AI German couplet private teaching system

Abstract

The invention relates to the technical field of German learning, and discloses an AI German couplet private teaching system, which comprises a voice input module used for receiving German voice input of a user, providing a voice signal as a basis for subsequent recognition and processing, and ensuring that data is timely transmitted to a subsequent module; the voice recognition module is used for extracting voice features, recognizing pronunciation and intonation of a user and converting voice into a text by using a voice recognition algorithm; and the corpus training and scene simulation module is used for simulating a real German interview scene, providing a practice environment, processing training data and generating scene simulation content, and is based on a German corpus. According to the German learning system, through cooperative operation of all the modules of the system, an efficient German learning environment is provided, personalized feedback and learning suggestions are provided, comprehensive support is provided for German learners, scoring and feedback are rapidly and accurately completed, it is ensured that users can obtain improvement suggestions in time, and the users are helped to improve.

Description

Technical Field

[0001] The present invention relates to the technical field of AI language practice, and in particular to an AI German couplet private teaching system. Background Art

[0002] AI language training uses intelligent technology to monitor the learners' learning process in real time and provide real-time guidance and feedback. Whether it is simple speech practice or real-life scenario drills, learners can get AI guidance at any time to improve and enhance training results in a timely manner.

[0003] At present, domestic AI language practice systems are mainly concentrated in the English field, while German is one of the most commonly learned foreign languages ​​in the world. More than 100 million people speak German as their mother tongue. It is the most common mother tongue in Europe after English and one of the most important cultural, scientific and communication languages. It is difficult to meet the learning needs of users across the country. Therefore, an AI German couplet private tutoring system is proposed to improve the above situation. Summary of the invention

[0004] In order to make up for the above shortcomings, the present invention provides an AI German couplet private teaching system, which aims to improve the problem that domestic AI language practice systems are mainly concentrated in the English field and are difficult to meet the learning needs of users across the country.

[0005] In a first aspect, the present invention provides the following technical solution, an AI German couplet private teaching system, comprising: Voice input module: responsible for receiving the user's German voice input, providing voice signals as the basis for subsequent recognition and processing, and ensuring that data is delivered to subsequent modules in a timely manner; Speech recognition module: used to extract speech features, identify the user's pronunciation and intonation, and use speech recognition algorithms to convert speech into text; Corpus training and scenario simulation module: used to simulate real German interview scenarios, provide a practice environment, process training data, generate scenario simulation content, and train AI models based on the German corpus; Human-computer interaction module: used to generate natural language responses through natural language processing technology and provide multi-round dialogue interactions to simulate real German communication; AI agent scoring module: used to comprehensively score users based on their sparring performance and provide personalized feedback and learning suggestions; Learning content recommendation module: used to provide personalized learning paths based on the user's learning progress and performance, recommend suitable learning content and practice questions, and dynamically adjust the recommended content.

[0006] Preferably, the voice input module includes an audio receiving unit and a signal processing unit, the audio receiving unit is used to receive a variety of audio formats, the signal processing unit is used to clarify the voice signal and transmit it to the voice recognition module; the voice recognition module includes a voice recognition algorithm unit, a feature extraction unit, an acoustic model, and a language model, the voice recognition algorithm unit is used to convert voice into text, the feature extraction unit is used to recognize the user's pronunciation and intonation, the acoustic model is a basic model for voice recognition, and the language model is a language prediction model for improving recognition accuracy.

[0007] Preferably, the corpus training and scenario simulation module includes a corpus management unit, a training algorithm unit, a data preprocessing unit, and a simulation environment unit. The corpus management unit is used to manage German corpus and update the data in real time. The training algorithm unit is used to adjust the speech recognition and comprehension capabilities according to the training algorithm. The data preprocessing unit is used to clean and format the corpus. The simulation environment unit is used to simulate real German conversation scenarios, which include interview scenarios and daily scenarios.

