Description:FORM 2
THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENT RULES, 2003

COMPLETE SPECIFICATION
(See Section 10 and Rule 13)

Title of Invention:
METHOD AND SYSTEM FOR LEARNING PATHWAY ASSESSMENT OF A USER

APPLICANT:
ORCALEX TECHNOLOGIES LLP
An Indian entity having address as:
Flat 402, Bandari Residency, Uma Nagar, Begumpet, Hyderabad 500016, Telangana, India

The following specification particularly describes the invention and the manner in which it is to be performed.

CROSS-REFERENCE TO RELATED APPLICATIONS AND PRIORITY
[0001] The present application does not claim priority from any other application.
TECHNICAL FIELD
[0002] The present invention, in general, relates to an education field, and more particularly, relates to a method and system for learning pathway assessment of a user.
BACKGROUND
[0003] This section is intended to introduce the reader to various aspects of art (the relevant technical field or area of knowledge to which the invention pertains), which may be related to various aspects of the present disclosure that are described or claimed below. This discussion is believed to be helpful in providing the reader with background information to facilitate a better understanding of the various aspects of the present disclosure. Accordingly, it should be understood that these statements in this background section are to be read in this light, and not as admissions of prior art. Similarly, a problem mentioned in the background section.
[0004] Digital education platforms have increasingly adopted artificial intelligence to personalize instruction, automate assessment, and adapt learning pathways. Contemporary systems commonly employ large language models, adaptive testing algorithms, or rule-based tutoring frameworks to analyse student responses and recommend remedial content. While these systems have improved scalability and accessibility of education, their assessment mechanisms remain fundamentally limited in their ability to capture the cognitive processes involved in problem solving, particularly in tasks requiring abstraction, conceptual reasoning, and transfer across domains.
[0005] Conventional widely used AI-driven tutoring systems assess learner performance at a topic or subtopic level. For example, AI tutors deployed in large-scale platforms evaluate whether a learner’s response is correct and, based on aggregate performance, suggest revisiting an associated topic or concept. Such systems may indicate that a learner needs to review quadratic equations, trigonometry, or algebraic manipulation. However, they do not identify which internal reasoning operation within the topic has failed. Distinct cognitive errors, such as failure to recognize problem structure, inappropriate selection of a solution strategy, or incorrect symbolic transformation, are treated uniformly as topic-level weaknesses. Consequently, remediation is broad and imprecise, often resulting in repeated exposure to content that does not address the learner’s actual difficulty.
[0006] Other conventional systems attempt to increase diagnostic granularity by decomposing curricula into fine-grained knowledge points. In these systems, learner performance is mapped to thousands of discrete content units and missing or weak knowledge points are identified for remediation. While this approach provides more detailed content-level insight than topic-based systems, it remains focused on what information or rules the learner lacks, rather than how the learner reasons with the information they possess. A learner may possess all required knowledge points yet consistently fail due to an incorrect mental model or flawed reasoning transition, such as misunderstanding proportional relationships or equivalence. These reasoning failures do not correspond to a single missing knowledge point and therefore remain undetected by such systems.
[0007] Certain intelligent tutoring systems further track explicit procedural steps taken during problem solving, particularly in mathematics and science education. These systems monitor whether a learner follows a predefined solution path and provide feedback when a step is skipped or incorrectly executed. However, procedural step tracking presumes that correct reasoning corresponds to adherence to a specific sequence of steps. It does not capture implicit cognitive transitions such as strategy selection, abstraction, or representation switching. As a result, learners may execute procedurally valid steps while relying on incorrect or fragile reasoning or may fail despite understanding the underlying concept but choosing an alternative solution path not anticipated by the system.
[0008] Additional adaptive learning platforms incorporate neuroscience-inspired models or learning efficiency optimizations to adjust content sequencing and pacing. While such systems may improve engagement and delivery efficiency, their assessment capabilities remain limited to identifying surface-level performance trends at the topic or concept level. They infer where a learner is struggling, but not why the struggle occurs at the level of internal cognitive operations.
[0009] As a result of these limitations, existing EdTech systems share several fundamental deficiencies. First, assessment granularity is insufficient, as no existing system evaluates learner performance at the level of cognitive transitions that bridge one reasoning state to another. These transitions often involve non-obvious mental operations that are critical to successful problem solving but are not directly observable through final answers or procedural steps. Second, existing systems cannot reliably distinguish between lower-order procedural execution and higher-order conceptual or strategic reasoning, even when both produce correct outcomes. This leads to inaccurate signals of mastery and ineffective instructional decisions. Third, current systems operate in domain-specific silos and fail to recognize that identical cognitive operations occur across different subject areas, such as ratio reasoning in mathematics, proportionality in physics, and stoichiometric relationships in chemistry. Consequently, learner difficulties are not correlated or addressed across domains.
