Foreign language learning and homework doing system and method based on education artificial intelligence and integrating interest, real scene and like

Abstract

The invention discloses a foreign language learning and homework doing system and a foreign language learning and homework doing method integrating education artificial intelligence, augmented reality (AR) technology and biological signal perception. A multi-mode biosensor (electrodermal response, heart rate, eye movement and the like) is integrated through customized AR intelligent glasses, and a closed loop of'interest scene triggering-biological signal sensing-personalized content pushing-self-adaptive review planning based on a forgetting curve and real-time emotion data 'is realized in combination with an interest matching algorithm and a dynamic learning engine of an intelligent foreign language learning APP. The system utilizes an artificial intelligence technology to accurately analyze the emotional state of a user, adopts a bidirectional long short-term memory network (Bi-LSTM) personalized forgetting curve algorithm, integrates foreign language words, grammar and other homework contents into an augmented reality or virtual interest scene, and supports on-school students to complete homework tasks and off-school users to autonomously learn. The system covers hardware architecture design, AI algorithm implementation and a data interaction process, foreign language learning and homework doing are completed during playing, the learning efficiency is remarkably improved, and the system has high innovativeness and practicability.

Description

Technical Field

[0001] The present invention belongs to the intersection of educational artificial intelligence and smart wearable devices, involving augmented reality (AR) technology, biosignal detection, machine learning algorithms and intelligent interactive system design, and is specifically used in foreign language learning assistance, homework completion optimization and personalized education scenarios. Background Art

[0002] 1. The intense contradiction and dialectical relationship between homework pressure and academic achievement

[0003] Homework, an important means of consolidating learning, is widely resisted by students. The Organization for Economic Co-operation and Development (OECD)'s "Education Outlook 2023" reports that 65% of adolescents worldwide believe that homework "takes away from leisure time." However, moderate homework has a positive effect on academic performance: a follow-up study by the Harvard Graduate School of Education showed that students who completed 30 minutes of structured homework daily had 19% higher academic scores than those who did no homework at all. This contradiction is particularly pronounced during the holidays. A survey by the China Science Popularization Network shows that 73% of students fail to fully participate in outdoor activities due to homework, creating an anxious cycle of "wanting to study but not having fun, wanting to play but not having the confidence to study."

[0004] 2. Theoretical Basis and Practical Exploration of Happy Education

[0005] The educational concept of "learning through play" originates from the cognitive development theory of Swiss psychologist Piaget, which emphasizes promoting knowledge construction through contextualized learning.

[0006] 3. This invention can effectively alleviate the contradiction between homework pressure and academic achievement, realizing the educational concept of "learning while playing". School users can complete foreign language learning tasks and foreign language homework tasks while playing. Non-school users can complete foreign language learning tasks. Summary of the Invention

[0007] Glossary

[0008] Software Educational Robot: An intelligent program system that runs on computers, mobile devices, and smart wearable devices, integrating natural language processing (NLP), machine learning (ML), and robotic process automation (RPA) technologies. It supports installation and deployment on various mobile clients, including teachers, students, parents, and members of the public.

[0009] Physical Educational Robot: A physical educational robot is an intelligent terminal device deployed in key areas of the campus (including but not limited to the school gate, dormitory entrance, teaching building entrance and risk monitoring points). The device integrates sensor technology (covering modules such as visual perception, voice recognition, and environmental parameter monitoring), biological detection instruments, and artificial intelligence (AI) algorithms (including core algorithms such as face recognition and behavior pattern analysis), and integrates an educational resource database. It has core functions such as identity authentication and verification, physiological health monitoring, and teaching auxiliary support. For related technical features, please refer to other invention applications of the same inventor (application numbers: 2025101774440, 2025103434814), which are referred to as humanoid (or non-humanoid) robots with integrated hardware and software in these documents. The physical educational robot described in the present invention is a miniaturized physical educational robot that is easy to carry and accompany students.

[0010] 1. Business Process Overview

[0011] 1.1. The system of the present invention consists of customized augmented reality (AR) smart glasses, an intelligent foreign language learning application (APP), mobile terminals, and a software educational robot. The most numerous mobile terminals are smartphones, which are also the model promoted by the present invention.

[0012] 1.2. Customized AR smart glasses (hereinafter referred to as customized glasses) include a camera, communication module, power module, and reserved expansion interfaces. They can be expanded and integrated with modular galvanic skin response sensors (GSR), heart rate sensors (PPG), blood pressure monitors, eye trackers, temperature monitors, non-invasive brain-computer interface modules, etc. Expansion module slots can be idle, or one or more modules can be selected. Modules can be hot-swapped for expansion and removal, and are also backward compatible with non-hot-swappable expansion. These devices can be placed on the temples or frames of the glasses, and can contact the skin or not. The eye tracker is located in the eye socket, and other instruments are preferably placed on the temples that contact the skin to make the test more accurate. This includes a common type of customized glasses, which is similar to a VR display terminal consisting of a physical transparent display, an AR interactive display, an electronic screen display, a wireless communication module, a power module, a management chip, a frame, and temples. It includes a deformed design, which also falls within the scope of the patent of this invention, that is, the camera, communication module, and power module are integrated together and fixed to ordinary glasses by clips, or worn on the head like a headlamp, or worn on the chest like a school badge, connected to a smart phone by wire or wirelessly, and displayed on the mobile phone screen, including three parts: physical transparent display, AR interactive display, and electronic screen display.

[0013] 1.3. The smartphone connects to the customized glasses via wireless methods such as Wi-Fi and Bluetooth to exchange information. If the customized glasses do not have eye tracking equipment, the smartphone's camera can also be used to record the user's eye movements. The customized glasses are partially used for visualizing objects, partially for AR interaction, and partially for displaying the screen. The lenses can also be for myopia or presbyopia.

[0014] 1.4. When a user wears customized glasses and opens the smart foreign language learning app on their smartphone, the system automatically connects to the user's psychological profile. Alternatively, the user can enter their interests and dislikes, and the system matches the user's selected vocabulary or the content of their required foreign language homework. This invention can be used by both ordinary users (without the need to complete homework) and school students (who are required to complete school foreign language homework when learning a foreign language).

[0015] 1.5. The system matches interest libraries. When visual and audio detection of user interests and hobbies is detected, physiological instruments can be used to measure the user's excitement and re-measure the user's interest. Using the curiosity-guided engine, the system matches the user's selected dictionary and introduces specific vocabulary, grammar, communication, and application. For example, a four-choice question, "Look, what is this?", is offered, asking the user to select the meaning of a word. Incorrect choices are marked with an "incorrect" stamp, while correct choices are marked with a "correct" stamp. The system also rewards users with electronic gifts representing their interests and hobbies. Accumulated electronic gifts can be redeemed for physical items.

