Interactive word learning method and system

Abstract

The present invention discloses an interactive word learning method and system, which belongs to the field of artificial intelligence assisted teaching technology, and aims to solve the problem that the word memorization method in the related art lacks interactivity with the learner, resulting in a decrease in learning enthusiasm and affecting the learning effect; the method comprises the following steps: controlling a display screen to display a word interactive learning interface, the word interactive learning interface comprising a first display area, an interactive control and a switching control; the first display area is used to display at least one word in a first word group; receiving question information input by a first user through the interactive control, and calling a preset large language model to generate an answer corresponding to the question information; in response to triggering the switching control, the word interactive learning interface displays at least one word in the first word group that has not yet been learned. The present invention can enhance the interactivity, fun and personalization of learning, so that users are more proactive and engaged in the learning process, thereby improving the learning effect.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence assisted teaching technology, and in particular to an interactive word learning method and system. Background Art

[0002] Vocabulary accumulation is a crucial step in language learning. Mastering a broad vocabulary not only improves reading, writing, listening, and speaking skills but also forms the foundation for understanding and using the language. However, traditional vocabulary memorization methods often rely on rote memorization and repetitive testing, lacking learner interaction. This single-minded approach often leaves learners feeling tedious, leading to a decrease in learning motivation and, consequently, poor learning outcomes.

[0003] The method of using mobile applications to memorize words in related technologies has a certain degree of interactivity in the learning process, but it is still insufficient and may make learners feel boring, resulting in a decrease in learning enthusiasm, which in turn affects the learning effect.

[0004] The disclosure of the above background technology content is only used to assist in understanding the concept and technical solution of the present invention. It does not necessarily belong to the prior art of this patent application. In the absence of clear evidence that the above content has been disclosed on the filing date of this patent application, the above background technology should not be used to evaluate the novelty and creativity of this application. Summary of the Invention

[0005] The present application provides an interactive word learning method and system, aiming to solve the problem that the word memorization method in the related art lacks interactivity with learners, resulting in a decrease in learning enthusiasm and affecting learning results.

[0006] To achieve the above objectives, the present application discloses the following technical solutions:

[0007] In a first aspect, an embodiment of the present application provides an interactive word learning method, which is applied to an electronic device, the electronic device being configured with a display screen, the method comprising:

[0008] Controlling the display screen to display a word interactive learning interface, the word interactive learning interface comprising a first display area, an interactive control, and a switch control; the first display area is used to display at least one word in the first word group;

[0009] receiving a question input by a first user through an interactive control, and calling a preset large language model to generate an answer corresponding to the question;

[0010] In response to triggering of the switch control, the word interactive learning interface displays at least one word in the first word group that has not been learned.

[0011] In the embodiment of the present application, through interactive controls and a preset large language model, users can have a conversation with the large language model to obtain detailed word content, clever memorization methods, and personalized questions. In addition, the large language model can also generate challenging questions for testing based on the words learned by the user and the context of the conversation. In this way, the interactivity, fun, and personalization of learning can be effectively enhanced, making users more proactive and engaged in the learning process, thereby improving learning outcomes.

[0012] Furthermore, by using a large language model for content generation, there is no need to rely on a large vocabulary database. This not only simplifies system design and reduces maintenance costs, but also makes the system more flexible and easy to expand.

[0013] In some possible implementations of the first aspect, the word interactive learning interface further includes a second display area; the second display area is used to display a preset question information set; the question information set includes the first question information;

[0014] In response to the first user's first operation on the first question, the interactive control obtains the first user's question and invokes the preset large language model to generate an answer corresponding to the question. In this way, the user only needs to operate on the preset question to trigger the interactive control to obtain the question and generate an answer, simplifying the user's operation steps and improving the convenience and efficiency of learning.

[0015] In some possible implementations of the first aspect, before controlling the display screen to display the word interactive learning interface, the method further includes:

[0016] Controlling the display screen to display an information collection interface, the information collection interface including input controls;

[0017] receiving basic information and learning needs input by a first user through an input control; the basic information includes language level, age stage, and learning habits; the learning needs include learning objectives and selected vocabulary books;

[0018] A first prompt word is generated according to the basic information and learning needs of the first user; and a preset large language model generates an answer corresponding to the question information based on the first prompt word.

