Psychological class interaction method, device and system based on artificial intelligence and medium
By using artificial intelligence-based interaction methods in online mental health classes, we can evaluate and feedback students' learning effects, and solve the problem of poor interaction effects in the existing technology, achieving a more efficient and easy-to-use learning experience.
Patent Information
- Application Number
- CN202411780854.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-05
- Publication Date
- 2025-05-02
AI Technical Summary
There are shortcomings in the interactive effects of existing online mental health classes and cannot provide real-time feedback based on students' status.
Using an interactive method based on artificial intelligence, the user's face features, actions and language data are obtained, and input into the learning effect artificial intelligence model, the learning effect is evaluated and the order and marking of the learning units are dynamically adjusted.
It improves the interactive effect in the online teaching process, enhances real-time feedback and dynamic adjustment of students' learning status, and improves the ease of use and effect of learning.
Smart Images
Figure CN119919251A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of intelligent education technology, and in particular to an artificial intelligence-based psychology course interaction method, device, system and medium. Background Art
[0002] As a form of mental health education, mental health classes have been widely used in schools and society. Some existing online mental health class applications cannot provide feedback based on the students' status during the mental health class, and the overall interaction effect is poor. Therefore, how to improve the interaction effect during online teaching is a technical issue that needs to be studied urgently in the industry. Summary of the invention
[0003] The present invention provides an artificial intelligence-based psychology class interaction method, device, system and medium to solve one or more technical problems existing in the prior art and at least provide a beneficial choice or create conditions.
[0004] The present invention provides an artificial intelligence-based psychology course interaction method, comprising: determining the triggering of a first triggering control on a main interaction interface, and displaying a teaching interaction interface according to the triggering; Determine a learning list of the user according to the user's account information, wherein the learning list records a number of learning units in order, and the learning list is used as a target learning list; Determine a learning unit from the first one in the target learning list, and record the learning unit as the target learning unit; Loading the target learning unit into the teaching interaction interface for playback; Acquire facial feature time series data, action feature time series data, and language feature time series data of the user during the learning process of the target learning unit; After the user has completed the learning of the target learning unit, the facial feature time series data, the action feature time series data and the language feature time series data are input into a pre-set learning effect artificial intelligence model, and the user's current learning effect for the target learning unit is determined by the learning effect artificial intelligence model, and the learning effect is recorded as the target learning effect; Determine a learning unit to be studied in the next class according to the target learning effect, and record the learning unit as a future learning unit; Determine whether the future learning unit is the same as the target learning unit. When the future learning unit is not the same as the target learning unit, insert the future learning unit into the first position in the current target learning list. In the current target learning list, mark the target learning unit first to prompt the target learning unit to complete the learning and update the current target learning list.
[0005] Furthermore, the artificial intelligence-based psychology class interaction method also includes: when the future learning unit is the same as the target learning unit, a second mark is made on the target learning unit in the current target learning list to indicate that the target learning unit has not completed learning, and the current target learning list is updated.
[0006] Furthermore, the artificial intelligence-based psychology class interaction method also includes: counting the number of first marks in the target learning list, and generating a corresponding number of first element objects on the main interaction interface according to the number.
[0007] Furthermore, the artificial intelligence-based psychology class interaction method also includes: counting the number of second marks in the target learning list, and generating a corresponding number of second element objects on the main interaction interface according to the number.
[0008] Furthermore, the artificial intelligence-based psychology course interaction method also includes: determining the trigger of the first element object, and displaying a first query interface according to the trigger, wherein the first query interface records the playback link tags of all completed learning units.
[0009] Furthermore, the artificial intelligence-based psychology course interaction method also includes: determining a trigger of a second element object, and displaying a second query interface according to the trigger, wherein the second query interface records the playback link labels of all unfinished learning units.
[0010] On the other hand, a device for interactive psychology classes based on artificial intelligence is provided, comprising: a processor and a memory, wherein the memory is used to store a computer-readable program; when the computer-readable program is executed by the processor, the processor implements the method for interactive psychology classes based on artificial intelligence as described in any one of the above technical solutions.