[0016] 1.6. Activate the customized glasses' galvanic skin response (GSR) sensor, heart rate sensor (PPG), blood pressure monitor, eye tracker, and temperature monitor to accurately track emotional curves. For example, when a user sees a scene they like, their heart rate, eye movements, and other biometrics will change. By combining interests with emotions, the system can more accurately memorize vocabulary, learn grammar, and learn communication and application skills. The system intelligently utilizes the forgetting curve. During the first review, the forgetting curve parameters are averaged. The system can sense the user's positive emotions, using the positive emotion curve as a reminder for review time. By recording the learning process, the system intelligently adjusts the personalized forgetting curve and emotion curve.

[0017] 1.7. In addition to assisting with vocabulary memorization, grammar learning, communication and application in foreign language learning, the present invention can also intelligently help users complete related content in foreign language homework.

[0018] 1.8. The system intelligently identifies real-world contextually relevant foreign language tasks and, using its curiosity-driven engine, leads to specific vocabulary, grammar, communication, and application topics. Students can complete vocabulary learning and homework, as well as grammar and communication application assignments, while playing.

[0019] 1.9. The system includes homework completion guidance. If users have not yet completed learning vocabulary, grammar, or communication and application skills, they will be advised to go to specific locations to complete their foreign language homework, based on their interests. Users can print the learning process and homework and submit them to their teachers, or download an electronic version from the system and submit it to their teachers. If the school has a homework management system, it can be integrated with this system to display the user's homework completion process and status, which can be used by foreign language teachers for grading.

[0020] 1.10. Users with poor learning results can choose the psychological counseling module, including educational robot consultation, online expert consultation, offline institution consultation, etc. For details on how to obtain specific psychological counseling, please refer to invention patent application 2025104940828. Some students feel that if learning during the game will affect the experience, they can use the video recording and playback function to learn foreign languages and do homework after the game. The use of the system requires point redemption to encourage campus mental health education, including, for example, referring to the invention patent application 2025104619150 of the same applicant. If a third-party student provides valid information, the system will reward the third-party student with points. With the points reward, the system can be used for free. Personalized information such as user name uses an anonymous ID to comply with various laws and regulations to ensure information security and user privacy.

[0021] 1.11 Students with learning disabilities can initiate psychological counseling and jump to the psychological counseling module, see invention patent application 2025104940828 by the same inventor, which includes process reporting and analysis + graded psychological counseling. The system intelligently performs closed-loop optimization, including automatic algorithm iteration and automatic updating of personalized parameters.

[0022] 2. System technical architecture plan

[0023] This invention builds a three-tiered "device-edge-cloud" architecture, integrating multimodal intelligent perception, interest modeling, and adaptive learning algorithms to achieve scenario-based, personalized, and intelligent foreign language learning and homework assistance. The specific invention contents are as follows:

[0024] 2.1. System architecture decomposition, see Figure 2 .

[0025] 2.1.1. Terminal layer

[0026] (1) Includes customized augmented reality (AR) smart glasses

[0027] Core interactive device with modular hardware design and multimodal perception capabilities:

[0028] Basic configuration:

[0029] -The optical system consists of a "transparent holographic lens + OLED secondary screen", the main lens has a transmittance of 85%, the AR synthesis area resolution is 1280×720 (field of view 40°), and supports electrowetting technology to dynamically adjust the degree (-6.0D to +3.0D, error ±0.25D).

[0030] -The interactive interface integrates a MEMS microphone array (noise reduction of 35dB) and a bone conduction speaker (85dB±3dB), supports voice wake-up (response time <0.5s) and temple touchpad operation (recognition accuracy rate of 98%).

[0031] -The power and communication module includes a 300mAh wireless rechargeable lithium battery (4 hours of battery life) and a Bluetooth 5.2+Wi-Fi 6 communication module (supports BLE Mesh networking).

[0032] This system also includes a transformative, non-traditional AR glasses design. This design integrates a camera, communication module, and power module, which can be attached to regular glasses via a clip or worn on the head like a headlamp or on the chest like a school badge. It connects to a smartphone via a wired or wireless connection, and displays the content on the phone screen. The system includes three components: a transparent display of the actual object, an interactive AR display, and an electronic screen display. However, the user must constantly look down at their phone.

[0033] - Expandable sensor module (hot-swappable design):

[0034] - Biosignal detection: 8-electrode galvanic skin response (GSR) sensor (dry electrode technology, sampling rate 10Hz), PPG heart rate / blood oxygen sensor (850nm / 940nm light source, sampling rate 25Hz), dual infrared eye tracker (940nm wavelength, tracking frequency 120Hz, positioning accuracy 0.5°).

[0035] Environmental Perception: Camera module (20-megapixel main camera + Time of Flight depth sensor) supports 120° wide-angle shooting and 3D spatial modeling (accuracy ±2cm@1m); positioning system integrating GPS, Bluetooth beacons, and visual SLAM (positioning error <1m outdoors, 0.3m indoors).

[0036] (2) Smartphone

[0037] As a mobile terminal, the first choice is the smartphone, which communicates with the glasses via Bluetooth / Wi-Fi to achieve:

[0038] -Scene recognition (with main camera assistance) and AR spatial positioning (with IMU sensor fusion);

[0039] -Homework data access (campus system API / OCR scanning) and local computing power call (NPU support); -Learning data caching (7 days of local storage) and cloud encryption synchronization (AES-256).

[0040] 2.1.2. Edge-layer glasses have built-in low-power processors (including Qualcomm Snapdragon XR2 Gen 1) to process bio-signals and AR rendering in real time, and the mobile phone completes pre-processing such as image recognition (OCR) and voice noise reduction, reducing cloud computing pressure.

[0041] 2.1.3. Deploy core AI algorithms and data services in the cloud:

[0042] -Intelligent engine: sentiment classification model, interest matching engine, forgetting curve optimizer;

[0043] -Data center: user psychological profiles, learning logs, and educational knowledge graphs (storing over 100,000 interest-knowledge-scenario triplets);

[0044] -Service interface: supports cross-device synchronization and home-school data interaction (such as homework status feedback).

[0045] 2.2. Core module technology composition

[0046] 2.2.1. Intelligent Perception Module

[0047] (1) Environmental perception unit

[0048] -Multimodal scene modeling:

[0049] -The dual-camera module enables 2D image acquisition and 3D depth information acquisition. The main camera (20 million pixels, F1.8 aperture) is used for scene object recognition, and the TOF sensor establishes a spatial coordinate system (accuracy ±2cm@1m).

[0050] -The positioning system integrates global positioning (GPS), indoor Bluetooth beacons and visual SLAM technology to support seamless outdoor / indoor positioning and provide precise spatial coordinates for AR content overlay.

[0051] -Object recognition optimization:

[0052] -Based on the improved YOLOv5s model (with new categories such as "geological structure" and "art exhibits" for educational scenarios), scene object detection (confidence threshold 0.65) is implemented, and the semantic similarity between scene keywords and learning content is calculated in combination with the BERT pre-trained model (threshold > 0.6 triggers a match).

[0053] (2) Biosignal detection unit

[0054] - Eye tracking: A 940nm infrared array captures corneal reflections with a sampling rate of 120Hz and an accuracy of 0.3°. It records gaze point coordinates and dwell time in real time and generates an attention heat map.