[0019] In this way, the large language model can adjust the content and difficulty of generated questions according to the user's basic information and learning needs, and generate personalized learning content, avoiding the problems of single content and inadaptability to individual differences in traditional methods, reducing the user's learning difficulty, and improving the user's learning experience and motivation.

[0020] In some possible implementations of the first aspect, the information collection interface further includes a first navigation control;

[0021] In response to triggering of the first navigation control, controlling the display screen to display a main interface; the main interface includes a second navigation control;

[0022] In response to triggering of the second navigation control, unlearned words and / or words to be reviewed are selected from a vocabulary database in a preset proportion based on the basic information and learning needs of the first user to generate a first word group; and the display screen is controlled to display a display interface for displaying words to be memorized; the display interface for displaying words to be memorized includes a first display area, a plurality of delete controls, and a third navigation control, the first display area being used to display the first word group; the plurality of delete controls being provided in a one-to-one correspondence with a plurality of words in the first word group;

[0023] In response to a first operation of a first user on a certain word, displaying a preset translation corresponding to the word and a deletion control;

[0024] In response to triggering of the delete control, the word corresponding to the delete control is deleted, the proficiency status of the deleted word is marked, and a new word is extracted from the vocabulary database and displayed;

[0025] In response to the triggering of the third navigation control, the display screen is controlled to display the word interactive learning interface. In this way, users can dynamically adjust the learning content, mark the words they have mastered, and update new words in real time, so that the learning content always remains fresh and improves the continuity of learning.

[0026] In some possible implementations of the first aspect, in response to triggering a switch control corresponding to the last word learned in the first word group, calling a preset large language model to generate a first short article including each word in the first word group, and controlling the display screen to display a word stringing interface; the word stringing interface includes a third display area, and the third display area is used to display the first short article;

[0027] In response to a first user's second operation on a first word in a first word group in a first passage, the display screen is controlled to display a word interactive learning interface corresponding to the first word. This allows users to comprehensively apply learned words in a more realistic context, improving their ability to apply the words in practice. Furthermore, operations on specific words in a passage can trigger the corresponding interactive learning interface, repeatedly reinforcing memory and helping users retain the words more deeply.

[0028] In some possible implementations of the first aspect, the word-to-text interface further includes a first control;

[0029] In response to the triggering of the first control, based on the interaction results between the first user and the preset large language model, a preset machine learning model is used to generate the next review time and proficiency of each word in the first word group, and the results are recorded in the vocabulary database. In this way, the machine learning model can analyze the user data, fit the forgetting curve of each user, and dynamically adjust the review plan. In this way, the review plan can be tailored to each person, in line with individual differences, more scientific, and can effectively avoid forgetting, optimize memory effects, and improve review efficiency.

[0030] In a second aspect, an embodiment of the present application provides an interactive word learning system, which is applied to an electronic device, wherein the electronic device is equipped with a display screen, and the system includes:

[0031] A first control module is configured to control the display screen to display a word interactive learning interface, the word interactive learning interface comprising a first display area, an interactive control, and a switch control; the first display area is configured to display at least one word in the first word group;

[0032] A first interaction module is configured to receive a question input by a first user through an interactive control, and to invoke a preset large language model to generate an answer corresponding to the question;

[0033] The first switching module is configured to, in response to triggering of the switching control, enable the word interactive learning interface to display at least one word in the first word group that has not been learned.

[0034] In some possible implementations of the second aspect, the word interactive learning interface also includes a second display area; the second display area is used to display a preset question information set; the question information set includes a first question information; the first interaction module is specifically used to: respond to the first user's first operation on the first question information, enable the interactive control to obtain the first user's question information, and call the preset large language model to generate an answer corresponding to the question information.

[0035] In some possible implementations of the second aspect, the first control module is specifically used to: before controlling the display screen to display the word interactive learning interface, control the display screen to display the information collection interface, the information collection interface including an input control; receive basic information and learning needs input by the first user through the input control; the basic information includes language level, age stage and learning habits; the learning needs include learning objectives and selected word books; generate a first prompt word based on the basic information and learning needs of the first user; and the preset large language model generates an answer corresponding to the question information based on the first prompt word.