[0008] Preferably, the human-computer interaction module includes a dialogue management unit, a natural language generation unit, a context understanding unit, and a response generation unit. The dialogue management unit is used to manage the context of the user dialogue, the natural language generation unit is used to generate natural and fluent responses, the context understanding unit is used to analyze the context of the user input, and the response generation unit is used to generate appropriate responses based on the context.

[0009] Preferably, the AI ​​agent scoring module includes an AI Agent role unit, a scoring algorithm unit, a feedback generation unit, a pronunciation evaluation unit, and a fluency evaluation unit. The AI ​​Agent role unit is used to provide personalized services and support in an intelligent manner. The scoring algorithm unit is used to comprehensively evaluate the user's performance, and the performance includes pronunciation accuracy, fluency, and emotional expression. The feedback generation unit is used to generate personalized learning suggestions based on the score. The pronunciation evaluation unit is used to evaluate pronunciation accuracy. The fluency evaluation unit is used to evaluate the fluency of the user's spoken language.

[0010] Preferably, the AI ​​Agent role unit includes a user support subunit, a bilingual translation subunit, an AI German HR subunit, a daily scenario dialogue subunit, and a feedback analysis subunit. The user support subunit is used to provide system introduction and operation guidance, guide users to use system functions, and answer user questions. The bilingual translation subunit is used to translate the input Chinese content into German and answer in German, and translate the German input content into Chinese. The AI ​​German HR subunit is used to conduct simulated interviews based on the interview scenarios simulated by the corpus training and scenario simulation modules. The daily scenario dialogue subunit conducts dialogues based on the daily scenarios simulated by the corpus training and scenario simulation modules, and the daily scenarios include shopping, restaurants, and transportation. The feedback analysis subunit is used to provide targeted evaluation criteria and timely feedback based on the German context and oral requirements, while emphasizing the user's strengths and progress, and pointing out areas that need improvement. The feedback includes pronunciation accuracy and grammatical errors.

[0011] Preferably, the learning content recommendation module includes a progress tracking unit, a content recommendation algorithm unit, a learning path planning unit, and an exercise question bank management unit. The progress tracking unit is used to record the user's learning progress, the content recommendation algorithm unit is used to recommend personalized learning content based on performance, the learning path planning unit is used to design a personalized learning route for the user, and the exercise question bank management unit is used to maintain and update the exercise question bank.

[0012] In a second aspect, the present invention provides the following technical solution, a working method of an AI German couplet private teaching system, comprising the following steps: S1. Speech input and recognition: German speech is input into the speech input module, and then the speech recognition module converts the speech into text and extracts speech features to obtain basic information; S2. Corpus training and scenario simulation: Based on the German corpus, train the AI ​​model and generate scenario simulation content based on basic information to simulate real German conversation scenarios, including interview scenarios and daily scenarios. S3. Human-computer interaction: Using natural language processing technology, according to the German dialogue scenario, provide multiple rounds of dialogue interaction, generate natural responses to basic information dialogues, and obtain training data, which includes pronunciation accuracy, fluency, and emotional expression; S4, AI agent scoring: Comprehensive scoring based on training data, and providing personalized feedback and learning suggestions based on the scoring; S5. Learning content recommendation: Based on personalized feedback and learning suggestions, recommend suitable learning content and practice questions, and provide personalized learning paths.

[0013] In a third aspect, the invention provides the following technical solution: a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the working method of the above-mentioned AI German couplet private tutoring system when executing the computer program.

[0014] In a fourth aspect, the present invention provides the following technical solution: a readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the working method of the above-mentioned AI German couplet private tutoring system is implemented.

[0015] The present invention has the following beneficial effects: 1. In the present invention, through the coordinated operation of various modules of the system, an efficient German learning environment is provided, and personalized feedback and learning suggestions are provided, all-round support is provided for German learners, and scoring and feedback are completed quickly and accurately, ensuring that users can obtain improvement suggestions in a timely manner, helping users to improve, thereby solving the problem of insufficient teachers and helping users to comprehensively improve their listening, speaking, reading and writing skills.