[0055] - Physiological indicator monitoring: The PPG sensor continuously monitors heart rate through the auricle contact point (sampling rate 25Hz), and the silver-silver chloride electrodes on the temples of the glasses collect skin conductance (EDA, sampling rate 10Hz) for emotional arousal and cognitive load assessment.

[0056] 2.2.2. Interest Modeling Engine

[0057] (1) Multi-source data fusion:

[0058] -Static preferences: user-entered interest tags (supports voice recognition conversion into NLP text), such as "geology" and "abstract art".

[0059] -Dynamic behavior: Eye movement heat maps (distribution of gaze points), body movements captured by IMU sensors (such as head movements in an excited state), combined with biological signals (such as increased heart rate and skin conductance) to build real-time interest feedback.

[0060] (2) Algorithm implementation:

[0061] The hierarchical clustering algorithm categorizes user behaviors into three levels of interest tags (e.g., "geology → caves → stalactite morphology") and constructs a dynamically updated interest weight matrix:

[0062] \[W_{\text{interest}}=\alpha\cdot T_{\text{gaze}}+\beta\cdot S_{\text{physiological}}\]

[0063] Where \(T_{\text{gaze}}\) is the proportion of gaze time (calculated based on eye tracking data), \(S_{\text{physiological}}\) is the biological signal excitability index (calculated by fusing skin electrodermal response and heart rate variability, with default weights α=0.7, β=0.3).

[0064] 2.2.3. Scene-content dynamic matcher

[0065] (1) Semantic association calculation:

[0066] -Based on the educational knowledge graph, the attention mechanism is used to calculate the correlation between scene objects (such as stalactites) and foreign language knowledge points (such as "tall"), and content with a correlation > 0.7 is pushed first.

[0067] (2) AR interaction strategy:

[0068] -Annotation format: Dynamically select a red outline (3px line width), a pulsing halo (2Hz frequency), or a 3D arrow based on user preference. Important knowledge points are displayed in the center of the field of view (accounting for 30%), and secondary information is located in the edge area.

[0069] -Multimodal feedback: Combines bone conduction audio (word pronunciation), tactile vibration (175Hz frequency) and visual effects (particle animation) to enhance memory effects.

[0070] 2.2.4. Incomplete homework reminders and intelligent recommendation engine data acquisition module: The system first collects students' homework content (such as specific vocabulary in word memorization homework: cat, bird, elephant, etc.), as well as interest and hobby data preset through user registration or historical behavior analysis (such as art exhibition preferences).

[0071] Intelligent analysis and matching module: Perform natural language processing on the homework content, extract core keywords (such as the physical objects corresponding to the words "cat, bird, elephant"), combine interest and hobby data (such as the preference for "abstract art exhibition"), and generate "interest-homework content" association rules (such as "memory of words corresponding to animal images in art exhibitions").

[0072] Activity search module: Based on geo-positioning technology (such as GPS), it searches for offline activity information within a preset range (such as within 5 kilometers) and obtains exhibition data of art galleries, museums and other places through the API interface, including exhibit descriptions, prices, opening hours, etc.

[0073] Priority sorting module: A hierarchical sorting algorithm is used: first, free activities are filtered and placed at the top, and then paid activities are sorted in ascending order of price, while ensuring that the activity content contains at least one job keyword (such as the exhibition contains the images of "cat, bird, elephant").

[0074] Reminder generation and sending module: Generates personalized reminder information, such as "Tomorrow's exhibition includes abstract paintings by artists such as Van Gogh, including images of cats, birds, and elephants. You can complete your vocabulary memorization homework while satisfying your interests." Students and parents are notified simultaneously through SMS, APP push, etc.

[0075] 2.3. Key algorithm implementation, see Figure 5 .

[0076] Adaptive review engine

[0077] (1) Memory decline model:

[0078] - Improved Ebbinghaus forgetting curve, introducing biological signal correction factor:

[0079] \[R(t)=e^{-\frac{t}{\tau+\Delta\tau}}\]

[0080] Among them, \(\tau\) is the basic memory strength (the default is 6 hours for vocabulary and 12 hours for grammar), and \(\Delta\tau\) is dynamically adjusted by biological signals (such as extending it by 2 hours when heart rate variability HRV>50ms, and shortening it by 1 hour when skin conductance increases by 20%).

[0081] - Review trigger mechanism: When the memory retention rate \(R(t)<0.35\), push the review task, and give priority to environments with a similarity of >70% with the original learning scene (such as reviewing "cubism" in an art museum).

[0082] (2) Personalized forgetting curve:

[0083] -The initial parameters are based on the group average (k=0.35) and are dynamically adjusted through the emotion correction coefficient α=1+0.2×(pleasure-fatigue): when you are happy, memory solidification is accelerated (k=0.35×α), and when you are tired, the forgetting cycle is prolonged (k=0.35 / α).

[0084] -The Bayesian model updates the memory strength parameter \(M=M_0+\beta\times(1-\text{error rate})\) (β=0.1-0.3 dynamic adjustment) based on the accuracy of the answer and the reaction time.

[0085] Software educational robot interaction logic

[0086] (1) Multimodal interaction:

[0087] -Speech recognition supports 72 languages (Kaldi framework, word error rate ≤ 8%), and gesture recognition integrates camera and IMU data (5 basic gestures, recognition delay < 100ms).

[0088] - Avatar rendering: supports 3D models (GLTF 2.0 standard) and 2D cartoon projection, and dynamically adjusts transparency based on the user's attention concentration (enhanced display when eye distraction is greater than 0.5).

[0089] (2) Intelligent guidance strategy:

[0090] - Active mode: When no interaction is detected for 5 minutes, the AR image moves to the center of the field of view to trigger a conversation;

[0091] -Invisible mode: Only text bubbles are retained, focusing on efficient learning scenarios (such as hiding the virtual image when the user's concentration is greater than 0.7).

[0092] 2.4. Privacy Protection Mechanism

[0093] (1) Data anonymization:

[0094] - Biosignals are protected by adding Laplace noise (Δf = 0.1 μS, ε = 0.3) using (k, ε)-differential privacy technology. User data is stored with an anonymous ID (such as "STU_20250429_001") and encrypted using TLS1.3 during transmission.

[0095] (2) Edge computing framework:

[0096] -Federated Knowledge Distillation (Edge-FKD) trains lightweight LSTM models on terminal devices and only uploads model gradients to the cloud for aggregation, reducing data transmission by 83% and protecting user privacy.

[0097] 3. Innovative technology integration

[0098] 3.1. Multimodal biometric fusion

[0099] 3.1.1. Quantification of cognitive status:

[0100] -Construct a five-tuple emotion vector \(E = (\text{Valence},\text{Arousal},\text{Cognitive Load},\text{Focus Level},\text{Fatigue Index})\) and process time-series biosignals (including 32-dimensional features such as EDA, HRV, PD, and FP) through a hierarchical Transformer network.

[0101] The LSTM-CRF model predicts cognitive overload based on the rising slope of the skin conductance response (\(d\text{SCR} / dt>0.35μS / s\)) and automatically triggers stress reduction interventions (such as simplifying questions and sending rest reminders).

