Systems and methods for learning new subjects

Abstract

This concerns computer implementation methods for learning new subjects. [Solution] This method includes the step of generating a plurality of character mascots on a display device of a computing device, each character mascot being associated with one of a plurality of learning preferences, which include data-driven learning preferences, aesthetic learning preferences, and experiential learning preferences. Furthermore, this method includes the step of receiving a selection of one of the character mascots from a user input device based on the user's visual preferences, which are determined to be consistent with the user's learning preferences. This method also includes the step of displaying learning content corresponding to the user's learning preferences on the display device, which is associated with one of the plurality of learning preferences.

Description

Technical Field

[0001] The present disclosure generally relates to learning methods and systems designed to facilitate the development of specific skills. In particular, the present disclosure aims to address the drawbacks inherent in existing educational frameworks by adopting a more individualized and comprehensive learning approach, and discloses systems and methods for learning new subjects.

Background Art

[0002] The present disclosure generally relates to learning methods and systems designed to facilitate the development of specific skills. In particular, the present disclosure aims to address the drawbacks inherent in existing educational frameworks by adopting a more individualized and comprehensive learning approach, and discloses systems and methods for learning new subjects.

[0003] The following discussion regarding the background of the present invention is intended to facilitate the understanding of the present invention. However, it should be understood that this discussion does not admit or indicate that any of the materials referred to were known, part of the common knowledge, or published in any jurisdiction at the priority date of the present application.

[0004] Education has long faced significant challenges in meeting the diverse learning needs of children as a fundamental pillar of personal and social growth. From the moment a child is born, the parent, as the first educator, recognizes the uniqueness of their child. Each child is considered to have unique talents and strengths waiting to be discovered. This initial understanding of the child's potential is often clouded when the child begins formal education. Existing educational systems based on economic efficiency typically transmit instructions in a standardized manner. This generalized teaching method often ignores the individualized strengths of each child, resulting in a loss of connection between the child's inherent potential and the education they receive.

[0005] In the current system, classrooms are built around efficiency and uniformity. Teachers impart knowledge to large groups of students in a standardized format, which means that learning often becomes a competition where the best listeners, or in some cases the most obedient, survive. For example, in a class of 40 students, typically only one or a very small number of children are praised and recognized for their talent or sharp learning abilities. The remaining students who do not thrive in this standardized learning environment may develop a dangerous misconception that they lack talent or the ability to learn. This false belief becomes a self-fulfilling prophecy, hindering their ability to explore personal growth and reducing their potential contribution to society.

[0006] Such misunderstandings have far-reaching consequences that extend beyond individual development. Vibrant communities and economies fundamentally depend on individuals who contribute to society with their unique strengths and talents. When children are unable to recognize or develop their talents due to the constraints of the education system, their potential for optimal contribution to society as a whole diminishes, resulting in overall economic prosperity.

[0007] Prior technologies have attempted to address these challenges by classifying learners into three main types: auditory learners, kinesthetic learners, and visual learners. While this classification acknowledges the existence of different learning styles, it is overly simplistic and does not adequately consider the complexity of individual learning needs. By fitting learners into rigid categories, these systems ignore the fluid and dynamic nature of the individual learning process. This approach fails to provide learners with the necessary tools and confidence to effectively engage with new information. As a result, many students remain unable to adapt to the learning environment, perpetuating a vicious cycle of misunderstanding and inadequacy.

[0008] These prior-technical solutions also lack a comprehensive and integrated approach that can address the multifaceted nature of learning. For example, students classified as auditory learners are taught using methods that primarily emphasize listening, kinesthetic learners are encouraged to engage in hands-on activities, and visual learners are primarily exposed to visually appealing content. However, this classification approach overlooks the fact that most learners benefit from a combination of multiple learning styles. Restricting students to a single method fails to foster the deep understanding necessary for long-term knowledge retention and personal growth.

[0009] Furthermore, existing solutions fail to instill confidence in learners from the early stages of education. Instead of enabling students to approach new information with clarity and confidence, existing systems often foster confusion and uncertainty, especially for those who do not clearly fit into any of the designated learning categories.

[0010] The primary shortcomings of current educational systems and prior technological solutions lie in their inability to accommodate the unique starting points of individual learners. By imposing uniform approaches or strictly categorizing students as auditory, kinesthetic, or visual learners, the systems ultimately stifle the personal growth and development of many students. These methods fail to provide learners with the means and strategies necessary to confidently approach new information, which is crucial for creating an effective learning environment.

[0011] Furthermore, the classification approach used in conventional methods fails to consider the dynamic nature of learning. Focusing on a particular mode often leads to a superficial understanding of the subject. For example, auditory learners may not benefit from visual aids, while visual learners may struggle to grasp concepts best understood through hands-on activities. This lack of an integrated approach limits students' ability to fully engage with educational content, often leading to loss of interest, frustration, and poor academic performance.

[0012] Furthermore, conventional solutions fail to address the far-reaching impacts their shortcomings have on society as a whole. When learners do not fully develop their talents, their potential contributions to society and the economy are limited. Failure to cultivate diverse strengths in the classroom prevents individuals from leveraging their unique abilities, resulting in a community lacking vitality and innovation.

[0013] Therefore, the present invention aims to overcome at least partially some of the aforementioned problems and to provide an improved approach to address these challenges. [Overview of the project]

[0014] According to a first aspect of the present invention, a computer implementation method for learning a new subject is provided. The method includes the steps of: generating a plurality of character mascots on a display device of a computing device, each character mascot associated with one of a plurality of learning preferences, the plurality of learning preferences including data-driven learning preferences, aesthetic learning preferences, and experiential learning preferences; receiving a selection of one of the character mascots from a user input device based on the user's visual preferences, wherein it is determined that the user's visual preferences are consistent with the user's learning preferences; and displaying learning content corresponding to the user's learning preferences on the display device, the learning content associated with one of the plurality of learning preferences.

[0015] According to various embodiments, the plurality of character mascots include a first character mascot, a second character mascot, and a third character mascot, each character mascot including a predetermined arrangement of geometric shapes associated with any one of a plurality of learning preferences.

[0016] According to various embodiments, the first character mascot includes a first predetermined arrangement of a symmetrical geometric shape, the first predetermined arrangement of the geometric shape being associated with a data-driven learning preference.

[0017] According to various embodiments, the second character mascot includes a second predetermined arrangement of geometric shapes in which parts of the geometric shapes overlap each other, and the second predetermined arrangement of geometric shapes is associated with aesthetic learning preferences.

[0018] According to various embodiments, the third character mascot includes a third predetermined arrangement of geometric shapes, which include geometric shapes having pointed edges, and the third predetermined arrangement of geometric shapes is associated with experiential learning preferences.

[0019] According to various embodiments, the first predetermined arrangement of geometric shapes includes circles, semicircles, and partial circles.

[0020] According to various embodiments, the second predetermined arrangement of geometric shapes includes circles, quadrilaterals, and triangles.

