A system and method for learning a new subject matter

Abstract

The present disclosure generally relates to a computer-implemented method for learning a new subject matter, the method comprising generating, on a display of a computing device, a plurality of character mascots, wherein each character mascot is associated with a one of a plurality of learning preferences, the plurality of learning preferences including a data-driven learning preference, an aesthetic learning preference, and an experiential learning preference, receiving, from a user input device, a selection of one of the character mascots based on a visual preference of a user, wherein the visual preference of the user is determined to be aligned with the user's learning preference, and displaying a learning content on the display that corresponds to the user's learning preference, wherein the learning content is associated with one of the plurality of learning preferences.

Description

A System and Method for Learning a new subject matterBackground

[0001] The present disclosure generally relates to learning methods and systems designed to enhance the development of specific skills Particularly, the present disclosure aims to address inherent shortcomings in existing educational frameworks by adopting a more individualized and comprehensive approach to learning, and discloses a system and method for learning anew subject matter.Technical Field

[0002] The present disclosure generally relates to learning methods and systems designed to enhance the development of specific skills. Particularly, the present disclosure aims to address inherent shortcomings in existing educational frameworks by adopting a more individualized and comprehensive approach to learning, and discloses a system and method for learning anew subject matter.

[0003] The following discussion of the background to the invention is intended to facilitate an understanding of the present invention. However, it should be appreciated that the discussion is not an acknowledgment or admission that any of the material referred to was published, known or part of the common general knowledge in any jurisdiction as at the pnority date of the application.

[0004] Education, as a fundamental pillar of personal and societal growth, has traditionally faced significant challenges in catering to the diverse learning needs of children. From the moment a child is bom, parents, as their first educators, recognize the uniqueness of their offspring. Each child is perceived as possessing distinct talents and strengths waiting to be discovered. This initial understanding of the child's potential is often clouded when the child enters formal education. The existing educational system, driven by the economies of scale, typically delivers instruction in a one-size-fits-all manner. This generalized approach to teaching frequently neglects the individualized strengths of each child, thereby causing a loss of connection between the child’s inherent potential and the education they receive.|0005| In current systems, classrooms are structured around efficiency and uniformity. Teachers deliver knowledge to large groups of students in a standardized format, which often means that learning becomes a matter of survival for the fittest listeners or, in some cases, the most compliant students. In a class of 40 students, for example, it is common for only one or a small handful of children to be celebrated and recognized for their talents or keen learning abilities. The remaining students, who may not thrive in this standardized learning environment, may develop a dangerous misconception that they' lack talent or the capacity' to leam. This false belief can lead to a self-fulfilling prophecy, inhibiting their ability7to seek personal growth and diminishing their potential contributions to society.

[0006] Such misconceptions have broader implications beyond individual development. A thriving community' and economy are fundamentally dependent on individuals contributing their unique strengths and talents to society. When children fail to recognize or develop their talents due to the limitations of the education system, the potential for optimal societal contribution diminishes, resulting in reduced overall economic prosperity.

[0007] Prior art solutions have attempted to address these challenges by categorizing learners into three primary7types: auditory learners, kinaesthetic learners, and visual learners. While this classification system acknowledges the existence of different learning modalities, it remains overly simplistic and fails to account for the complexity of individual learning needs. By placing learners into rigid categories, these systems fail to account for the fluid and dynamic nature of individual learning processes. This approach does not provide the necessary tools or confidence for learners to engage with new information effectively. As a result, many students may still struggle to find their footing in the learning environment, perpetuating the cycle of misunderstanding and underachievement.

[0008] These prior art solutions also lack a comprehensive, integrated approach that can accommodate the multifaceted nature of learning. Students classified as auditory learners, for instance, are typically taught using methods that emphasize listening, while kinaesthetic learners are encouraged to engage in hands-on activities, and visual learners are exposed primarily to visually appealing content. Thiscategorical approach, however, overlooks the fact that most learners benefit from a combination of modalities. Restricting students to a single method does not foster the deep understanding necessary' for long-term knowledge retention and personal growth.

[0009] Moreover, prior art solutions fail to instill confidence in learners from the outset of their educational journey. Instead of empowering students to approach new information with clarity and self- assurance, the existing systems often perpetuate confusion and uncertainty, particularly for those who do not neatly fit into one of the predetermined learning categories.[0010| The major shortcoming of the current educational system and prior art solutions lies in their inability to cater to the unique starting points of individual learners. By enforcing a one-size-fits-all approach or rigidly categorizing students into auditory, kinaesthetic, or visual learners, the system inadvertently stifles the individual growth and development of most students. These methods do not provide learners with the tools or strategies necessary to approach new information with confidence, which is crucial for fostering an effective learning environment.

[0011] Further, the categorical approach used in prior methods does not account for the dynamic nature of learning. The focus on one particular modality' often results in a superficial understanding of the subject matter. For instance, auditory learners may miss out on the benefits of visual aids, while visual learners may struggle to grasp concepts that are best understood through hands-on activities. This lack of an integrated approach limits the ability of students to engage fully with educational content, often resulting in disengagement, frustration, and underperformance.

[0012] Additionally, prior art solutions do not address the broader societal impact of their inadequacies. When learners do not fully develop their talents, their potential contributions to society and the economy are limited. The failure to nurture diverse strengths within the classroom ultimately leads to a less dynamic and innovative community, as individuals are not empowered to leverage their unique abilities.

[0013] Therefore, the present invention attempts to overcome at least in part some of the aforementioned problems and to provide for an improved approach for addressing the foregoing challenges.Summary of the Invention

[0014] According to a first aspect of the invention, there is provided a computer-implemented method for learning a new subject matter, the method comprising generating, on a display of a computing device, a plurality of character mascots, wherein each character mascot is associated with a one of a plurality of learning preferences, the plurality of learning preferences including a data-dnven learning preference, an aesthetic learning preference, and an expenential learning preference, receiving, from a user input device, a selection of one of the character mascots based on a visual preference of a user, wherein the visual preference of the user is determined to be aligned with the user's learning preference, and displaying a learning content on the display that corresponds to the user’s learning preference, wherein the learning content is 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, a third character mascot, wherein each character mascot includes a predetermined arrangement of geometrical shapes associated with one of the plurality of learning preferences.

[0016] According to various embodiments, the first character mascot includes a first predetermined arrangement of geometrical shapes in a symmetrical configuration, wherein the first predetermined arrangement of geometric shapes is associated with the data-driven learning preference.

[0017] According to various embodiments, the second character mascot includes a second predetermined arrangement of geometrical shapes in a non-symmetrical configuration, wherein some of the geometrical shapes overlap with one another, and wherein the second predetermined arrangement of geometrical shapes is associated with an aesthete learning preference.

[0018] According to various embodiments, the third character mascot includes a third predetermined arrangement of geometncal shapes including geometrical shapes with pointed edges, wherein the third predetermined arrangement of geometrical shapes is associated with an experiential learning preference.