[0036] In some possible implementations of the second aspect, the information collection interface also includes a first navigation control; the first control module is specifically further used to: in response to the triggering of the first navigation control, control the display screen to display the main interface; the main interface includes a second navigation control; in response to the triggering of the second navigation control, select unlearned words and / or words to be reviewed from the vocabulary database in a preset proportion according to the basic information and learning needs of the first user to generate a first word group; and control the display screen to display a word display interface to be memorized; the word display interface to be memorized includes a first display area and multiple deletion controls and a third navigation control, the first display area is used to display the first word group; the multiple deletion controls are set in a one-to-one correspondence with multiple words in the first word group; in response to the first user's first operation on a certain word, display the preset translation and deletion control corresponding to the word; in response to the triggering of the deletion control, delete the word corresponding to the deletion control, mark the proficiency status of the deleted word, and extract and display a new word from the vocabulary database; in response to the triggering of the third navigation control, control the display screen to display a word interactive learning interface.

[0037] In some possible implementations of the second aspect, the first control module is specifically further used to: in response to the triggering of the switch control corresponding to the last learned word in the first word group, call the preset large language model to generate a first short article containing each word in the first word group, and control the display screen to display the word stringing interface; the word stringing interface includes a third display area, and the third display area is used to display the first short article; in response to the first user's second operation on a first word belonging to the first word group in the first short article, control the display screen to display the word interactive learning interface corresponding to the first word.

[0038] In some possible implementations of the second aspect, the word-to-text interface also includes a first control; the first control module is specifically used to: in response to the triggering of the first control, based on the interaction results between the first user and the preset large language model, use a preset machine learning model to generate the next review time and proficiency of each word in the first word group, and record them in the vocabulary database.

[0039] In a third aspect, an embodiment of the present application provides an electronic device comprising one or more processors; a storage device storing one or more programs; and one or more display screens, wherein the storage device, the processor, and the display screen are coupled; when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any technical solution of the first aspect.

[0040] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method described in any technical solution of the first aspect is implemented.

[0041] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the method described in any technical solution of the first aspect.

[0042] Among them, the technical effects brought about by any design method in the second to fifth aspects can refer to the technical effects brought about by different design methods in the first aspect, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the embodiments or the description of the prior art. Obviously, the drawings described below are merely exemplary, and those skilled in the art can derive other implementation drawings based on the provided drawings without inventive effort.

[0044] Figure 1 A schematic diagram of the process of an interactive word learning method provided in some embodiments of the present application Figure 1 ;

[0045] Figure 2 A schematic diagram of the main interface provided in some embodiments of the present application;

[0046] Figure 3 A schematic diagram of the process of an interactive word learning method provided in some embodiments of the present application Figure 2 ;

[0047] Figure 4 A schematic diagram of a word-to-be-remembered display interface provided in some embodiments of the present application;

[0048] Figure 5 A schematic diagram of the process of an interactive word learning method provided in some embodiments of the present application Figure 3 ;

[0049] Figure 6 Schematic diagram of the interactive learning interface provided in some embodiments of the present application Figure 1 ;

[0050] Figure 7 Schematic diagram of the interactive learning interface provided in some embodiments of the present application Figure 2 ;

[0051] Figure 8 A schematic diagram of the process of an interactive word learning method provided in some embodiments of the present application Figure 4 ;

[0052] Figure 9A schematic diagram of a word-to-text interface provided in some embodiments of the present application;

[0053] Figure 10 A schematic diagram of the structure of an interactive word learning system provided in some embodiments of the present application;

[0054] Figure 11 It is a schematic structural diagram of an electronic device suitable for implementing some embodiments of the present application. DETAILED DESCRIPTION

[0055] Specific embodiments of the present invention will now be mentioned in detail. Although the present invention is described in conjunction with these specific embodiments, it should be appreciated that the present invention is not intended to be limited to these specific embodiments. On the contrary, these embodiments are intended to cover substitutions, changes, or equivalent embodiments that may be included within the spirit and scope of the invention defined by the claims. In the following description, a large number of specific details are set forth in order to provide a comprehensive understanding of the present invention. The present invention may be implemented without some or all of these specific details.

[0056] When used in conjunction with "including," "methods comprising," or similar language in this specification and the appended claims, the singular forms "a," "an," and "the" include plural references unless the context clearly dictates otherwise. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0057] Vocabulary accumulation is a crucial step in language learning. Mastering a broad vocabulary not only improves reading, writing, listening, and speaking skills but also forms the foundation for understanding and using the language. However, traditional vocabulary memorization methods often rely on rote memorization and repetitive testing, lacking learner interaction. This single-minded approach often leaves learners feeling tedious, leading to a decrease in learning motivation and, consequently, poor learning outcomes.