[0021] According to various embodiments, the third predetermined arrangement of geometric shapes includes quadrilaterals and triangles.

[0022] According to various embodiments, a computer implementation method further includes the steps of receiving user input in one or more forms, including text, audio, and video, in response to displayed learning content, and generating new learning content for the user based on the user input and determined learning preferences.

[0023] According to various embodiments, the step of generating new learning content is performed by a recommendation engine that communicates with a large-scale language model.

[0024] According to various embodiments, a preference for experiential learning is associated with learning content that includes instructional video content involving practical experience.

[0025] According to various embodiments, a data-driven learning preference is associated with learning content that includes a story with visual content and an auditory accompaniment.

[0026] According to various embodiments, an aesthetic learning preference is associated with aesthetically appealing visual learning content.

[0027] According to various embodiments, a computer-implemented method further includes receiving user input in one or more modalities including text, voice, or video in response to the displayed learning content, obtaining user-specific context data based on the user input, determining relevant learning content for the user based on the user-specific context data and the determined learning preference, and generating one or more relevant learning contents for the user based on the user-specific context data and the determined learning preference.

[0028] According to a second aspect of the present invention, a system for learning a new subject is provided. The system includes a computing device having a display device, a user input device, a processor, and a memory, the memory stores instructions, and when the instructions are executed by the processor, the system provides learning content on the display device, the learning content is associated with one of a plurality of learning preferences including a data-driven learning preference, an aesthetic learning preference, and an experiential learning preference, and when the instructions are executed by the processor, the system generates a plurality of character mascots on the display device of the computing device, each character mascot is associated with one of the plurality of learning preferences, receives a selection of one of the character mascots based on the user's visual preference from the user input device, determines that the visual preference is consistent with the user's learning preference, and displays on the display device learning content corresponding to the user's learning preference, the learning content being associated with one of the plurality of learning preferences. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In the drawings, the same reference numerals generally denote the same parts in different drawings. The drawings are not necessarily drawn to scale; rather, they generally focus on explaining the principles of the present invention. The dimensions of various features and elements may be arbitrarily enlarged or reduced for clarity. In the following description, various embodiments of the present invention will be better understood by reference to non-limiting examples and the accompanying drawings.

[0030] [Figure 1] FIG. 1 is a schematic diagram showing an exemplary infrastructure of a system for learning a new subject according to an embodiment of the present invention.

[0031] [Figure 2] FIG. 2 shows an exemplary embodiment of a character mascot used in the system shown in FIG. 1.

[0032] [Figure 3] FIG. 3 is a flowchart showing a method for learning a new subject according to an embodiment of the present invention.

[0033] [Figure 4] FIG. 4 shows an example of a toy kit used in a computer-implemented method for learning a new subject according to an embodiment of the present invention.

[0034] [Figure 5] FIG. 5 is a flowchart showing another method for learning a new subject according to an embodiment of the present invention.

[0035] [Figure 6] FIG. 6 is a schematic block diagram of a system architecture for learning a new subject according to an embodiment of the present invention.

[0036] [Figure 7]Figure 7 is a flowchart showing another method for providing personalized, adaptive learning content according to one embodiment of the present invention. Detailed explanation

[0037] The following describes exemplary embodiments of the present invention in detail, and these embodiments are shown in the accompanying drawings. While the present invention is described in conjunction with these embodiments, it should be understood that these embodiments are not intended to limit the invention. Conversely, the present invention is intended to encompass alternatives, modifications, and equivalents that may fall within the spirit and scope of the invention as defined by the accompanying description. Furthermore, the following detailed description of embodiments of the present invention includes numerous specific details to provide a full understanding of the invention. However, those skilled in the art will understand that the invention can be carried out without these specific details. In other cases, well-known methods, procedures, components, and circuits are not described in detail so as not to unnecessarily obscure aspects of the embodiments of the present invention.

[0038] In this specification, the term “comprising” shall be understood to have the same broad meaning as the term “including,” meaning to include a specific integer or step, or a group of integers or steps, but not to exclude any other integer or step, or a group of integers or steps. This definition also applies to the variations of “comprising,” namely “comprise” and “comprises.”

[0039] It should be understood that embodiments of the present invention discussed below are preferably software algorithms, programs, or code residing on a computer-available medium having control logic to be executable on a machine having a computer processor. The machine typically includes memory storage configured to execute the computer algorithm or program and provide output.

[0040] As used herein, the term “software” means any code or program that can reside within the processor of a host computer, whether the implementation is a software computer product on hardware, firmware, disk, or memory storage device, or whether it is downloaded from a remote machine. Embodiments described herein include such software that implements the equations, relationships, and algorithms described herein. Those skilled in the art will understand further features and advantages of the present invention based on the embodiments described above.

[0041] Embodiments described with respect to one of the server and the method are equally valid for the other of the server and the method. Similarly, embodiments described with respect to the server are equally valid for the method, and vice versa.

[0042] Features described in one embodiment are applicable to the same or similar features in other embodiments. Features described in one embodiment are applicable to other embodiments even if they are not explicitly described in other embodiments. Furthermore, additions and / or combinations and / or substitutions described in one embodiment are applicable to the same or similar features in other embodiments.

[0043] In relation to the various embodiments described herein, the articles "a," "an," and "the" are understood to refer to one or more of the features or elements in question.

[0044] As used herein, the term "and / or" is understood to include any combination of one or more items listed in relation to it.

[0045] As used throughout this specification, the term “module” may be understood to mean an application-specific integrated circuit (ASIC), an electronic circuit, a combinational logic circuit, a field-programmable gate array (FPGA), a processor that executes code, other suitable hardware components that provide the described functionality, or any combination thereof. The term “module” may also include memory that stores the code executed by the processor.

[0046] To achieve the aforementioned features, advantages, and objectives, the present invention relates to a system and method for learning a new subject.

[0047] This disclosure discloses a system and method for learning a new subject. The subject may include a new language, or a subject encompassing mathematics, science, art, or other forms of knowledge. The invention offers a new perspective on learning. Instead of classifying learners into fixed categories, the invention creates clarity at the starting point of the learning experience. The invention enables learners to gain confidence almost immediately when engaging with information and content. This granting of confidence is important, as it allows learners to engage with learning materials without being constrained by preconceived notions about their own abilities.

[0048] This invention also introduces a comprehensive workflow that encompasses the following three learning preferences: • Data-driven learning preference • Aesthetic learning preference • Preference for experiential learning Based on the user's visual preferences for character mascots (which align with the user's natural and optimal learning preferences), the workflow includes the use of narrative-based approaches, experiential approaches, and aesthetically appealing visuals that are consistent with each of the aforementioned learning preferences. By integrating these elements, the present invention ensures that content is delivered in a way that is consistent with each user's optimal and natural learning preferences and is accessible and engaging for all learners.