[0011] On the other hand, there is provided an artificial intelligence-based psychology course interactive system, comprising: a first determination module, a second determination module, a third determination module, a loading module, an acquisition module, a fourth determination module, a fifth determination module, and an update module; The first determination module is used to: determine the triggering of the first trigger control on the main interaction interface, and display the teaching interaction interface according to the triggering; The second determination module is used to: determine the user's learning list according to the user's account information, wherein the learning list has a number of learning units recorded in order, and the learning list is used as the target learning list; The third determination module is used to: determine a learning unit from the first position in the target learning list, and the learning unit is recorded as the target learning unit; The loading module is used to: load the target learning unit into the teaching interaction interface for playing; The acquisition module is used to: acquire the facial feature time series data, action feature time series data and language feature time series data of the user during the learning process of the target learning unit; The fourth determination module is used to: after the user completes the learning of the target learning unit, input the facial feature time series data, the action feature time series data and the language feature time series data into a preset learning effect artificial intelligence model, determine the user's current learning effect for the target learning unit through the learning effect artificial intelligence model, and record the learning effect as the target learning effect; The fifth determination module is used to: determine the learning unit to be studied in the next class according to the target learning effect, and record the learning unit as a future learning unit; The update module is used to determine whether the future learning unit is the same as the target learning unit. When the future learning unit is not the same as the target learning unit, the future learning unit is inserted into the first position in the current target learning list. In the current target learning list, the target learning unit is marked as the first position to prompt the target learning unit to complete the learning and update the current target learning list.
[0012] Furthermore, the update module is also used for: when the future learning unit is the same as the target learning unit, a second mark is made on the target learning unit in the current target learning list to indicate that the target learning unit has not completed learning, and the current target learning list is updated.
[0013] On the other hand, a computer-readable storage medium is provided, in which a program executable by a processor is stored. When the program executable by the processor is executed by the processor, it is used to implement the artificial intelligence-based psychology class interaction method as described in any one of the above technical solutions.
[0014] The present invention has at least the following beneficial effects: the present invention uses an artificial intelligence model to evaluate the learning effect of the user in the learning process, and selects the learning unit for the next lesson according to the learning effect, and places the selected learning unit at the top of the target learning list. This facilitates better learning for users and improves the ease of learning for users. The present invention also provides corresponding devices, systems and media, and the beneficial effects of the devices, systems and media are similar to those of the method, so they will not be described again here. BRIEF DESCRIPTION OF THE DRAWINGS
[0015] The accompanying drawings are used to provide a further understanding of the technical solution of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the technical solution of the present invention and do not constitute a limitation on the technical solution of the present invention.
[0016] Figure 1It is a flowchart of the steps based on the interactive method of artificial intelligence psychology class; Figure 2 It is a schematic diagram of the device structure based on the artificial intelligence psychology class interactive device; Figure 3 It is a schematic diagram of the system connection structure based on the artificial intelligence psychology course interactive system; Figure 4 It is a schematic diagram of the interface structure of the main interactive interface. DETAILED DESCRIPTION
[0017] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.
[0018] It should be noted that, although the functional modules are divided in the system schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the system or the order in the flowchart. The terms "first", "second", etc. in the specification, claims and the above drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.
[0019] For ease of description, some terms in the text are explained below.
[0020] Artificial Intelligence Model, Artificial Intelligence (AI) model refers to a system that can simulate human intelligent behavior through computer algorithms and data training.
[0021] The learning effect artificial intelligence model refers to the model that reflects the learning effect obtained through training of the artificial intelligence model.
[0022] Human-Machine Interface, Human-Machine Interface (HMI) refers to the communication media or means between people and computer systems. It is a platform for two-way information exchange of various symbols and actions between people and computers.
[0023] Intelligent device, an intelligent device refers to any device, instrument or machine with computing processing capabilities.
[0024] refer to Figure 1 and Figure 4 , Figure 1 It is a flowchart of the steps of the interactive method of artificial intelligence psychology class. Figure 4 It is a schematic diagram of the interface structure of the main interactive interface.