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

[0020] According to various embodiments, the second predetemiined arrangement of geometrical shapes includes circles, squares and triangles.

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

[0022] According to various embodiments, the computer-implemented method further comprises receiving a user input in one or more modalities, including text, audio, and video, in response to the displayed learning content; and generating a new learning content for the user based on the user input and the determined learning preference.

[0023] According to various embodiments, generating the new learning content is performed by a recommendation engine in communication with a large language model.

[0024] According to various embodiments, the experiential learning preference is associated with learning content comprising instructional video content that involves a hands-on experience.|0025| According to various embodiments, the data-dnven learning preference is associated with learning content comprising a story with visual content and aural accompaniment.

[0026] According to various embodiments, the aesthetic learning preference is associated with aesthetically-pleasing and visual learning content.

[0027] According to various embodiments, the computer-implemented method further comprises the steps of receiving a user input in one or more modalities, including text, audio, or video, in response to the displayed learning content; and retrieving a user-specific contextual data based on the user input, determining relevant learning content for the user based on the user-specific contextual data and determined learning preference, generating one or more relevant learning content for the user based on the user-specific contextual data and determined learning preference.[0028| According to a second aspect of the invention, there is provided a system for learning new subj ect matter, the system comprising a computing device having a display, a user input device, a processorand a memory, wherein the memory store instructions, which when executed by the processor, cause the system to provide learning content on the display, the learning content being associated with one of a plurality of learning preferences including a data-driven learning preference, an aesthetic learning preference, and an experiential learning preference, characterised in that the instructions, when executed by the processor, cause the system to generate, on the display of the computing device, a plurality of character mascots, wherein each character mascot is associated with a one of a plurality of learning preferences, receive, from the user input device, a selection of one of the character mascots based on a visual preference of a user, wherein the visual preference of the user is determined to be aligned with the user’s learning preference, and display the learning content on the display that corresponds to the user’s learning preference, wherein the learning content is associated with one the plurality of learning preferences.Brief Description of the Drawings

[0029] In the drawings, like reference characters generally refer to the same parts throughout the different views. The drawings are not necessarily to scale, emphasis instead generally being placed upon illustrating the principles of the invention. The dimensions of the various features or elements may be arbitrarily expanded or reduced for clarity. In the following description, various embodiments of the invention will be better understood with reference to the detailed description when considered in conjunction with the non-limiting examples and the accompany drawings, in which:

[0030] Figure 1 is a schematic diagram illustrating an exemplary infrastructure of a system for learning new' subject matter, according to an embodiment of the invention;

[0031] Figure 2 illustrates exemplar}' embodiments of character mascots for use with the system of Figure 1, according to an embodiment of the invention;10032| Figure 3 is a flow' chart illustrating a method for learning new' subject matter, according to an embodiment of the invention;

[0033] Figure 4 is an example toy kit for use with the computer-implemented method for learning a new' subject matter according to an embodiment of the invention;

[0034] Figure 5 is a flow chart illustrating another method for learning new subject matter, according to an embodiment of the invention;

[0035] Figure 6 is a schematic block diagram a system architecture for learning new subject matter, according to an embodiment of the invention;

[0036] Figure 7 is a flow' chart illustrating another method for providing personalized and adaptive learning content according to an embodiment of the invention.Detailed Description

[0037] Reference will now be made in detail to an exemplary embodiment of the present invention, examples of which are illustrated in the accompanying drawings. While the invention will be described in conjunction with the embodiment, it will be understood that they are not intended to limit the invention to these embodiments. On the contrary, the invention is intended to cover alternatives, modifications, and equivalents, which may be included within the spirit and scope of the invention as defined by the appended description. Furthermore, in the following detailed description of embodiments of the present invention, numerous specific details are set forth in order to provide a thorough understanding of the present invention. However, it will be recognized by one of ordinary skill in the art that the present invention may be practiced without these specific details. In other instances, well-known methods, procedures, components, and circuits have not been described in detail as not to unnecessarily obscure aspects of the embodiments of the present invention.[ 00381 In the specification the term “comprising7’ shall be understood to have a broad meaning similar to the term “including” and will be understood to imply the inclusion of a stated integer or step or group of integers or steps but not the exclusion of any other integer or step or group of integers or steps. This definition also applies to variations on the term “comprising” such as “comprise” and “comprises”.

[0039] It is to be appreciated that the embodiments of this invention as discussed below are preferably a software algorithm, program or code residing on a computer useable medium having control logic forenabling execution on a machine having a computer processor. The machine typically includes memory storage configured to provide output from execution of the computer algorithm or program.

[0040] As used herein, the term “software” is meant to be synonymous with any code or program that can be in a processor of a host computer, regardless of whether the implementation is in hardware, firmware or as a software computer product available on a disc, a memory storage device, or for download from a remote machine. The embodiments described herein include such software to implement the equations, relationships and algorithms described. One skilled in the art will appreciate further features and advantages of the invention based on the above-described embodiments.

[0041] Embodiments described in the context of one of a server and a method are analogously valid for the other server and method. Similarly, embodiments described in the context of a server are analogously valid for a method, and vice-versa.

[0042] Features that are described in the context of an embodiment may correspondingly be applicable to the same or similar features in the other embodiments. Features that are described in the context of an embodiment may correspondingly be applicable to the other embodiments, even if not explicitly described in these other embodiments. Furthermore, additions and / or combinations and / or alternatives as described for a feature in the context of an embodiment may correspondingly be applicable to the same or similar feature in the other embodiments.10043| In the context of various embodiments, the articles “a”, “an” and “the” as used with regard to a feature or element include a reference to one or more of the features or elements.

[0044] As used herein, the term “and / or” includes any and all combinations of one or more of the associated listed items.

[0045] Throughout the description, the term “module” may be understood as an application specific integrated circuit (ASIC), an electronic circuit, a combinational logic circuit, a field programmable gatearray (FPGA), a processor which executes code, other suitable hardware components which provide the described functionality, or any combination thereof. The term of “module” may include a memoty which stores code executed by the processor.

[0046] To achieve the stated features, advantages and objects, the present invention is directed to a system and method for learning a new subject matter.

[0047] The present disclosure discloses a system and method for learning a new subject matter. Subject matter can include a new language or subject matters involving mathematics, science, art, or other forms of knowledge. The present invention offers a fresh perspective on learning. Rather than categorizing learners into fixed buckets, the present invention creates clarity at the starting point of the learning experience. It empowers learners to acquire confidence almost immediately when approaching information and content. This empowerment is crucial, as it allows individuals to engage with learning materials without the constraints of preconceived notions about their abilities.