[0049] In one embodiment, the present invention is designed to teach any subject to students with dyslexia or learning disabilities or learning difficulties. Furthermore, the present invention can broadly teach any subject to students of all learning abilities, including children and adults, based on the selection of the student's visual preferences for character mascots (aligned with the student's natural optimal learning preferences). The present invention is designed to be language-independent and can be adapted to one or more of several languages ​​for localization in various countries where English is not the primary language of instruction or official language. While this disclosure focuses on English, other languages ​​are also envisioned. Details of the scientific rationale behind each character mascot, which aligns with natural optimal learning preferences, will be discussed later.

[0050] This invention relates to a system and method for learning a new subject by determining a learner's optimal learning style through the selection of a character mascot designed with a specific predetermined arrangement of geometric shapes. The selection of a specific character mascot has been found to be a highly accurate predictor of the cognitive style that most strongly resonates with the learner's intrinsic preferences. The accuracy of this determination has been validated through extensive testing on a large and diverse sample of learners, details of which are described below.

[0051] One of the advantages of this invention lies in its ability to facilitate the overall learning experience. By applying an optimal learning experience aligned with the user's learning preferences in the early stages of the learning process, it has been shown that learners from diverse backgrounds, including those with learning differences and difficulties, can easily and accurately acquire information. This inclusivity is extremely important in today's diverse educational environments, which aim to guarantee opportunities for success for all learners.

[0052] The objective of this invention is to provide clarity at the starting point of the learning experience, enabling learners to confidently and effectively engage with new information. This invention aims to create a more comprehensive and effective educational environment by providing a workflow that encompasses multiple learning profiles. By recognizing and nurturing individual potential, this approach aims to transform the learning environment so that all learners can grow and make meaningful contributions to society.

[0053] This invention discloses a system and method for learning a new subject. In one embodiment, this may include language skills such as pronunciation, reading comprehension, writing, and spelling.

[0054] Figure 1 shows a block diagram of a learning management system for learning a new subject according to an embodiment of the present invention. The learning management system 100 includes a server 110 configured to communicate wirelessly with a computing device 130 via a communication network 120. The computing device 130 may be a dedicated learning machine or a general-purpose device such as a personal computer, tablet, or smartphone equipped with a learning application 140.

[0055] Server 110 is configured to manage user profiles, authenticate users, maintain and update learning content for specific learning profiles, and exchange data (e.g., learning content and user progress). To facilitate user authentication and content delivery, Server 110 holds user profiles, learning profiles, and learning content materials or content associated with user profiles or learning profiles. In some cases, Server 110 may run on dedicated hardware infrastructure, or it may be hosted on a cloud platform. In its broadest sense, learning content is any resource, activity, information, or experience used by or designed for a user or learner to acquire knowledge, develop skills, or cultivate new attitudes or behaviors.

[0056] In this specification, the term “server” can mean a single computing device or a group of interconnected computing devices that work together to perform a particular function. That is, a server may be housed in a single hardware unit or distributed across multiple or many different hardware units. In one embodiment, the server 110 may be activated on a remote cloud server or a co-located server and may include a database 115 for holding various data inputs and outputs necessary for the operation of the system 100. In one embodiment, the data server 110 may store data input to or output generated by the computing device 130. In one embodiment, a Large Language Model (LLM) used by the computing device 130 is stored in the data server 115. In one embodiment, examples of LLMs include, but are not limited to, Zephyr, Code Large Language Model Meta AI (LLAMA), and Generative Pre-training Transformer (GPT). The LLM stored in server 110 functions as a foundational component for various computing tasks and applications. In one embodiment, computing device 130 may be connected to server 110 via communication network 120 for communication.

[0057] In one embodiment, the communication network 120 may be a wired network, a wireless network, or a combination thereof. The communication network 120 may be implemented as any of different types of networks, including, but is not limited to, an Ethernet IP network, an intranet, a local area network (LAN), a wide area network (WAN), or a Metropolitan Area Network (MAN). Various devices within the system 100 may be configured to connect to the communication network 120 according to various wired and wireless communication protocols. Examples of such wired and wireless communication protocols include, but are not limited to, Transmission Control Protocol and Internet Protocol (TCP / IP), User Datagram Protocol (UDP), Hypertext Transfer Protocol (HTTP), File Transfer Protocol (FTP), ZigBee, EDGE, IEEE 802.11, Light Fidelity (Li-Fi), 802.16, IEEE 802.11s, IEEE 802.11g, multi-hop communication, wireless access point (AP), device-to-device communication, cellular communication protocols, and Bluetooth (BT) communication protocols. Furthermore, the communication network 120 may include various network devices such as routers, bridges, servers, computing devices, and storage devices.

[0058] The computing device 130 is a standalone computing device that can perform the functions described herein by installing and running the learning application 140 or learning software program. The computing device 130 may be any suitable device such as a personal computer, tablet, or smartphone. In some embodiments, the learning application 140 may be part of a server-client architecture, and the learning application includes a client that communicates with a server hosting the learning application or learning software program. In other embodiments, the learning application may be accessible from a web server by one or more third-party web applications of a website. In such cases, the third-party web application or website may invoke the learning application 140 by installing a widget or add-on, or by providing a link to the learning application 140.

[0059] The computing device 130 includes a memory 132 for storing the learning application 140. In one embodiment, the memory 132 may store instructions and data, including one or more modules that, when executed by the processor 131, can cause the processor 131 to act to present appropriate learning content to the user, as will be described in detail later in this specification. The memory 132 may be non-volatile or volatile. In one embodiment, the memory 132 may also store a single module or a combination of several different modules to present learning content to the user via the learning application 140. Examples of non-volatile memory include, but are not limited to, flash memory, read-only memory (ROM), programmable ROM (PROM), erasable PROM (EPROM), and electrically erasable PROM (EEPROM) memory. Furthermore, examples of volatile memory, though not limited to them, include Dynamic Random Access Memory (DRAM) and Static Random-Access Memory (SRAM).

[0060] The learning application 140 includes data executed by the processor 131, such as a user profile 141, a learning profile 142, and learning content 143. The learning content 143 and the learning profile 142 are implemented in the form of an application or software application that can be downloaded and installed on a computing device by the user, like the learning application 140.

[0061] Furthermore, the data may be stored in one or more databases 115 maintained by the server 110. For example, one database may store program modules, another may store learning content 143, yet another may maintain the user's learning progress, and yet another may store the user profile 122. These databases may be implemented as relational databases in one embodiment.

[0062] The computing device 130 also includes a display device 133 that provides output. The display device includes a touchscreen display or other display such as an LCD or LED display to display a user interface. Other output devices, such as audio devices (speakers, headphones, or other acoustic devices), can be configured to output sound according to instructions received from the processor 131. In some embodiments, the learning content 143 may include a unit that generates haptic feedback (e.g., vibration) in response to one or more actions.

[0063] The computing device 130 further includes one or more user input devices 134 for receiving user input. These input devices may take the form of a pressure-sensitive panel (not shown) that is physically associated with the display device and integrally forms a touchscreen, a keypad (not shown) for communicating information and command selections to the processor 131, a cursor controller (not shown) for communicating directional information and command selections to the processor 131 to control cursor movement on the display device, a microphone for detecting voice input from the user, and / or an image capture device such as a camera or video recorder (for detecting image input or acquiring images of the user).

