An English teaching system based on artificial intelligence

Abstract

The present application provides an English teaching system based on artificial intelligence. When the system detects that the user is engaged in an English learning activity, it determines the English learning goal selected by the user; outputs the initial English learning content corresponding to the English learning goal; detects the user's learning environment information, physiological parameters, and the analysis result of the emotional state; the analysis result of the emotional state represents the current anxiety level of the user; obtains the learning result of the user for the initial English learning content; determines the current target coefficient of the user according to the physiological parameters, learning result, and the analysis result of the emotional state; inputs the learning environment information and the target coefficient into a pre-trained classification model to obtain the current classification result of the user, and the prediction result includes the learning material category of the English learning goal; outputs the English learning material corresponding to the learning material category. By applying the system provided in the embodiments of the present application, a personalized English learning experience can be provided for the user.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to an English teaching system based on artificial intelligence. Background Art

[0002] In traditional English teaching, teachers mainly rely on experience and standardized teaching materials to arrange course content and progress. Although this method can provide systematic knowledge transfer, it is incapable of dealing with individual differences among students. With the development of information technology and artificial intelligence, intelligent teaching tools have gradually emerged, aiming to optimize the learning experience in a data-driven way. However, existing technologies still face many challenges in achieving truly personalized education.

[0003] Most existing teaching systems use a unified teaching schedule and content setting, which is difficult to meet the personalized needs of different students. Each student's learning foundation, learning ability and learning style are significantly different. A unified teaching method will inevitably lead to some students finding it difficult to learn, while others find the course content too simple. This unified teaching method not only reduces the overall learning efficiency, but may also dampen students' enthusiasm for learning, affecting their long-term learning motivation and effectiveness. Summary of the invention

[0004] The technical problem to be solved by this application is to provide an English teaching system based on artificial intelligence, which can provide users with a personalized English learning experience. The specific solution is as follows:

[0005] An English teaching system based on artificial intelligence, comprising:

[0006] A first determining unit, configured to determine an English learning goal selected by the user when detecting that the user is performing an English learning activity;

[0007] A first output unit, configured to output initial English learning content corresponding to the English learning goal;

[0008] A first detection unit is used to detect the user's learning environment information, physiological parameters and emotional state analysis results; the emotional state analysis results represent the user's current anxiety level;

[0009] A first acquisition unit, configured to acquire the learning result of the user on the initial English learning content;

[0010] A second determining unit, configured to determine a current target coefficient of the user according to the physiological parameter, the learning result and the emotional state analysis result, wherein the target coefficient represents a degree of information processing burden borne by the user when performing English learning activities;

[0011] An execution unit, configured to input the learning environment information and the target coefficient into a pre-trained classification model to obtain the current classification result of the user, where the prediction result includes the learning material category of the English learning objective;

[0012] A second output unit, configured to output the English learning materials corresponding to the learning material category.

[0013] For the above system, optionally, the second determination unit includes:

[0014] A calculation subunit, configured to calculate the physiological score of the user according to the physiological parameters;

[0015] A non-linear conversion subunit, configured to perform non-linear conversion on the physiological score, the learning result, and the emotional state analysis result;

[0016] An execution subunit, configured to input the physiologically scored, learning result, and emotionally state analyzed results after non-linear conversion into a pre-constructed multi-layer perceptron to obtain the current target coefficient of the user.

[0017] For the above system, optionally, the non-linear conversion subunit includes:

[0018] A calculation module, configured to perform weighted processing on the physiological score, the learning result, and the emotional state analysis result to obtain the weighted terms corresponding to the physiological score, the learning result, and the emotional state analysis result respectively;

[0019] A non-linear conversion module, configured to perform non-linear conversion on each weighted term by using the non-linear conversion function corresponding to each weighted term.

[0020] For the above system, optionally, the first detection unit includes:

[0021] An environment detection subunit, configured to collect environmental noise, environmental light intensity, environmental temperature, and environmental humidity; and obtain the learning environment information of the user according to the environmental noise, environmental light intensity, environmental temperature, and environmental humidity;

[0022] An emotion detection subunit, configured to identify the expression and voice of the user to obtain the emotional state analysis result of the user;

[0023] A physiological detection subunit, configured to collect at least one physiological parameter of the user's heart rate, skin conductivity, respiratory information, and electroencephalogram information.