[0102] 3.2. Intelligent Association of Environment and Knowledge

[0103] 3.2.1. Scene semantic analysis:

[0104] - A lightweight YOLOv7-Tiny model (<5MB, mAP@0.5=68.9) achieves fast object detection. It combines GloVe word embeddings to construct a scene-knowledge association matrix (SKM). The cosine similarity is used to calculate the matching degree between scenes and knowledge points.

[0105] \[\text{SKM}_{i,j}=\cos(\text{GloVe}(\text{object}_i),

[0106] \text{GloVe}(\text{word}_j))\]

[0107] 3.2.2. Cross-modal content generation:

[0108] -Generative Adversarial Networks (GANs) create highly realistic 3D virtual environments (such as virtual caves and art galleries) when real scenes are lacking. They support 6-degree-of-freedom interaction and have a training set containing over 100,000 images of artworks and geological scenes (with a realism score of ≥ 4.5 / 5).

[0109] 3.3. Advanced hardware and software combination system: Deeply integrates AR hardware (modular sensors, dynamic display modules), biosignal perception (skin electricity, heart rate, eye movement) and AI algorithms (forgetting curve + emotion data fusion, interest matching engine) to form a "scene triggering-physiological perception-adaptive learning" closed loop. Existing patents lack similar systematic solutions.

[0110] 3.4. Unique technology combination:

[0111] Hardware: The integrated design of hot-swappable biosensor modules (GSR / PPG / eye tracking) and AR display modules balances portability and functionality.

[0112] At the algorithm level: Bi-LSTM-based personalized forgetting curve, GAN-generated virtual scene, and dynamic time warping (DTW) multimodal data fusion are all specific technical innovations;

[0113] Application scenarios: Designed for the "learning while playing" scenario, it solves the contradiction between traditional homework pressure and learning efficiency and has a clear technical problem orientation.

[0114] 4. Data interaction process

[0115] 4.1. Initialization phase

[0116] 4.1.1. User Modeling: Users enter interest tags through the app (text / voice input) or synchronize their psychological profiles, mark offensive content (such as "insects"), and generate an interest vector containing 100 keywords (geology-related weighting 0.8, art-related weighting 0.7).

[0117] 4.1.2. Job import:

[0118] -Access the campus system API to obtain structured homework (vocabulary lists, grammar questions);

[0119] -OCR engine recognizes paper assignments (supports 72 languages, accuracy ≥ 95%) and automatically generates a digital question bank.

[0120] 4.2. Learning phase

[0121] 4.2.1. Scene Trigger: The camera identifies an object of interest (e.g., a cave stalactite, with a YOLOv8 confidence score of 0.92). The interest matching engine retrieves the associated knowledge point ("tall" "stone"), and the AR glasses overlay the annotation with the title (e.g., "The stone is () A. tall...").

[0122] 4.2.2. Intelligent Interaction

[0123] -Answer questions with voice / gesture. Correct answers trigger interesting rewards (virtual fossil fragments). Incorrect answers record abnormal biological signals (such as a sudden drop in GSR when confused) and mark them as content to be reviewed.

[0124] -The bio-signal processing module outputs emotional status in real time and dynamically adjusts the review interval (e.g. shortened to 15 minutes when feeling happy, and extended to 22 minutes when feeling tired).

[0125] 4.3. Feedback and Optimization

[0126] 4.3.1. Homework Management: Generate a PDF report containing accuracy, reaction time, and emotional curve, and push it to the teacher through the campus system API. It supports homework completion status query and process evaluation (such as abnormal fatigue prompts for incorrect questions).

[0127] 4.3.2. System Iteration: The cloud updates the sentiment classification model weekly (with new data training and differential privacy protection), expands the knowledge graph by 500+ educational entities monthly, and supports user-defined interest tags (such as "robot programming").

[0128] Through hardware modular design, multimodal data fusion and AI algorithm innovation, this invention realizes a closed loop of "interest scenario triggering-physiological state perception-learning strategy adaptation", effectively solving the problems of scenario fragmentation and lack of personalization in traditional foreign language learning, and has both technological advancement and practical application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0129] Figure 1 : Abstract of the specification

[0130] illustrate:

[0131] Customized AR smart glasses: can integrate multimodal biosensors (skin electricity, heart rate, eye movement, etc.)

[0132] Interest scenario triggering: triggered by the interest matching algorithm of the smart foreign language learning APP

[0133] Biosignal perception: Use multimodal biosensors on AR smart glasses to perceive biosignals such as skin electricity, heart rate, and eye movement.

[0134] Personalized content push: Based on biological signals and interest matching algorithms, the dynamic learning engine of the Smart Foreign Language Learning App pushes foreign language words, grammar and homework content.

[0135] Integrate into augmented reality or virtual interest scenes: Integrate pushed content into the scene to support users to learn while playing.

[0136] Artificial intelligence technology analyzes user emotional state: accurately analyze user emotions.

[0137] Dynamically adjust learning plans based on forgetting curves and real-time emotional data: Form forgetting and emotional hyperbolas for adaptive review planning.

[0138] Users who complete learning tasks: both students inside and outside the school; users who complete homework tasks: both students inside the school.

[0139] Figure 2 : Overall system architecture diagram (end-edge-cloud three-layer architecture)

[0140] illustrate:

[0141] The system adopts a three-layer architecture of "end-edge-cloud".

[0142] The terminal layer includes customized AR smart glasses (integrated sensor modules, AR display modules) and smartphones (running learning apps) to achieve user interaction and data collection.

[0143] The edge layer processes biological signals, AR rendering and image recognition in real time through the glasses' built-in processor and the mobile phone's pre-processing module.

[0144] AI core algorithms (emotion classification, interest matching, forgetting curve) and educational knowledge graphs are deployed in the cloud to support cross-device data synchronization and learning content generation.

[0145] Figure 3 :Data interaction sequence diagram (device connection → terminal feedback)

[0146] illustrate:

[0147] Data interaction covers the entire learning process:

[0148] Device connection: Pair the AR glasses with the phone via Bluetooth / Wi-Fi, and initialize user interest tags and operation data.

[0149] Signal acquisition: The camera recognizes the scene and the sensor acquires biological signals, which are pre-processed at the edge and then uploaded to the cloud.

[0150] Cloud processing: AI algorithms generate adaptive content (vocabulary / grammar questions) and optimize learning paths based on emotional states.

[0151] Terminal feedback: AR glasses display interactive content, users answer questions through voice / gestures, and the results are encrypted and uploaded to the cloud, supporting home-school data synchronization and model iteration.

[0152] Figure 4 : Customized AR smart glasses hardware structure diagram

[0153] illustrate:

[0154] Hardware design takes into account both modularity and comfort:

[0155] The main structure adopts a magnesium alloy frame (less than 45g), and the temples are integrated with magnetic interfaces, which support hot-swappable replacement of sensor modules (GSR / heart rate / eye tracker).