[0064] According to various embodiments, the learning content 143 requires receiving input from the user. For example, this may be text input, selection input, voice input, or handwriting input by the user, which is performed in response to instructions provided by the learning content. The learning application applies speech recognition and handwriting recognition software to identify the learner's approach.

[0065] The computing device 130 also includes a communication unit 135. The communication unit 135 provides a bidirectional data communication connection to a network link connected to the communication network 120. For example, the communication unit 135 may be an Integrated Services Digital Network (ISDN) card, a cable modem, a satellite modem, etc. Another example is that the communication unit 135 may be a local area network (LAN) card that provides a data communication connection to a compatible LAN. A wireless link may also be implemented. In such an implementation, the communication unit 135 sends and receives electrical, electromagnetic, or optical signals carrying digital data streams representing various types of information.

[0066] Figure 2 shows exemplary embodiments of three character mascots according to various embodiments. In one embodiment, the learner is presented with a selection of three character mascots on the display device of the computing device 130: a first character mascot 200, a second character mascot 300, and a third character mascot 400. Each of these character mascots is uniquely constructed by combining geometric shapes such as circles, triangles, and squares in a predetermined arrangement, based on the concept of a tangram. Tangrams are known for their psychometric properties, which help the development of cognitive processing by facilitating the sequential assembly of separate elements. Under the widely used concept of “learning styles” in education, the VARK model (visual, auditory, reading / writing, kinesthetic) for example shows that people learn most effectively when taught in a way that matches their preferred learning preferences. In this invention, the arrangement of geometric shapes within each character mascot is configured to appeal to the learner’s intrinsic cognitive ordering ability and learning preferences, which include one of data-driven learning preferences, aesthetic learning preferences, and experiential learning preferences. According to these learning preferences, aesthetic learners benefit most from the use of diagrams and images, data-driven learners benefit most from audio-based or story-based learning, and experiential learners benefit most from sensory-based or hands-on approaches.

[0067] Referring to Figure 2, in one embodiment, the first character mascot 200, also known as Ollie, is designed in a predetermined arrangement of geometric shapes. The first character mascot 200 includes a predetermined arrangement of geometric shapes. Specifically, this arrangement is symmetrical. In the illustrated embodiment, the mascot comprises a body portion 210 formed from a first complete circle. A pair of semicircles 220a, 220b are symmetrically arranged on the upper part of the body portion 210 to represent ears. A pair of second smaller complete circles 230a, 230b are symmetrically arranged inside the body portion 210 to represent eyes. A partial circle 240 is positioned below the center of the eyes to represent a mouth. In one embodiment, this symmetrical arrangement of basic geometric shapes, including circles, semicircles, and partial circles, has been found to appeal to data-driven learners with logical and data-driven tendencies. This structured and predictable design resonates with cognitive modes, and the choice of mascot 200 serves as a strong indicator that the learner has a data-driven learning preference.

[0068] In other embodiments, the second character mascot 300, also known as Chloe, features a second predetermined arrangement of geometric shapes including circles, triangles, and quadrilaterals. In contrast to the first embodiment, this arrangement is asymmetrical and aesthetically pleasing. As shown, the second character mascot 300 has a body portion 310 configured in the shape of a cloud. In one embodiment, the combination and / or overlap of asymmetric geometric shapes may provide a basis for imaginative thinking and encourage aesthetic learners to imagine what is hidden behind the cloud. Aesthetic learners who show a preference for the second character mascot 300 are typically visually inclined and prefer learning in an aesthetic way. The freeform, organic, and aesthetically guided design of the second character mascot 300 resonates with aesthetic learners who prioritize a visual and creative mode of cognition.

[0069] In other embodiments, the third character mascot 400, also known as Drew, comprises a third predetermined arrangement of geometric shapes including circles, triangles, and quadrilaterals. In contrast to the first and second character mascots, the third character mascot 400 is distinguished by its constituent elements. Specifically, the third character mascot 400 is formed from an arrangement of multiple geometric shapes with sharp points. In addition, the multiple geometric shapes are selected to convey a sense of movement, function as motivational graphics, or represent activity-based characteristics. As shown, the third character mascot 400 has a body portion 420 comprising a quadrilateral, a triangle, and two semicircles at each opposite end of the quadrilateral. The body portion 420 includes corners 430a, 430b at the top of the body portion 420. Facial features include two circular eyes and a semicircular mouth. It has simple curved, arch-shaped arms. This unique combination of geometric shapes is configured to appeal to experiential learners who derive the greatest benefit from sensory-based learning or hands-on approaches. Experiential learners who show a preference for the third character mascot 400 typically have a sensory inclination and prefer learning in a hands-on manner. The third character mascot 400 resonates with learners who prioritize sensory-based learning.

[0070] Referring to Figure 3, a flowchart 600 of a method for learning a new subject according to one embodiment of the present disclosure is disclosed. Figure 3 is described in conjunction with Figures 1 and 2. In one embodiment, the flowchart 600 may include a number of steps that can be performed by various modules of a computing device 130 to present learning content to a user via a learning application 140.

[0071] In step 610, the method includes the step of generating a plurality of character mascots on a display device of a computing device. Each character mascot is associated with a learning preference, which includes one of data-driven learning preferences, aesthetic learning preferences, and experiential learning preferences. A character mascot can be understood as a symbolic figure or representation that embodies a particular characteristic or theme, in this case, the learning preference described above. A learning preference refers to the way an individual prefers to process information and can be data-driven, aesthetically based, or experiential.

[0072] One advantage of this configuration is that it allows learners to engage with content in a way that aligns with their natural inclinations, potentially enhancing their engagement with and retention of information. By presenting character mascots that cater to different learning preferences or styles, the present invention can leverage the capabilities of computing devices to tailor content to the user's learning preferences, providing a personalized, interactive experience and making more efficient use of processing power and memory capacity.

[0073] In addition, the present invention proposes delivering content or information through a combination of aesthetically appealing visuals, auditory-based experiences incorporating narrative or storytelling techniques, and sensory experiences and practical approaches that go beyond mere written text, tailored to the user's learning preferences. This tripartite strategy fosters a comprehensive and deep understanding of the subject matter. Empirical evidence has shown that this approach significantly improves the ability of learners from diverse backgrounds, including those with learning differences, to acquire information more easily and accurately.

[0074] In step 620, the method further includes receiving a user's selection of one of a set of character mascots based on visual preferences via an input device on a computing device. The user's visual preferences are consistent with the user's learning preferences as described above. The input device is any hardware used to transmit data to the computer, such as a keyboard or mouse. The selection of a character mascot allows the system to adapt the learning content to the user's preferred style, which increases the likelihood of effective learning.