[0025] This artificial intelligence-based psychology course interaction method is mainly operated through smart devices. When the smart device is running, the steps that can be executed include: Step 1: Determine the triggering of the first trigger control 110 on the main interaction interface 100, and display the teaching interaction interface according to the triggering.
[0026] When a user (student) needs to study a course, he or she can do so through a smart device. The smart device displays a login interface in its human-computer interaction interface, and the user enters the set account information on the login interface. After the smart device determines that the account information is correct, it can display the main interaction interface 100 on its human-computer interaction interface. In the main interaction interface 100, a first trigger control 110 is provided. The first trigger control 110 is an interactive button control.
[0027] When the user needs to conduct formal learning, he can trigger the sensing area of the first trigger control 110. The smart device determines that the user has triggered the sensing area of the first trigger control 110, and responds to the trigger. The teaching interaction interface is displayed on the human-computer interaction interface.
[0028] Step 2: Determine the user's learning list based on the user's account information, wherein the learning list records a number of learning units in order, and the learning list is used as a target learning list.
[0029] In order to determine the learning unit that the user currently needs to learn, the smart device will query the user's account information. The account information is used to find the learning list under the user's account. For the sake of ease of description, the found learning list is recorded as the target learning list. Among them, the target learning list records various learning units. These learning units are set to divide the user's learning stage into multiple learning intervals, and each learning interval corresponds to a learning unit. Therefore, by breaking the whole into parts, the effect of the user's stage learning is improved. Among them, the learning units on the learning list are sorted from the first to the last, and the priority of the learning units gradually decreases. The learning unit at the first place on the learning list is the unit with the highest priority for learning.
[0030] Step 3: Determine the first learning unit in the target learning list, and record the learning unit as the target learning unit.
[0031] After the smart device finds the target learning list under the user's account, the smart device can determine the learning unit that needs to be learned currently from the first one in the target learning list. For the convenience of description, the learning unit is recorded as the target learning unit.
[0032] Step 4: Load the target learning unit into the teaching interaction interface for playback.
[0033] After the smart device has determined the target learning unit, it can load the target learning unit into the teaching interaction interface and run and play the target learning unit through the teaching interaction interface.
[0034] Step 5: Obtain the user's facial feature time series data, action feature time series data, and language feature time series data during the learning process of the target learning unit.
[0035] During the playback of the target learning unit, the smart device will use its built-in sensors to collect the user's facial changes during the learning process, thereby obtaining facial feature time series data. The smart device will use its built-in sensors to collect the user's movement changes during the learning process, thereby obtaining movement feature time series data. The smart device will use its built-in sensors to collect the user's voice during the learning process, thereby obtaining language feature time series data.
[0036] In some further specific embodiments, the smart device can obtain the user's image information through the camera unit, and through the recognition of the image information, extract the user's facial feature time series data and action feature time series data during the learning process of the target learning unit. The smart device can obtain the user's voice information through the recorder unit, and through the recognition of the voice information, extract the user's language feature time series data during the learning process of the target learning unit.
[0037] Step 6. After the user completes the learning of the target learning unit, the facial feature time series data, motion feature time series data and language feature time series data are input into a pre-set learning effect artificial intelligence model. The user's current learning effect on the target learning unit is determined by the learning effect artificial intelligence model, and the learning effect is recorded as the target learning effect.
[0038] After obtaining the facial feature time series data, action feature time series data, and language feature time series data of the user during the learning process of the target learning unit, the smart device will temporarily store these data and wait for the user to complete the learning of the target learning unit. In some specific embodiments, the smart device can determine that the user has completed the learning of the target learning unit by obtaining the instructions input by the user. Of course, the input of this instruction can be achieved by responding to the triggering of the button control on the human-computer interaction interface of the smart device.