[0024] For the above system, optionally, the first determination unit includes:

[0025] A recommendation subunit, configured to recommend multiple alternative learning objectives to the user according to the user's historical learning performance, learning preferences, and learning progress;

[0026] A receiving subunit, configured to receive the learning objective selected by the user from among the various alternative learning objectives.

[0027] For the above system, optionally, the first determination unit further includes:

[0028] A planning subunit, configured to receive the available time resources and learning syllabus set by the user, and generate a learning plan for the user according to the available time resources and the learning syllabus;

[0029] A time management subunit, configured to determine the user's learning progress according to the user's historical learning situation, the learning plan, and the current time.

[0030] For the above system, optionally, the first acquisition unit includes:

[0031] A learning status detection subunit, configured to obtain the learning behavior data of the user for the initial English learning content, and obtain the user's online quiz results, interactive exercise evaluation results, and self-report questionnaire results according to the learning behavior data;

[0032] An evaluation subunit, configured to obtain the learning result of the user for the initial English learning content according to the user's online quiz results, interactive exercise evaluation results, and self-report questionnaire results.

[0033] For the above system, optionally, the second output unit includes:

[0034] A matching subunit, configured to obtain the English learning materials corresponding to the learning material category in a preset learning resource library;

[0035] An output subunit, configured to output the English learning materials corresponding to the learning material category.

[0036] For the above system, optionally, it further includes:

[0037] A second acquisition unit, configured to obtain the learning result of the user for the English learning materials, and return to trigger the process of the second determination unit to re-execute the determination of the user's current target coefficient according to the physiological parameters, the learning result, and the emotional state analysis result.

[0038] For the above system, optionally, it further includes:

[0039] A first detection unit, configured to detect whether the target coefficient is greater than a preset target coefficient threshold;

[0040] A third output unit, configured to output a prompt message when the continuous number of times that the detected target coefficient is greater than the target coefficient threshold reaches a preset number threshold; the prompt message is used to prompt the user to take a break.

[0041] An English teaching method based on artificial intelligence, comprising:

[0042] When it is detected that the user is engaged in an English learning activity, determining the English learning goal selected by the user;

[0043] Outputting the initial English learning content corresponding to the English learning goal;

[0044] Detecting the user's learning environment information, physiological parameters, and emotional state analysis result; the emotional state analysis result represents the current anxiety level of the user;

[0045] Obtaining the learning result of the user for the initial English learning content;

[0046] According to the physiological parameters, the learning result, and the emotional state analysis result, determining the current target coefficient of the user, where the target coefficient represents the degree of information processing burden borne by the user when engaged in an English learning activity;

[0047] Inputting the learning environment information and the target coefficient into a pre-trained classification model to obtain the current classification result of the user, where the prediction result includes the learning material category of the English learning goal;

[0048] Outputting the English learning materials corresponding to the learning material category.

[0049] Based on the English teaching system based on artificial intelligence provided by the embodiments of the present application, the system includes: a first determination unit, configured to determine the English learning goal selected by the user when detecting that the user is engaged in an English learning activity; a first output unit, configured to output the initial English learning content corresponding to the English learning goal; a first detection unit, configured to detect the user's learning environment information, physiological parameters, and emotional state analysis result; the emotional state analysis result represents the current anxiety level of the user; a first acquisition unit, configured to acquire the learning result of the user for the initial English learning content; a second determination unit, configured to determine the current target coefficient of the user according to the physiological parameters, the learning result, and the emotional state analysis result, where the target coefficient represents the degree of information processing burden borne by the user when engaged in an English learning activity; an execution unit, configured to input the learning environment information and the target coefficient into a pre-trained classification model to obtain the current classification result of the user, where the prediction result includes the learning material category of the English learning goal; a second output unit, configured to output the English learning material corresponding to the learning material category. Applying the system provided by the embodiments of the present application can provide a personalized English learning experience for users. Description of the Drawings

[0050] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained according to the provided drawings without creative efforts.