[0075] For example, consider a scenario where the user selects the first character mascot 200 (Olly). The first character mascot preferably includes a predetermined arrangement of substantially symmetrical geometric shapes. In one embodiment, this symmetrical arrangement of basic geometric shapes, including circles, semicircles, and partial circles, has been found to appeal to data-driven learners with logic-driven, narrative-driven, and data-driven tendencies. This structured and predictable design resonates with cognitive modes, and the selection of the first character mascot 200 serves as a strong indicator that the user has a preference for data-driven learning.

[0076] In step 630, the method includes the step of displaying learning content on a display device of a computing device. The learning content is associated with the user's learning preferences. The learning content refers to educational materials presented to the user. This configuration optimizes the learning process by presenting the content in the format most readily accepted by the user.

[0077] In addition, in another embodiment, the method also includes the step of receiving user input from one or more user input devices in response to learning content. The user input relates to an output associated with a learning objective, which is a specific purpose or skill that the learning process aims to achieve. This configuration allows for real-time feedback and adaptation of the learning process, thereby enhancing the effectiveness of the educational experience. For example, the learning content may request voice and handwritten input from the user in response to instructions provided by the learning content. The learning application may then apply speech recognition and handwriting recognition software to identify the learner's approach.

[0078] In another embodiment, the method further includes the step of generating a user progress report based on user input and a determination of whether the user has achieved the learning objectives of the learning content. The progress report is a document that provides an overview of the user's achievements and areas for improvement. This configuration allows the user to gain useful insights into their learning progress, adjust their learning approach as needed, and facilitate continuous improvement.

[0079] In various embodiments, the techniques described herein are implemented by the learning management system 100 in response to the processor 131 executing one or more instruction sequences (e.g., the aforementioned modules) contained in the main memory 132. Such instructions may be read into the memory 132 from a remote database (not shown) or other storage medium such as non-volatile memory. By executing the instruction sequences in the memory 132, the processor 131 performs the steps described herein. In alternative embodiments, hardwired circuits may be used as an alternative to or in combination with software instructions.

[0080] According to various embodiments, learning content is structured, provided, and displayed on a display device to include bite-sized outcomes based on the objectives of the topic. When a learner achieves a bite-sized outcome, it means their learning has been optimized. The learning content includes multiple subjects covering various types of knowledge. All subjects have learning objectives and outcomes, and the outcomes include bite-sized outcomes associated with the learning objectives.

[0081] In one embodiment, for example, in the Alphabet Explorer learning content, there is a mini-activity for the user to perform for each letter of the alphabet, and the completion of each activity is recorded as an achievement and added to the overall progress report on learning the ABCs.

[0082] In another embodiment, learning content on renewable energy teaches renewable energy and solar energy using different physical DIY learning kits. Results are incorporated into the completion of the physical products of the learning kits and the understanding of the digital story. Results can be determined by the degree of completion of the physical products and the level of application of the content from the digital story within the activity booklet.

[0083] This invention includes an innovative aspect of incorporating character mascots or learning mascots into learning applications or learning software programs. These character mascots help to accurately identify the learning style best suited to the learner. Specifically, the mascots are designed to align with one of the following three main learning preferences: (i) Data-driven learning preference - Learners who prefer story-based content and want to understand the foundation of the information they are learning. (ii) Aesthetic learning preference - Learners who tend to acquire information through aesthetically appealing visuals. (iii) Experiential learning preference - Learners who prefer to absorb information through sensory approaches. The selection of an appropriate character mascot helps identify the learner's optimal learning style, which aligns with the most suitable method in which the learner feels most confident when absorbing new knowledge. However, it should be noted that the selection of a character mascot does not prevent the learner from engaging in activities based on other learning preferences. This invention is based on the premise that a learner's confidence and interest in learning are inherently related to the early stages of the learning experience. By facilitating the intake of new information through the learner's preferred learning preferences, this invention ensures a more effective learning process and a deeper understanding of the subject matter.

[0084] The present invention also provides a computer implementation method for learning a new subject through the assembly of a toy kit. This computer implementation method delivers learning content through learning preferences that may be data-driven, aesthetic, or experiential, or a combination thereof. This tripartite strategy promotes a comprehensive and deep understanding of the subject. Empirical evidence has shown that this approach significantly improves the ability of learners with diverse backgrounds, including learning differences, to acquire information more easily and accurately.

[0085] Figure 4 shows a toy kit used in a computer implementation method for learning a new subject. As those skilled in the art will understand, various types of toy kits can be used to learn a new subject. For example, Figure 4 shows a toy kit of a solar-powered house with a windmill. The solar-powered house is a DIY toy kit that allows children to build their own model house. The subject learned in connection with the toy kit is renewable energy. In an exemplary embodiment, a child can assemble a house equipped with solar panels and, using an interactive computer implementation method, learn the basics of renewable energy, such as how solar energy is collected and converted into electrical energy, and how this energy powers various devices in the house. Children can also learn the basics of sustainable architecture and design through building a solar-powered house with a windmill.

[0086] Toy kits and computer implementation methods offer several advantages in learning new subjects. (i) Practical learning: Toy kits provide practical and experiential learning opportunities. For example, children can more easily understand and grasp complex concepts by assembling models and directly interacting with their components. (ii) Environmental awareness: By addressing topics such as renewable energy, the toy kit fosters an understanding of and interest in sustainable practices. This cultivates a sense of responsibility towards the environment and emphasizes the importance of renewable energy sources. (iii) Creative expression: The process of assembling and customizing toy kits promotes creativity. Children can engage in technical learning while exploring the artistic aspects, and creativity is integrated with education.

[0087] Figure 5 shows a flowchart of a computer implementation method 500 for learning a new subject through the assembly of a toy kit. One aspect of the present invention relates to a computer implementation method for learning a new subject through the assembly of a toy kit. A computer implementation method can be understood as a series of steps or actions that utilize a computing device to facilitate a task or function in order to achieve a desired outcome. In this case, it is learning a new subject through an interactive engagement with a toy kit.

[0088] In step 510, the first step includes displaying subject-related learning content on the user interface of a computing device, where the learning content comprises at least two learning modules, each learning module associated with a learning preference comprising one or more of the following: visual learning preferences, auditory learning preferences, and kinesthetic learning preferences. Learning content refers to informational materials presented to the user for educational purposes, which may include text, images, audio, video, interactive simulations, etc.

[0089] A learning module is an independent section of learning content tailored to a specific learning style. Each learning module may be associated with a particular learning preference. A learning preference refers to an individual characteristic that influences how learners absorb information most effectively. The three main learning styles are auditory, visual, and kinesthetic. Auditory learners prefer story-based content and enjoy acquiring knowledge through listening and speaking. Visual learners are drawn to acquiring new concepts through aesthetically appealing visuals. Kinesthetic learners benefit from learning through sensory approaches. One advantage of this configuration is its ability to accommodate diverse learning preferences, thereby enhancing user engagement and information retention. By presenting multiple learning modules tailored to various styles, this method effectively leverages the processing power of computing devices to provide a tailored educational experience, optimizing both user interaction and information absorption.