[0039] After the smart device determines that the user has completed the learning of the target learning unit, it will input the facial feature time series data, the action feature time series data and the language feature time series data into a pre-set learning effect artificial intelligence model. Among them, the learning effect artificial intelligence model can be run in a remote artificial intelligence server. The smart device transmits the facial feature time series data, the action feature time series data and the language feature time series data to the remote artificial intelligence server through the network. The artificial intelligence server judges the user's learning effect through the internally integrated learning effect artificial intelligence model, thereby obtaining a parameter that can reflect the user's learning effect on the target learning unit, and this parameter is recorded as the target learning effect. The artificial intelligence server feeds back the target learning effect to the smart device.
[0040] Step 7: Determine the learning unit to be studied in the next class from the target learning list according to the target learning effect, and record the learning unit as a future learning unit.
[0041] After the smart device determines that the target learning effect has been obtained, it can query the learning unit corresponding to the target learning effect from the effect and learning unit comparison table through the target learning effect.
[0042] The effect and learning unit comparison table is pre-set and stored in the local device of the smart device. The effect and learning unit comparison table records the comparison relationship between the effect and the learning unit. The smart device determines the corresponding learning unit from the effect and learning unit comparison table by a table lookup method. For the convenience of description, the learning unit is recorded as a future learning unit.
[0043] Step 8. Determine whether the future learning unit is the same as the target learning unit. When the future learning unit is not the same as the target learning unit, insert the future learning unit into the first position in the current target learning list. In the current target learning list, mark the target learning unit first to prompt the target learning unit to complete the learning and update the current target learning list.
[0044] After the smart device determines the future learning unit, in order to realize that it will appear in the next learning course. Therefore, the future learning unit will be inserted into the first place in the current target learning list. As a result, the user will learn the future learning unit when he / she studies the next time. Of course, there will also be a situation where the target learning effect obtained by the user after learning the target learning unit reflects that the future learning unit is the same as the target learning unit (after evaluating the user's learning effect, it is considered that the user needs to re-learn the current target learning unit). Therefore, before the smart device inserts the future learning unit into the first place in the current target learning list, it is necessary to compare the future learning unit and the target learning unit to see if they are the same. After the smart device determines that the future learning unit is not the same as the target learning unit through comparison, it will insert the future learning unit into the first place in the current target learning list. Of course, at this time, the target learning unit in the current target learning list will also be marked, and the mark here is called the first mark. The first mark is used to prompt that the target learning unit has completed learning. After that, the current target learning list will be updated.
[0045] The present invention uses an artificial intelligence model to evaluate the learning effect of the user in the learning process, and selects the learning unit for the next lesson according to the learning effect, and places the selected learning unit at the top of the target learning list, thereby facilitating better learning for the user.
[0046] In some further specific embodiments, the artificial intelligence-based psychology class interaction method also includes: when the future learning unit is the same as the target learning unit, a second mark is made on the target learning unit in the current target learning list to indicate that the target learning unit has not completed learning, and the current target learning list is updated.
[0047] When the smart device determines that the future learning unit is the same as the target learning unit, the position of the target learning unit will remain unchanged. And in the current target learning list, the target learning unit will be marked for the second time. By marking the target learning unit for the second time, it is indicated that the target learning unit has not completed learning. After marking the target learning unit in the target learning list, the smart device will update the current target learning list.
[0048] In some further specific embodiments, the artificial intelligence-based psychology course interaction method also includes: by counting the number of first marks in the target learning list, generating a corresponding number of first element objects 101 on the main interaction interface 100 according to the number. Among them, the first element object 101 can be a decorative element object or a UI element object with interactive function. The number of first element objects 101 is in a certain proportional relationship with the number of first marks. The user can know the number of completed learning units in the current study by perceiving the number of first element objects 101. Moreover, the first element object 101 can be set to various interesting images, such as the image of a star, the image of the moon, or the image of the sun. Thereby increasing the fun of the user.
[0049] In some further specific embodiments, the first element object 101 is limited to a UI element object with interactive functions, and the user can trigger it by touching the sensing area of the first element object 101. When the smart device determines that the first element object 101 is triggered, it will display a first query interface according to the trigger. The first query interface records the play link tags of all completed learning units. The smart device responds to the triggering of the first element object 101 by displaying the first query interface, and records the play link tags of all completed learning units through the first query interface. The user can use the play link notes to review the completed learning units.