[0010] Due to these shortcomings, remediation in existing systems is limited to repetition of content, increased practice volume, or generic instructional interventions. Such approaches often fail to correct the underlying reasoning flaw, resulting in persistent errors, learner frustration, and inefficient use of instructional time. The inability to diagnose and address the true source of learner difficulty represents a significant technical limitation in the current state of the art.
[0011] Given these challenges, there is a need for an educational assessment system that diagnoses learner performance at the level of cognitive operations.
[0012] Further limitations and disadvantages of conventional and traditional approaches will become apparent to one of skill in the art, through comparison of described systems with some aspects of the present disclosure, as set forth in the remainder of the present application and with reference to the drawings.
SUMMARY
[0013] This summary is provided to introduce concepts related to a method and a system for learning pathway assessment of a user and the concepts are further described below in the detailed description. This summary is not intended to identify essential features of the claimed subject matter nor is it intended for use in determining or limiting the scope of the claimed subject matter.
[0014] According to embodiments illustrated herein, a method for learning pathway assessment of a user is disclosed. The method may comprise various steps performed by a processor. The method may include a step of receiving one or more responses of the user corresponding to one or more educational problems. Further, the method may include a step of identifying a problem-solving pattern, associated with the one or more educational problems, in the one or more responses of the user. Further, the method may include a step of decomposing the problem-solving pattern into a plurality of ordered cognitive bridges. In an embodiment, each of the plurality of ordered cognitive bridges may represent a non-trivial cognitive transition between successive problem-solving steps. Further, the method may include a step of evaluating each of the plurality of ordered cognitive bridges by comparing the corresponding one or more responses of the user against an expected bridge-specific cognitive solution stored in a bridge repository. Further, the method may include a step of generating a bridge level learning pathway assessment of the user based on the evaluation.
[0015] According to embodiments illustrated herein, a system for learning pathway assessment of the user is disclosed. The system may comprise the processor, a memory coupled with the processor. The memory may be configured to store programmed instructions that cause the processor to perform various steps. The processor may be configured to receive the one or more responses of the user corresponding to the one or more educational problems. Further, the processor may be configured to identify the problem-solving pattern, associated with the one or more educational problems, in the one or more responses of the user. Further, the processor may be configured to decompose the problem-solving pattern into the plurality of ordered cognitive bridges. In an embodiment, each of the plurality of ordered cognitive bridges may represent the non-trivial cognitive transition between successive problem-solving steps. Further, the processor may be configured to evaluate each of the plurality of ordered cognitive bridges by comparing the corresponding one or more responses of the user against the expected bridge-specific cognitive solutions stored in the bridge repository. Further, the processor may be configured to generate the bridge level learning pathway assessment of the user based on the evaluation.
[0016] According to embodiments illustrated herein, a non-transitory computer-readable storage medium for learning pathway assessment of the user is disclosed. The non-transitory computer-readable storage medium having stored thereon, a set of computer-executable instructions causing a computer comprising a processor to perform steps. The step may involve receiving the one or more responses of the user corresponding to the one or more educational problems. Further, the step may involve identifying the problem-solving pattern, associated with the one or more educational problems, in the one or more responses of the user. Furthermore, the step may involve decomposing the problem-solving pattern into the plurality of ordered cognitive bridges. In an embodiment, each of the plurality of ordered cognitive bridges may represent the non-trivial cognitive transition between successive problem-solving steps. Moreover, the step may involve evaluating each of the plurality of ordered cognitive bridges by comparing the corresponding one or more responses of the user against the expected bridge-specific cognitive solutions stored in the bridge repository. Further, the step may involve generating the bridge level learning pathway assessment of the user based on the evaluation.
[0017] The foregoing summary is illustrative only and is not intended to be in any way limiting. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features will become apparent by reference to the drawings and the following detailed description.
BRIEF DESCRIPTION OF DRAWINGS
[0018] The accompanying drawings illustrate the various embodiments of systems, methods, and other aspects of the disclosure. Any person with ordinary skills in art will appreciate that the illustrated element boundaries (e.g., boxes, groups of boxes, or other shapes) in the figures represent one example of the boundaries. In some examples, one element may be designed as multiple elements, or multiple elements may be designed as one element. In some examples, an element shown as an internal component of one element may be implemented as an external component in another, and vice versa. Further, the elements may not be drawn to scale.
[0019] Various embodiments will hereinafter be described in accordance with the appended drawings, which are provided to illustrate and not to limit the scope in any manner, wherein similar designations denote similar elements, and in which:
[0020] FIG. 1 is a block diagram that illustrates a system (100) for learning pathway assessment of a user, in accordance with an embodiment of present subject matter.