[0090] Step 520 involves the user selecting one of the learning modules via an input device on the user interface. An input device can be understood as any mechanism through which the user can communicate selections or commands to the computing device. This configuration allows for personalization of the learning experience, enabling the user to choose the format that best suits their learning style. One advantage of this feature is that the user gains active control over their learning process, thereby increasing motivation and satisfaction. The input device facilitates efficient data acquisition, allows the system to respond dynamically to the user's selections and preferences, and improves the overall learning experience.

[0091] In step 530, interactive visual content may be generated on the user interface in response to the user's selection. Here, the interactive visual content includes one of the following: a story with visual and auditory accompaniment illustrating concepts related to the subject, and an instructional video for assembling the toy kit. Interactive visual content refers to multimedia elements that engage the user through visual and auditory stimuli. A story is a narrative form of communication used to persuasively convey information. In this context, it plays a role in illustrating concepts related to the subject through engaging visual and auditory elements. If selected by the user, the instructional video content provides step-by-step guidance on assembling the toy kit and offers learners a practical approach to understanding the relevant principles.

[0092] In various embodiments, users progress through all learning modules sequentially, regardless of their preferred learning style. This combines multiple learning styles, user interaction, and rich multimedia content to create a comprehensive and engaging educational experience, thereby promoting effective learning for the user through the assembly of the toy kit. This configuration also creates an immersive learning environment that caters to visual, auditory, and kinesthetic learners, making complex concepts more accessible and understandable. The generation of interactive content leverages the processing power of computing devices to present high-quality visuals and audio, maximizing the efficient use of bandwidth and storage resources and enhancing subject comprehension and retention.

[0093] Figure 6 is a schematic block diagram of a system architecture for learning a new subject, relating to one embodiment of the present invention. An exemplary computing environment suitable for implementing one or more embodiments described herein is presented below. This computing environment is provided for illustrative purposes only and should not be construed as limiting the scope or functionality of any embodiment described herein. References to known computing components and processes are not intended to suggest that the disclosed embodiments are merely a collection of such well-known elements. Rather, the disclosed embodiments include specific configurations and programming that result in a machine specifically adapted to perform the described methods and systems.

[0094] The system 500 includes a central server 110 which is communicably coupled to one or more computing devices 130 via a communication network 120. In one embodiment, one or more computing devices 130 may include user-operable computing devices such as desktop computers, laptops, smartphones, tablets, point-of-sale (POS) terminals, or kiosks. These computing devices 130 may include one or more processors, memory, display interface, input / output subsystems, and network communication hardware. Each computing device 130 is configured to run a user interface application such as a web-based dashboard, mobile app, or voice interface, through which the user may interact with the learning system 100 in the form of text, voice, or video.

[0095] In one embodiment, the communication network 120 described above may be configured to enable secure, bidirectional data exchange between a learning system 100 deployed on a remote server and a computing device. The communication network 120 may include a combination of public and private networks such as the Internet, VPN, LAN, and wireless cellular networks (e.g., 4G, 5G), and may support various communication protocols including HTTPS, WebSockets, and gRPC. The network may incorporate encryption, authentication, and API gateway layers to facilitate real-time and asynchronous interactions across text, voice, and video formats, and to ensure data integrity and access control. In addition, the network may interface with external systems such as analytics platforms and support backend operations triggered by user interactions.

[0096] In one embodiment, a system 500 implemented on a remote server comprises at least one processor 112 (or a cluster of processors). One or more processors 112 are configured to execute the core logic of the learning system and include specialized sub-components such as a learning preference detection unit 180, a context detection unit 190, and a recommendation engine 116. These modules may be configured to work together to classify incoming user input, apply enterprise-level interaction policies, and estimate user intent based on both semantic input and contextual metadata.

[0097] Server 110 includes a processor 112 configured to execute instructions and manage the operation of each component of the server. Server 110 further includes a database 115 for storing data, an I / O interface 117 for handling data input and output, one or more large language models (LLM models) 118, and one or more application programming interfaces (APIs) 119 for interacting with other software and services. Server 110 includes a learning preference detection unit 180 that communicates with the processor 112. Server 110 also includes a context detection unit 190 and a recommendation engine 116, which also communicate with the processor 112 and the database 115. The processor 112 integrates the functions of these units to provide a personalized learning experience to the user interacting with the computing device 130.

[0098] In one embodiment, the system 500 comprises at least one processor 112, which may be implemented using one or more processors composed of hardware, software, firmware, or any combination thereof. Depending on the system requirements, the processor 112 may comprise a central processing unit (CPU), a graphics processing unit (GPU), or other special processing hardware such as a TPU or FPGA. The processor 112 may be operably coupled with other system components via a system bus and is responsible for coordinating and executing the core functions of the learning system 500. In a software or firmware-based implementation, the processor 112 may execute computer-executable instructions stored in a database / memory. These instructions are written in any suitable programming language and are configured to perform operations such as intent classification, context management, tool invocation, recommendation generation, and system management. In a distributed environment, multiple processor instances may operate in parallel across the cloud or edge infrastructure to support scalable real-time performance.

[0099] In one embodiment, system 100 further includes an input / output (I / O) interface 117 configured to manage communication between system 500 and external devices or networks. The I / O interface 117 may include hardware and software components that can receive user input in multiple forms, including text, voice, and video, and transmit system responses or interaction elements, such as learning content tailored to the user's learning preferences. It may also support integration with peripherals such as microphones, cameras, touchscreens, keyboards, and display panels. The I / O interface 117 may be operably coupled to processor 112 and other system components via a system bus and may be responsible for formatting, routing, and validating input and output data. In some embodiments, the I / O interface 117 supports communication via various protocols such as HTTP, HTTPS, WebSockets, gRPC, or proprietary APIs, enabling seamless interaction with client devices, external systems, and third-party platforms. The I / O interface 117 may also include input filtering and output formatting logic that are consistent with the overall system configuration parameters.

[0100] In one embodiment, the database 115 may be configured to store both persistent and temporary data used during execution by one or more processors 112. The database 115 may include a combination of volatile memory (e.g., random access memory or RAM) and non-volatile memory (e.g., solid-state drives, hard disk drives, or flash memory). Furthermore, it may support removable memory or external memory such as CompactFlash cards, Secure Digital (SD) cards, or other portable storage formats. The database 115 may be implemented in the form of primary and secondary storage to provide dedicated areas for system instructions, intermediate calculations, and long-term data persistence. The database 115 may be configured to store executable program instructions, configuration parameters, and system-wide data including user profiles, learning profiles, context memory states, session history, access control rules, and interaction logs. The database 115 may also hold parameters for pre-trained language models, tool call mappings, and rule sets for learning content modules. During operation, the processor 112 may be configured to retrieve, update, and write data from the database 115 to support real-time conversation processing, tool execution, personalization, and recommendation generation. Depending on performance and consistency requirements, the memory module may be implemented using a relational database, a NoSQL database, a key-value store, or an in-memory data grid.