[0050] In some further specific embodiments, the artificial intelligence-based psychology course interaction method also includes: by counting the number of second marks in the target learning list, generating a corresponding number of second element objects 102 on the main interaction interface 100 according to the number. Among them, the second element object 102 can be a decorative element object or a UI element object with interactive function. The number of second element objects 102 is in a certain proportional relationship with the number of second marks. The user can know the number of unfinished learning units in the current study by perceiving the number of second element objects 102. Moreover, the second element object 102 can be set to various interesting images, such as the image of a star, the image of the moon, or the image of the sun. Thereby increasing the fun of the user. Of course, in order to distinguish, the images of the first element object 101 and the second element object 102 are not the same.
[0051] In some further specific embodiments, the second element object 102 is limited to a UI element object with interactive functions, and the user can trigger it by touching the sensing area of the second element object 102. When the smart device determines that the second element object 102 is triggered, it will display a second query interface according to the trigger. The second query interface records the play link tags of all unfinished learning units. The smart device responds to the triggering of the second element object 102 by displaying the second query interface, and records the play link tags of all unfinished learning units through the second query interface. The user can use the play link notes to review the unfinished learning units.
[0052] On the other hand, reference Figure 2 , Figure 2 It is a schematic diagram of the device structure based on the artificial intelligence psychology class interactive device.
[0053] Provided is an artificial intelligence-based psychology course interaction device, comprising: a processor and a memory; wherein the memory is used to store a computer-readable program. When the computer-readable program is executed by the processor, the processor implements the artificial intelligence-based psychology course interaction method as described in any one of the above technical solutions.
[0054] It will be appreciated by those skilled in the art that all or some of the steps and systems in the methods disclosed above may be implemented as software, firmware, hardware, and appropriate combinations thereof. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or non-transitory medium) and a communication medium (or transient medium). As known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disk (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and can be accessed by a computer. As is well known to those skilled in the art, communication media typically embodies computer readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.
[0055] On the other hand, reference Figure 3, Figure 3 It is a schematic diagram of the system connection structure based on the artificial intelligence psychology course interactive system.
[0056] Provided is an artificial intelligence-based psychology course interactive system, comprising: a first determination module, a second determination module, a third determination module, a loading module, an acquisition module, a fourth determination module, a fifth determination module and an update module.
[0057] The first determination module is used to determine the triggering of the first trigger control 110 on the main interaction interface 100, and display the teaching interaction interface according to the triggering.
[0058] When the user needs to conduct formal learning, he can trigger the sensing area of the first trigger control 110. The first determination module determines that the user has triggered the sensing area of the first trigger control 110, and then responds to the trigger. The teaching interaction interface is displayed on the human-computer interaction interface.
[0059] The second determination module is used to determine the user's learning list according to the user's account information, wherein the learning list records a number of learning units in order, and the learning list is used as a target learning list.
[0060] In order to determine the learning unit that the user currently needs to learn, the second determination module will query the user's account information. The learning list under the user's account is found through the account information. For the sake of ease of description, the found learning list is recorded as the target learning list. Among them, the target learning list records various learning units. These learning units are set to divide the user's learning stage into multiple learning intervals, and each learning interval corresponds to a learning unit. Therefore, by breaking the whole into parts, the effect of the user's stage learning is improved. Among them, the learning units on the learning list are sorted from the first to the last, and the priority of the learning units gradually decreases. The learning unit at the first place on the learning list is the unit with the highest priority for learning.
[0061] The third determination module is used to determine a learning unit from the first position in the target learning list, and the learning unit is recorded as the target learning unit.
[0062] After the third determination module finds the target learning list under the user's account, the third determination module can determine the learning unit that needs to be learned currently from the first one in the target learning list. For ease of description, the learning unit is recorded as the target learning unit.
[0063] The loading module is used to load the target learning unit into the teaching interaction interface for playback.
[0064] After the loading module determines the target learning unit, it can load the target learning unit into the teaching interaction interface and run and play the target learning unit through the teaching interaction interface.