[0021] FIG. 2 is a block diagram that illustrates various components of an application server (104) configured for learning pathway assessment of the user, in accordance with an embodiment of the present subject matter.
[0022] FIG. 3 is a flowchart that illustrates a method (300) for learning pathway assessment of the user, in accordance with an embodiment of the present subject matter.
[0023] FIG. 4 illustrates a block diagram (400) of an exemplary computer system for implementing embodiments consistent with the present subject matter.
[0024] It should be noted that the accompanying figures are intended to present illustrations of exemplary embodiments of the present disclosure. These figures are not intended to limit the scope of the present disclosure. It should also be noted that accompanying figures are not necessarily drawn to scale.
DETAILED DESCRIPTION
[0025] The present disclosure may be best understood with reference to the detailed figures and description set forth herein. Various embodiments are discussed below with reference to the figures. However, those skilled in the art will readily appreciate that the detailed descriptions given herein with respect to the figures are simply for explanatory purposes as the methods and systems may extend beyond the described embodiments. For example, the teachings presented, and the needs of a particular application may yield multiple alternative and suitable approaches to implement the functionality of any detail described herein. Therefore, any approach may extend beyond the particular implementation choices in the following embodiments described and shown.
[0026] References to “one embodiment,” “at least one embodiment,” “an embodiment,” “one example,” “an example,” “for example,” and so on indicate that the embodiment(s) or example(s) may include a particular feature, structure, characteristic, property, element, or limitation but that not every embodiment or example necessarily includes that particular feature, structure, characteristic, property, element, or limitation. Further, repeated use of the phrase “in an embodiment” does not necessarily refer to the same embodiment. The terms “comprise”, “comprising”, “include(s)”, or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a setup, system or method that comprises a list of components or steps does not include only those components or steps but may include other components or steps not expressly listed or inherent to such setup or system or method. In other words, one or more elements in a system or apparatus preceded by “comprises… a” does not, without more constraints, preclude the existence of other elements or additional elements in the system or apparatus.
[0027] An objective of the present invention is to provide a method and system for learning pathway assessment of a user by evaluating a problem-solving pattern at a bridge level, rather than at a topic level or final-answer level.
[0028] An objective of the present invention is to overcome limitations of existing educational assessment systems by diagnosing learner performance at the level of cognitive reasoning transitions, rather than at topic, knowledge-point, or final-answer levels.
[0029] Another objective of the present invention is to identify where learner reasoning breaks down within a problem-solving process, including failures occurring between intermediate cognitive steps.
[0030] Yet another objective of the present invention is to generate bridge level learning pathway assessment that distinguishes lower-order cognitive operations from higher-order cognitive operations, thereby enabling differentiation between rote learners and higher-order thinkers even when final answers are identical.
[0031] Yet another objective of the present invention is to enable identification of failed cognitive bridges and associated cognitive error types, and to generate targeted remedial content specific to the failed cognitive bridge.
[0032] Yet Another objective of the present invention is to achieve increased diagnostic granularity and cross-domain predictive accuracy compared to topic-level or answer-based assessment systems.
[0033] Yet Another objective of the present invention is to provide an implementation-agnostic assessment architecture executable using rule-based systems, neural networks, multi-agent architectures, or hybrid approaches.
[0034] Yet Another objective of the present invention is to reduce assessment latency and improve diagnostic responsiveness by enabling execution on an edge computing device.
[0035] Yet another objective of the invention is to provide automated error pattern repository generation, multi-modal input processing including handwriting, voice, digital pen, and visual diagrams, temporal decay tracking for mastery prediction, and automated remediation content generation tailored to identified bridge-level failures.
[0036] FIG. 1 is a block diagram that illustrates a system (100) for learning pathway assessment of the user, in accordance with an embodiment of present subject matter. The system (100) typically includes a database server (102), an application server (104), a communication network (106), and one or more portable devices (108). The database server (102), the application server (104), and the one or more portable devices (108) are typically communicatively coupled with each other via the communication network (106). In an embodiment, the application server (104) may communicate with the database server (102), and the one or more portable devices (108) using one or more protocols such as, but not limited to, Hypertext Transfer Protocol (HTTP), Transmission Control Protocol/Internet Protocol (TCP/IP), Wireless Application Protocol (WAP), RF mesh, Bluetooth Low Energy (BLE), and the like, to communicate with one another. In an exemplary embodiment, the system (100) may be implementation-agnostic with respect to rule-based systems, neural networks, multi-agent architectures, or hybrid approaches.