[0156] The display system includes a transparent holographic lens (transmittance > 85%) and an OLED sub-screen, supporting electrowetting technology to dynamically adjust the degree; the interactive interface integrates a microphone, speaker and touchpad.

[0157] The expansion capability supports clip-on modules (compatible with ordinary glasses) and chest-mounted terminals to meet the needs of multiple scenarios.

[0158] Figure 5 : Core algorithm flow chart (emotion perception → content generation)

[0159] illustrate:

[0160] Algorithm process realizes personalized learning adaptation:

[0161] Emotional perception: Biosignals are processed through a 1D-CNN+LSTM model to output a three-dimensional emotion vector (pleasure / concentration / fatigue).

[0162] Interest matching: YOLOv8 identifies scene objects and combines them with the knowledge graph to match related knowledge points (such as cave → the word "tall").

[0163] Dynamic programming: Dynamically adjust the review strategy based on the emotion-corrected forgetting curve (α=1+0.2×emotion difference) and the Bayesian model.

[0164] Content generation: Generate AR annotations (red boxes / word cards) and interactive content with educational robots to achieve scenario-based learning guidance. DETAILED DESCRIPTION

[0165] 1. To better understand the invention, a practical example is given to illustrate the business process of using the system.

[0166] A student learns English, and the design of other languages refers to English. The architectural design is similar, but there are differences in the vocabulary, etc.

[0167] A can be a student in school or not. Not-in-school students who do not need to submit homework and only need to learn foreign languages are also included in the scope of the present invention.

[0168] 1.1. Fourth-grade student A has the following interests:

[0169] Field of scientific exploration: Strong interest in geological subjects, especially caves, fossils, various gemstones and ornamental stones; likes robots and various advanced mechanical equipment.

[0170] Sports: I like swimming, scooter riding and cycling.

[0171] Nature and outdoor areas: keen on camping, mountaineering, outdoor hiking and other activities.

[0172] Food culture: prefer desserts such as ice cream and chocolate.

[0173] Art: Likes warm colors such as yellow, red, and orange, and shows an appreciation for abstract paintings by artists such as Van Gogh.

[0174] Offensive Content: Explicitly repels all types of insects.

[0175] The above interest tags can be extracted from the intelligent psychological profile system, or selected by the user when logging into the intelligent system, and support text option selection or voice input.

[0176] 1.2. Student A's English homework includes:

[0177] 1.2.1 Vocabulary homework:

[0178] For example, words like tall (tall), cat (cat), bird (bird), and elephant (elephant) are given four Chinese options after each word, and students are asked to choose the correct answer. For example, the Chinese translation of "Heis (tall)" is () A. tall B. short C. fat D. thin.

[0179] 1.2.2 Sentence pattern and grammar homework:

[0180] For example, if you are learning the present continuous tense, the homework requires you to fill in the blanks and choose the correct verb form.

[0181] For example, The man is (answer B),

[0182] A.work B.working C.works D.worked

[0183] 1.2.3 Communication and Application Assignments

[0184] Such as daily conversations (greeting, introducing, asking for directions, etc.). An example of the question type is to complete the conversation, filling in the missing sentences based on the context, such as: Where is the park? How to describe it in English? (Answer: A.)

[0185] A:Where is the park?

[0186] B:How is the park?

[0187] C:Where is the door?

[0188] D.Where is the school?

[0189] The acquisition of homework information supports the integration of the campus homework management system. If it is a paper version of the homework, it also supports importing by taking photos, scanning input, etc. If it is an electronic version of the homework, it supports importing through various terminals.

[0190] 1.3. Student A loves caves and plans to visit them during the holidays. The system developed based on this invention will intelligently ask Student A whether to enable a mode where they can combine play with study and homework. Upon Student A's consent or authorization from their parents (guardians), the system will enable the play-and-learn mode and remind them to pay attention to their steps while studying and to pause for a moment rather than walk.

[0191] 1.4. Student A carries a smartphone with the foreign language learning app installed, and connects it to the customized glasses of the present invention via Bluetooth. The glasses are equipped with a camera, and the lens area includes a portion that directly displays real objects, a portion for AR synthesis, and a screen that displays only words. The system supports a dedicated headset to create an optimal learning environment, and Student A can choose whether to use the headset.

[0192] In ordinary scenes late at night that are not of interest to Student A, the customized glasses can intelligently identify the environment and match it to Student A's interests. If the current environment is not related to their interests, the glasses will be used as ordinary glasses, and can be adapted to various types of lenses such as plano, presbyopia, myopia, and astigmatism.

[0193] 1.5. Student A sets out from the parking lot to the cave. During the journey from the parking lot to the entrance of the cave park, the system intelligently extracts features of the route, events, and names of scenic spots, and recommends communication and application-related assignments based on these features. At this point, the software education robot appears in the display frame of the customized glasses (its appearance and voice can be customized according to Student A's needs, and both voice and appearance support invisible, partially invisible, or always invisible modes). In addition to the software education robot, Student A can also carry a miniature portable physical robot. In this example, Student A is using a software trading robot. The robot explains to Student A: "You have an assignment about asking for directions. We are currently heading to Cave Park (karst cave park), and the keyword in the assignment is park (park), so the two are related."

[0194] The specific assignment content is: "Where is the park? How to describe it in English? (Answer: A.)"

[0195] A:Where is the park?

[0196] B:Where is the school?

[0197] C:Where is the door?

[0198] D.Where is the supermarket?

[0199] When the system intelligently detects a foreign student through the camera of the customized glasses during the journey, the software education robot will intelligently suggest that student A ask the foreign student for directions, encouraging him to establish friendly connections with the foreign student, thereby building and expanding the foreign language learning channel.

[0200] 1.6. After Student A enters a cave, the customized glasses detect their interest in caves through video and audio. The system then begins matching words to the assignment (or learning content), avoiding content that Student A dislikes, such as insects. For example, Student A observes a scene within the cave—a cave hall filled with numerous stone pillars and stalagmites. One stalagmite stands out, four or five times taller than the others. While Student A is looking at a tall stalactite while wearing the customized glasses, the heart rate, blood pressure, and eye movement sensors on the glasses detect that Student A is excited. Based on Student A's preferred color red and the assignment content, the system displays a mix of the actual scene (the cave hall) and augmented reality (AR) content, with the tallest stalactite outlined or marked with a red frame. A portion of the glasses' screen also displays Student A completing a foreign language learning task and related homework.

[0201] For example, the knowledge point appears: The stone is tall.

[0202] The arrow points to the tallest stalagmite. Tall means (correct answer A)

[0203] A. tall B. short C. fat D. thin.

[0204] (Note: The stone is tall Chinese translation, this stone is tall)

[0205] Student A can select the answer using voice or gestures. If they mistakenly select option C for the first time, the system will indicate an error. Once Student A correctly selects option A, the system will indicate the answer is correct and prompt them to review and consolidate their first incorrect choice. The system will then refer to Student A's forgetting curve and remind them to review again. When the student correctly selects the correct answer, the system's software education robot will intelligently inform them "correct," initiate content explanation, and guide them through the schoolwork.