[0065] The acquisition module is used to obtain facial feature time series data, action feature time series data and language feature time series data of the user during the learning process of the target learning unit.
[0066] During the playback of the target learning unit, the acquisition module will use its built-in sensors to collect the user's facial changes during the learning process, thereby obtaining facial feature time series data. The acquisition module will use its built-in sensors to collect the user's movement changes during the learning process, thereby obtaining movement feature time series data. The smart device will use its built-in sensors to collect the user's voice during the learning process, thereby obtaining language feature time series data.
[0067] In some further specific embodiments, the acquisition module can acquire the user's image information through the camera unit, and through the recognition of the image information, extract the user's facial feature time series data and action feature time series data during the learning process of the target learning unit. The acquisition module can acquire the user's voice information through the recorder unit, and through the recognition of the voice information, extract the user's language feature time series data during the learning process of the target learning unit.
[0068] The fourth determination module is used to: after the user completes the learning of the target learning unit, input the facial feature timing data, motion feature timing data and language feature timing data into a pre-set learning effect artificial intelligence model, determine the user's current learning effect on the target learning unit through the learning effect artificial intelligence model, and record the learning effect as the target learning effect.
[0069] After obtaining the facial feature time series data, action feature time series data and language feature time series data of the user in the process of learning the target learning unit, the fourth determination module will temporarily store these data and wait for the user to complete the learning of the target learning unit. In some specific embodiments, the fourth determination module can determine that the user has completed the learning of the target learning unit by obtaining the instruction input by the user. Of course, the input of this instruction can be achieved by responding to the triggering of the button control on the human-computer interaction interface of the fourth determination module.
[0070] After the fourth determination module determines that the user has completed the learning of the target learning unit, the facial feature time series data, the action feature time series data and the language feature time series data will be input into a pre-set learning effect artificial intelligence model. Among them, the learning effect artificial intelligence model can be run in a remote artificial intelligence server. The fourth determination module transmits the facial feature time series data, the action feature time series data and the language feature time series data to the remote artificial intelligence server through the network. The artificial intelligence server judges the user's learning effect through the internally integrated learning effect artificial intelligence model, thereby obtaining a parameter that can reflect the user's learning effect on the target learning unit, and this parameter is recorded as the target learning effect. The artificial intelligence server feeds back the target learning effect to the fourth determination module.
[0071] The fifth determination module is used to determine the learning unit that needs to be learned in the next class according to the target learning effect, and record the learning unit as a future learning unit.
[0072] After the fifth determination module determines that the target learning effect has been obtained, the learning unit corresponding to the target learning effect can be obtained by searching the effect and learning unit comparison table through the target learning effect.
[0073] The effect and learning unit comparison table is pre-set and stored in the local device. The effect and learning unit comparison table records the comparison relationship between the effect and the learning unit. The fifth determination module determines the corresponding learning unit from the effect and learning unit comparison table by a table lookup method. For the convenience of description, the learning unit is recorded as a future learning unit.
[0074] The update module is used to determine whether the future learning unit is the same as the target learning unit. When the future learning unit is not the same as the target learning unit, the future learning unit is inserted into the first position in the current target learning list. In the current target learning list, the target learning unit is marked as the first position to prompt the target learning unit to complete the learning and update the current target learning list.
[0075] After the update module determines the future learning unit, in order to realize that it will appear in the next learning course. Therefore, the future learning unit will be inserted into the first place in the current target learning list. As a result, the user will learn the future learning unit when he / she studies the next time. Of course, there will also be a situation where the target learning effect obtained by the user after learning the target learning unit reflects that the future learning unit is the same as the target learning unit (after evaluating the user's learning effect, it is considered that the user needs to re-learn the current target learning unit). Therefore, before the update module inserts the future learning unit into the first place in the current target learning list, it is necessary to compare the future learning unit and the target learning unit to see if they are the same. After the update module determines through comparison that the future learning unit is not the same as the target learning unit, it will insert the future learning unit into the first place in the current target learning list. Of course, at this time, the target learning unit in the current target learning list will also be marked, and the mark here is called the first mark. The first mark is used to prompt that the target learning unit has completed learning. After that, the data of the current target learning list will be updated.