[0016] 2. In the present invention, the user support subunit provides system introduction and operation guidance, guides users to use system functions, and answers user questions. The AI ​​German HR subunit and the daily scenario dialogue subunit conduct dialogues based on the scenarios simulated by the corpus training and scenario simulation modules, provide targeted evaluation standards, and provide timely feedback. At the same time, it emphasizes the user's strengths and progress, and points out areas for improvement, so that it is easy to use and can be widely applicable to German learners of different levels without the need for complex configuration and adjustment.

[0017] 3. In the present invention, the AI ​​Agent role unit provides personalized services and support in an intelligent manner, the scoring algorithm unit comprehensively evaluates the user's performance, the feedback generation unit generates personalized learning suggestions based on the scores, the pronunciation evaluation unit evaluates the pronunciation accuracy, and the fluency evaluation unit evaluates the user's spoken fluency, so as to score the user's practice situation, provide personalized learning suggestions and improvement measures, and improve the learning effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a system architecture diagram of the AI ​​German couplet private teaching system proposed by the present invention; Figure 2 This is a diagram of the voice input module architecture of the AI ​​German couplet private teaching system proposed by the present invention; Figure 3 This is a diagram of the speech recognition module architecture of the AI ​​German couplet private teaching system proposed by the present invention; Figure 4This is a diagram of the corpus training and scenario simulation module architecture of the AI ​​German couplet private teaching system proposed by the present invention; Figure 5 This is a human-computer interaction module architecture diagram of an image recognition system proposed by the present invention; Figure 6 This is an architecture diagram of an AI agent scoring module of an image recognition system proposed by the present invention; Figure 7 This is the AI ​​Agent role unit architecture diagram of the AI ​​German couplet private teaching system proposed by the present invention; Figure 8 This is a learning content recommendation module architecture diagram of the AI ​​German couplet private teaching system proposed by the present invention; Fig. 9 This is a flow chart of the working method of the AI ​​German couplet private teaching system proposed by the present invention; Fig.10 This is a workflow diagram of the AI ​​Agent role unit of the AI ​​German couplet private teaching system proposed in the present invention. DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] Embodiment 1 Reference Figure 1 In the first embodiment of the present invention, the present invention provides an AI German couplet private teaching system, comprising: Voice input module: responsible for receiving the user's German voice input, providing voice signals as the basis for subsequent recognition and processing, and ensuring that data is delivered to subsequent modules in a timely manner; Speech recognition module: used to extract speech features, identify the user's pronunciation and intonation, and use speech recognition algorithms to convert speech into text; Corpus training and scenario simulation module: used to simulate real German interview scenarios, provide a practice environment, process training data, generate scenario simulation content, and train AI models based on the German corpus; Human-computer interaction module: used to generate natural language responses through natural language processing technology and provide multi-round dialogue interactions to simulate real German communication; AI agent scoring module: used to comprehensively score users based on their sparring performance and provide personalized feedback and learning suggestions; Learning content recommendation module: used to provide personalized learning paths based on the user's learning progress and performance, recommend suitable learning content and practice questions, and dynamically adjust the recommended content.