[0037] In one embodiment, the database server (102) may refer to a computing device that may be configured to store one or more educational problems, one or more responses, the problem-solving pattern, a plurality of ordered cognitive bridges, an expected bridge-specific cognitive solutions, the learning pathway assessment of the user, one or more pattern embeddings, description of the patterns, error patterns, a subject agnostic problem-solving pattern, predefined cognitive transition templates, lower-order cognitive operations, higher-order cognitive operations, bridges, and cross-domain correlations. Further, the one or more responses may include at least one of handwritten input, typed input, voice input, digital pen stroke data, image-based diagrammatic input, or a combination thereof. Further, one or more time-based hesitation metrics may be derived from the digital pen stroke data. Further, each of the plurality of ordered cognitive bridges may represent a non-trivial cognitive transition between successive problem-solving steps.
[0038] In an embodiment, the database server (102) may include a special purpose operating system specifically configured to perform one or more database operations on the one or more responses of the user. Examples of database operations may include, but are not limited to, Select, Insert, Update, and Delete. In an embodiment, the database server (102) may include hardware that may be configured to perform one or more predetermined operations. In an embodiment, the database server (102) may be realized through various technologies such as, but not limited to, Microsoft® SQL Server, Oracle®, IBM DB2®, Microsoft Access®, PostgreSQL®, MySQL®, SQLite®, or any vector database, distributed database technology and the like. In an embodiment, the database server (102) may be configured to utilize the application server (104) for implementing the method for learning pathway assessment of the user.
[0039] A person with ordinary skills in art will understand that the scope of the disclosure is not limited to the database server (102) as a separate entity. In an embodiment, the functionalities of the database server (102) can be integrated into the application server (104) or into the one or more portable device (108).
[0040] In an embodiment, the application server (104) may refer to a computing device or a software framework hosting an application or a software service. In an embodiment, the application server (104) may be implemented to execute procedures such as, but not limited to, programs, routines, or scripts stored in one or more memories for supporting the hosted application or the software service. In an embodiment, the hosted application or the software service may be configured to perform one or more predetermined operations. The application server (104) may be realized through various types of application servers such as, but are not limited to, a Java application server, a .NET framework application server, a Base4 application server, a PHP framework application server, django server, FastAPI, Express.js or any other application server framework.
[0041] In an embodiment, the application server (104) may be configured to utilize the database server (102) and the one or more portable device (108), in conjunction, for implementing the method for learning pathway assessment of the user. In an implementation, the application server (104) corresponds to an infrastructure for implementing the method for learning pathway assessment of the user.
[0042] In an embodiment, the application server (104) may be configured to receive the one or more responses of the user corresponding to the one or more educational problems. In an embodiment, the one or more responses may include at least one of handwritten input, typed input, voice input, digital pen stroke data, image-based diagrammatic input, or a combination thereof. Further, the application server (104) may be configured to identify the problem-solving pattern, associated with the one or more educational problems, in the one or more responses of the user. Further, the application server (104) may be configured to decompose the problem-solving pattern into the plurality of ordered cognitive bridges. In an embodiment, each of the plurality of ordered cognitive bridges may represent the non-trivial cognitive transition between successive problem-solving steps. Moreover, the application server (104) may be configured to evaluate each of the plurality of ordered cognitive bridges by comparing the corresponding one or more responses of the user against the expected bridge-specific cognitive solutions stored in a bridge repository. Further, the application server (104) may be configured to generate the bridge level learning pathway assessment of the user based on the evaluation.
[0043] In an exemplary embodiment, the process described as being performed on the application server (104) may instead, or additionally, be carried out locally on the in-vehicle device, allowing the system to operate independently of a network connection and enabling faster processing when local resources are sufficient.
[0044] In an embodiment, the communication network (106) may correspond to a communication medium through which the application server (104), the database server (102), and the one or more portable device (108) may communicate with each other. Such a communication may be performed in accordance with 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), Wireless Application Protocol (WAP), File Transfer Protocol (FTP), ZigBee, EDGE, infrared IR), IEEE 802.11, 802.16, 2G, 3G, 4G, 5G, 6G, 7G cellular communication protocols, and/or Bluetooth (BT) communication protocols. The communication network (106) may either be a dedicated network or a shared network. Further, the communication network (106) may include a variety of network devices, including routers, bridges, servers, computing devices, storage devices, and the like. The communication network (106) may include, but is not limited to, the Internet, intranet, a cloud network, a Wireless Fidelity (Wi-Fi) network, a Wireless Local Area Network (WLAN), a Local Area Network (LAN), a cable network, the wireless network, a telephone network (e.g., Analog, Digital, POTS, PSTN, ISDN, xDSL), a telephone line (POTS), a Metropolitan Area Network (MAN), an electronic positioning network, an X.25 network, an optical network (e.g., PON), a satellite network (e.g., VSAT), a packet-switched network, a circuit-switched network, a public network, a private network, and/or other wired or wireless communications network configured to carry data.