[0048] The present invention also introduces a comprehensive workflow that encompasses the following three learning preferences:• Data-driven learning preference;• Aesthetic learning preference: and• Experiential learning preference.Depending on the selection of the user based on the user’s visual preference of a character mascot that is aligned with the user’s natural optimal learning preference, the workflow includes a story-based approach, a hands-on approach, and the use of aesthetically pleasing visuals that are aligned with each of the above learning preferences. By integrating these elements, the present invention ensures that every piece of content is delivered in a manner that is engaging and accessible to all learners and aligned with the optimal natural learning preference of each user.

[0049] In an embodiment, the present invention is designed to teach any subject matter to dyslexics and students with learning disabilities or difficulties. Additionally, the present invention is broadly capableof teaching any subject matter to any student of any learning capability , including children and adults based on the selection of the student’s visual preference of a character mascot aligned with the student’s natural optimal learning preference. The present invention is designed to be language agnostic, and may employ one or more of multiple languages for localization in various countries where English may not be the primary teaching or official language of education, even though the present disclosure is focused on the English language, and other languages are envisioned. The details of the science behind each character mascot that is aligned with a natural optimal learning preference will be detailed later.100501 The present invention relates to a system and method for learning new subject matter by determining a learner's optimal learning style through selection of a character mascot designed with a specific, predetermined arrangement of geometric shapes. It has been found that a learner's selection of a particular character mascot is a highly accurate predictor of the cognitive modalities that resonate most strongly with their inherent preferences. The accuracy of this determination has been validated through extensive testing on a large and diverse sample of learners and is detailed hereinafter.

[0051] One advantage of the present invention lies in its ability to facilitate holistic learning experiences. By employing an optimal learning experience that is aligned with a user’s learning preferences at the beginning of the user’s learning journey, it has been shown to enable learners from various backgrounds, including those with learning differences or difficulties, to acquire information easily and accurately. This inclusivity is vital in today's diverse educational landscape, where the goal is to ensure that every learner has the opportunity to succeed.

[0052] The objective addressed by the present invention is to provide clarity' at the starting point of the learning experience, enabling learners to gain confidence and engage effectively with new information. The present invention provides a workflow, which encompasses multiple learning profiles, that aims to create a more inclusive and effective educational environment. By recognizing and nurturing individual potential, this approach seeks to transform the learning landscape, ensuring that all learners can thrive and contribute meaningfully to society.

[0053] The present invention discloses a system and method for learning a new subject matter. In an embodiment, this can involve language skills such as articulation, reading, writing, and spelling.

[0054] FIG. 1 illustrates a block diagram of a learning management system for learning a new subject matter according to embodiments of the invention. The learning management system 100 includes a server 1 1 configured for wireless communication through a communication network 120 to a computing device 130. The computing device 130 could be a dedicated learning machine or a multipurpose device like a PC, tablet, or smartphone with a learning application 140 installed.

[0055] The server 110 is configured to manage user profiles, authenticate users, maintain and update learning content for specific learning profiles, and exchange data (such as learning content and user progress). To facilitate user authentication and content delivery', the server 110 maintains user profiles, learning profiles and learning content material or content associated with a user profile or a learning profile. In some cases, the server 1 10 may operate on dedicated hardware infrastructure. Alternatively, it can be hosted on a cloud platform. In the broadest sense, the learning content is any resource, activity', information, or experience that is designed for or can be used by a user or a learner to acquire knowledge, develop skills, or cultivate new attitudes and behaviors.

[0056] Use of the term ‘server’ herein can mean a single computing device or a plurality of interconnected computing devices which operate together to perform a particular function. That is, the server may be contained within a single hardware unit or be distributed among several or many different hardware units. In an embodiment, the server 110 may be enabled in a remote cloud server or a co-located server and may include a database 115 for retaining various data inputs and outputs necessary for operations of the system 100. In an embodiment, the data server 110 may store data input by the computing device 130 or output generated by' the computing device 130. In an embodiment, within the data server 1 15, a Large Language Model (LLM) is stored for use by the computing device 130. In an embodiment, examples of the LLM may include, but are not limited to, zephyr, code Large Language Model Meta Al (LLAMA), Generative Pre-training Transformer (GPT), etc The LLM stored within the server 110 serves as a foundationalcomponent for various computational tasks and applications. In an embodiment, the computing device 130 may be communi cably coupled with the server 110 through the communication network 120.

[0057] In an embodiment, the communication network 120 may be a wired or a wireless network or a combination thereof. The communication network 120 can be implemented as one of the different ty pes of networks, such as but not limited to, ethemet IP network, intranet, local area network (LAN), wide area network (WAN), or a Metropolitan Area Network (MAN). Various devices in the system 100 may be configured to connect to the communication network 120, in accordance with various wired and wireless communication protocols. Examples of such wired and wireless communication protocols may include, but are not limited to, a Transmission Control Protocol and Internet Protocol (TCP / IP), User Datagram Protocol (UDP), Hypertext Transfer Protocol (HTTP), File Transfer Protocol (FTP), Zig Bee, 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. Further the communication network 120 can include a variety of netw ork devices, including routers, bridges, servers, computing devices, storage devices, and the like.[0058| The computing device 130 is a standalone computing device onto which a learning application 140 or a learning software program can be installed and executed to perform the functions described herein. 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, in which the learning application includes a client in communication wi th a server that hosts 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 websites. In such cases, the third party7web applications or websites may be able to invoke the learning application 140 by installing a widget or addon or providing a link to the learning application 140.

[0059] The computing device 130 includes a memory 132 for storing a learning application 140. In an embodiment, the memory 132 may7store instructions and data, including one or more modules that, when executed by the processor 131, may cause the processor 131 to present suitable learning content to theuser, as will be discussed in greater detail herein below. In an embodiment, the memory 132 may be a nonvolatile memory or a volatile memory7. In an embodiment, the memory 132 may also store a single module or a combination of different modules to present learning content to a user through the learning application 140. Examples of non-volatile memory7may include but are not limited to, a flash memory7, a Read Only Memory (ROM), a Programmable ROM (PROM), Erasable PROM (EPROM), and Electrically EPROM (EEPROM) memory7. Further, examples of volatile memory may include but are not limited to. Dynamic Random Access Memory (DRAM), and Static Random- Access memory7(SRAM).|0060| The learning application 140 includes data such as user profiles 141, learning profiles 142, and learning content 143, for execution by the processor 131. The learning content 143, the learning profiles 142 are implemented as a form of an application or software application such as a learning application 140 that is downloadable by a user and installed on the computing device.

[0061] Further, data may be stored in one or more databases 115 maintained by the server 110. For example, one database may7store program modules, another database may7store learning content 143, another database may maintain a user's learning progress and a further database may store user profiles 122, for example. These databases may be implemented as relational databases in one example.

[0062] The computing device 130 may also include a display 133 to provide an output. The display includes a touch screen display or other display, for example, such as an LCD, LED display, for displaying a user interface. Other output devices such as an audio device (e g. a speaker, headphones, or other audio device) can be configured to output sound in accordance with instructions received from the processor 131. In some embodiments, the learning content 143 may also include a unit that generates haptic feedback (e.g., vibrations) in response to one or more actions.