[0058] The existing method of memorizing words using mobile applications has a certain degree of interactivity in the learning process, but it is still insufficient and can make learners feel boring, resulting in a decrease in learning enthusiasm, which in turn affects learning outcomes.

[0059] In response to the above technical problems, the overall idea of ​​the technical solution provided by this application is as follows: An embodiment of this application provides an interactive word learning method, which is applied to an electronic device, and the electronic device is equipped with a display screen. The method includes: controlling the display screen to display a word interactive learning interface, the word interactive learning interface includes a first display area, an interactive control and a switching control; the first display area is used to display at least one word in the first word group; receiving question information input by the first user through the interactive control, and calling a preset large language model to generate an answer corresponding to the question information; in response to the triggering of the switching control, the word interactive learning interface displays at least one word in the first word group that has not yet been learned.

[0060] This allows users to engage in conversations with the large language model through interactive controls and a pre-set large language model, obtaining detailed vocabulary information, clever memorization techniques, and personalized questions. Furthermore, the large language model can generate challenging questions for users to test based on the vocabulary they've learned and the context of the conversation. This effectively enhances the interactivity, fun, and personalization of learning, making users more proactive and engaged in the learning process, and improving learning outcomes.

[0061] Furthermore, by using a large language model for content generation, there is no need to rely on a large vocabulary database. This not only simplifies system design and reduces maintenance costs, but also makes the system more flexible and easy to expand.

[0062] After introducing the basic principles of the present application, various non-limiting implementation methods of the present application will be specifically introduced in conjunction with the drawings in the specification.

[0063] See also Figure 1 and Figure 2 The embodiment of the present application provides an interactive word learning method, which is applied to an electronic device, wherein the electronic device is equipped with a display screen, and the method comprises the following steps:

[0064] S101: Controlling the display screen to display an information collection interface, where the information collection interface includes an input control and a first navigation control;

[0065] S102: receiving basic information and learning needs input by a first user through an input control; the basic information includes language level, age stage, and learning habits; the learning needs include learning objectives and selected vocabulary books;

[0066] S103: In response to the triggering of the first navigation control, the display screen is controlled to display the main interface; the main interface includes the second navigation control ( Figure 2 Start memorizing words in the middle).

[0067] Specifically, the first navigation control may be triggered by a user single-clicking, double-clicking, or swiping upward.

[0068] It can be understood that the main interface can be used to display the main information of the word book selected by the first user, including the name of the word book, the total number of words, the number of mastered words, the number of words to be reviewed, the number of reviewed words, the number of words to be learned today, and the number of words learned today.

[0069] See also Figure 3 and combined Figure 4 , the interactive word learning method further comprises the following steps:

[0070] S104: In response to the triggering of the second navigation control, select unlearned words and / or words to be reviewed from the vocabulary database according to the basic information and learning needs of the first user in a preset proportion to generate a first word group; and control the display screen to display a display interface for displaying words to be memorized; the display interface for displaying words to be memorized includes a first display area and a plurality of delete controls ( Figure 4 ) and the third navigation control ( Figure 4 In the next step), the first display area is used to display the first word group; multiple deletion controls are set in a one-to-one correspondence with multiple words in the first word group;

[0071] Of course, the present application is not limited to this. In other embodiments, learning process settings can also be automatically generated based on the basic information and learning needs of the first user. Learning process settings include but are not limited to the number of words in each group of learning, whether to display word definitions by default, whether to automatically read dialogues, and the default language of dialogues. In this way, the need for manual intervention is reduced, allowing the system to adapt to the needs and changes of different users more quickly.

[0072] S105: In response to a first user's first operation on a word, displaying a preset translation corresponding to the word and a delete control;

[0073] S106: In response to the triggering of the delete control, the word corresponding to the delete control is deleted, the proficiency status of the deleted word is marked, and a new word is extracted from the vocabulary database and displayed;

[0074] In this way, users can dynamically adjust learning content, mark words they have mastered, and update new words in real time, so that the learning content always remains fresh and the continuity of learning is improved.