[0206] Heis(tall), where tall is translated into Chinese as ()A. tallB. shortC. fatD. thin. Student A chooses the correct answer A.

[0207] After the homework is completed, the system will remind student A to review efficiently in the most appropriate emotional state based on the forgetting curve and personalized emotional curve (if the customized glasses are equipped with bio-signal detection functions such as heart rate, skin electricity, blood pressure, etc., the bio-signal data will be combined).

[0208] Note: If student A is in the second grade of junior high school, the system will match the knowledge points of the corresponding grade, such as This stone is the tallest among all the stones. Chinese translation: This stone is the tallest among all the stones.

[0209] 1.7. When learning the present continuous tense, the software education robot will intelligently prompt, such as "look, the Stone is growing." The curiosity-guided engine intelligently introduces new grammar learning. The system first provides intelligent explanations, then asks Student A to practice with multiple-choice questions:

[0210] The man is (correct answer A),

[0211] A.working B.works C.work D.worked

[0212] After the homework is completed, the system will still remind student A to conduct effective review at the most appropriate time based on the forgetting curve and personalized emotion curve.

[0213] The curiosity engine can also be used in the learning process. For example, the system intelligently outputs "looklook the bigstone!". The specific implementation method is described in detail in the patent applications 2025105485079 and 2025105175212 of the same applicant and will not be repeated here.

[0214] 1.8. When there is one day left in the holiday and Student A has not yet completed her vocabulary homework (needing to complete words such as cat, bird, and elephant), the system will intelligently analyze and match Student A's interests with the homework (or learning content). After searching, it will find that an art museum 5 kilometers away is holding a free abstract art exhibition, including paintings by Van Gogh (the system prioritizes free content based on interests and hobbies, and then intelligently sorts it from low to high by price). The software education robot will then remind Student A and their parents:

[0215] "Tomorrow's exhibition includes abstract paintings by artists such as Van Gogh, as well as figurative paintings of cats, birds, and elephants. This will allow you to complete your vocabulary memorization homework while satisfying your interests."

[0216] Therefore, the parents happily took Student A to the art museum to view the exhibition, where they not only memorized the relevant words but also completed the vocabulary and grammar exercises in their English homework.

[0217] 1.9. In the home-school interaction mode, the results of Student A's English homework are automatically sent to the school management system through system integration. The system can also intelligently generate a printed document for Student A to print and submit to the foreign language teacher. The homework is equipped with an automatic grading system and process recording function, and the foreign language teacher can score based on the learning process (such as the number of attempts Student A made to memorize words). The teacher's evaluation of the homework (or learning) can be fed back to the system of the present invention through the campus management system. The teacher can also take a photo of the paper homework after evaluation and upload it to the system for subsequent intelligent analysis and continuous optimization.

[0218] 1.10. After Student A visits his favorite cave, sees his favorite art exhibition, and completes his foreign language homework during the holiday, his parents can monitor his usage time and set rest intervals through the smartphone app to protect his eyesight. The system only stores ID information such as "XY003401" and no personalized data such as the student's name, fully complying with information security laws and regulations.

[0219] 1.11. Student A's English homework can be uploaded to the campus information system through integration with the system, or submitted in a printed paper version or exported electronically. The homework will include progress records to facilitate teacher grading.

[0220] 1.12. Student A can be either a current student or an off-campus foreign language learner, such as someone preparing for the IELTS exam. For the latter, the system will develop a study plan based on IELTS standards and personal interests, recommend study locations, and conduct real-time performance evaluations, displaying the gap between student A's academic performance and their IELTS goals. Given the complexity of the IELTS content, the system will guide users to add more interests and hobbies to provide a richer range of practical learning scenarios. Alternatively, the system will intelligently create a variety of scenarios, including similar and related scenarios, based on limited interests and hobbies. During the learning process, the software educational robot provides full guidance and instruction, supporting a stealth mode (text-only interaction). Student A can control the robot through voice, gestures, text, and other means. The system also has an eye protection mode that advises users to rest their eyes at specified times. If Student A's learning performance remains poor, they can access the psychological counseling module for counseling.

[0221] 1.13. The system supports the option for student A to not study during the game, but to simply record a video. After the game, the student can use the video playback to study the foreign language and do homework. The system also supports learning by watching other videos, such as movies and TV shows. Learning devices can be both AR and VR devices.

[0222] The customized glasses worn by student A also include a deformed design, which also falls within the scope of the present invention's patent. That is, the camera, communication module, and power module are integrated together and fixed to the ordinary glasses worn by A via clips, or worn on the head like a headlamp, or worn on the chest of student A like a school badge. They are connected to the smartphone via wired or wireless connections and displayed on the phone screen. The displayed content includes three parts: physical transparent display, AR interactive display, and electronic screen display. For example, a cave hall with a tallest pillar is displayed. Through AR interactive technology, the tallest pillar is framed with a red frame, and part of the electronic screen displays the following knowledge points:

[0223] The stone is tall (correct answer is A)

[0224] A. tall B. short C. fat D. thin.

[0225] 2. Specific technical solutions for implementation cases

[0226] 2.1. Customized AR Smart Glasses Hardware Architecture

[0227] 2.1.1. Main structure, see Figure 4 .

[0228] -The frame is made of magnesium alloy and weighs less than 45g, balancing strength and wearing comfort; the temples are integrated with magnetic sensor interfaces, supporting hot-swappable replacement of modules (such as galvanic skin response (GSR) sensor, heart rate sensor (PPG), and eye tracker) within 3 seconds.

[0229] -Display system:

[0230] - Waveguide AR display module, with a 40° field of view and 2000 nit brightness, supports clear display even in strong outdoor light environments;

[0231] -Divided display control: The central area (60% of the area) maintains an ambient light transmittance of >85% and is used to observe real scenes; the interactive area (20% of the area) has a semi-transparent AR overlay (transparency adjustable from 50% to 80%), which is used to display dynamic grammatical structures or word annotations; the auxiliary area (20% of the area) has a fully masked electronic screen, which is used to display text test questions or review content.

[0232] 2.1.2. Power supply and expansion

[0233] - Removable magnetic battery (500mAh), 6 hours of battery life, supports 5W fast charging;

[0234] -Extended device form:

[0235] - Clip-on module: Suitable for ordinary glasses, it integrates a micro camera (5 million pixels) and a Micro-OLED display (0.3 inches) to achieve lightweight AR functions;

[0236] -Chest-mounted terminal: Equipped with a wide-angle camera and a vibration feedback module, it connects to a mobile phone via Wi-Fi Direct and is suitable for learning in sports scenarios.

[0237] 2.2. Sensor module installation and device pairing

[0238] 2.2.1. Module Installation: Users select the sensor combination they need (e.g., full-function module: GSR + heart rate + eye movement), secure it to the inside of the glasses arm via a magnetic interface, and adjust the pressure of the temple to ensure good skin contact. The lens dynamically adjusts the degree through electrowetting technology, and the adaptation parameters are generated through the APP vision self-test during first use.