[0063] The computing device 130 also includes one or more user input devices 134 to receive user input. These input devices may be in the form of a touch sensitive panel (not shown) physically associated with the display to collectively7form a touch-screen, a keypad (not shown) for communicating information and command selections to processor 131, a cursor control (not shown) for communicating directioninformation and command selections to processor 131 and for controlling cursor movement on the display, a microphone for detecting audio input from a user, and / or an image capturing device (not shown) such as a camera or video recorder (for detecting visual inputs, capturing footage of the user, etc ).

[0064] According to various embodiments, the learning content 143 requires receiving an input from the user, for example, a text input, a selection input, a voice input or a handwriting input by the user in response to instructions provided by the learning content. The learning application adopts voice recognition and handwriting recognition software to identify the learner’s approach.10065| The computing device 130 also includes a communication unit 135. The communication unit 135 provides a two-way data communication coupling to a network link that is connected to a communicarion network 120. For example, communication unit 135 may be an integrated services digital network (ISDN) card, cable modem, satellite modem, etc. As another example, communication unit 135 may be a local area network (LAN) card to provide a data communication connection to a compatible LAN. Wireless links may also be implemented. In any such implementation, communication unit 135 sends and receives electrical, electromagnetic or optical signals that carry digital data streams representing various types of information.

[0066] Fig. 2 illustrates example embodiments of three character mascots according to various embodiments. In an embodiment, learners are presented with a selection of three character mascots: a first character mascot 200, a second character mascot 300, and a third character mascot 400, on a display of a computing device 130. These character mascots are each uniquely constructed using a combination of geometric shapes, such as circles, triangles, and squares, in a predetermined arrangement based on the concept of a tangram. The tangram, known for its psychometric properties, aids in the development of cognitive processing by facilitating the sequential assembly of discrete elements. The concept of "learning styles" has also been widely popularized in education, with theories like the VARK model (Visual, Auditory , Reading / Writing, Kinesthetic) suggesting that people learn best when taught in a way that matches their preferred learning preference. In the present invention, the arrangement of geometric shapes within each character mascot is configured to engage a learner's innate cognitive sequencing abilities andlearning preferences compnsing one of the following: a data-driven learning preference, an aesthetic learning preference and an experiential learning preference. According to these learning preferences, an aesthetic learner would benefit most from use of pictures and diagrams, a data-driven learner would benefit most from sound-based learning or story-based learning, and an experiential learner would benefit most from a sensory-based learning or hands-on approach.

[0067] Referring to Figure 2, in an embodiment, a first character mascot 200, also referred to as Ollie, is designed in a predetennined arrangement of geometric shapes. The first character mascot 200 comprises a predetermined arrangement of geometric shapes. Specifically, this arrangement is symmetrical. In the depicted embodiment, the mascot comprises a main body 210 formed from a first full circle. A pair of semicircles 220a,220b are arranged symmetrically on the upper portion of the main body 210 to represent ears. A pair of second, smaller full circles 230a, 230b are arranged symmetrically within the main body 210 to represent eyes. A quadrant of a circle 240 is arranged centrally below the eyes to represent a mouth. In an embodiment, this symmetrical arrangement of fundamental geometric shapes — including circles, semicircles, and quadrants — has been found to appeal to a data-driven learner who is characteristically logic-driven and data-driven. The structured and predictable nature of the design resonates with these cognitive modalities, making the selection of mascot 100 a strong indicator that the learner possesses a data-driven learning preference.

[0068] In another embodiment, a second character mascot 300 referred to as Chloe, comprises a second predetermined arrangement of geometric shapes including circles, triangles and squares. In contrast to the first embodiment, this arrangement is non-symmetrical and aesthetically pleasing. As illustrated, the second character mascot 300 has a main body 310 configured in the shape of a cloud. In an embodiment, the non-sy mmetrical combination of geometrical shapes and / or overlap of geometrical shapes provides a basis for imaginative thinking, as it might lead an aesthetic learner to imagine what is hidden behind the clouds. Aesthetic learners who demonstrate a preference for selecting the second character mascot 300 are typically visually inclined and enjoy learning in an aesthetic manner. The free-fonn, organic, and aesthetically driven design of the second character mascot 300 resonates with aesthetic learners who prioritise visual and creative cognitive modalities.|0069| In another embodiment, a third character mascot 400 referred to as Drew, comprises a third predetermined arrangement of geometric shapes including circles, triangles and squares. In contrast to the first and second character mascots, the third character mascot 400 is distinguished from the first and second character mascots by its compositional elements. Specifically, the third character mascot 400 is formed from an arrangement of a plurality of geometrical shapes comprising sharp pointed edges. Additionally, the plurality of geometrical shapes is selected to convey a sense of movement, to serve as motivating graphics, or to represent action-based attributes. As illustrated, the third character mascot 400 has a main body 420 comprising a square and triangles two semi-circles at each opposed end of the square. The main body 420 includes horns 430a, 430b on the upper portion of the main body 420. Its facial features comprises two circular eyes and a mouth in the shape of a semicircle. It has an arm that is a simple, curved arc-like shape. This unique combination of geometrical shapes is configured to appeal to an experiential learner who would benefit most from a sensory-based learning or hands-on approach. Expenential learners who demonstrate a preference for selecting the third character mascot 400 are typically sensory- inclined and enjoy learning in a hands-on manner. The third character mascot 300 resonates with learners who prioritise sensory-based learning.

[0070] Refemng to FIG. 3, a flowchart 600 of a method of learning a new subject matter in accordance with an embodiment of present disclosure is disclosed. FIG. 3 is explained in conjunction with FIGs. 1 and 2. Tn an embodiment, the flowchart 600 may include a plurality of steps that may be performed by various modules of the computing device 130 so as to present learning content to a user through the learning application 140.

[0071] At step 610, the method involves generating a plurality of character mascots, each associated with a learning preference on the display of a computing device, wherein the learning preferences include one of the following: a data-driven learning preference, an aesthetic learning preference, and an experiential learning preference. A character mascot can be understood as a symbolic figure or representation that embodies certain characteristics or themes in this case, the aforesaid learning preferences. A learning preference refers to the preferred way an individual processes information, which can be data-driven, aesthetic-based, or experiential.|0072| One advantage of this arrangement is that it allows learners to engage with content in a manner that aligns with their natural inclinations, potentially enhancing their engagement and retention of information. By presenting character mascots that correspond to different learning preferences or styles, the present invention leverages the computing device's ability to provide personalized and interactive experiences, which can lead to more efficient use of processing power and storage capacity as the content is tailored to the user's learning preferences.