[0076] In some further specific embodiments, the update module is also used to: generate a corresponding number of first element objects 101 on the main interactive interface 100 according to the number by counting the number of first marks in the target learning list. Among them, the first element object 101 can be a decorative element object or a UI element object with interactive function. The number of first element objects 101 is in a certain proportional relationship with the number of first marks. The user can know the number of completed learning units in the current learning by perceiving the number of first element objects 101. Moreover, the first element object 101 can be set to various interesting images, such as the image of a star, the image of the moon, or the image of the sun. Thereby increasing the fun of the user.
[0077] In some further specific embodiments, the first element object 101 is limited to a UI element object with interactive functions, and the user can trigger it by touching the sensing area of the first element object 101. When the update module determines that the first element object 101 is triggered, it will display the first query interface according to the trigger. The first query interface records the play link tags of all completed learning units. The update module responds to the triggering of the first element object 101 by displaying the first query interface, and records the play link tags of all completed learning units through the first query interface. The user can use the play link notes to review the completed learning units.
[0078] In some further specific embodiments, the update module is also used to: generate a corresponding number of second element objects 102 on the main interactive interface 100 according to the number by counting the number of second tags in the target learning list. Among them, the second element object 102 can be a decorative element object or a UI element object with interactive functions. The number of second element objects 102 is in a certain proportional relationship with the number of second tags. The user can know the number of unfinished learning units in the current learning by perceiving the number of second element objects 102. Moreover, the second element object 102 can be set to various interesting images, such as the image of a star, the image of the moon, or the image of the sun. Thereby increasing the fun of the user. Of course, in order to distinguish, the images of the first element object 101 and the second element object 102 are not the same.
[0079] In some further specific embodiments, the second element object 102 is limited to a UI element object with interactive functions, and the user can trigger it by touching the sensing area of the second element object 102. When the update module determines that the second element object 102 is triggered, it will display a second query interface according to the trigger. The second query interface records the play link tags of all unfinished learning units. The update module responds to the triggering of the second element object 102 by displaying the second query interface, and records the play link tags of all unfinished learning units through the second query interface. The user can use the play link notes to review the unfinished learning units.
[0080] On the other hand, a computer-readable storage medium is provided, which stores a program executable by a processor. When the program executable by the processor is executed by the processor, it is used to implement the artificial intelligence-based psychology class interaction method as described in any one of the above-mentioned specific embodiments.
[0081] An embodiment of the present application also discloses a computer program product, including a computer program or computer instructions, wherein the computer program or computer instructions are stored in a computer-readable storage medium, a processor of a computer device reads the computer program or computer instructions from the computer-readable storage medium, and the processor executes the computer program or computer instructions, so that the computer device executes the artificial intelligence-based psychology class interaction method as described in any of the previous embodiments.
[0082] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein, for example. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.
[0083] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.
[0084] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.
[0085] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
[0086] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.
[0087] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store program codes.
[0088] Although the description of the present application has been quite detailed and specifically describes several described embodiments, it is not intended to be limited to any of these details or embodiments or any particular embodiment, but should be regarded as providing a broad possible interpretation of these claims by reference to the attached claims, taking into account the prior art, so as to effectively cover the intended scope of the present application. In addition, the above description of the present application is based on the embodiments foreseeable by the inventor, and its purpose is to provide a useful description, and those non-substantial changes to the present application that have not yet been foreseen may still represent equivalent changes to the present application.