[0051] Figure 1 It is a schematic structural diagram of an English teaching system based on artificial intelligence provided by the present application;

[0052] Figure 2 It is a schematic structural diagram of a second determination unit provided by the present application;

[0053] Figure 3 It is a system flowchart of an English teaching method based on artificial intelligence provided by the present application;

[0054] Figure 4 It is a flowchart of the process of determining the current target coefficient of the user provided by the present application. Detailed Embodiments

[0055] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the protection scope of the present application.

[0056] In the present application, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, system, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, system, article or device. Without further limitation, an element defined by the phrase "comprising a..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the element.

[0057] Most existing teaching systems adopt a unified teaching progress and content setting, which are difficult to meet the personalized needs of different students. There are significant differences in the learning foundation, learning ability and learning style of each student. The unified teaching method will inevitably cause some students to feel difficult in learning, while some other students will find the course content too simple. This unified teaching method not only reduces the overall learning efficiency, but may also dampen the learning enthusiasm of students and affect their long-term learning motivation and effect.

[0058] Although some existing teaching systems are also trying to conduct personalized teaching, however, the existing teaching systems usually rely on single-dimensional data to evaluate the learning status of students, resulting in inaccurate evaluation of the learning status of students. The evaluation mechanism of the existing systems is usually static, making recommendations based on preset rules or models, while ignoring real-time dynamic changes. For example, when the information processing burden of students is too high, the system fails to detect and adjust the recommended content in time, resulting in the learning materials recommended may exceed the tolerance of students, or be too simple to effectively challenge the abilities of students.

[0059] Based on this, the embodiments of the present application provide an English teaching system based on artificial intelligence. This system can be applied to an electronic device, and the electronic device can be a controller, a personal computer, a server, a tablet device, a smart phone, a smart wearable device, etc. The system flowchart of the system is as Figure 1 shown, and specifically includes:

[0060] A first determination unit 101, a first output unit 102, a first detection unit 103, a first acquisition unit 104, a second determination unit 105, an execution unit 106 and a second output unit 107.

[0061] The first determination unit 101 is configured to determine the English learning objective selected by the user when it detects that the user is engaged in an English learning activity.

[0062] In this embodiment, the first determination unit can connect to the API of the education platform or teaching material system to synchronize the user's course progress in real time. At the same time, sensors (such as keyboard input frequency, mouse click pattern) or in-app events (such as opening learning materials, starting practice questions) are used to detect whether the user is engaged in an English learning activity. When it detects that the user is engaged in an English learning activity, the system will activate the recommendation mechanism and provide the user with multiple alternative learning objectives. The system receives the selection made by the user among the various alternative learning objectives, thereby determining the English learning objective selected by the user.

[0063] In an embodiment provided by the present application, based on the above implementation process, optionally, the first determination unit 101 includes:

[0064] A recommendation subunit, configured to recommend multiple alternative learning objectives to the user according to the user's historical learning performance, learning preferences, and learning progress.

[0065] A reception subunit, configured to receive the learning objective selected by the user among the various alternative learning objectives.

[0066] In this embodiment, the learning objective can be the specific learning purpose or task that the user hopes to achieve during the English learning process. These objectives can be short-term (such as mastering a specific grammar point, completing a certain reading comprehension exercise) or long-term (such as passing a certain level of English exam, improving the overall English level).

[0067] For example, when the system detects that the user is engaged in an English learning activity, it pops up a learning prompt window, asking the user what they want to learn today, and provides the following options: Option 1, review yesterday's content (based on the user's historical learning data); Option 2, learn a new grammar point (according to the course syllabus); Option 3, improve listening skills (based on the results of the user's most recent quiz). When the user selects Option 2, this objective is further refined by asking the user which part of the grammar they specifically want to learn, and the user selects "past perfect tense" as the learning objective.

[0068] Xiaoming selects "learn a new grammar point". At this time, the system further refines this objective by asking him which part of the grammar he specifically wants to learn. Xiaoming selects "past perfect tense".

[0069] In an embodiment provided by the present application, based on the above implementation process, optionally, the first determination unit 101 further includes:

[0070] A planning subunit, configured to receive available time resources and a learning outline set by a user, and generate a learning plan for the user according to the available time resources and the learning outline;

[0071] The time management subunit is used to determine the learning progress of the user according to the historical learning situation of the user, the learning plan and the current time.