[0101] In one embodiment, the recommendation engine 116, shown and described with reference to Figure 6, is configured to generate personalized, context-aware learning content materials for a user. The recommendation engine 116 may be configured to analyze real-time driving data, user behavior patterns, historical interaction logs, and user performance metrics to determine relevant behaviors or insights. These recommendations may include learning content materials and engagement boosting suggestions in response to user input related to user interactions. The recommendation engine 116 may work in conjunction with a learning preference detection unit and a context detection unit, as well as one or more large-scale language models, to construct user-tailored learning content materials and structured outputs such as system-generated prompts. The recommendation engine 116 may include rule-based logic, statistical models, or machine learning algorithms to evaluate thresholds, detect anomalies, or predict trends. The generated recommendations may be provided to the user in response to specific queries or proactively, and presented via an output unit in an interactive format that supports user acceptance, modification, or rejection.

[0102] In one embodiment, one or more Large Language Models (LLMs) 118 may be configured to facilitate natural language understanding, semantic interpretation, prompt generation, and dynamic response generation. The LLMs 118 may be implemented as a finely tuned transformer-based architecture, such as GPT, BERT, or other foundational models trained on a large corpus. Depending on performance, latency, and deployment constraints, these LLM models 118 may be hosted locally within the system infrastructure or accessed via secure API calls to a third-party AI service provider. When in operation, the LLMs 118 are invoked by a learning preference detection unit 180, or a context detection unit 190, or a recommendation engine 116 to perform functions such as query interpretation, entity extraction, context inference, and natural language generation. The LLMs 118 may consume structured prompts constructed from context memory and system parameters to return structured or free-form text output for further processing or presentation. Multiple LLM118 instances may be used simultaneously for different business domains or user roles, and their output may be filtered or refined by post-processing logic in accordance with the overall configuration policy.

[0103] In one embodiment, one or more Application Programming Interfaces (APIs) 119 may be configured to facilitate secure and structured communication between internal system components and external platforms. These APIs 119 may be implemented using standard protocols such as REST, gRPC, GraphQL, or WebSockets, and operate to send and receive data related to user queries, system responses, tool executions, business metrics, and action card payloads. The APIs 119 enable seamless interoperability with third-party systems, including other learning or education platforms. Internally, the APIs 119 may also be used to execute data flows between system modules, such as recommendation engines and context discovery units, and others. These interfaces ensure the modularity, extensibility, and abstraction of functionality, allowing components to evolve independently while maintaining system consistency. The APIs 119 may be protected by authentication mechanisms such as OAuth 2.0, API keys, or mutual TLS, and may include rate limiting, logging, and access control layers to enforce security, privacy, and compliance policies.

[0104] In one embodiment, the components of system 100 may include a processor 112, an I / O interface 117, a database 115, a large-scale language model 118, a recommendation engine 116, and an API 119, and may be configured to communicate with each other via a system bus (not shown). The system bus may be configured to provide a communication backbone that facilitates synchronization and structured, high-speed data exchange between functional modules. The system bus may include one or more hardware and / or software-based interconnects, such as a parallel bus, a serial bus, or a message delivery interface, and may support one-to-one or one-to-many communication patterns. Depending on the operational requirements of each module, the system bus may be configured to support event-driven, synchronous, or asynchronous communication. The system bus may be implemented using technologies such as shared memory, message queues, middleware brokers (e.g., RabbitMQ, Kafka), or an internal API layer. This ensures modularity, scalability, and harmony across the entire distributed system, enabling consistent, low-latency execution of workflows, real-time tool invocation, and context-aware recommendation delivery.

[0105] In one embodiment, a non-temporary computer-readable medium may be provided which stores instructions, and when these instructions are executed by one or more data processing devices, the devices perform the methods and processes described herein. The non-temporary computer-readable medium may include, but is not limited to, magnetic disks, optical disks, solid-state drives, flash memory, or other suitable medium capable of storing executable instructions. When operably connected to a processing system configured to execute such instructions, the stored program code enables the system to perform the functions and operations described above.

[0106] The operation of the system 500 will be described below with reference to the flowchart in Figure 7, which illustrates a method 700 for providing personalized learning content. Method 700 is preferably executed by the processor 112 of the server 110.

[0107] Figure 7 shows the interactive method 700 in more detail. The initial steps 710, 720, and 730 directly correspond to the steps of the aforementioned method 600. Method 700 further includes an interactive feedback loop.

[0108] The method begins in step 710, where multiple character mascots are generated on the display device of the user's computing device (130). Each mascot is associated with one of several learning preferences, namely data-driven learning preferences, aesthetic learning preferences, and experiential learning preferences. This initial step creates clarity at the starting point of the learning experience and presents character mascots that incorporate a unique combination and arrangement of geometric shapes aligned with the associated learning preferences.

[0109] In step 720, the system receives a selection of one of the character mascots from the user via a user input device. This selection is based on the user's visual preference for a particular mascot, and the system determines that it aligns with the user's natural optimal learning preference. This selection act instills confidence in the user, enabling them to immediately gain confidence when engaging with new information.

[0110] Next, in step 730, the processor 112 displays learning content associated with the determined learning preference on the user's display device. For example, a user with a data-driven learning preference is presented with a narrative approach accompanied by auditory accompaniment, while a user with an experiential learning preference is presented with a hands-on approach. This ensures that the content is delivered in an engaging and accessible manner.

[0111] Method 700 further comprises an interactive and adaptive loop. In step 740, the system receives user input in one or more forms, such as text, voice, or video, in response to commands from the learning content. The I / O interface 117 is configured to support multimode user interaction and can operate to receive user input in one or more forms, including text, voice, or video. System 500 is also configured to acquire user-specific and domain-level context data. This enables System 500 to operate across a wide range of access interfaces, such as web-based dashboards, mobile applications, and voice-activated terminals.

[0112] In one embodiment, the user may input a response in text format via a graphical user interface (GUI). For example, the user may select a response indicating a preference for a particular genre of stories on a GUI interface on a desktop dashboard. The I / O interface 117 may be configured to capture the text and convert it into a structured format suitable for subsequent processing by the processor.

[0113] In step 750, the context discovery unit (190) on server 110 processes this response to obtain user-specific context data. Along with the response, the I / O interface 117 may also be configured to capture or associate one or more context metadata elements that characterize the user and their environment at the time the query is sent. Such metadata may include, but is not limited to, a user identifier (e.g., usr_02345 or the user's email address), an age group identifier, a session identifier for state-holding interactions (e.g., sess_00981), a device type (e.g., desktop_browser, mobile_app, or kiosk_terminal), a timestamp indicating the time the query was received (e.g., 2025-05-23T14:42:11Z), arbitrary location coordinates (e.g., 37.7749° N, 122.4194° W), and language or localization settings (e.g., en-US). In one embodiment, user-specific context data may include one or more of the user's role designation (e.g., "student," "adult," "kindergartener," "male," "female"), user identifier, active permissions, past query history, and current session metadata.