[0045] In an embodiment, the one or more portable devices (108) may refer to a computing device used by the plurality of users. The one or more portable devices (108) may comprise of one or more processors and one or more memory. The one or more memories may include computer readable code that may be executable by one or more processors to perform predetermined operations. In an embodiment, the one or more portable devices (108) may receive the one or more responses of the user corresponding to the one or more educational problems and provide the bridge level learning pathway assessment of the user. Examples of the one or more portable devices (108) may include, but are not limited to, a personal computer, a laptop, a computer desktop, a personal digital assistant (PDA), a mobile device, a tablet, or any other computing device.
[0046] The system (100) can be implemented using hardware, software, or a combination of both, which includes using where suitable, one or more computer programs, mobile applications, or “apps” by deploying either on premises over the corresponding computing terminals or virtually over cloud infrastructure. The system (100) may include various micro-services or groups of independent computer programs which can act independently in collaboration with other micro-services. The system (100) may also interact with a third-party or external computer system. A critical attribute of the system (100) is that it automatically creates and provides the one or more interventions during usage of the application by the one or more users at a pre-defined time instant.
[0047] FIG. 2 illustrates a block diagram illustrating various components of the application server (104) configured for learning pathway assessment of the user, in accordance with an embodiment of the present subject matter. Further, FIG. 2 is explained in conjunction with elements from FIG. 1. Here, the application server (104) preferably includes a processor (202), a memory (204), a transceiver (206), an input/output (I/O) unit (208), a user interface (210), an identification unit (212), a decomposition unit (214), an evaluation unit (216) and a generation unit (218). The processor (202) is further preferably communicatively coupled to the memory (204), the transceiver (206), the input/output (I/O) unit (208), the user interface (210), the identification unit (212), the decomposition unit (214), the evaluation unit (216) and the generation unit (218) while the transceiver (206) is preferably communicatively coupled to the communication network (106).
[0048] In an exemplary embodiment, learning pathway assessment of the user may be executed by using a multi-agent architecture comprising a coordination agent, an assessment agent, a diagnostics agent, a vision agent, and a remediation agent. Further, the coordination agent may dynamically allocate computational resources based on the number and complexity of cognitive bridges. Further, the coordination agent may be configured to manage message passing between the multi-agents. Further, the coordination agent may be configured for load balancing and error handling.
[0049] The processor (202) comprises suitable logic, circuitry, interfaces, and/or code that may be configured to execute a set of instructions stored in the memory (204), and may be implemented based on several processor technologies known in the art. The processor (202) works in coordination with the transceiver (206), the input/output (I/O) unit (208), the user interface (210), the identification unit (212), the decomposition unit (214), the evaluation unit (216) and the generation unit (218) for learning pathway assessment of the user. Examples of the processor (202) include, but not limited to, standard microprocessor, microcontroller, central processing unit (CPU), an X86-based processor, a Reduced Instruction Set Computing (RISC) processor, an Application- Specific Integrated Circuit (ASIC) processor, and a Complex Instruction Set Computing (CISC) processor, distributed or cloud processing unit, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions and/or other processing logic that accommodates the requirements of the present invention.
[0050] The memory (204) comprises suitable logic, circuitry, interfaces, and/or code that may be configured to store the set of instructions, which are executed by the processor (202). Preferably, the memory (204) is configured to store one or more programs, routines, or scripts that are executed in coordination with the processor (202). Additionally, the memory (204) may include any computer-readable medium or computer program product known in the art including, for example, volatile memory, such as static random-access memory (SRAM) and dynamic random-access memory (DRAM), and/or non-volatile memory, such as read only memory (ROM), erasable programmable ROM, a Hard Disk Drive (HDD), flash memories, Secure Digital (SD) card, Solid State Disks (SSD), optical disks, magnetic tapes, memory cards, virtual memory and distributed cloud storage. The memory (204) may be removable, non-removable, or a combination thereof. Further, the memory (204) may include routines, programs, objects, components, data structures, etc., which perform particular tasks or implement particular abstract data types. The memory (204) may include programs or coded instructions that supplement applications and functions of the system (100). In one embodiment, the memory (204), amongst other things, serves as a repository for storing data processed, received, and generated by one or more of the programs or the coded instructions. In yet another embodiment, the memory (204) may be managed under a federated structure that enables adaptability and responsiveness of the application server (104). In an exemplary embodiment, the memory (204) may comprise a vector database.
