is formed.

[0111] Specifically, the extracted key knowledge points are correlated with employee learning records and practical feedback, and clustering algorithms (such as K-means and hierarchical clustering) are used to classify them according to knowledge attributes and application scenarios. For example, knowledge points related to "customer complaint handling" are clustered into categories such as "communication skills," "emotional management," and "process standardization." Based on the high-frequency errors in employee practice, weak knowledge points are identified and marked as "key areas for reinforcement." This is just an example, but not a limitation.

[0112] Using a graph database as a platform, knowledge points are treated as nodes, and logical relationships (such as "containment," "association," and "preconditions"), difficulty levels, and application scenarios are used as edges to construct a visual knowledge network. For example, "product function explanation" and "customer needs analysis" are connected by a "causal relationship," labeled as "intermediate difficulty," and their application scenario is "sales negotiation." Simultaneously, cutting-edge industry developments are incorporated as supplementary nodes, forming a dynamic and comprehensive knowledge system that supports on-demand retrieval by employees and knowledge sharing and iteration within the enterprise.

[0113] The AI-based employee training and assessment method provided in this embodiment collects learning records and practical feedback to deeply explore training effectiveness, making the knowledge system more aligned with employees' actual needs. It uses natural language processing technology to accurately extract key knowledge points, ensuring the professionalism and accuracy of the knowledge system content. The combination of clustering algorithms and graph database technology effectively organizes knowledge threads and constructs a logically clear and hierarchically distinct multi-dimensional knowledge graph, which helps employees systematically consolidate training results and achieve efficient internalization and flexible application of knowledge.

[0114] Step S204: Generate personalized assessment content and conduct intelligent assessment based on the personalized assessment content to obtain the assessment results.

[0115] Specifically, step S204 includes:

[0116] Step S2041: Construct a multi-factor authentication system based on face recognition, voiceprint recognition, and behavioral feature analysis, and use the multi-factor authentication system to authenticate employee identities.

[0117] Specifically, terminal devices are used to collect employees' biometric and behavioral data: cameras are used to capture employees' facial images and extract features such as facial proportions and facial textures; microphones are used to record voice samples and analyze the acoustic features of voiceprints such as timbre, tone, and frequency; keyboard and mouse sensors are used to record employees' typing speed, mouse clicking habits, and other behavioral data to form a multi-dimensional identity authentication dataset.

[0118] The collected data is input into a multi-factor authentication system. The system uses deep learning models (such as convolutional neural networks for image processing and recurrent neural networks for voiceprint analysis) to extract and compare features from different dimensions of the data. During authentication, employees are required to pass facial recognition, voiceprint verification, and behavioral feature matching simultaneously. For example, when logging into the assessment system, employees need to complete a facial scan, voice command verification, and pass a similarity test between their actions and a preset template. Only after passing these three verifications can they enter the assessment interface, ensuring the authenticity and reliability of the assessment subject's identity.

[0119] Step S2042: During the assessment process, employee behavior is acquired in real time, and abnormal behavior is identified based on the employee behavior.

[0120] Specifically, during the assessment process, employee behavior and environmental data are continuously collected. Cameras are used to capture employees' body movements and gaze direction in real time; microphones are used to monitor environmental noise and voice communication; and the system background records operation logs such as screen switching, window opening, and document access. For example, it records whether employees frequently switch screens to non-assessment interfaces, or whether there are abnormal behaviors such as multiple people appearing on the same screen or engaging in voice conversations.

[0121] The system utilizes computer vision and machine learning algorithms to analyze the collected data. Object detection algorithms (such as YOLO) are used to identify unusual people or objects in the image; an attention model is employed to analyze whether employees' gaze is off-target from the assessment interface; and a behavior pattern clustering algorithm compares real-time behavior with normal assessment behavior templates. If preset abnormal patterns such as "prolonged use of mobile phones with the head down" or "frequent communication with others" are observed, the system automatically flags them and triggers an alert, ensuring the fairness and seriousness of the assessment process.

[0122] Step S2043: Combine the content of the standard answer with the semantic understanding model to analyze the employee's answer and determine the assessment score.

[0123] Specifically, the employee-submitted assessment answers undergo preprocessing such as word segmentation and part-of-speech tagging to remove redundant information. Natural language processing techniques are then used to convert the text into vector representations and extract semantic features. For example, keywords and logical structures in open-ended question-and-answer answers are transformed into numerical features that computers can understand.