[0073] Additionally, the present invention advocates for the delivery of content or information through a combination of aesthetically appealing visuals, an auditor -based experience comprising narrative or storytelling approach, and a sensorial experience and hands-on approach that extends beyond mere written text that are in line with the user’s learning preference. This tripartite strategy’ fosters a comprehensive and profound understanding of subject matters. Empirical evidence suggests that this approach significantly enhances the ability of learners from diverse backgrounds, including those with learning differences, to acquire information with greater ease and accuracy.

[0074] At step 620, the method further includes receiving, by a user via an input device on the computing device, a selection of one of the character mascots based on a visual preference of the user, wherein the visual preference of the user is aligned with the user’s learning preference as mentioned above. An input device is any hardware used to send data to a computer, such as a keyboard or mouse The selection of the character mascot allows the system to adapt the learning content to the user's preferred sty le, thereby increasing the likelihood of effective learning.

[0075] For example, if the user selects the first character mascot 200 (Ollie). The first character mascot comprises a predetermined arrangement of geometric shapes which is preferably substantially symmetrical. In an embodiment, this symmetrical arrangement of fundamental geometric shapes — including circles, semicircles, and quadrants — has been found to appeal to data-driven learners who are characteristically logic-driven, story-driven, and data-driven. The structured and predictable nature of thedesign resonates with these cognitive modalities, making the selection of the first character mascot 200 a strong indicator that the user possesses a data-driven learning preference.

[0076] At step 630, the method involves displaying learning content on the display of the computing device, wherein the learning content is associated with the user's learning preference. Learning content refers to the educational material presented to the user. This arrangement ensures that the content is presented in a format that is most likely to be well-received by the user, thereby optimizing the learning process.|0077| Additionally, in another embodiment, the method also includes receiving a user input from the user from one or more user input devices in response to the learning content, wherein the user input is associated with an outcome linked with a learning objective. A learning objective is a specific goal or skill that the learning process aims to achieve. This arrangement allows for real-time feedback and adaptation of the learning process, which can enhance the effectiveness of the educational experience. For example, the learning content requires voice input and handwriting input by the user in response to instructions provided by the learning content. The learning application adopts voice recognition and handwriting recognition software to identify the learner's approach.

[0078] In another embodiment, the method further involves generating a progress report for the user based on a determination of whether the user has met the learning obj ective of the learning content, based on the user input. A progress report is a document that provides an overview of the user's achievements and areas for improvement. This arrangement provides the user with valuable insights into their learning journey, enabling them to adjust their approach as needed and facilitating continuous improvement.

[0079] According to various embodiments, the techniques herein are performed by the learning management system 100 in response to processor 131 executing sequences of one or more instructions (e.g. the modules described previously) contained in main memory 132. Such instructions may be read into memory 132 from another storage medium, such as a remote database (not shown) or a non-transient memory'. Execution of the sequences of instructions contained in the memory 132 causes the processor131 to perform the process steps described herein. In alternative embodiments, hard-wired circuitry7may be used in place of or in combination with software instructions.

[0080] According to various embodiments, the learning content is organized, delivered and presented in a manner on the display that comprises bite-sized outcomes that are topic objective driven. Once the learner achieves the bite-sized outcome, it means their learning is optimised. The learning content comprises a plurality of subject matters covering various types of knowledge. Every subject matter has learning objectives and outcomes, and the outcomes comprise bite-sized outcomes that are associated with a learning objective.

[0081] In an embodiment, for example, in an Alphabet Explorer learning content, at each letter of the alphabet, there are mini activities for the user to do and accomplishment of each activity would then be recorded as an outcome, adding up to the overall progress report where mastering the ABCs is concerned.

[0082] In another embodiment, a learning content on renewal energy7utilises different physical DIY learning kits to teach renewable energy’ and solar energy7. The outcomes are incorporated in the physical product completion of the learning kit and comprehension of a digital story. The outcomes can be determined by the level of completion of the physical product and the application of content from the digital story In activity booklets.

[0083] The present invention comprises an innovative aspect pertaining to the incorporation of character mascots or learning mascots within the learning application or learning software program. These character mascots are instrumental in accurately identifying a learner's optimal learning style. Specifically, the mascots are designed to align with one of three primary learning preferences:(i) Data-driven learning preference - learners who prefer story-based content and exhibit a desire to understand the foundational basis of the information they acquire;(ii) Aesthetic learning preference - learners who are inclined towards acquiring information through aesthetically pleasing visuals.(iii) Experiential learning preference - learners who favor a sensorial approach to information absorption.The selection of an appropriate character mascot serves to pinpoint the learner's optimal learning style, which aligns with their preferred and most confident method for assimilating new knowledge. It should be noted, however, that the selection of character mascot does not preclude the learner from engaging in alternative learning preferences. The invention posits that a learner's confidence and interest in learning are intrinsically linked to the initial phase of their learning experience. By facilitating the intake of new information through the learner’s preferred learning preference, the invention ensures a more effective learning process and a deeper comprehension of the subject matter.

[0084] The present invention also provides for a computer-implemented method for learning a new subject matter via assembly of a toy kit. The computer-implemented method delivers learning content via a combination of learning preferences which can be data-driven, aesthetic, or experiential. This tripartite strategy fosters a comprehensive and profound understanding of subject matters. Empirical evidence suggests that this approach significantly enhances the ability of learners from diverse backgrounds, including those with learning differences, to acquire information with greater ease and accuracy.

[0085] Figure 4 illustrates a toy kit for use with the computer-implemented method for learning a new subject matter. As will be appreciated by one skilled in the art, a variety of toy kits can be employed for learning anew subject matter. For example, Figure 4 shows a toy kit featuring a solar-powered house with a windmill. The solar-powered house is a do-it-yourself toy kit where a child can build his or her own model house. The subject matter to be learned relating to the toy kit is about renewable energy. In this example embodiment, a child can assemble a house equipped with solar panels and using the interactive computer-implemented method, learn the fundamentals of renewal energy, such as how solar energy' is harnessed and converted into electrical energy or how this energy can power various appliances w ithin the house. The child can also learn the fundamentals of sustainable architecture and design through building the solar-powered house with a windmill.

[0086] The toy kit and computer-implemented method offer several advantages for learning new subject matter:(i) Hands-On Learning: The toy kit provides practical, experiential learning opportunities. For instance, children can build models and directly interact with components, making complex concepts more accessible and easier to understand.(ii) Environmental Awareness: By engaging with topics such as renewable energy, the toy kit helps instil an understanding and appreciation of sustainable practices. This fosters a sense of responsibility towards the environment and highlights the importance of renewable energy sources.(iii) Creative Expression: The process of building and customizing the toy kit encourages creativity. Children can explore their artistic side while also engaging in technical learning, blending creativity with education.