[0051] In another embodiment, the memory (204) may comprise the one or more educational problems, the one or more responses, the problem-solving pattern, the plurality of ordered cognitive bridges, the expected bridge-specific cognitive solutions, the learning pathway assessment of the user, the one or more pattern embeddings, description of the patterns, error patterns, the subject agnostic problem-solving pattern, the predefined cognitive transition templates, the lower-order cognitive operations, the higher-order cognitive operations, the bridges, and the cross-domain correlations. Further, the one or more responses may include at least one of handwritten input, typed input, voice input, digital pen stroke data, image-based diagrammatic input, or a combination thereof. Further, the one or more time-based hesitation metrics may be derived from the digital pen stroke data. Further, each of the plurality of ordered cognitive bridges may represents the non-trivial cognitive transition between successive problem-solving steps.
[0052] The transceiver (206) comprises suitable logic, circuitry, interfaces, and/or code that may be configured to receive, process or transmit information, data or signals, which are stored by the memory (204) and executed by the processor (202). The transceiver (206) is preferably configured to receive, process or transmit, one or more programs, routines, or scripts that are executed in coordination with the processor (202). The transceiver (206) is preferably communicatively coupled to the communication network (106) of the system (100) for communicating all the information, data, signal, programs, routines or scripts through the network.
[0053] The transceiver (206) may implement one or more known technologies to support wired or wireless communication with the communication network (106). In an embodiment, the transceiver (206) may include, but is not limited to, an antenna, a radio frequency (RF) transceiver, one or more amplifiers, a tuner, one or more oscillators, a digital signal processor, a Universal Serial Bus (USB) device, a coder-decoder (CODEC) chipset, a subscriber identity module (SIM) card, and/or a local buffer. Also, the transceiver (206) may communicate via wireless communication with networks, such as the Internet, an Intranet and/or a wireless network, such as a cellular telephone network, a wireless local area network (LAN) and/or a metropolitan area network (MAN). Accordingly, the wireless communication may use any of a plurality of communication standards, protocols and technologies, such as: Global System for Mobile Communications (GSM), Enhanced Data GSM Environment (EDGE), wideband code division multiple access (W-CDMA), code division multiple access (CDMA), time division multiple access (TDMA), Bluetooth, Wireless Fidelity (Wi-Fi) (e.g., IEEE 802.11a, IEEE 802.11b, IEEE 802.11g and/or IEEE 802.11n), voice over Internet Protocol (VoIP), Wi-MAX, a protocol for email, instant messaging, and/or Short Message Service (SMS).
[0054] The input/output (I/O) unit (208) comprises suitable logic, circuitry, interfaces, and/or code that may be configured to receive or present information. The input/output (I/O) unit (208) comprises various input and output devices that are configured to communicate with the processor (202). Examples of the input devices include, but are not limited to, a keyboard, a mouse, a joystick, a touch screen, a microphone, a camera, and/or a docking station. Examples of the output devices include, but are not limited to, a display screen and/or a speaker. The input/output (I/O) unit (208) may include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, and the like. The input/output (I/O) unit (208) may allow the system (100) to interact with the user directly or through the portable devices (108). Further, the input/output (I/O) unit (208) may enable the system (100) to communicate with other computing devices, such as web servers and external data servers (not shown). The input/output (I/O) unit (208) can facilitate multiple communications within a wide variety of networks and protocol types, including wired networks, for example, LAN, cable, etc., and wireless networks, such as WLAN, cellular, or satellite. The user input/output (I/O) unit (208) may include one or more ports for connecting a number of devices to one another or to another server. In one embodiment, the input/output (I/O) unit (208) allows the application server (104) to be logically coupled to other portable devices unit (108), some of which may be built in. Illustrative components include tablets, mobile phones, camera, micro phone, desktop computers, wireless devices, a cell phone, personal digital assistant (PDA), stationary personal computer, IPTV remote control, laptop computer, pocket PC, a television set capable of receiving IP based video services, mobile IP device etc.
[0055] In an embodiment, the input/output (I/O) unit (208) may be configured to receive the one or more responses of the user corresponding to the one or more educational problems. Further, the one or more responses may include at least one of handwritten input, typed input, voice input, digital pen stroke data, image-based diagrammatic input, or a combination thereof. Further, the one or more time-based hesitation metrics may be derived from the digital pen stroke data. In an exemplary embodiment, the input/output (I/O) unit (208) may comprise at least one of a scanner, a camera, a digital pen, a microphone, or a keyboard.
[0056] In another embodiment, the user interface (210) of the application server (104), is disclosed. In an embodiment, the user interface unit (210) may be configured to provide the bridge level learning pathway assessment of the user. In an exemplary embodiment, the user interface unit (210) may be configured to display the at least at least one of the remediation, score, marks, percentage, percentile, grade, recommendation, HOT (Higher-order thinking) level, LOT (Higher-order thinking) level or a combination thereof. Further, the user interface (210) may be configured the user to interact and render real-time feedback.