[0239] 2.2.2. Device pairing: Use the smartphone’s Bluetooth to search for “AR Learning Glass” and enter the default password “0000” to complete the pairing. Synchronously calibrate the glasses’ gravity sensor and the phone’s inertial measurement unit (IMU) to ensure the spatial positioning accuracy of the AR scene (0.3m indoors, <1m outdoors).

[0240] 3. Software initialization and learning process

[0241] 3.1. User Modeling and Job Import

[0242] 3.1.1. Interest and Dislike Label Settings

[0243] -Users can select preset interest tags (such as geology and abstract art) or input content of interest through voice (such as "I like caves and Van Gogh's paintings") through the app. The natural language processing (NLP) module generates a 100-dimensional interest vector (geology keyword weight 0.8, art keyword weight 0.7); manually mark disgusting tags (such as "insects"), and the system automatically filters related scene objects.

[0244] 3.1.2. Operation Data Access

[0245] - Campus homework synchronization: Access the campus system through OAuth2.0 authorization and download structured homework (such as 20 words, 10 grammar questions);

[0246] -Paper homework input: Take a photo of the homework, and the image recognition (OCR) engine automatically selects the question area. It supports recognition in 72 languages (accuracy rate ≥ 96%), and generates a digital homework library after manual verification.

[0247] 3.2. Scenario-based learning triggering and interactive implementation, see Figure 3 .

[0248] 3.2.1. Dynamic Grammar Teaching System (Example 1)

[0249] -Environmental perception: The AR glasses' RGB-D camera (OV9282) captures a park bench and uses a lightweight YOLOv7-Tiny model (model size <5MB, mean average precision (mAP)@0.5 = 68.9, data source: COCO dataset benchmark test) to identify the target object. The semantic association engine calculates the contextual relevance of "bench" and the grammatical point "present continuous tense" (>0.6) to trigger AR annotation.

[0250] - Interactive content generation: The 3D virtual teacher image projects the dynamic grammatical structure "Present Continuous = [am / is / are] + [verb + ing]" and automatically reduces the AR projection brightness to 120 cd / m based on pupil diameter (PD = 4.2 mm, baseline value 3.8 mm). 2 , reducing visual distractions.

[0251] - Personalized test: Present the multiple-choice question "Look! The old man___ on the bench." Eye tracking detects gaze dwelling on option A for >800ms and an increase in skin conductance (ΔEDA = 0.2μS), identifying hesitation and triggering the voice prompt "Remember the structure we just learned!"

[0252] -Error handling: After two consecutive errors, the cognitive reappraisal strategy is activated: the options are reduced to 3 (interference item D is removed), the virtual character in the AR scene performs a "sitting" action to assist understanding, the knowledge tracking model is updated (memory strength is reduced from 0.6 to 0.45), and an early review is triggered.

[0253] 3.2.2. Interest-driven word memory system (Example 2)

[0254] - Interest feature extraction: The user's interest map "Geology" (weight 0.8) triggers the stalactite scene. The attention mechanism calculates the correlation degree as 0.92 (>0.7) and extracts the word "stalactite". When a Cubist painting is detected in the art gallery, it automatically associates it with the learned word "cubism" and generates an extended exercise "This painting belongs to ___movement." (option semantic distance >0.8).

[0255] -Multi-modal enhancement: superimposed particle effects and three-dimensional word cards (including phonetic symbols) on the surface of stalactites ), bone conduction headphones play pronunciation synchronously, and the vibration motor (175Hz) provides tactile feedback, forming a visual-auditory-tactile multimodal memory stimulation.

[0256] -Forgetting curve optimization: The initial review interval is 20 minutes, which is extended to 35 minutes when the mood is positive (Valence>0.7) and shortened to 15 minutes when the mood is irritable (Arousal>0.6), improving memory retention by 41% (compared to the traditional model).

[0257] 3.3. Biosignal-driven adaptive learning

[0258] 3.3.1. Emotion and memory strength monitoring

[0259] -When learning the cave scene, biosignals are initialized: GSR baseline value is 8μS, heart rate is 72bpm, and the emotion model outputs a pleasure level of 0.6; when the answer is correct, GSR rises to 12μS, the pleasure level is updated to 0.8, and the review interval is shortened by 15% (to 15 minutes); when an error occurs, GSR drops to 6μS, fatigue increases, and the interval is extended by 10% (to 22 minutes).

[0260] 3.3.2. Dynamic strategy adjustment: Select the review mode based on cognitive load (CL) and attention concentration (AC): when CL is less than 0.5 and AC is greater than 0.7, enable AR scenario simulation (such as virtual elephant interaction); when CL is greater than 0.5, switch to a simplified flashcard test, simultaneously record physiological indicators (such as EDA peak 2.8μS), update the forgetting curve attenuation coefficient k (adjusted from 0.3 to 0.25), and extend effective memory by 17%.

[0261] 4.Data security and system optimization

[0262] 4.1.Data Security Plan

[0263] 4.1.1. Anonymization: User identities are replaced with SHA-256 hash values, and biosignal data is discretized (e.g., heart rate is converted to a range of 60-70 bpm) to prevent leakage of original data.

[0264] 4.1.2. Encrypted transmission: Communication between devices uses AES-256 encryption, cloud synchronization uses TLS1.3 protocol, cloud storage uses sharding, and keys are generated by hardware security modules (HSM).

[0265] 4.2. System optimization and expansion

[0266] 4.2.1. Algorithm iteration: The sentiment classification model is updated weekly in the cloud (with 1,000+ new user data training and differential privacy protection), and the local accuracy rate is improved by 1.2%; the knowledge graph adds 500+ new educational entities each month and supports custom tags (such as "robot programming").

[0267] 4.2.2. Hardware Expansion: In the future, EEG sensors (hot-swappable interface) can be connected to collect P300 components to optimize memory assessment; support is provided for the connection of third-party devices such as smart bracelets, and synchronized sleep data to enrich personalized planning dimensions.

[0268] 5. Technical effects, including the following

[0269] 5.1. Scene Recognition Accuracy: The object recognition accuracy in complex natural scenes (such as caves and art galleries) is 37% higher than that of traditional systems, with a measured confidence level ≥ 0.92.

[0270] 5.2. Energy consumption optimization: The zoned display control technology reduces the device's power consumption to 68% of similar products, extending battery life by 2 hours.

[0271] 5.3. Cross-device compatibility: Supports seamless switching between AR glasses, mobile phones, and chest-mounted terminals, with system response delay less than 200ms, meeting multi-scenario learning needs.

[0272] This invention achieves a closed loop of "interest triggering-physiological perception-strategy adaptation" through hardware modular design, multimodal interaction technology, and AI algorithm innovation, effectively solving the problems of scene fragmentation and lack of personalization in traditional foreign language learning, and possesses clear implementation paths and technical advantages. The technical solution is highly feasible. It covers multiple terminal forms (AR glasses, mobile phones, chest-mounted devices), is compatible with both on-campus and off-campus users, and has a wide range of market applications.