[0075] See also Figure 5 Combined with Figure 6 , the interactive word learning method further comprises the following steps:

[0076] S107: In response to the triggering of the third navigation control, the display screen is controlled to display the word interactive learning interface; the word interactive learning interface includes a first display area, an interactive control ( Figure 6 ) and toggle controls ( Figure 6 The first display area is used to display at least one word in the first word group.

[0077] Exemplarily, the first display area displays a word and its definition, and the user can click on the definition to switch the display and hiding status of the definition.

[0078] S108: Receive question information input by the first user through the interactive control, and call a preset large language model to generate an answer corresponding to the question information.

[0079] This allows users to engage in conversations with the model, gaining detailed information about the word, clever memorization techniques, practice with scenario-based conversations, and ask other personalized questions. Users can directly interact with the large language model, asking specific questions about the word, and the model will generate answers and explanations based on the user's question. This approach not only enhances the interactivity of learning but also provides users with a more personalized learning experience.

[0080] Furthermore, content generation no longer relies on massive databases: users simply input a single word, and the large language model automatically generates the required learning content, including example sentences, synonyms, antonyms, and word meaning explanations. Thanks to its powerful natural language processing capabilities, the large language model can generate rich learning content based on individual words without relying on a massive vocabulary database. This approach significantly increases the system's flexibility and content diversity.

[0081] For details, please refer to Figure 7 In some embodiments, the word interactive learning interface further includes a second display area ( Figure 7 The second display area is used to display a preset question information set; the question information set includes a first question information; the electronic device can receive the question information input by the first user through the interactive control by the following steps:

[0082] In response to the first user's first operation on the first question information, the interactive control obtains the first user's question information. In this way, the user only needs to operate on the preset question information to trigger the interactive control to obtain the question information and generate an answer, which simplifies the user's operation steps and improves the convenience and efficiency of learning. The first question information can specifically provide a detailed analysis of the word (definition, examples, synonym analysis, root affix etymology, etc.), provide clever ways to memorize the word, provide usage scenarios for the word (through simulated scenario dialogue, etc.), and other questions related to the current word.

[0083] Of course, the application is not limited to this. In other embodiments, the user can also manually input information (e.g., keyboard input, voice input) through the interactive control.

[0084] Furthermore, in some embodiments, the electronic device may call a preset large language model to generate an answer corresponding to the question information through the following steps:

[0085] The first step is to generate a first prompt word based on the basic information and learning needs of the first user;

[0086] In the second step, the preset large language model generates an answer corresponding to the question information based on the first prompt word. In this way, the large language model can adjust the content and difficulty of the generated questions according to the user's basic information and learning needs, generate personalized learning content, and avoid the problems of single content and incompatibility with individual differences in traditional methods, reduce the user's learning difficulty, and improve the user's learning experience and learning motivation. Of course, the present application is not limited to this. In other embodiments, the preset large language model can also generate answers corresponding to the question information based on historical interaction data, thereby adjusting the content and difficulty of the generated questions.

[0087] More specifically, the electronic device can use a pre-set large language model to generate responses to questions in the following manner: When a user asks a custom question, the electronic device first analyzes the question to determine whether it is relevant to language learning. If so, the large language model is used to generate a corresponding answer or explanation based on the user's input.

[0088] In some embodiments, the interactive control can display the conversation between the user and the large language model in the form of message bubbles. The user can long press a message bubble to pop up a secondary menu and choose to translate, read aloud or copy the content of the message.

[0089] Building on the above-mentioned examples, the word memorization process can also incorporate training in listening, speaking, reading, and writing. By engaging multiple senses, such as hearing, vision, and oral language, this improves memory effectiveness and enhances the fun and interactivity of learning. Furthermore, through AI-powered scene simulations, it creates an immersive experience, thereby activating sensory nerves to achieve a memorization effect.

[0090] Specifically, visual training allows users to listen to audio recordings of words and conversations, view the words, their definitions, and the conversations, and practice speaking. Auditory training is achieved by playing pronunciation files of words and converting the responses of the large language model into speech using text-to-speech technology. Visual training involves displaying text of words, their definitions, and the conversations. Oral training is achieved through speech recognition technology, allowing users to record their voices to interact with the large language model and play back their own pronunciations.

[0091] S109: In response to the triggering of the switching control, the word interactive learning interface displays at least one word in the first word group that has not been learned.