[0057] In an embodiment, the processor (202) may be configured to preprocess the one or more responses of the user. Further, the preprocessing of the one or more response of the user may include adaptive binarization, de-skewing with angular correction (of up to ±15 degrees) and noise removal using morphological operations. Further, the vision agent may be configured to process the one or more responses of the user corresponding to one or more educational problems using vision-language models or custom fine-tuned SLM models, for achieving 97%+ accuracy on mathematical symbol recognition. Further, the vision agent may be configured for detecting a region of interest in the one or more responses of the user. Further, the region of interest may include mathematical expressions, symbols, text annotations, diagrams, or a combination thereof. In an exemplary embodiment, the vision agent may be configured to convert the region of interest into a LaTeX representation using a custom grammar parser. Further, the assessment agent may validate the LaTeX representation against one or more mathematical syntax rules. Further, the assessment agent may be configured to identify the pattern by performing a hybrid search that combines dense vector similarity matching using a cosine similarity metric exceeding a predefined threshold and sparse keyword-based matching using a BM25 scoring model for pattern names.
[0058] In another embodiment, the identification unit (212) of the application server (104) is disclosed. The identification unit (212) may be configured for identifying the problem-solving pattern, associated with the one or more educational problems, in the one or more responses of the user. Further, the problem-solving pattern may be identified by performing retrieval-augmented generation (RAG) over the vector database storing the one or more pattern embeddings. In an exemplary embodiment, the problem-solving pattern may be the subject agnostic problem-solving pattern. In an exemplary embodiment, the retrieval-augmented generation process may include the hybrid dense-sparse retrieval mechanism. Further, the processor (202) implements a multi-agent architecture leveraging large language models (LLMs) as cognitive reasoning engines, combined with specialized vision-language models for multimodal input processing, and retrieval-augmented generation (RAG) for pattern-bridge knowledge retrieval.
[0059] In an exemplary embodiment, the assessment agent may be configured for bridge evaluation and cross-domain correlation. Further, the assessment agent may be configured to identify the problem-solving pattern, associated with the one or more educational problems, in the one or more responses of the user. Furthermore, the assessment agent may be configured to evaluate bridge execution using fine-tuned LLM (Large Language Model) / SLM (Small Language Model) or AI (Artificial Intelligence) API with specialized prompt engineering for pattern-bridge evaluation, with and without Agentic AI frameworks.
[0060] In an exemplary embodiment, the multi-agent architecture may comprise the coordination agent. Further, the coordination agent may be configured to orchestrate multi-agent workflow using an Agentic AI framework, implementing novel task delegation protocol based on bridge complexity scores. In an embodiment, the processor (202) implements the RAG architecture specifically designed for pattern-bridge retrieval, addressing the technical challenge of rapidly accessing relevant patterns from the database/memory (204) of 10,000+ problems and 500+ unique patterns. In an exemplary embodiment, the remediation agent may be configured to generate personalized learning content using RAG over an educational content database.
[0061] In another exemplary embodiment, the RAG architecture may include the vector database configured to store the embeddings of patterns, the bridges, and the cross-domain correlations. Further, the embeddings may be generated using sentence-transformers. Further, each pattern description may be embedded along with representative problem examples, bridge sequences, and error patterns, thereby enabling multi-aspect retrieval capability. Further, the retrieval process may employ the hybrid search combining dense vector similarity. Further, the cosine similarity may exceed a threshold of 0.75, with sparse BM25 keyword matching for pattern names, achieving a retrieval accuracy of approximately 92%. Further, the processor (202) may comprise a reranking module. Further, the reranking module may be implemented as a cross-encoder and may re-rank a top-k set of candidates. Further, k may be equals 10, based on bridge sequence similarity and subject domain context. In an exemplary embodiment, the RAG implementation may provide pattern retrieval latency of approximately 150-200 milliseconds, compared to 800-1200 milliseconds for traditional database queries, thereby enabling real-time assessment during student problem-solving.
[0062] In another embodiment, the decomposition unit (214) of the application server (104), is disclosed. The decomposition unit (214) comprises suitable logic, circuitry, interfaces, and/or code that may be configured for decomposing the problem-solving pattern into the plurality of ordered cognitive bridges. Further, each of the plurality of ordered cognitive bridges may represent the non-trivial cognitive transition between successive problem-solving steps. Further, the plurality of ordered cognitive bridges may be decomposed corresponding to a single education problem. In an exemplary embodiment, a bridge-level granularity may decompose cognitive assessment from topic-level (1 data point) to bridge-level (5-15 data points per problem), enabling precise failure localization. In an exemplary embodiment, a bridge-level granularity may decompose cognitive assessment from topic-level (1 data point) to bridge-level (5-15 data points per problem), enabling precise failure localization. Further, the plurality of ordered cognitive bridges may include, for example, Bridge 1 for recognizing standard form, Bridge 2 for identifying a factoring approach, Bridge 3 for executing factorization, Bridge 4 for applying the zero-product property, and Bridge 5 for solving and verifying the solution. The decomposition into these cognitive bridges may allow assessment at transition points between operations, providing granularity not suggested by any prior art system.