[0087] Figure 5 illustrates a flowchart of a computer-implemented method 500 for learning a new subject matter via assembly of a toy kit. One aspect of the present invention relates to a computer-implemented method of learning a new subject matter via assembly of a toy kit. A computer-implemented method may be understood as a sequence of steps or actions taken to achieve a desired outcome utilizing a computing device to facilitate tasks or functions, in this case, learning a new subject matter through interactive engagement with a toy kit.

[0088] At step 510, the first step involves displaying learning content associated with the subject matter on a user interface of a computing device, wherein the learning content comprises at least two learning modules, and each learning module is associated with a learning preference comprising one or more of the following: a visual learning preference, an auditory learning preference, and a kinesthetic learning preference. Learning content refers to the informational materials presented to the user for educational purposes. This may include text, images, audio, video, interactive simulations, and more.

[0089] Learning modules are distinct sections of the learning content tailored to specific learning styles. It may be provided that each learning module is associated with a specific learning preference. Learning preferences are individual characteristics of learners that influence how they best absorb information. The three main learning styles are auditory, visual, and kinesthetic. An auditory learner enjoys story -based content and prefers to acquire knowledge through listening and speaking. A visual learner is attracted to aesthetically pleasing visuals for acquiring new concepts. A kinesthetic learner benefits from a sensorial approach towards learning. One advantage of this arrangement lies in its ability to address diverse learning preferences, thereby enhancing user engagement and retention of information. By presenting multiple learning modules that cater to various styles, the method effectively utilizes the processing capabilities of the computing device to deliver tailored educational experiences, optimizing both user interaction and information absorption.

[0090] At Step 520, this involves a user selecting one of the learning modules via an input device on the user interface. An input device may be understood as any mechanism through which a user can communicate their choices or commands to the computing device. This arrangement allows for personalization of the learning experience, as users can choose the modality' that best suits their learning style. One advantage of this feature is that it empowers users to take control of their learning process, thereby increasing motivation and satisfaction. The input device facilitates efficient data capture, allowing the system to respond dynamically to user selections and preferences, which enhances the overall learning experience.

[0091] At step 530, it may be provided that interactive visual content is generated on the user interface in response to the user’s selection, wherein the interactive visual content includes one of the following: a story comprising visual content and aural accompaniment illustrating concepts associated with the subject matter, and an instructional video content for assembling the toy kit. Interactive visual content refers to multimedia elements that engage users through visual and auditory stimuli. A story' is a narrative form of communication used to convey information in a compelling manner. In this context, it ser es to illustrate concepts associated with the subject matter through engaging visuals and auditory elements. Aninstructional video content, if selected by the user, provides step-by-step guidance for assembling the toy kit, offering learners a hands-on approach towards understanding the related principles.

[0092] In various embodiments, a user will go through all the learning modules regardless of preferred learning preferences. By doing so, this combines multiple learning styles, user interactivity, and rich multimedia content to create a comprehensive and engaging educational experience, thus promoting effective learning for the user through the assembly of a toy kit. This arrangement also creates an immersive learning environment that caters to visual, auditory' and kinesthetic learners, thereby making complex concepts more accessible and easier to understand. The generation of interactive content leverages the processing capabilities of the computing device to present high-quality visuals and audio, maximizing the effective use of bandwidth and storage resources, which enhances user comprehension and retention of the subject matter.

[0093] Figure 6 is a schematic block diagram of a system architecture for learning new subject matter, according to an embodiment of the invention. An exemplary' computing environment suitable for implementing one or more embodiments described herein is presented below. This computing environment is provided for illustration 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 any of the embodiments disclosed are mere aggregations of such known elements. Rather, the disclosed embodiments involve specific configurations and programming that result in specially adapted machines for carrying out the described methods and systems.

[0094] The system 500 comprises a central server 110 communicatively coupled with one or more computing devices 130 via a communication network 120. In an embodiment, the one or more computing device 130 may comprise computing devices operable by users, such as desktop computers, laptops, smartphones, tablets, point-of-sale terminals, or kiosks. These computing devices 130 may include one or more processors, memory', display interfaces, input / output subsystems, and network communication hardware. Each computing device 130 is configured to execute a user interface application, such as a webbased dashboard, a mobile app, or a voice interface, through which the user may interact with the learning system 100 via text, audio, or video modalities.

[0095] Tn an embodiment, the communication network 120, as mentioned above, may be configured to enable secure, bidirectional data exchange between the computing devices and the learning system 100 deployed on remote servers. It may include a combination of public and private networks, such as the Internet, VPNs, LANs, and wireless cellular networks (e g., 4G, 5G), and supports various communication protocols including HTTPS, WebSockets, and gRPC. The network facilitates real-time and asynchronous interactions across text, audio, and video modalities, and may incorporate encryption, authentication, and API gateway layers to ensure data integrity and access control. Additionally, the network may interface with external systems, such as analytics platforms, to support backend operations triggered by user interactions.

[0096] In an embodiment, the system 500 as implemented on the remote server, comprises at least one processor 112 (or a cluster of processors). The one or more processors 112 may be configured to execute the core logic of the learning system, and comprises specialized subcomponents such as a learning preference detection unit 180, a context detection unit 190 and a Recommendation Engine 116. These modules may be configured to operate collectively to classify incoming user input, apply enterprise-level interaction policies, and infer user intent based on both semantic input and contextual metadata.

[0097] The server 110 comprises a processor 112 that is configured to execute instructions and manage the operations of the server's components. The 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. The server 1 10 includes a Learning Preference Detection Unit 180, which is in communication with the processor 112. The ser er 110 also includes a Context Detection Unit 190 and a Recommendation Engine 116, which are also in communication with the processor 112 and database 115. The processor 112 orchestrates the functions of these units to deliver a personalized learning experience to a user operating a computing device 130.[0098| In an embodiment, the system 500 comprises at least one processor 112, which may be implemented using one or more processors configured in hardware, software, firmware, or any suitable combination thereof. The processor 112 may comprise central processing units (CPUs), graphics processing units (GPUs), or other specialized processing hardware such as TPUs or FPGAs, depending onsystem requirements. The processor 112 may be operatively coupled to other system components via a system bus and is responsible for coordinating and executing the core functions of the learning system 500. In software or firmware-based implementations, the processor 112 may execute computer-executable instructions stored in database / memory. These instructions may be written in any suitable programming language and are configured to perform operations such as intent classification, context management, tool invocation, recommendation generation, and system orchestration. In distributed environments, multiple processor instances may operate in parallel across cloud or edge infrastructure to support scalable and realtime performance.

[0099] In an embodiment, the system 100 further comprises input / output (I / O) interface 117, which is configured to manage communication between the system 500 and external devices or networks. The I / O interface 117 may include hardware and software components capable of receiving user input in multiple modalities, including text, voice, and video, and transmitting system responses or interactive elements such as learning content adapted for a learning preference of the user. It may also support integration with peripheral devices such as microphones, cameras, touchscreens, key boards, and display panels. The I / O interface 117 may be operatively coupled to the processor 1 12 and other system components via the system bus, and is responsible for formatting, routing, and validating incoming and outgoing data. In some embodiments, the I / O interface 117 may support communication over 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 filters and output formatting logic aligned with system-wide configuration parameters.