[0114] These metadata parameters are sent to the processor along with the response and are used to support domain identification, personalization, role-based access control, and consistent formatting of system responses. User-specific contextual data is used by the context discovery unit 190 to constrain and guide the domain selection process, ensuring that system 100 identifies business functions to which the user is authorized and which are contextually consistent. In one embodiment, the context discovery unit 190 is configured to maintain conversational continuity and personalization by storing multi-level contextual data, including session-based interaction history and long-term behavioral profiles. The unit supplies relevant and appropriate context to the user profile to enhance query interpretation and tool selection. It also retains user responses, tracks system interactions over time, and progressively refines responses to enable proactive engagement. By retaining context across multiple user interactions, the context discovery unit 190 supports a consistent, efficient, and personalized user experience throughout the system's workflow.

[0115] Finally, in step 760, the recommendation engine 116 works with the processor 112 to determine and then generate one or more new and relevant learning content. In addition, it also determines the content flow for the user. For example, different character mascots guide Ollie to different entry points to learning content, such as stories, Chloe to coloring pages, and Drew to hands-on activities, although they later converge. In one embodiment, the recommendation engine 116 comprises, but is not limited to, one or more recommendation engines and one or more recommendation domains. The recommendation engines may be configured to generate suggestions based on various user inputs, including, but is not limited to, user behavior, user preferences, social media profiles, user reviews, and feedback. Recommendation domains may include functional domains. In one embodiment, functional domains include content types, including visual content, auditory content, text content, and kinesthetic content. In another embodiment, functional domains include forms of user participation in content, such as passive content, active content, interactive content, or collaborative content. In yet another embodiment, the functional domain may also include contextual learning content, defined as the context in which learning occurs. Examples of contextual learning content include regular learning content, non-regular learning content, and informal learning content. The delivery of generated recommendations may be through one or more channels, such as an AI-assisted interface, inbox notifications, and email communications. The engine may include a data store (implemented as a database) configured to persistently store recommendation-related attributes at the store ID level.

[0116] This new content is based on both the user's inherent learning preferences, determined at the start of the session, and user-specific contextual data. This comprehensive workflow ensures a dynamic and highly personalized learning path that goes beyond traditional written representation, fostering a deeper understanding of any subject.

[0117] Although the above process is described as a series of steps, this is merely for the sake of explanation. Therefore, some steps may be added, some may be omitted, the order of the steps may be replaced, or some steps may be performed simultaneously. While embodiments have been described with reference to specific exemplary embodiments, it will be apparent that various modifications and changes can be made to these embodiments without departing from the broader scope of System 100 and Method disclosed herein. Therefore, this specification and the drawings should be understood as illustrative, not limiting.

[0118] While the present invention has been described in detail with reference to specific embodiments, those skilled in the art will understand that various modifications can be made in form and detail without departing from the spirit and scope of the invention (defined by the appended claims). Therefore, the scope of the invention is indicated by the appended claims, and it is intended that all modifications falling within the meaning and equivalents of the claims are included.

Claims

1. A computer implementation method for learning a new subject, The aforementioned method, A step of generating a plurality of character mascots on a display device of a computing device, wherein each character mascot is associated with one of a plurality of learning preferences, and the plurality of learning preferences include data-driven learning preferences, aesthetic learning preferences, and experiential learning preferences. A step of receiving a selection of one of the character mascots based on the user's visual preferences from a user input device, wherein it is determined that the user's visual preferences are consistent with the user's learning preferences. A step of displaying learning content on a display device that corresponds to the user's learning preferences, wherein the learning content is associated with one of the plurality of learning preferences, Computer implementation methods, including those mentioned above.

2. The aforementioned plurality of character mascots include a first character mascot, a second character mascot, and a third character mascot. The computer implementation method according to claim 1, wherein each of the character mascots includes a predetermined arrangement of geometric shapes associated with one of the plurality of learning preferences.

3. The computer implementation method according to claim 2, wherein the first character mascot includes a first predetermined arrangement of a symmetrically configured geometric shape, and the first predetermined arrangement of the geometric shape is associated with data-driven learning preferences.

4. The computer implementation method according to claim 2, wherein the second character mascot includes a second predetermined arrangement of geometric shapes having an asymmetric configuration, wherein a portion of the geometric shapes overlaps with one another, and the second predetermined arrangement of geometric shapes is associated with aesthetic learning preferences.

5. The computer implementation method according to claim 2, wherein the third character mascot includes a third predetermined arrangement of geometric shapes, the geometric shapes having pointed edges, and the third predetermined arrangement of geometric shapes is associated with experiential learning preferences.

6. The computer implementation method according to claim 3, wherein the first predetermined arrangement of the geometric shape includes circles, semicircles, and partial circles.

7. The computer implementation method according to claim 4, wherein the second predetermined arrangement of the geometric shapes includes circles, quadrilaterals, and triangles.

8. The computer mounting method according to claim 5, wherein the third predetermined arrangement of the geometric shapes includes quadrilaterals and triangles.

9. The steps include receiving user input in one or more forms, including text, audio, and video, in response to the displayed learning content, A step of generating new learning content for the user based on the user input and the determined learning preference for geometric shapes, The computer implementation method according to claim 1, further comprising:

10. The computer implementation method according to claim 1, wherein the step of generating the new learning content is performed by a recommendation engine that communicates with a large-scale language model.

11. The computer implementation method according to claim 1, wherein the aforementioned experiential learning preference is associated with learning content that includes instructional video content involving practical experience.

12. The computer implementation method according to claim 1, wherein the data-driven learning preference is associated with learning content comprising a story with visual content and auditory accompaniment.

13. The computer implementation method according to claim 1, wherein the aesthetic learning preference is associated with aesthetically appealing visual learning content.

14. The steps include receiving user input in one or more forms, including text, audio, or video, in response to the displayed learning content, The steps include obtaining user-specific context data based on the user input, The steps include determining relevant learning content for the user based on the user-specific contextual data and the determined learning preferences, The steps include generating one or more relevant learning contents for the user based on the user-specific context data and the determined learning preferences, The computer implementation method according to claim 1, further comprising:

15. A system for learning a new subject, wherein the system is A computing device comprising a display device, a user input device, a processor, and memory, wherein the memory stores instructions, and when the instructions are executed by the processor, the system provides learning content on the display device, and the learning content is associated with one of a plurality of learning preferences, including data-driven learning preferences, aesthetic learning preferences, and experiential learning preferences. When the aforementioned instruction is executed by the processor, the system Multiple character mascots are generated on the display device of the computing device, and each character mascot is associated with one of the multiple learning preferences. The user input device receives a selection of one of the character mascots based on the user's visual preferences, and it is determined that the visual preferences are consistent with the user's learning preferences. A system that displays learning content corresponding to the user's learning preferences on the display device, and the learning content is associated with one of the plurality of learning preferences.

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