[0072] In this embodiment, the planning subunit can generate a detailed study plan based on the available time resources and study outline set by the user. The user can enter the specific time period available for study every day or every week through the system interface, and the system can also automatically detect the user's free time through calendar integration. In addition, the user can upload or select a preset study outline, such as a textbook catalog or an exam outline. Based on this information, the planning subunit will generate a reasonable and personalized schedule to ensure that the time allocation of learning tasks is scientific and reasonable, avoiding excessive concentration or dispersion, thereby providing users with an efficient learning framework.

[0073] Optionally, the time management subunit can dynamically adjust and determine the user's learning progress based on the user's historical learning situation, current learning plan and current time. Collect and analyze the user's past learning data, including completed tasks, test scores and interactive exercise results, to evaluate the user's learning efficiency and weak links. Combined with the current learning plan, the time management subunit compares historical data, evaluates the rationality of the plan, and recommends adjustments when appropriate. The system monitors the current time in real time and flexibly adjusts learning tasks to ensure that users complete high-priority tasks within the appropriate time period, avoid learning interruptions caused by time conflicts, and dynamically adjust the learning progress to ensure that the learning burden is moderate.

[0074] For example, when a user logs into the system and starts a new learning session, the system automatically detects that the user is doing an English learning activity and pops up a prompt window to ask him what he wants to learn today. The user sets 7 to 9 pm every day as his English learning time and selects "CET-4 preparation" as the learning outline. Based on this information, the system generates a detailed study plan, including specific tasks for each day, such as grammar review, vocabulary accumulation, and mock tests. Subsequently, the system analyzes Xiao Ming's learning data for the past week and finds that the user is weak in the listening part, so it is recommended to increase the time for listening practice and arrange a complete mock test on the weekend. The system also reminds users that on the upcoming weekdays, due to other arrangements, they can shorten their study time in the evening and focus on efficient task completion.

[0075] The first output unit 102 is used to output the initial English learning content corresponding to the English learning goal.

[0076] In this embodiment, after determining the user's English learning goal, the first output unit 102 outputs the initial English learning content corresponding to the goal. This can ensure that the user can immediately obtain learning materials that match their learning goals, such as explanations of specific grammar points, exercises on relevant vocabulary, listening or reading materials, etc. Through precise content output, the first output unit not only provides the user with a clear starting point for learning, but also enhances the pertinence and efficiency of learning, enabling the user to enter an effective learning state in the shortest possible time.

[0077] The first detection unit 103 is configured to detect the user's learning environment information, physiological parameters, and emotional state analysis result; the emotional state analysis result represents the user's current anxiety level.

[0078] In this embodiment, detecting the user's learning environment information, physiological parameters, and emotional state analysis result, especially the emotional state used to represent the user's current anxiety level. Through multi-dimensional data collection and analysis, this unit can provide a scientific basis for subsequent personalized learning adjustments to ensure that the user learns in the best state.

[0079] In an embodiment provided by the present application, based on the above implementation process, optionally, the first detection unit 103 includes:

[0080] The environment detection subunit is configured to collect environmental noise, environmental light intensity, environmental temperature, and environmental humidity; and obtain the user's learning environment information based on the environmental noise, environmental light intensity, environmental temperature, and environmental humidity;

[0081] The emotion detection subunit is configured to identify the user's facial expressions and speech to obtain the emotional state analysis result of the user;

[0082] The physiological detection subunit is configured to collect at least one psychological parameter of the user's heart rate, skin conductivity, respiratory information, and electroencephalogram information.

[0083] In this embodiment, the environment detection subunit collects a variety of physical parameters related to the user's learning environment, including environmental noise, environmental light intensity, environmental temperature, and environmental humidity. Among them, too high or too low noise levels may affect the user's concentration. Appropriate lighting helps protect eyesight and improve reading comfort. Comfortable temperature and humidity conditions can improve learning efficiency and avoid distraction due to discomfort. By integrating these data, the environment detection subunit can evaluate whether the user's learning environment is suitable and provide optimization suggestions or automatically adjust the difficulty and type of learning content accordingly.