[0124] The preprocessed answer vectors are compared with the semantic vectors of the standard answers to calculate similarity. A semantic understanding model is then used to analyze the completeness, accuracy, and logic of the answers. For example, for essay questions, the model not only determines whether the answer contains key knowledge points but also assesses the depth of argumentation and the fluency of language. For open-ended solution design questions, a knowledge graph is used to match the technical points in the answer with the actual business scenario. Finally, a quantitative score is generated according to preset scoring rules to ensure that the scoring results are objective, comprehensive, and aligned with the job competency requirements.

[0125] The AI-based employee training and assessment method provided in this embodiment establishes a scientific and authentic training and assessment mechanism by constructing a multi-factor authentication system, promptly identifying abnormal behaviors by acquiring employee behavior data in real time, maintaining the fairness of the assessment, and using AI technology to ensure that the assessment results accurately reflect employee capabilities, thus providing enterprises with a reliable basis for employee evaluation.

[0126] Step S205: Evaluate the effectiveness of employee training based on the results of practice and assessment.

[0127] Specifically, step S205 includes:

[0128] Step S2051: Establish an evaluation index system for training effectiveness. The evaluation index system includes knowledge mastery, skill improvement, work performance improvement rate, and practical task completion quality score.

[0129] Specifically, a comprehensive evaluation indicator system should be constructed, including knowledge mastery (such as assessment scores and knowledge point test scores), skill improvement (efficiency in completing practical tasks and changes in error rate), work performance improvement rate (sales growth and customer conversion rate improvement), and practical task completion quality score (project outcome quality and customer satisfaction). For example, for sales personnel, the focus can be on performance improvement rate and customer objection handling practice score; for technical personnel, the emphasis should be on practical skills and project delivery quality.

[0130] Step S2052: By comparing the employee's assessment scores, practical case completion status and job performance data before and after training, the weight of each evaluation indicator is determined using the analytic hierarchy process (AHP), and the weighted average algorithm is used to calculate the comprehensive score of the employee training effect.

[0131] Specifically, data is collected through multiple channels. Assessment results come from objective scoring by the intelligent assessment system (such as multiple-choice scores) and subjective question analysis by the semantic understanding model (such as scoring of solution design ideas); practical results rely on real-time records in the work scenario, such as sales order data, customer service work order processing records, and engineer project progress feedback, to ensure that the data truly reflects the transformation of employee capabilities.

[0132] The weights of each indicator are determined by using the analytic hierarchy process. For example, if the company's current strategy focuses on performance growth, the "performance improvement rate" can be given a higher weight. Then, the weighted average algorithm is used to calculate the overall score, and the data of various indicators before and after employee training are compared to quantitatively evaluate the training effect.

[0133] The AI-based employee training assessment method provided in this embodiment comprehensively considers multiple indicators such as knowledge mastery and skill improvement, avoiding the one-sidedness of single-dimensional evaluation. It uses the analytic hierarchy process to determine weights, and can flexibly adjust the importance of each indicator according to the actual needs of the enterprise, making the assessment more aligned with business objectives. The weighted average algorithm integrates data to generate a quantitative comprehensive score, achieving objective and accurate quantification of training effectiveness.

[0134] This embodiment also provides an artificial intelligence-based employee training and assessment device, which is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0135] This embodiment provides an employee training and assessment device based on artificial intelligence, such as... Figure 3 As shown, it includes:

[0136] The profile building module 301 is used to acquire basic employee data, work behavior data, and historical training data, and integrate the basic data, work behavior data, and historical training data to build a multi-dimensional employee profile.

[0137] The training customization module 302 is used to determine employee training objectives based on performance targets and generate personalized training content based on employee profiles and corresponding training objectives.

[0138] The practice record module 303 is used to build a knowledge system based on the personalized training content after employees complete the personalized training content, and to make real-time knowledge recommendations based on the employees' real-time work scenarios, and record the employees' practice results.

[0139] The assessment module 304 is used to generate personalized assessment content and perform intelligent assessment based on the personalized assessment content to obtain assessment results.

[0140] The effectiveness evaluation module 305 is used to evaluate the effectiveness of employee training based on practical results and assessment results.

[0141] Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0142] In this embodiment, the AI-based employee training and assessment device is presented in the form of functional units. Here, a unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0143] This invention also provides a computer device having the above-described features. Figure 3 The device shown is an AI-based employee training and assessment system.

[0144] Please see Figure 4 , Figure 4 This is a schematic diagram of the structure of a computer device provided in an optional embodiment of the present invention, such as... Figure 4 As shown, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 4 Take a processor 10 as an example.

[0145] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0146] The memory 20 stores instructions executable by at least one processor 10 to cause the at least one processor 10 to perform the method shown in the above embodiments.

[0147] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0148] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0149] The computer device also includes a communication interface 30 for communicating with other devices or communication networks.