[0100] In an embodiment, the database 115 may be configured to store both persistent and transient data used by the one or more processors 112 during execution. 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 ). It may also support removable or external memory such as CompactFlash cards, Secure Digital (SD) cards, or other portable storage formats. The database 1 15 may be implemented in the form of primary and secondary storage, providing dedicated locations for system instructions, intermediate computations, and long-term data persistence. The database 115 may be configured to store executable program instructions, configuration parameters, and system-wide dataincluding user profiles, learning profiles, contextual memory states, session histones, access control rules, and interaction logs. The database 115 may also retain pre-trained language model parameters, tool invocation mappings, and rule sets for learning content modules. During operation, the processors 112 may be configured to retrieve, update, and write data to the database 115 to support real -rime conversation handling, tool execution, personalization, and recommendation generation. The memory module may be implemented using relational databases, NoSQL databases, key-value stores, or in-memory data grids, depending on performance and consistency requirements.

[0101] In an embodiment, the recommendation engine 116, as illustrated and described with reference to FIG. 6, which is configured to generate personalized, context-aware, learning content material for users. The recommendation engine 116 may be configured to analyze real-time operational data, user behavior patterns, historical interaction logs, and user performance metrics to determine relevant actions or insights. These recommendations may comprise suggestions on learning content material, engagement nudges, depending on the user input associated with the user interaction. The recommendation engine 116 may operate in conjunction with the learning preference detection unit and context detection unit, and one or more large language models to construct structured outputs such as learning content material adapted for the user or system-generated prompts. The recommendation engine 116 compnses of rule-based logic, statistical models, or machine learning algorithms to evaluate thresholds, detect anomalies, or forecast trends. The generated recommendations may be delivered to users proactively or in response to specific queries, and are rendered via the output unit in an interactive format that supports user approval, modification, or dismissal.

[0102] In an embodiment, the one or more large language models (LLMs) 118 may be configured to facilitate natural language understanding, semantic interpretation, prompt generation, and dynamic response formulation. The LLMs 118 may be implemented as fine-tuned transformer-based architectures, such as GPT, BERT, or other foundation models trained on large-scale corpora. These LLM models 118 may be hosted locally within the system infrastructure or accessed via secure API calls to third-party' Al service providers, depending on performance, latency, and deployment constraints. In operation, the LLMs 118 may be invoked by the learning preference detection unit 180 or the context detection unit 190, or the recommendation engine 116 to perform functions such as query' interpretation, entity' extraction.contextual reasoning, and natural language generation. The LLM models 118 may consume structured prompts assembled from contextual memory and system parameters, and return structured or free-text outputs for further processing or presentation. Multiple LLM model 118 instances may be used concurrently for different business domains or user roles, and their outputs may be filtered or refined by post-processing logic in accordance with global configuration policies.

[0103] In an embodiment, the 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 are operable to transmit and receive data related to user quenes, system responses, tool executions, business metrics, and action card payloads. The APIs 119 enable seamless interoperability with other third-party systems, including other learning or educational platforms. Internally, APIs 119 may also be used to execute data flow between system modules such as the recommendation engine, context detection unit, and among others. These interfaces ensure modularity', scalability, and abstraction of functionality, allowing components to evolve independently while maintaining system cohesion. The APIs 1 19 may be secured through 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 an embodiment, the components of the system 100, including the processors 112, I / O interface 1 17, database 115, large language models 1 18, recommendation engine 1 16, and APIs 1 19, among others 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 structured, high-speed data exchange and synchronization between functional modules. The system bus may include one or more hardware and / or software-based interconnects, such as parallel buses, serial buses, or message-passing interfaces, and may support point-to-point or broadcast communication patterns. The system bus may be configured to support event-driven, synchronous, or asynchronous communication, depending on the operational requirements of each module. The system bus may be implemented using technologies such as shared memory, message queues, middleware brokers (e.g., RabbitMQ, Kafka), or internal API layers. It ensures modularity, scalability', and coordination across the distributed system, enabling coherent andlow-latency execution of workflows, real-time tool invocation, and context-aware recommendation delivery.

[0105] In an embodiment, a non-transitory computer-readable medium may be provided, wherein the non-transitory computer-readable medium stores instructions that, when executed by one or more data processing devices, cause the devices to perform the methods and processes described herein. The non- transitory computer-readable medium may include, but is not limited to, magnetic disks, optical disks, solid-state drives, flash memory, or any 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 carry out the functionalities and operations as set forth in the foregoing description.

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

[0107] Figure 7 shows a more detailed, interactive method 700. The initial steps 710, 720, 730 correspond directly to the steps of method 600 described above. The method 700 further includes an interactive feedback loop.

[0108] The method begins at step 710, wherein a plurality of character mascots are generated on a display of a user's computing device (130). Each mascot is associated with one of a plurality of learning preferences, namely a data-driven learning preference, an aesthetic learning preference, and an experiential learning preference. This initial step creates clarity at the starting point of the learning experience, presenting character mascots each incorporating a unique combination and arrangement of geometrical shapes that are aligned with an associated learning preference.

[0109] At 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, which the system determines is aligned with the user's natural optimal learning preference. This act of choiceempowers the user and allows them to acquire confidence almost immediately when approaching new information.

[0110] Next, at step 730, the processor 112 displays learning content on the user's display that is associated with the determined learning preference. For example, a user with an data-driven learning preference may be presented with a story-based approach with aural accompaniment, while a user with an experiential learning preference may be presented with a hands-on approach. This ensures that the content is delivered in a manner that is engaging and accessible.[ 001111 The method 700 further comprises an interactive and adaptive loop. At step 740, the sy stem receives a user input in one or more modalities, such as text, audio, or video, in response to an instruction from the learning content. The I / O interface 117 is configured to support multimodal user interaction and is operable to receive user input in one or more modalities, including text, audio, or video. The system 500 is also configured to retrieve user-specific contextual data and domain-level contextual data. This enables the system 500 to operate across a range of access interfaces, such as web-based dashboards, mobile applications, and voice-activated terminals.

[0112] In an 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 stones of a specific genre into the GUI interface on a desktop dashboard. The I / O interface 117 may be configured to capture the text and convert it into a structured fonnat suitable for downstream processing by the processor.