[0084] Optionally, the emotion detection sub-unit mainly obtains the analysis result of the user's emotional state by recognizing the user's expressions and speech, with particular attention to the user's anxiety level. Specifically, a camera can be used to capture the user's facial expressions, and machine learning algorithms can be combined to analyze their emotional changes. The user's speech can be recorded through a microphone, and features such as intonation and speech rate can be analyzed to judge their emotional state. This non-intrusive emotion monitoring method not only improves the convenience of data collection but also can reflect the user's psychological state in real time, helping the system to take corresponding support measures in a timely manner, such as providing relaxation techniques or adjusting the difficulty of learning tasks. In this embodiment, the analysis result of the user's emotional state can be obtained in an existing manner, which will not be elaborated here.

[0085] The first acquisition unit 104 is configured to acquire the learning result of the user for the initial English learning content.

[0086] In this embodiment, the first acquisition unit can evaluate the user's understanding and mastery of the initial English learning content. By collecting the user's learning behavior data, such as online quiz results, interactive exercise evaluations, and self-report questionnaires, the learning result of the user can be comprehensively obtained. This unit can not only quantify the user's knowledge mastery level but also identify the advantages and disadvantages in the learning process, providing a basis for subsequent personalized adjustment and feedback to ensure the continuous optimization of the learning path and the maximization of the learning effect.

[0087] In an embodiment provided by the present application, based on the above implementation process, optionally, the first acquisition unit includes:

[0088] The learning status detection sub-unit is configured to acquire the learning behavior data of the user for the initial English learning content, and obtain the user's online quiz result, interactive exercise evaluation result, and self-report questionnaire result according to the learning behavior data;

[0089] The evaluation sub-unit is configured to obtain the learning result of the user for the initial English learning content according to the user's online quiz result, interactive exercise evaluation result, and self-report questionnaire result.

[0090] In this embodiment, the learning status detection sub-unit can acquire the learning behavior data of the user for the initial English learning content, and obtain the user's online quiz result, interactive exercise evaluation result, and self-report questionnaire result according to these data. Specifically: the online quiz result includes the quiz results in the forms of multiple-choice questions, fill-in-the-blank questions, true or false questions, etc., quantifying the user's memory and understanding of knowledge points. The interactive exercise evaluation result includes the evaluation results of interactive forms such as listening exercises, speaking exercises, and writing exercises. The self-report questionnaire result can be obtained through the user's subjective feedback. The learning status detection sub-unit ensures the comprehensive collection of the user's learning behavior data from different angles through a variety of evaluation tools.

[0091] Optionally, the evaluation subunit may form a comprehensive data set based on summarizing the online quiz results, interactive exercise evaluation results, and self-report questionnaire results. Weights are assigned according to the importance of different types of evaluation results (such as quizzes, exercises, questionnaires). For example, quiz scores may account for 60%, interactive exercise evaluations for 30%, and self-report questionnaires for 10%. Based on the weights, a comprehensive learning score, that is, the learning outcome, is calculated.

[0092] A second determination unit 105, configured to determine a current target coefficient of the user according to the physiological parameter, the learning outcome, and the emotional state analysis result, where the target coefficient represents the degree of information processing burden borne by the user when performing an English learning activity.

[0093] In an embodiment provided by the present application, based on the above implementation process, optionally, the second determination unit, as Figure 2 shown, includes:

[0094] A calculation subunit 201, configured to calculate a physiological score of the user according to the physiological parameter;

[0095] A non-linear conversion subunit 202, configured to perform non-linear conversion on the physiological score, the learning outcome, and the emotional state analysis result;

[0096] An execution subunit 203, configured to input the non-linearly converted physiological score, learning outcome, and emotional state analysis result into a pre-constructed multi-layer perceptron to obtain the current target coefficient of the user.

[0097] In this embodiment, the calculation subunit may calculate a physiological score S of the user according to the physiological parameter of the user phys , specifically, the heart rate (HR), skin conductance (GSR), respiratory information (BR), and electroencephalogram information (EEG) of the user may be obtained from the physiological detection subunit. Using a preset algorithm or model, these physiological parameters are converted into a comprehensive physiological score S phys . For example, the following formula may be used:

[0098] S phys = w1*f(HR)+w2·f(GSR)+w3·f(BR)+w4·f(EEG)

[0099] where w1, w2, w3, w4 are the weights of each physiological parameter, and f(·) is a function for normalizing or standardizing each parameter to ensure comparison of different parameters under the same dimension.