Claims
1. An interactive method for psychology courses based on artificial intelligence, characterized in that: include: Determine the triggering of the first trigger control on the main interactive interface, and display the teaching interactive interface according to the triggering; Determine a learning list of the user according to the user's account information, wherein the learning list records a number of learning units in order, and the learning list is used as a target learning list; Determine a learning unit from the first one in the target learning list, and record the learning unit as the target learning unit; Loading the target learning unit into the teaching interaction interface for playback; Acquire facial feature time series data, action feature time series data, and language feature time series data of the user during the learning process of the target learning unit; After the user has completed the learning of the target learning unit, the facial feature time series data, the action feature time series data and the language feature time series data are input into a pre-set learning effect artificial intelligence model, and the user's current learning effect for the target learning unit is determined by the learning effect artificial intelligence model, and the learning effect is recorded as the target learning effect; Determine a learning unit to be studied in the next class according to the target learning effect, and record the learning unit as a future learning unit; Determine whether the future learning unit is the same as the target learning unit. When the future learning unit is not the same as the target learning unit, insert the future learning unit into the first position in the current target learning list. In the current target learning list, mark the target learning unit first to prompt the target learning unit to complete the learning and update the current target learning list.
2. The method for interactive psychology class based on artificial intelligence according to claim 1, characterized in that: Also includes: When the future learning unit is the same as the target learning unit, a second mark is made on the target learning unit in the current target learning list to indicate that the target learning unit has not been completed, and the current target learning list is updated.
3. The method for interactive psychology class based on artificial intelligence according to claim 1, characterized in that: Also includes: By counting the number of first marks in the target learning list, a corresponding number of first element objects are generated on the main interaction interface according to the number.
4. The method for interactive psychology class based on artificial intelligence according to claim 1, characterized in that: Also includes: By counting the number of second markers in the target learning list, a corresponding number of second element objects are generated on the main interaction interface according to the number.
5. The method for interactive psychology class based on artificial intelligence according to claim 1, characterized in that: Also includes: A trigger of the first element object is determined, and a first query interface is displayed according to the trigger, wherein the first query interface records the play link tags of all the learning units that have completed learning.
6. The method for interactive psychology class based on artificial intelligence according to claim 1, characterized in that: Also includes: A trigger of the second element object is determined, and a second query interface is displayed according to the trigger, wherein the second query interface records the play link labels of all unfinished learning units.
7. An interactive device for psychology class based on artificial intelligence, characterized in that: include: processor; A memory for storing a computer readable program; When the computer-readable program is executed by the processor, the processor implements the artificial intelligence-based psychology class interaction method as described in any one of claims 1 to 6.
8. An interactive system for psychology lessons based on artificial intelligence, characterized in that: include: a first determining module, a second determining module, a third determining module, a loading module, an acquiring module, a fourth determining module, a fifth determining module and an updating module; The first determination module is used to: determine the triggering of the first trigger control on the main interaction interface, and display the teaching interaction interface according to the triggering; The second determination module is used to: determine the user's learning list according to the user's account information, wherein the learning list has a number of learning units recorded in order, and the learning list is used as the target learning list; The third determination module is used to: determine a learning unit from the first position in the target learning list, and the learning unit is recorded as the target learning unit; The loading module is used to: load the target learning unit into the teaching interaction interface for playing; The acquisition module is used to: acquire the facial feature time series data, action feature time series data and language feature time series data of the user during the learning process of the target learning unit; The fourth determination module is used to: after the user completes the learning of the target learning unit, input the facial feature time series data, the action feature time series data and the language feature time series data into a preset learning effect artificial intelligence model, determine the user's current learning effect for the target learning unit through the learning effect artificial intelligence model, and record the learning effect as the target learning effect; The fifth determination module is used to: determine the learning unit to be studied in the next class according to the target learning effect, and record the learning unit as a future learning unit; The update module is used to determine whether the future learning unit is the same as the target learning unit. When the future learning unit is not the same as the target learning unit, the future learning unit is inserted into the first position in the current target learning list. In the current target learning list, the target learning unit is marked as the first position to prompt the target learning unit to complete the learning and update the current target learning list.
9. The artificial intelligence-based psychology course interactive system according to claim 8, characterized in that: The update module is also used for: when the future learning unit is the same as the target learning unit, a second mark is made on the target learning unit in the current target learning list to indicate that the target learning unit has not completed learning, and the current target learning list is updated.
10. A computer-readable storage medium, characterized in that: A processor-executable program is stored therein, and when the processor-executable program is executed by the processor, it is used to implement the artificial intelligence-based psychology class interaction method as described in any one of claims 1 to 6.