Kinesthetic learning system with ai-generated quizzes and customizable movement-based user responses

Abstract

A computer-implemented system for providing a kinesthetic learning experience generates quiz questions from educational content using one or more trained computational models configured to analyze textual, audio, or video-derived data. The system presents the quiz questions to a user and receives user responses in the form of detected physical body movements. The physical body movements are detected using computer vision and / or wearable sensor data and are interpreted as answer selections based on configurable movement-to-answer mappings. The system provides feedback indicative of answer correctness, tracks user performance data, and generates analytical output for individual or group evaluation. The system further supports integration with external educational platforms via an application programming interface.

Description

[0001] Description

[0002] Title of the Invention

[0003] Kinesthetic Learning System with Al-Generated Quizzes and Customizable Movement-Based User Responses

[0004] Cross-Reference to Related Applications

[0005] This application claims priority to U.S. Provisional Patent Application No.

[0006] 63 / 746,931, filed on 01 / 18 / 2025 titled “Kinesthetic Learning System with

[0007] Al-Generated Quizzes and Customizable Body Movement-Based Responses”, filed by Jawad Sahloul, the entirety of which is incorporated herein by reference.

[0008] Technical Field

[0009] The present invention relates generally to educational technology and

[0010] human-computer interaction. More specifically, it relates to systems and methods for generating educational assessments using artificial intelligence and enabling users to respond to such assessments through detected physical body movements using computer vision and / or wearable sensor technologies.

[0011] Background of the Invention

[0012] Digital learning platforms have become widely adopted across academic, professional, and consumer education contexts. These platforms rely on traditional interaction mechanisms, including keyboard input, touchscreens, and mouse-based selection. Such interaction paradigms often fail to promote physical engagement and may contribute to prolonged sedentary behavior, which is associated with adverse health outcomes.

[0013] In parallel, the rapid expansion of online educational content has increased cognitive load on learners, reducing attention span and retention. Research in embodied cognition demonstrates that learning outcomes can be improved when cognitive tasks are coupled with physical movement that is meaningfully associated with the learning material.

[0014] Existing interactive learning systems lack the ability to dynamically generate assessments from arbitrary online content while simultaneously enabling configurable, movement-based input mechanisms. Further, known systems do not provide sufficient flexibility to accommodate different physical abilities, learning preferences, or hardware configurations such as camera-based motion tracking versus wearable sensors.Accordingly, there exists a need for an adaptable learning system that integrates artificial intelligence-based content analysis with customizable, movement-driven user interaction to improve engagement, retention, and accessibility.

[0015] Summary of the Invention

[0016] The invention provides a computer-implemented system and method for creating interactive, kinesthetic learning experiences. The system automatically generates quizzes from user-provided or externally sourced content using one or more artificial intelligence models. Users respond to quiz questions by performing predefined physical movements that are detected and interpreted as answers.

[0017] The system supports multiple movement detection modalities, including

[0018] camera-based computer vision and wearable sensor-based detection. A configurable movement-mapping framework allows educators or users to assign specific body movements to answer options. The system further provides real-time feedback, performance tracking, personalized learning recommendations, and reporting capabilities.

[0019] The invention may be deployed as a standalone web-based application or integrated into third-party educational platforms through an application programming interface (API).

[0020] Brief Description of the Drawings

[0021] • Figure 1 illustrates a block diagram of the system architecture.

[0022] • Figure 2 illustrates a flowchart of the quiz generation process.

[0023] • Figure 3 illustrates a flowchart of user interaction and movement-based response processing.

[0024] Detailed Description of the Invention

[0025] System Overview

[0026] The system comprises one or more computing devices executing software modules configured to ingest content, generate assessments, detect user movements, interpret responses, and provide feedback. The system may operate in a

[0027] cloud-based environment and communicate with client devices via a network.

[0028] Content Ingestion Module

[0029] The content ingestion module is configured to receive educational content in multiple formats, including: - Plain text or webpage URLs - Video content identified by URLs or video files - Audio content identified by URLs or audio files

[0030] Audio and video content may be transcribed into text using automated speech recognition prior to analysis.Quiz Generation Module

[0031] The quiz generation module applies one or more large language models to analyze the ingested content, identify key concepts, and generate quiz questions and corresponding answer options. Generated questions may include multiple-choice and true / false formats. For time-based media, questions may be associated with specific timestamps.

[0032] Movement Mapping and Customization Module

[0033] The system includes a configuration interface enabling assignment of physical movements to answer options. Movements may include, without limitation, jumping, squatting, raising one or both arms, nodding, shaking the head, clapping, or stomping. Custom mappings may be stored and reused.

[0034] Movement Detection Module

[0035] The movement detection module supports alternative detection pathways:

[0036] • Computer Vision Pathway: Utilizes camera input and pose estimation models to identify user movements in real time.

[0037] • Wearable Sensor Pathway: Receives motion data from wearable devices including accelerometers and gyroscopes and classifies detected gestures. Detected movements are mapped to answer selections based on the active configuration.