[0092] See also Figure 8 Combined with Figure 9 , the interactive word learning method further comprises the following steps:

[0093] S110: In response to the triggering of the switch control corresponding to the last word learned in the first word group, the preset large language model is called to generate a first short article containing each word in the first word group, and the display screen is controlled to display the word stringing interface; the word stringing interface includes a third display area and a first control ( Figure 9 The third display area is used to display the first short text.

[0094] For example, the words in the first short text that belong to the first word group may be highlighted so that the user can recognize the words that have been learned.

[0095] Preferably, in some embodiments, the preset large language model can also generate a first short article containing each word in the first word group based on the aforementioned first prompt word. In this way, the reading difficulty of the first short article can be reduced, ensuring the user's learning effect.

[0096] S111: In response to a first user performing a second operation on a first word in a first word group in a first short text, controlling a display screen to display a word interactive learning interface corresponding to the first word. The first word is any word in the first word group. Specifically, the second operation can be designed with reference to the first operation and will not be further described herein.

[0097] This allows users to apply the words they've learned in a more realistic context, improving their ability to apply them in practice. Furthermore, interacting with specific words in a passage triggers the corresponding interactive learning interface, reinforcing memory and helping users retain the words more deeply.

[0098] S112: In response to the triggering of the first control, based on the interaction results between the first user and the preset large language model, a preset machine learning model is used to generate the next review time and proficiency of each word in the first word group, and recorded in the vocabulary database.

[0099] In this way, machine learning models can analyze user data, fit each user's forgetting curve, and dynamically adjust the review plan. This allows the review plan to be tailored to individual differences, making it more scientific, effectively preventing forgetting, optimizing memory effects, and improving review efficiency.

[0100] For example, in some embodiments, the following method can be used to generate the next review time and proficiency of each word in the first word group based on the interaction result between the first user and the preset large language model using a preset machine learning model:

[0101] Input data is collected from the user's learning behavior, including learning and memorization time, number of historical reviews of the same vocabulary and timestamps, number of questions asked in word interactive learning, number of answers and accuracy in word interactive learning, and number of times the user clicks on the highlighted word that has not been remembered to return to the word interactive learning page. These data will be input into the BP neural network (with parameters initialized according to the Ebbinghaus forgetting curve) to predict the user's proficiency in each word as output.

[0102] Based on the proficiency calculated by the model, the system will determine the probability of extracting review. The lower the proficiency, the higher the probability of extracting review, and vice versa.

[0103] During actual review, the system will determine whether the review time is too early or too late based on the user's actions. Specifically, if the user's memory of the word is good at the predicted review time, the system will determine that the review time may be too early; if the user's memory of the word is vague at the predicted review time, the system will determine that the review time is accurate; if the user's memory of the word is poor at the predicted review time, the system will determine that the review time may be too late.

[0104] Whether the user has a good memory is judged through interaction. If the user stays on the interactive learning page of the word for less than 10 seconds and does not ask any questions, the user is considered to have a good memory of the word. If the user stays on the interactive learning page of the word for more than 10 seconds and less than 1 minute, and asks questions less than or equal to once, the user is considered to have a vague memory of the word. In other cases, the user's memory of the word is considered to be poor. Based on this judgment, the reward function is set through reinforcement learning to adjust the review strategy, so that the system can gradually adjust and optimize to obtain a proficiency evaluation mechanism that is more suitable for different users, thereby reflecting personalized and scientific review time and frequency, ensuring that future review times are more in line with the user's memory patterns, thereby improving learning effects and reducing forgetting.

[0105] See also Figure 10 Based on the inventive concept of an interactive word learning method in the aforementioned embodiment, the embodiment of the present application provides an interactive word learning system, which is applied to an electronic device, the electronic device being equipped with a display screen, and the system comprising:

[0106] The first control module 201 is used to control the display screen to display a word interactive learning interface, which includes a first display area, an interactive control, and a switch control; the first display area is used to display at least one word in the first word group;

[0107] The first interaction module 202 is configured to receive a question input by a first user through an interactive control, and invoke a preset large language model to generate an answer corresponding to the question;

[0108] The first switching module 203 is configured to, in response to triggering of the switching control, cause the word interactive learning interface to display at least one word in the first word group that has not yet been learned.

[0109] In some embodiments, the word interactive learning interface also includes a second display area; the second display area is used to display a preset question information set; the question information set includes a first question information; the first interaction module 202 is specifically used to: respond to the first user's first operation on the first question information, so that the interactive control obtains the first user's question information, and calls the preset large language model to generate an answer corresponding to the question information.