[0100] In an embodiment provided by the present application, based on the above implementation process, optionally, the non-linear conversion subunit includes:

[0101] A calculation module, configured to perform weighted processing on the physiological score, the learning result, and the emotional state analysis result to obtain weighted terms corresponding to the physiological score, the learning result, and the emotional state analysis result respectively;

[0102] A non-linear conversion module, configured to perform non-linear conversion on each weighted term by using a non-linear conversion function corresponding to each weighted term.

[0103] In this embodiment, the calculation module performs weighted processing on the physiological score S phys , the learning result S learn , and the emotional state analysis result S emo respectively, and assigns different weights w phys , w learn , w emo according to their importance in evaluating the user information processing burden. For example:

[0104] S′ phys =w phys *S phys

[0105] S′ learn =w learn *S learn

[0106] S′ emo =w emo *S emo

[0107] Calculate the weighted value of each dimension to form the corresponding weighted terms S′ phys , S′ learn and S′ emo .

[0108] Optionally, a pre-set non-linear conversion function (such as the Sigmoid function or the ReLU function ReLU(x)=max(0, x)) can be used to perform non-linear conversion on each weighted term. For example:

[0109] S″ phys =σ(S′ phys )

[0110] S″ learn =σ(S′ learn )

[0111] S″ emo =σ(S′emo )

[0112] Optionally, the non - linear conversion functions corresponding to different weighting terms may be the same or different.

[0113] In this embodiment, the execution subunit may use the data after non - linear conversion as the input feature x = [S″ phys , S″ learn , S″ emo and send it to the multi - layer perceptron. The multi - layer perceptron performs complex calculations and analyses on the input data through its internal neural network structure, and outputs a comprehensive target coefficient T. Specifically, assuming that the output layer of the multi - layer perceptron uses a linear activation function, the target coefficient T can be expressed as:

[0114] T = MLP(x)=W L *σ(W L-1 *σ(...σ(W (1) *x + b (1) )...+b (L-1) )+b (L) )

[0115] where W i and b (i) are the weight matrix and bias vector of the i - th layer respectively, L is the number of network layers, and σ(·) is the activation function.

[0116] In this embodiment, the training process of the multi - layer perceptron may include: obtaining an initial multi - layer perceptron and a first training data set. The first training data set includes a plurality of first training data samples and the sample label corresponding to each first training sample. The first training sample includes historical physiological scores, historical learning results, and historical emotional state analysis results. The sample label may be the target coefficient determined by an expert system or a rule set. Using the first training samples in the first training data set to train the initial multi - layer perceptron until the number of training times of the initial multi - layer perceptron is greater than a preset number threshold, and determining the initial multi - layer perceptron as the trained multi - layer perceptron.

[0117] In this embodiment, the target coefficient T is a quantization index, indicating the degree of information processing burden borne by the user when learning English in the current state. A higher target coefficient means that the user may face a greater cognitive load and needs to appropriately adjust the learning content or take a break.

[0118] For example, the user is using the improved English teaching system. When the user completes a learning session on "past perfect tense", the system performs the following operations through the second determination unit 105:

[0119] The calculation subunit calculates the physiological score S based on the user's heart rate, skin conductivity, and respiration informationphys 。

[0120] The non - linear conversion sub - unit weights the physiological score S phys , the learning result S learn and the emotional state analysis result S emo for weighted processing.

[0121] Use the Sigmoid function to perform non - linear conversion on each weighted item to obtain S″ phys , S″ learn , S″ emo 。

[0122] Execution sub - unit: Input the data after non - linear conversion into a multi - layer perceptron to calculate the target coefficient T.

[0123] If the target coefficient T is high, the system will remind Xiaoming to rest or simplify the learning task to reduce his cognitive load; if T is low, the system may add some challenging exercises to improve the learning effect.

[0124] Execution unit 106 is used to input the learning environment information and the target coefficient into a pre - trained classification model to obtain the current classification result of the user, and the prediction result includes the learning material category of the English learning objective.

[0125] In this embodiment, the classification model can be a multi - layer perceptron model, a random forest model, a support vector machine model, etc.