[0021] Specifically, it receives the user's German voice input and provides voice signals as the basis for subsequent recognition and processing, and ensures that the data is delivered to the subsequent modules in a timely manner, thus serving as the basis for subsequent recognition and processing; it extracts voice features, recognizes the user's pronunciation and intonation, and uses the voice recognition algorithm to convert the voice into text, thereby quickly and accurately completing voice recognition and ensuring the effectiveness of subsequent processing; it simulates real German interview scenarios, provides a practice environment, processes training data, generates scenario simulation content, and trains AI models based on the German corpus, thereby efficiently processing training data, facilitating the rapid generation of scenario simulation content, and providing a realistic practice environment, thereby improving voice recognition and comprehension capabilities; it generates natural language responses through natural language processing technology, and provides multi-round dialogue exchanges It simulates real German communication to ensure the continuity and fluency of the conversation; it gives a comprehensive score based on the user's practice performance and provides personalized feedback and learning suggestions, so as to quickly and accurately complete the scoring and feedback, ensuring that users can get improvement suggestions in time to help them improve; it provides a personalized learning path based on the user's learning progress and performance, recommends suitable learning content and practice questions, and dynamically adjusts the recommended content, so as to help users improve their German level in a systematic way and ensure the effectiveness of the learning plan; it can process user input in real time, quickly generate high-quality practice content and feedback, and meet users' immediate learning needs, thereby improving the problem that domestic AI language practice systems are mainly concentrated in the English field and cannot meet the learning needs of users across the country.

[0022] Reference Figure 2 , Figure 3 The voice input module includes an audio receiving unit and a signal processing unit. The audio receiving unit is used to receive a variety of audio formats. The signal processing unit is used to clarify the voice signal and transmit it to the voice recognition module. The voice recognition module includes a voice recognition algorithm unit, a feature extraction unit, an acoustic model, and a language model. The voice recognition algorithm unit is used to convert voice into text. The feature extraction unit is used to recognize the user's pronunciation and intonation. The acoustic model is the basic model for voice recognition. The language model is a language prediction model for improving recognition accuracy.

[0023] Specifically, a variety of audio formats are received through the audio receiving unit, the voice signal is clarified through the signal processing unit and transmitted to the voice recognition module, the voice is converted into text through the voice recognition algorithm unit, the user's pronunciation and intonation are recognized through the feature extraction unit, the acoustic model is used as the basic model of voice recognition, and the language model is used as a language prediction model to improve recognition accuracy, thereby enhancing the accuracy of voice recognition.

[0024] Reference Figure 4 The corpus training and scenario simulation module includes a corpus management unit, a training algorithm unit, a data preprocessing unit, and a simulation environment unit. The corpus management unit is used to manage German corpus and update the data in real time. The training algorithm unit is used to adjust the speech recognition and understanding capabilities according to the training algorithm. The data preprocessing unit is used to clean and format the corpus. The simulation environment unit is used to simulate real German conversation scenarios, including interview scenarios and daily scenarios.

[0025] Specifically, a large amount of German corpus is managed through the corpus management unit and the data is updated in real time. The training algorithm unit adjusts the speech recognition and comprehension capabilities according to the training algorithm. The data preprocessing unit cleans and formats the corpus. The simulation environment unit simulates real German conversation scenarios, thereby conducting special training for German interview scenarios to help users overcome language barriers, pass interviews smoothly, and improve their job search success rate.

[0026] Reference Figure 5 The human-computer interaction module includes a dialogue management unit, a natural language generation unit, a context understanding unit, and a reply generation unit. The dialogue management unit is used to manage the context of the user dialogue, the natural language generation unit is used to generate natural and fluent replies, the context understanding unit is used to analyze the context of user input, and the reply generation unit is used to generate appropriate replies according to the context.

[0027] Specifically, the context of user conversations is managed by the dialogue management unit, natural and fluent responses are generated by the natural language generation unit, the context of user input is analyzed by the context understanding unit, and appropriate responses are generated according to the context by the response generation unit, thereby systematically improving the user's listening, speaking, reading and writing skills, providing all-round support for German learners and improving their German level.

[0028] Reference Figure 6The AI ​​agent scoring module includes an AI Agent role unit, a scoring algorithm unit, a feedback generation unit, a pronunciation evaluation unit, and a fluency evaluation unit. The AI ​​Agent role unit is used to provide personalized services and support in an intelligent way. The scoring algorithm unit is used to comprehensively evaluate the user's performance, including pronunciation accuracy, fluency, and emotional expression. The feedback generation unit is used to generate personalized learning suggestions based on the score. The pronunciation evaluation unit is used to evaluate pronunciation accuracy. The fluency evaluation unit is used to evaluate the fluency of the user's spoken language.