[0038] User Interface Module

[0039] The user interface presents quiz questions, displays movement instructions, and provides visual feedback regarding detected movements and answer correctness. The interface further allows users to select detection modes and customize movement mappings.

[0040] Feedback and Analytics Module

[0041] The feedback module provides immediate correctness feedback, tracks user performance metrics, and generates personalized recommendations. In educational environments, aggregated reports may be generated at individual and group levels, including statistical summaries and visualizations.

[0042] Application Programming Interface (API)

[0043] A RESTful API enables third-party systems to submit content, retrieve quizzes, configure movement mappings, initiate quiz sessions, and obtain performance data.

[0044] Example Operation

[0045] In an exemplary embodiment, a teacher submits a video URL to the system. The system transcribes the audio, generates quiz questions, assigns movement-basedanswer mappings, and presents the quiz to students. Students answer by performing physical movements detected via webcam or wearable devices. Feedback is provided in real time, and performance data is recorded.

[0046] Advantages of the Invention

[0047] The present invention provides several technical and practical advantages over conventional digital learning and assessment systems.

[0048] First, the invention enables automatic generation of quizzes from heterogeneous content sources, including text, video, and audio, through the use of artificial intelligence models. This reduces the manual effort required to create assessments and allows educational content to be rapidly transformed into interactive learning materials.

[0049] Second, the invention introduces movement-based user input mechanisms as an alternative to traditional keyboard, mouse, or touchscreen inputs. By enabling answers to be provided through detected physical body movements, the system increases user engagement and facilitates kinesthetic learning without requiring specialized input devices.

[0050] Third, the system provides configurable mappings between physical movements and answer options, allowing movements to be customized based on user preference, physical ability, or instructional objectives. This customization improves accessibility for diverse users and enables adaptation to different learning environments.

[0051] Fourth, the invention supports multiple movement detection modalities, including computer vision-based pose estimation and wearable sensor-based motion detection. This hardware-agnostic architecture allows the system to operate across a wide range of devices and environments while maintaining consistent functionality. Fifth, the system provides real-time feedback and performance analytics, including tracking of accuracy, response timing, and learning trends. This enables immediate reinforcement for learners and data-driven insights for educators.

[0052] Sixth, the invention supports integration with external educational platforms through an application programming interface (API), enabling seamless deployment within existing learning management systems without requiring modification of underlying infrastructure.

[0053] Finally, by combining artificial intelligence-based content analysis with physical interaction and adaptive feedback, the invention improves learning engagement, retention, and flexibility while addressing the limitations of passive, sedentary digital learning systems.

Claims

Claims1. A computer-implemented method for providing a kinesthetic learning experience, comprising:receiving educational content in at least one of text, video, or audio format; analyzing the educational content using one or more artificial intelligence models to identify key concepts; generating, based on the analysis, a quiz comprising at least one question and a plurality of answer options; defining a mapping between each answer option and a corresponding physical body movement; presenting the at least one question and the corresponding physical body movements to a user via a user interface; detecting a physical body movement performed by the user using at least one movement detection modality; interpreting the detected physical body movement as a selected answer option based on the mapping; and providing feedback to the user based on whether the selected answer option is correct.

2. The method of claim 1 , wherein the educational content comprises a video or audio file, and further comprising transcribing the video or audio file into text prior to analysis.

3. The method of claim 1 , wherein the artificial intelligence model comprises a large language model configured to generate multiple-choice or true / false questions.

4. The method of claim 1 , wherein the movement detection modality comprises computer vision using a camera and a pose estimation model.

5. The method of claim 1 , wherein the movement detection modality comprises wearable sensor detection using motion data from at least one of an accelerometer or gyroscope.

6. The method of claim 1 , further comprising allowing a user to customize the mapping between physical body movements and answer options.

7. The method of claim 1 , further comprising storing user performance data including accuracy and response timing.

8. The method of claim 7, further comprising generating performance reports based on the stored user performance data.

9. A computer-implemented system for providing a kinesthetic learning experience, comprising:one or more processors; memory storing executable instructions that, when executed by the one or more processors, cause the system to: receive educational content; analyze the educational content using an artificial intelligence model; generate a quiz comprising at least one question and a plurality of answer options; associate each answer option with a physical body movement; detect user-performed physical body movements using computer vision and / or wearable sensor data; interpret detected movements as answerselections; and provide feedback based on correctness of the answer selections.

10. The system of claim 9, further comprising a user interface configured to display questions, movement instructions, and feedback.

11. The system of claim 9, further comprising an application programming interface configured to allow external systems to submit content and retrieve generated quizzes.

12. The system of claim 9, wherein the system is configured to allow selection between computer vision-based detection and wearable sensor-based detection.

13. The system of claim 9, further comprising a feedback module configured to generate personalized learning recommendations.

14. A non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform the method of claim 1.

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