[0110] In some embodiments, the first control module 201 is specifically used to: before controlling the display screen to display the word interactive learning interface, control the display screen to display the information collection interface, the information collection interface including input controls; receive basic information and learning needs input by the first user through the input controls; the basic information includes language level, age stage and learning habits; the learning needs include learning objectives and selected word books; generate a first prompt word based on the basic information and learning needs of the first user; and the preset large language model generates an answer corresponding to the question information based on the first prompt word.

[0111] In some embodiments, the information collection interface also includes a first navigation control; the first control module 201 is specifically used to: in response to the triggering of the first navigation control, control the display screen to display the main interface; the main interface includes a second navigation control; in response to the triggering of the second navigation control, select unlearned words and / or words to be reviewed from the vocabulary database in a preset proportion according to the basic information and learning needs of the first user to generate a first word group; and control the display screen to display the to-be-memorized word display interface; the to-be-memorized word display interface includes a first display area and multiple deletion controls and a third navigation control, the first display area is used to display the first word group; the multiple deletion controls are set in a one-to-one correspondence with the multiple words in the first word group; in response to the first user's first operation on a certain word, display the preset translation and deletion control corresponding to the word; in response to the triggering of the deletion control, delete the word corresponding to the deletion control, mark the proficiency status of the deleted word, and extract and display a new word from the vocabulary database; in response to the triggering of the third navigation control, control the display screen to display the word interactive learning interface.

[0112] In some embodiments, the first control module 201 is specifically further used to: in response to the triggering of the switch control corresponding to the last learned word in the first word group, call the preset large language model to generate a first short article containing each word in the first word group, and control the display screen to display the word stringing interface; the word stringing interface includes a third display area, and the third display area is used to display the first short article; in response to the first user's second operation on a first word belonging to the first word group in the first short article, control the display screen to display the word interactive learning interface corresponding to the first word.

[0113] In some embodiments, the word stringing interface also includes a first control; the first control module 201 is specifically used to: in response to the triggering of the first control, based on the interaction results between the first user and the preset large language model, use the preset machine learning model to generate the next review time and proficiency of each word in the first word group, and record it in the vocabulary database.

[0114] It is understandable that the modules and references recorded in this interactive word learning system Figure 1 Therefore, the operations, features and beneficial effects described above for the method are also applicable to the interactive word learning system and the modules contained therein, and will not be described in detail here.

[0115] See also Figure 11 , based on the inventive concept of an interactive word learning method in the aforementioned embodiment, an embodiment of the present application provides an electronic device. The electronic device may include but is not limited to mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), etc., and fixed terminals such as digital TVs, desktop computers, etc. The electronic device includes a processing device 301 (such as a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to the program stored in ROM 302 (read-only memory) or the program loaded from storage device 308 into RAM 303 (random access memory). In RAM 303, various programs and data required for the operation of the electronic device are also stored. The processing device 301, ROM 302, and RAM 303 are connected to each other via a bus 304. An input / output interface (i.e., an I / O interface 305) is also connected to the bus 304.

[0116] Typically, the following devices may be connected to the I / O interface 305: an input device 306 including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 307 including, for example, a display screen, a speaker, a vibrator, etc.; a storage device 308 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 309. The communication device 309 may allow the electronic device to communicate with other devices wirelessly or by wire to exchange data.

[0117] In particular, according to some embodiments of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, some embodiments of the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes program code for executing the method shown in the flowchart. In some such embodiments, the computer program can be downloaded and installed from the network via the communication device 309, or installed from the storage device 308, or installed from the ROM 302. When the computer program is executed by the processing device 301, the above-mentioned functions defined in the method of some embodiments of the present application are performed.

[0118] It should be noted that the computer-readable medium described in some embodiments of the present application may be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. The computer-readable storage medium may be an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or component, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In some embodiments of the present application, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device, or component. In some embodiments of the present application, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to wires, optical cables, RF (radio frequency), etc., or any suitable combination thereof.

[0119] In some embodiments, the client and server can communicate using any currently known or future developed network protocol, such as HTTP (HyperText Transfer Protocol), and can be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0120] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device. The computer-readable medium carries one or more programs, and when the one or more programs are executed by the electronic device, the method steps of any of the above technical solutions may be implemented.