[0029] Specifically, the AI ​​Agent role unit provides personalized services and support in an intelligent way, the scoring algorithm unit comprehensively evaluates the user's performance, the feedback generation unit generates personalized learning suggestions based on the scores, the pronunciation evaluation unit evaluates the pronunciation accuracy, and the fluency evaluation unit evaluates the user's oral fluency, so as to score the user's practice situation, provide personalized learning suggestions and improvement measures, and improve learning effects.

[0030] Reference Figure 7 , Fig.10 The AI ​​Agent role unit includes a user support subunit, a bilingual translation subunit, an AI German HR subunit, a daily scenario dialogue subunit, and a feedback analysis subunit. The user support subunit is used to provide system introduction and operation guidance, guide users to use system functions, and answer user questions. The bilingual translation subunit is used to translate the input Chinese content into German and answer in German, and translate the German input content into Chinese. The AI ​​German HR subunit is used to conduct simulated interviews based on the interview scenarios simulated by the corpus training and scenario simulation modules. The daily scenario dialogue subunit conducts dialogues based on the daily scenarios simulated by the corpus training and scenario simulation modules. Daily scenarios include shopping, restaurants, and transportation. The feedback analysis subunit is used to provide targeted evaluation criteria and timely feedback based on the German context and oral requirements, while emphasizing the user's strengths and progress, and pointing out areas that need improvement. Feedback includes pronunciation accuracy and grammatical errors.

[0031] Specifically, the user support sub-unit provides system introduction and operation guidance, guides users to use system functions, and answers users' questions. The bilingual translation sub-unit translates Chinese input content into German and answers in German, and translates German input content into Chinese. The AI ​​German HR sub-unit conducts simulated interviews based on the interview scenarios simulated by the corpus training and scenario simulation modules. The daily scenario dialogue sub-unit conducts conversations based on the daily scenarios simulated by the corpus training and scenario simulation modules, provides targeted evaluation standards and timely feedback, and at the same time emphasizes the users' strengths and progress and points out areas that need improvement, so that it is easy to use and can be widely applicable to German learners of different levels without the need for complex configuration and adjustment.

[0032] Reference Figure 8 ,The learning content recommendation module includes a progress tracking unit, a content ,recommendation algorithm unit, a learning path planning unit, and an exercise question bank management unit. ,The progress tracking unit is used to record the user’s learning progress, the ,content recommendation algorithm unit is used to recommend personalized learning content ,based on performance, the learning path planning unit is used to design personalized ,learning routes for users, and the exercise question bank management unit is used to maintain and ,update the exercise question bank.

[0033] Specifically, the progress tracking unit records the user's learning progress, the content recommendation algorithm unit recommends personalized learning content based on performance, the learning path planning unit designs personalized learning routes for users, and the exercise question bank management unit maintains and updates the exercise question bank, thereby providing an efficient German learning environment, solving the problem of insufficient teachers, and helping users comprehensively improve their listening, speaking, reading and writing skills.

[0034] Embodiment 2: Reference Fig. 9 In a second embodiment of the present invention, the present invention provides a working method of an AI German couplet private teaching system, comprising the following steps: S1. Speech input and recognition: German speech is input into the speech input module, and then the speech recognition module converts the speech into text and extracts speech features to obtain basic information; S2. Corpus training and scenario simulation: Based on the German corpus, train the AI ​​model and generate scenario simulation content based on basic information to simulate real German conversation scenarios, including interview scenarios and daily scenarios. S3. Human-computer interaction: Using natural language processing technology, according to the German dialogue scenario, provide multiple rounds of dialogue interaction, generate natural responses to basic information dialogues, and obtain training data, including pronunciation accuracy, fluency, and emotional expression; S4, AI agent scoring: Comprehensive scoring based on training data, and providing personalized feedback and learning suggestions based on the scoring; S5. Learning content recommendation: Based on personalized feedback and learning suggestions, recommend suitable learning content and practice questions, and provide personalized learning paths.