[0126] Optionally, the model training process can be to obtain an initial model and a second training data set. The second training data set includes multiple second training samples and the classification label corresponding to each sample, the historical environment information and the target coefficient of the second training sample. Use the second training samples in the second training data set to train the initial model until the prediction accuracy of the initial model is greater than a preset accuracy threshold, and determine the initial model with the prediction accuracy greater than the accuracy threshold as the trained classification model.

[0127] In this embodiment, the learning material category can include one or more of basic listening materials, basic reading materials, basic writing materials, basic word materials, intermediate listening materials, intermediate reading materials, intermediate writing materials, intermediate word materials, advanced listening materials, advanced reading materials, advanced writing materials, and advanced word materials, etc.

[0128] The second output unit 107 is used to output the English learning materials corresponding to the learning material category.

[0129] In an embodiment provided by the present application, based on the above implementation process, optionally, the second output unit includes:

[0130] A matching subunit, configured to obtain English learning materials corresponding to the learning material category in a preset learning resource library;

[0131] An output subunit, configured to output English learning materials corresponding to the learning material category.

[0132] In an embodiment provided by the present application, based on the above implementation process, optionally, it further includes:

[0133] A second obtaining unit, configured to obtain the learning result of the user for the English learning materials, and return to trigger the second determining unit to re - execute the process of determining the current target coefficient of the user according to the physiological parameters, the learning result, and the emotional state analysis result.

[0134] In an embodiment provided by the present application, based on the above implementation process, optionally, it further includes:

[0135] A first detection unit, configured to detect whether the target coefficient is greater than a preset target coefficient threshold;

[0136] A third output unit, configured to output a prompt message when the continuous number of times that the detected target coefficient is greater than the target coefficient threshold reaches a preset number threshold; the prompt message is used to prompt the user to take a rest.

[0137] This system integrates multi - dimensional data (including physiological parameters, learning results, emotional states, and environmental information), uses an advanced machine learning model to dynamically evaluate the user's learning burden, and provides personalized recommendations for the most suitable learning materials. It not only improves learning efficiency and user experience, but also ensures that users can learn efficiently in the most suitable state, truly realizing intelligent education with individualized teaching and situation adaptation.

[0138] An embodiment of the present application further provides an English teaching method based on artificial intelligence. This method can be applied to an electronic device, and the method flow chart of this method is as Figure 3 shown, and specifically includes:

[0139] S301: When it is detected that the user is engaged in an English learning activity, determine the English learning goal selected by the user.

[0140] S302: Output the initial English learning content corresponding to the English learning goal.

[0141] S303: Detect the user's learning environment information, physiological parameters, and emotional state analysis result; the emotional state analysis result represents the current anxiety level of the user.

[0142] S304: Obtain the learning result of the user for the initial English learning content.

[0143] S305: Determine the current target coefficient of the user according to the physiological parameter, the learning result, and the analysis result of the emotional state, where the target coefficient represents the degree of information processing burden borne by the user when performing English learning activities.

[0144] S306: Input the learning environment information and the target coefficient into a pre-trained classification model to obtain the current classification result of the user, where the prediction result includes the learning material category of the English learning target.

[0145] S307: Output the English learning materials corresponding to the learning material category.

[0146] In an embodiment provided by the present application, based on the above solution, optionally, the process of determining the current target coefficient of the user according to the physiological parameter, the learning result, and the analysis result of the emotional state is as Figure 4 shown and includes:

[0147] S401: Calculate the physiological score of the user according to the physiological parameter.

[0148] S402: Perform a non-linear transformation on the physiological score, the learning result, and the analysis result of the emotional state.

[0149] S403: Input the non-linearly transformed physiological score, learning result, and analysis result of the emotional state into a pre-constructed multi-layer perceptron to obtain the current target coefficient of the user.

[0150] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations.

[0151] For the convenience of description, when describing the above device, it is divided into various units according to functions for separate description. Of course, when implementing the present application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0152] From the description of the above embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions for causing a computer device (which can be a personal computer, server, etc.) to execute the system described in various embodiments or some parts of the embodiments of this application.