[0121] Computer program code for performing the operations of some embodiments of the present application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0122] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, program segment or a part of code, and the module, program segment or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.

[0123] The modules described in some embodiments of the present application may be implemented in software or hardware, and may also be provided in a processor.

[0124] The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, and without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chip (SOCs), complex programmable logic devices (CPLDs), and the like.

[0125] Some embodiments of the present application further provide a computer program product, including a computer program, which implements any of the above-mentioned interactive word learning methods when executed by a processor.

[0126] Those skilled in the art will appreciate that in one or more of the above examples, the functions described herein can be implemented using a combination of hardware and software. When software is used, the corresponding functions can be stored in a computer-readable medium or transmitted as one or more instructions or codes on a computer-readable medium. Computer-readable media include computer storage media and communication media, wherein communication media includes any medium that facilitates the transmission of computer programs from one place to another. The storage medium can be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0127] Although the present invention has been described in detail above using general descriptions and specific embodiments, it will be apparent to those skilled in the art that modifications and improvements may be made thereto. Therefore, such modifications and improvements, without departing from the spirit of the present invention, are intended to be within the scope of protection claimed herein.

Claims

1. An interactive word learning method, characterized in that Applied to an electronic device, the electronic device being equipped with a display screen, the method comprising: Controlling the display screen to display a word interactive learning interface, wherein the word interactive learning interface includes a first display area, an interactive control, and a switch control; the first display area is used to display at least one word in the first word group; receiving a question input by a first user through the interactive control, and calling a preset large language model to generate an answer corresponding to the question; In response to the triggering of the switching control, the word interactive learning interface displays at least one word in the first word group that has not been learned; Before controlling the display screen to display the word interactive learning interface, the method further includes: Controlling the display screen to display an information collection interface, wherein the information collection interface includes input controls; receiving basic information and learning needs input by a first user through the input control; the basic information includes language level, age stage and learning habits; the learning needs include learning objectives and selected vocabulary books; generating a first prompt word according to the basic information and learning needs of the first user; generating an answer corresponding to the question information based on the first prompt word by the preset large language model; The information collection interface further includes a first navigation control; In response to triggering of the first navigation control, controlling the display screen to display a main interface; the main interface includes a second navigation control; In response to triggering of the second navigation control, unlearned words and / or words to be reviewed are selected from a vocabulary database in a preset proportion based on the basic information and learning needs of the first user to generate the first word group; and the display screen is controlled to display a to-be-remembered word display interface; the to-be-remembered word display interface includes a first display area, a plurality of delete controls, and a third navigation control, the first display area being used to display the first word group; the plurality of delete controls being provided in a one-to-one correspondence with the plurality of words in the first word group; In response to a first user's first operation on a certain word, displaying a preset translation corresponding to the word and a deletion control; In response to the triggering of the delete control, the word corresponding to the delete control is deleted, the proficiency status of the deleted word is marked, and a new word is extracted from the vocabulary database and displayed; In response to triggering of the third navigation control, the display screen is controlled to display a word interactive learning interface.

2. The interactive word learning method according to claim 1, characterized in that; The word interactive learning interface further includes a second display area; the second display area is used to display a preset question information set; the question information set includes the first question information; In response to a first operation of the first user on the first question information, the interactive control obtains the question information of the first user and calls a preset large language model to generate an answer corresponding to the question information.

3. The interactive word learning method according to claim 1, wherein In response to triggering of a switch control corresponding to the last word learned in the first word group, calling a preset large language model to generate a first short article including each word in the first word group, and controlling the display screen to display a word stringing interface; the word stringing interface includes a third display area, and the third display area is used to display the first short article; In response to a second operation by the first user on a first word in the first word group in the first short article, the display screen is controlled to display a word interactive learning interface corresponding to the first word.

4. The interactive word learning method according to claim 3, characterized in that The word stringing interface also includes a first control; In response to the triggering of the first control, based on the interaction results between the first user and the preset large language model, a preset machine learning model is used to generate the next review time and proficiency of each word in the first word group, and recorded in the vocabulary database.

5. An electronic device, characterized in that: include: one or more processors; a storage device having one or more programs stored thereon; one or more display screens, wherein the storage device, the processor, and the display screens are coupled; When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 4.

6. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which implements the method according to any one of claims 1 to 4 when executed by a processing device.

7. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processing device, the method according to any one of claims 1 to 4 is implemented.

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