[0113] At step 750, the Context Detection Unit (190) on the server 110 processes this response to retrieve user-specific contextual data. Alongside the response, the I / O interface 117 may also be configured to capture or associate one or more contextual metadata elements that characterize the user and their environment at the time of query submission. Such metadata may include, but is not limited to, a user identifier (e.g., usr_02345 or a user email address), an age group identifier, a session identifier (e.g., sess_00981) for stateful interaction, a device type (e.g., desktop_browser, mobile_app, or kiosk_terminal), a timestamp indicating when the query was received (e.g., 2025-05-23T14:42: 11Z), optional geolocation coordinates (e.g., 37.7749° N, 122.4194° W), and language or localization preferences (e.g., en-US). In anembodiment, the user-specific contextual data may include one or more of: the user’s role designation (e.g., “student,” “adult”, “kindergarten”, “male”, “female”), user identifier, active permissions, prior query history, and current session metadata.

[0114] These metadata parameters may be transmitted along with the response to the processor, where they are used to support domain identification, personalization, role-based access control, and consistent formatting of system responses. The user-specific contextual data may be used by the context detection unit 190 to constrain and guide the domain selection process, ensuring that the system 100 identifies a business function the user is both authorized for and contextually aligned with In an embodiment, the context detection 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 This unit supplies relevant context to the user profiles to enhance query interpretation and tool selection. It also retains user responses and tracks system interactions over time, enabling progressive refinement of responses and proactive engagement. By preserving context across multiple user interactions, the contextual detection unit 190 supports a coherent, efficient, and personalized user experience throughout the system’s workflow.

[0115] Finally, at step 760, the Recommendation Engine 116, in conjunction with the processor 112, determines and then generates one or more new, relevant pieces of learning content. Additionally, it also determines a content flow for the user For example, different character mascots lead to different entry points for the learning content, such as story -first for Ollie, coloring for Chloe, and hands-on activities for Drew', even though they converge later. In an embodiment, the recommendation engine 116 comprises, but is not limited to, one or more recommendation engines, one or more recommendation domains. The recommendation engine may be configured to generate suggestions based on various user inputs, including but not limited to: user behaviour, user preferences, social media profiles, user review's and feedback. The recommendation domains may include functional areas. In an embodiment, a function area includes content type including visual content, auditory' content, textual content, kinesthetic content. Tn another embodiment, function area includes user participation of content for example, passive content, active content, interactive content, or collaborative content. In another embodiment, the function area can alsoinclude contextual learning content defined as the context in which learning occurs. Some examples of contextual learning content includes formal learning content, non-formal learning content, informal learning content. Delivery of the generated recommendations may occur through one or more channels, including, for example, an Al assistant interface, inbox notifications, and email communications. The engine may further 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-specific contextual data and the original learning preference determined at the start of the session. This comprehensive workflow ensures a dynamic and deeply personalized learning path that transcends traditional written words and fosters a deeper understanding of any subject matter.

[0117] It shall be noted that the processes described above are described as a sequence of steps; this was done solely for the sake of illustration. Accordingly, it is contemplated that some steps may be added, some steps may be omitted, the order of the steps may be re-arranged, or some steps may be performed simultaneously. Although embodiments have been described with reference to specific example embodiments, it will be evident that various modifications and changes may be made to these embodiments without departing from the broader scope of the system 100 and method described herein. Accordingly, the specification and drawings are to be regarded in an illustrative rather than a restrictive sense.

[0118] While the invention has been particularly shown and described with reference to specific embodiments, it should be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the spirit and scope of the invention as defined by the appended claims The scope of the invention is thus indicated by the appended claims and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced.

Claims

CLAIMS1. A computer-implemented method for learning new subject matter, the method comprising: generating, on a display of a computing device, a plurality of character mascots, wherein each character mascot is associated with a one of a plurality of learning preferences, the plurality of learning preferences including a data-driven learning preference, an aesthetic learning preference, and an experiential learning preference; receiving, from a user input device, a selection of one of the character mascots based on a visual preference of a user, wherein the visual preference of the user is determined to be aligned with the user’s learning preference; and displaying a learning content on the display that corresponds to the user’s learning preference, wherein the learning content is associated w ith one of the plurality of learning preferences.

2. The computer-implemented method according to claim 1, wherein the plurality of character mascots includes a first character mascot, a second character mascot, a third character mascot, wherein each character mascot includes a predetermined arrangement of geometrical shapes associated with one of the plurality' of learning preferences.

3. The computer-implemented method according to claim 2, wherein the first character mascot includes a first predetermined arrangement of geometrical shapes in a symmetrical configuration, wherein the first predetermined arrangement of geometric shapes is associated w ith the data- driven learning preference.

4. The computer-implemented method according to claim 2, wherein the second character mascot includes a second predetermined arrangement of geometrical shapes in a non-symmetrical configuration, wherein some of the geometrical shapes overlap with one another, and wherein the second predetermined arrangement of geometrical shapes is associated with an aesthetic learning preference.

5. The computer-implemented method according to claim 2, wherein the third character mascot includes a third predetermined arrangement of geometrical shapes including geometrical shapes with pointed edges, wherein the third predetermined arrangement of geometrical shapes is associated with an experiential learning preference.

6. The computer-implemented method according to claim 3, wherein the first predetermined arrangement of geometrical shapes includes circles, semicircles and quadrants.

7. The computer-implemented method according to claim 4, wherein the second predetermined arrangement of geometrical shapes includes circles, squares and triangles.

8. The computer-implemented method according to claim 5, wherein the third predetermined arrangement of geometrical shapes includes squares and triangles.

9. The computer-implemented method according to claim 1, further comprising: receiving a user input in one or more modalities, including text, audio, and video, in response to the displayed learning content; and generating a new learning content for the user based on the user input and the determined learning preference.

10. The computer-implemented method according to claim 1, wherein generating the new' learning content is performed by a recommendation engine in communication with a large language model.

11. The computer-implemented method according to claim 1, wherein the experiential learning preference is associated with learning content comprising instructional video content that involves a hands-on experience.

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

13. The computer-implemented method according to claim 1, wherein the aesthetic learning preference is associated with aesthetically pleasing and visual learning content.

14. The computer-implemented method according to claim 1, further comprising the steps of: receiving a user input in one or more modalities, including text, audio, or video, in response to the displayed learning content; and retrieving a user-specific contextual data based on the user input; determining relevant learning content for the user based on the user-specific contextual data and determined learning preference; generating one or more relevant learning content for the user based on the user-specific contextual data and determined learning preference.

15. A system for learning new subject matter, the system comprising: a computing device having a display, a user input device, a processor and a memory', wherein the memory store instructions, which when executed by the processor, cause the system to provide learning content on the display, the learning content being associated with one of a plurality' of learning preferences including a data-driven learning preference, an aesthetic learning preference, and an experiential learning preference; characterised in that the instructions, when executed by the processor, cause the system to:generate, on the display of the computing device, a plurality of character mascots, wherein each character mascot is associated with a one of a plurality of learning preferences; receive, from the user input device, a selection of one of the character mascots based on a visual preference of a user, wherein the visual preference of the user is determined to be aligned with the user’s learning preference; and display the learning content on the display that corresponds to the user’s learning preference, wherein the learning content is associated w ith one the plurality of learning preferences.

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