[0035] Specifically, the basic information is obtained by inputting the German speech into the speech input module, and then converting the speech into text through the speech recognition module, and extracting the speech features, so as to ensure that the required information is accurately extracted from the speech input by the user as the basis for subsequent processing; By training the AI ​​model based on the German corpus and generating scenario simulation content based on basic information, the system can simulate real German conversation scenarios, including interview scenarios and daily scenarios, to ensure that the system can provide accurate speech recognition and scenario simulation, thereby improving the user's learning effect. By using natural language processing technology, we provide multiple rounds of dialogue interaction based on German dialogue scenarios, generate natural responses to basic information dialogues, and obtain training data. The training data includes pronunciation accuracy, fluency, and emotional expression, thereby ensuring the naturalness and fluency of the dialogue and improving the user's learning experience. By comprehensively scoring the training data and providing personalized feedback and learning suggestions based on the scores, the accuracy of the scores and the pertinence of the feedback can be ensured, thus improving the learning effect of users. Based on personalized feedback and learning suggestions, we recommend suitable learning content and practice questions, and provide personalized learning paths, thereby ensuring the effectiveness and pertinence of the learning plan and improving the user's learning efficiency.

[0036] Embodiment 3 The third embodiment of the present invention is based on the same inventive concept and proposes a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps of the working method of the AI ​​German couplet private teaching system of the above embodiment are implemented.

[0037] Embodiment 4 The fourth embodiment of the present invention is based on the same inventive concept. A computer device proposed by the present invention includes: a processor and a memory; the processor and the memory communicate with each other; the memory is used to store instructions; the processor is used to execute the instructions in the memory to execute the working method of the AI ​​German couplet private teaching system of the above embodiment.

[0038] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0039] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. AI German couplet private teaching system, characterized by: include: Voice input module: responsible for receiving the user's German voice input, providing voice signals as the basis for subsequent recognition and processing, and ensuring that data is delivered to subsequent modules in a timely manner; Speech recognition module: used to extract speech features, identify the user's pronunciation and intonation, and use speech recognition algorithms to convert speech into text; Corpus training and scenario simulation module: used to simulate real German interview scenarios, provide a practice environment, process training data, generate scenario simulation content, and train AI models based on the German corpus; Human-computer interaction module: used to generate natural language responses through natural language processing technology and provide multi-round dialogue interactions to simulate real German communication; AI agent scoring module: used to comprehensively score users based on their sparring performance and provide personalized feedback and learning suggestions; Learning content recommendation module: used to provide personalized learning paths based on the user's learning progress and performance, recommend suitable learning content and practice questions, and dynamically adjust the recommended content.

2. The AI ​​German couplet private teaching system according to claim 1 is characterized in that: The voice input module includes an audio receiving unit and a signal processing unit. The audio receiving unit is used to receive multiple audio formats. The signal processing unit is used to clarify the voice signal and transmit it to the voice recognition module. The voice recognition module includes a voice recognition algorithm unit, a feature extraction unit, an acoustic model unit, and a language model unit. The voice recognition algorithm unit is used to convert voice into text. The feature extraction unit is used to recognize the user's pronunciation and intonation. The acoustic model unit is a basic model for voice recognition. The language model unit is a language prediction model for improving recognition accuracy.

3. The AI ​​German couplet private teaching system according to claim 1 is characterized in that: The corpus training and scenario simulation module includes a corpus management unit, a training algorithm unit, a data preprocessing unit, and a simulation environment unit. The corpus management unit is used to manage German corpus and update the data in real time. The training algorithm unit is used to adjust the speech recognition and comprehension capabilities according to the training algorithm. The data preprocessing unit is used to clean and format the corpus. The simulation environment unit is used to simulate real German conversation scenarios, which include interview scenarios and daily scenarios.