[0150] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0151] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. An employee training and assessment method based on artificial intelligence, characterized in that, The method includes: Acquire basic employee data, work behavior data, and historical training data, and integrate these data to construct a multi-dimensional employee profile. Employee training objectives are determined based on performance targets, and personalized training content is generated based on the employee profile and corresponding training objectives. After employees complete personalized training content, a knowledge system is built based on the personalized training content, and real-time knowledge recommendations are made in combination with the employees' real-time work scenarios, and the employees' practical results are recorded. Generate personalized assessment content, and conduct intelligent assessment based on the personalized assessment content to obtain assessment results; The effectiveness of employee training will be evaluated based on the described practice results and the described assessment results.

2. The method according to claim 1, characterized in that, Acquire basic employee data, work behavior data, and historical training data, and integrate these data to construct a multi-dimensional employee profile, including: The system collects basic employee data, historical training data, and real-time employee work behavior data through enterprise software service systems. After cleaning and deduplication of the basic data, historical training data, and work behavior data, the data is standardized and a mapping between the data is established. Based on the mapping of relationships between various data, and according to the evaluation index system, quantitative scores and visual employee profiles are generated for employees.

3. The method according to claim 1, characterized in that, Employee training objectives are determined based on performance targets, and personalized training content is generated based on the employee profile and corresponding training objectives, including: Determine employee skill requirements based on performance targets, and determine employee training objectives based on employee profiles and skill requirements. Based on the priority of training objectives and learning preference data in employee profiles, matching course modules are selected from the corporate training resource library. Knowledge graph technology is used to associate course knowledge points with learning preference data to dynamically generate personalized training content.

4. The method according to claim 1, characterized in that, After employees complete personalized training content, a knowledge system is built based on that content, including: Collect learning records and practical feedback data generated by employees during personalized training; Natural language processing technology is used to perform semantic analysis on textual materials and case descriptions in personalized training content to extract key knowledge points; By combining employees' learning records and practical feedback data during training, key knowledge points are classified and integrated through clustering algorithms. A knowledge graph is constructed using graph database technology. With knowledge points as nodes and logical relationships, difficulty levels, and application scenarios between knowledge points as edges, a multi-dimensional knowledge system is formed that includes theoretical knowledge, practical experience, and industry-leading dynamics.

5. The method according to claim 1, characterized in that, Intelligent assessment based on personalized assessment content includes: A multi-factor authentication system is constructed based on facial recognition, voiceprint recognition, and behavioral feature analysis, and the multi-factor authentication system is used to authenticate the identity of employees. During the assessment process, employee behavior is acquired in real time, and abnormal behavior is identified based on the employee behavior. By combining the content of the standard answers with semantic understanding models, employee answers are analyzed to determine performance evaluation scores.

6. The method according to claim 1, characterized in that, The effectiveness of employee training is evaluated based on the stated practical results and the stated assessment results, including: Establish an evaluation index system for training effectiveness, which includes knowledge mastery, skill improvement, work performance improvement rate, and practical task completion quality score. By comparing employee performance evaluations, completion of practical case studies, and job performance data before and after training, the weight of each evaluation indicator was determined using the analytic hierarchy process (AHP), and a weighted average algorithm was used to calculate the overall score of employee training effectiveness.

7. The method according to claim 6, characterized in that, The method further includes: The employee training effectiveness level is determined based on the comprehensive score, and whether the training effectiveness has achieved the expected results is determined based on the employee training effectiveness level. If the training results do not meet expectations, then based on the skill gaps and learning preference data presented in the employee profile, combined with the distribution of weak knowledge points in the knowledge system, the training course combination will be re-planned, the training duration and learning schedule will be adjusted, and practical projects that are more in line with the needs of employees will be introduced to generate a new training optimization plan. The new training optimization plan is linked and updated with employee profiles and knowledge systems to form a closed-loop employee training management system.

8. An employee training and assessment device based on artificial intelligence, characterized in that, The device includes: The profile building module is used to acquire basic employee data, work behavior data, and historical training data, and integrate these data to build a multi-dimensional employee profile. The training customization module is used to determine employee training objectives based on performance targets and generate personalized training content based on employee profiles and corresponding training objectives. The practice record module is used to build a knowledge system based on the personalized training content after employees complete the personalized training content, and to make real-time knowledge recommendations based on the employees' real-time work scenarios, and record the employees' practice results. The assessment module is used to generate personalized assessment content and perform intelligent assessments based on that content to obtain assessment results. The effectiveness evaluation module is used to evaluate the effectiveness of employee training based on practical results and assessment results.

9. A computer device, characterized in that, include: A memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, the processor executing the computer instructions to perform the method of any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to perform the method of any one of claims 1 to 7.

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