[0153] The above has introduced in detail a kind of English teaching system based on artificial intelligence provided by this application. Specific examples are used in this article to elaborate on the principle and implementation manner of this application. The description of the above embodiments is only used to help understand the system of this application and its core idea; at the same time, for those of ordinary skill in the art, according to the idea of this application, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation to this application.

Claims

1. An English teaching system based on artificial intelligence, characterized in that: include: A first determining unit, configured to determine an English learning goal selected by the user when detecting that the user is performing an English learning activity; A first output unit, configured to output initial English learning content corresponding to the English learning goal; A first detection unit, used to detect the user's learning environment information, physiological parameters and emotional state analysis results; The emotional state analysis result represents the current anxiety level of the user; A first acquisition unit, configured to acquire the learning result of the user on the initial English learning content; A second determining unit, configured to determine a current target coefficient of the user according to the physiological parameter, the learning result and the emotional state analysis result, wherein the target coefficient represents a degree of information processing burden borne by the user when performing English learning activities; An execution unit, configured to input the learning environment information and the target coefficient into a pre-trained classification model to obtain the current classification result of the user, wherein the classification result includes the learning material category of the English learning target; A second output unit, used for outputting English learning materials corresponding to the learning material category; The second determining unit includes: A calculation subunit, configured to calculate a physiological score of the user according to the physiological parameters; A non-linear conversion subunit, used for performing non-linear conversion on the physiological score, the learning result and the emotional state analysis result; An execution subunit, used for inputting the physiological score, learning result and emotional state analysis result after nonlinear conversion into a pre-built multi-layer perceptron to obtain the current target coefficient of the user; The nonlinear conversion subunit comprises: a calculation module, configured to perform weighted processing on the physiological score, the learning result, and the emotional state analysis result to obtain weighted items corresponding to the physiological score, the learning result, and the emotional state analysis result, respectively; The nonlinear conversion module is used to perform nonlinear conversion on each of the weighted items using a nonlinear conversion function corresponding to each of the weighted items.

2. The system according to claim 1, characterized in that The first detection unit comprises: The environment detection subunit is used to collect environmental noise, environmental light intensity, environmental temperature and environmental humidity; and obtain the learning environment information of the user according to the environmental noise, environmental light intensity, environmental temperature and environmental humidity; The emotion detection subunit is used to recognize the user's expression and voice to obtain the user's emotional state analysis result; The physiological detection subunit is used to collect at least one psychological parameter of the user's heart rate, skin conductivity, breathing information, and EEG information.

3. The system according to claim 1, characterized in that The first determining unit includes: A recommendation subunit, configured to recommend a plurality of candidate learning objectives to the user according to the user's historical learning performance, learning preferences, and learning progress; The receiving subunit is used to receive a learning goal selected by a user from among the candidate learning goals.

4. The system according to claim 1, characterized in that The first determining unit further includes: A planning subunit, configured to receive available time resources and a learning outline set by a user, and generate a learning plan for the user according to the available time resources and the learning outline; The time management subunit is used to determine the learning progress of the user according to the historical learning situation of the user, the learning plan and the current time.

5. The system according to claim 1, characterized in that The first acquisition unit includes: A learning status detection subunit, used to obtain the user's learning behavior data on the initial English learning content, and obtain the user's online test results, interactive exercise evaluation results, and self-report questionnaire results based on the learning behavior data; The evaluation subunit is used to obtain the user's learning results of the initial English learning content according to the user's online test results, interactive exercise evaluation results and self-report questionnaire results.

6. The system according to claim 1, characterized in that The second output unit comprises: A matching subunit, used for acquiring English learning materials corresponding to the learning material category in a preset learning resource library; The output subunit is used to output the English learning materials corresponding to the learning material category.

7. The system according to claim 1, characterized in that Also includes: The second acquisition unit is used to obtain the user's learning results of the English learning material, and return to trigger the second determination unit to re-execute the process of determining the user's current target coefficient based on the physiological parameters, the learning results and the emotional state analysis results.

8. The system according to claim 7, characterized in that Also includes: A first detection unit, used to detect whether the target coefficient is greater than a preset target coefficient threshold; A third output unit is used to output prompt information when it is detected that the target coefficient is greater than the target coefficient threshold for a continuous number of times reaching a preset number threshold; The prompt information is used to prompt the user to take a rest.

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