4. The AI ​​German couplet private teaching system according to claim 1, characterized in that: The human-computer interaction module includes a dialogue management unit, a natural language generation unit, a context understanding unit, and a response generation unit. The dialogue management unit is used to manage the context of user dialogues, the natural language generation unit is used to generate natural and fluent responses, the context understanding unit is used to analyze the context of user input, and the response generation unit is used to generate appropriate responses based on the context.

5. The AI ​​German couplet private teaching system according to claim 1 is characterized in that: The AI ​​agent scoring module includes an AI Agent role unit, a scoring algorithm unit, a feedback generation unit, a pronunciation evaluation unit, and a fluency evaluation unit. The AI ​​Agent role unit is used to provide personalized services and support in an intelligent manner. The scoring algorithm unit is used to comprehensively evaluate the user's performance, which includes pronunciation accuracy, fluency, and emotional expression. The feedback generation unit is used to generate personalized learning suggestions based on the score. The pronunciation evaluation unit is used to evaluate pronunciation accuracy. The fluency evaluation unit is used to evaluate the fluency of the user's spoken language.

6. The AI ​​German couplet private teaching system according to claim 5, characterized in that: The AI ​​Agent role unit includes a user support subunit, a bilingual translation subunit, an AI German HR subunit, a daily scenario dialogue subunit, and a feedback analysis subunit. The user support subunit is used to provide system introduction and operation guidance, guide users to use system functions, and answer user questions. The bilingual translation subunit is used to translate the input Chinese content into German and answer in German, and translate the German input content into Chinese. The AI ​​German HR subunit is used to conduct simulated interviews based on the interview scenarios simulated by the corpus training and scenario simulation modules. The daily scenario dialogue subunit conducts dialogues based on the daily scenarios simulated by the corpus training and scenario simulation modules. The daily scenarios include shopping, restaurants, and transportation. The feedback analysis subunit is used to provide targeted evaluation criteria and timely feedback based on the German context and oral requirements, while emphasizing the user's strengths and progress, and pointing out areas that need improvement. The feedback includes pronunciation accuracy and grammatical errors.

7. The AI ​​German couplet private teaching system according to claim 1, characterized in that: The learning content recommendation module includes a progress tracking unit, a content recommendation algorithm unit, a learning path planning unit, and an exercise question bank management unit. The progress tracking unit is used to record the user's learning progress, the content recommendation algorithm unit is used to recommend personalized learning content based on performance, the learning path planning unit is used to design a personalized learning route for the user, and the exercise question bank management unit is used to maintain and update the exercise question bank.

8. The working method of the AI ​​German couplet private teaching system is characterized by: The AI ​​German couplet private teaching system applied to any one of claims 1 to 7 comprises the following steps: S1. Speech input and recognition: German speech is input into the speech input module, and then the speech recognition module converts the speech into text and extracts speech features to obtain basic information; S2. Corpus training and scenario simulation: Based on the German corpus, train the AI ​​model and generate scenario simulation content based on basic information to simulate real German conversation scenarios, including interview scenarios and daily scenarios. S3. Human-computer interaction: Using natural language processing technology, according to the German dialogue scenario, provide multiple rounds of dialogue interaction, generate natural responses to basic information dialogues, and obtain training data, which includes pronunciation accuracy, fluency, and emotional expression; S4, AI agent scoring: Comprehensive scoring based on training data, and providing personalized feedback and learning suggestions based on the scoring; S5. Learning content recommendation: Based on personalized feedback and learning suggestions, recommend suitable learning content and practice questions, and provide personalized learning paths.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method for using the AI ​​German couplet private teaching system as claimed in claim 8 is implemented.

10. A readable storage medium, characterized in that: The readable storage medium stores a computer program, and when the computer program is executed by the processor, the method for using the AI ​​German couplet private teaching system according to claim 8 is implemented.

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