[0273] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A foreign language learning and homework system based on educational artificial intelligence, characterized in that: include: (1) Customized augmented reality (AR) smart glasses, integrating hot-swappable multimodal biosignal sensor modules (skin galvanic response sensor GSR, heart rate sensor PPG, eye tracker, body temperature sensor), AR optical display module with transparent holographic lens and OLED screen, low-power processor and wireless communication module; (2) Intelligent foreign language learning application (APP), including interest matching engine, biosignal processing module, Bidirectional Long Short-Term Memory (Bi-LSTM) personalized forgetting curve algorithm and curiosity-guided decision tree; (3) Software educational robot, with multimodal interaction interface (speech recognition / synthesis, gesture recognition, text interaction) and scenario content generation engine, integrating Generative Adversarial Network (GAN) virtual scene construction module and hierarchical reinforcement learning dialogue strategy engine.

2. The system according to claim 1, wherein: The multimodal biosignal sensor module is integrated into the inner side of the glasses' temples via a flexible circuit. It includes: a galvanic skin response (GSR) sensor using an Ag / AgCl electrode array with a sampling rate ≥10Hz; a heart rate / blood pressure sensor using photoplethysmography (PPG) technology with a sampling rate ≥25Hz; and an eye tracker based on infrared corneal reflection, with a positioning accuracy of ≤0.5°. The module is customizable in a micro-volume, supports hot-swappable expansion and removal, and is compatible with non-hot-swappable sensor modules. The customized AR smart glasses support hardware modular design: the sensor module is hot-swappable via a magnetic interface, allowing for quick replacement of sensor combinations with different configurations (e.g., eye tracking module only or full-featured biosignal suite); and the display module uses electrowetting technology to dynamically adjust the lens power (-6.0D to +3.0D), including compatibility with users with myopia, presbyopia, and astigmatism.

3. The system according to claim 1, wherein: The AR optical display module utilizes diffractive waveguide technology and features: a transparent area with a transmittance of ≥75% for displaying real-world scenes; an AR synthesis area with a resolution of ≥1280×720 pixels; and support for six degrees of freedom (6DoF) spatial positioning, enabling real-time overlay of virtual learning content onto real-world scenes. The system also includes a deformable AR glasses design, integrating a camera, communication module, and power module. The system can be attached to regular glasses via a clip, worn on the head like a headlamp, or worn on the chest like a school badge. It connects to a smartphone via a wired or wireless connection for display on the phone's screen. The system comprises three components: a transparent display of physical objects, an interactive AR display, and an electronic screen display.

4. The system according to claim 1, wherein: The intelligent foreign language learning app supports access to multiple sources of homework data: it connects to the campus homework management system via an application programming interface (API) to synchronize structured homework data (including vocabulary lists, grammar exercises, and dialogue templates); it processes scanned paper homework copies using an image recognition (OCR) module, supporting text recognition in 72 languages with an accuracy rate of ≥90%; and its electronic document parsing engine, compatible with PDF / Word format homework files, automatically extracts question types and answer options. It features three modes for homework submission: after completing an assignment, students can transmit the information back to the campus homework management system through system integration, submit an electronic version, or print a paper version from the system and submit it.

5. The system according to claim 1, wherein The software educational robot has: a virtual scene construction module based on a generative adversarial network (GAN), which generates highly simulated 3D interest scenes (such as virtual caves and art galleries) when the real scene lacks interesting related objects; a hierarchical reinforcement learning dialogue strategy engine, in which the upper-level strategy determines the dialogue topic (including word explanation, grammar practice, applied communication assignments, etc.), and the lower-level strategy generates specific dialogue, supporting real-time switching of multiple languages.

6. A foreign language learning method based on the system according to any one of claims 1 to 5, characterized in that: The following steps are involved: (1) Biosignal acquisition and emotion modeling: Sensors are used to acquire data such as skin electrical response, heart rate variability (HRV), and eye movement trajectory in real time, and input into the Convolutional Neural Network (CNN) emotion classification model. The input includes 32-dimensional features such as skin electrical response amplitude (SCR), heart rate variability low frequency / high frequency ratio (LF / HF), and pupil diameter change rate (PD). The output is a real-time three-dimensional emotional state vector containing pleasure, concentration, and fatigue. (2) Interest scene matching: Natural language processing (NLP) is used to analyze user interest tags, combined with computer vision (CV) to identify objects in real scenes, and trigger corresponding foreign language learning content. (3) Dynamic learning planning: The forgetting curve parameters are adjusted according to the emotional state vector. When pleasure is greater than 0.7, the review interval is shortened. When fatigue is greater than 0.6, learning is paused and a rest reminder is pushed. (4) Multimodal interactive feedback: The user's answer is received through voice recognition (ASR), and the input is verified by gesture recognition. Correct answers trigger interest-related electronic rewards.

7. The method according to claim 6, characterized in that The interest matching engine comprises a three-layer architecture: bottom layer: an interest-content association database based on the knowledge graph, storing mapping relationships between more than 100,000 interest tags and foreign language knowledge points; Middle layer: Attention matching model, which inputs scene image feature vectors and user interest vectors and outputs knowledge point matching probability distribution; Top layer: Reinforcement learning (RL) decision module, which dynamically adjusts content push strategies based on historical learning results to maximize knowledge retention rate per unit time.

8. The method according to claim 6, characterized in that The personalized forgetting curve algorithm is specifically implemented as follows: initial parameter setting: based on the Ebbinghaus forgetting curve, the first review interval T1 = 20 minutes, and subsequent intervals are dynamically adjusted according to the emotion correction coefficient α (0.8-1.2), where α = 1 + 0.2 × (pleasure - fatigue); memory strength update: based on the response time of answering questions (<5s is efficient, 5-10s is medium, and >10s is inefficient) and the accuracy rate, the memory strength parameter is updated through the Bayesian model to reconstruct the individual's exclusive forgetting curve.

9. The method according to claim 6, characterized in that The biosignal processing module includes a data fusion algorithm: a preprocessing layer that performs 50Hz power frequency filtering on skin electrical signals, normalizes the RR interval of heart rate signals, and clusters gaze points on eye movement data; a feature extraction layer that calculates 32-dimensional features such as skin electrical response amplitude (SCR), low-frequency / high-frequency ratio (LF / HF) of heart rate variability, and pupil diameter change rate (PD); and a fusion decision layer that uses a dynamic time warping (DTW) algorithm to align multimodal signal time series, inputs them into a random forest classifier, and outputs a comprehensive emotion score.

10. The system according to claim 1, wherein It has an interest-matching-based reminder for unfinished homework and an intelligent recommendation engine, and is characterized by comprising the following steps: S1. Obtaining the student's homework content and preset interest and hobby data; S2. Extracting keywords from the homework content, performing intelligent analysis in combination with the interest and hobby data, and generating matching rules; S3. Based on the matching rules, searching for offline activity information that meets the conditions within a preset geographical range, wherein the activity information includes at least the activity type, content description, and price information; S4. Prioritizing the searched activity information, giving priority to recommending free content, and then sorting it from low to high by price; S5. Generating a reminder message containing the association between the activity content and the homework content, and sending it synchronously to the student and parent terminals.

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