[0063] In another embodiment, the evaluation unit (216) of the application server (104), is disclosed. The evaluation unit (216) comprises suitable logic, circuitry, interfaces, and/or code that may be configured for evaluating each of the plurality of ordered cognitive bridges by comparing the corresponding one or more responses of the user against the expected bridge-specific cognitive solutions stored in the bridge repository. Further, the evaluation of each of the plurality of ordered cognitive bridges may correspond to analysing intermediate response steps corresponding to an educational problem rather than only a final answer. In an exemplary embodiment, the bridge repository may be configured to store the predefined cognitive transition templates. Further, the evaluation unit (216) may be configured to analyse crossed-out work, erasures, or corrected steps, by using a change detection algorithm by comparing ink density patterns.
[0064] In an embodiment, the processor (202) may be configured for classifying each of the evaluated cognitive bridges into a hierarchical cognitive complexity level distinguishing lower-order cognitive operations from higher-order cognitive operations. Further, the processor (202) configured for classifying the user as the higher-order thinker may require successful execution of at least one higher-order cognitive bridge independent of procedural correctness. Further, the hierarchical cognitive complexity level may be configured to distinguish procedural correctness from conceptual understanding. Furthermore, the hierarchical cognitive complexity level may comprise at least one of pattern recognition, procedural execution, strategic analysis, and conceptual reasoning.
[0065] In an embodiment, the processor (202) may be configured for correlating the plurality of evaluated ordered cognitive bridges with structurally equivalent cognitive bridges across multiple subject domains using a cross-domain structural equivalence engine. Further, the cross-domain structural equivalence engine may be configured to identify equivalence between cognitive bridges from at least one of mathematics, physics, chemistry domains, or a combination thereof. Further, the processor (202) may be configured to determine at least one failed cognitive bridge from the plurality of ordered cognitive bridges, where the user’s reasoning diverges from an expected cognitive transition.
[0066] For example, cross-domain structural equivalence corresponds to:
Mathematical Bridge (Example 1):
B_203: Factor quadratic → (x - 2) (x - 3) = 0
Cognitive Operations:
1. Identify factors of constant term (6 = 2 × 3)
2. Find combination that sums to linear coefficient (-5 = -2 + -3)
3. Write factored form
Physics Bridge (Example 2):
B_513: Solve v = u - gt for t
Cognitive Operations:
1. Recognize linear equation structure
2. Isolate variable through algebraic manipulation
3. Perform division operation
Structural Similarity Score: 0.87 (high correlation)
Prediction Accuracy: Student passed both bridges ✓
System predicted physics bridge success based on math bridge performance
Prediction made BEFORE student attempted physics problem
[0067] In another embodiment, the generation unit (218) of the application server (104), is disclosed. The generation unit (218) comprises suitable logic, circuitry, interfaces, and/or code that may be configured for generating the bridge level learning pathway assessment of the user based on the evaluation. Further, the bridge level learning pathway assessment may include at least one of the failed cognitive bridges, an associated cognitive error type, at least one predicted at-risk cognitive bridge in a different subject domain. Further, the bridge level learning pathway assessment may include generation of targeted remedial content specific to the failed cognitive bridge. In an embodiment, the remediation agent may be configured to generate targeted remediation content tailored to identified bridge-level failures. Further, generating the targeted remediation content may include selecting intervention type based on whether the failed bridge requires automatic operation fluency or analytical reasoning capability.
[0068] In an exemplary embodiment, the remediation agent may implement remediation strategies focused on decision-making scenarios. Further, the decision-making scenarios may include presenting problems having multiple valid solution approaches, prompting the user to select a preferred method and provide a rationale for the selection, and comparing efficiency across the multiple valid solution approaches. Further, the remediation strategies may be configured to build metacognitive awareness of strategy selection by guiding the user to reflect on the decision-making process associated with choosing among the multiple valid approaches. In an exemplary embodiment, remediation agent may be configured to tailor intervention content based on identified cognitive complexity level of failed bridge, providing procedural practice for lower-order thinking failures and conceptual explanations for higher-order thinking failures. Further, remediation agent may be configured to generate practice problems incorporating successful bridges to maintain mastery while targeting failed bridges for improvement, preventing regression on already mastered cognitive operations. Furthermore, the remediation agent may dynamically adjust problem difficulty by selecting problems requiring mastery of specific subsets of bridges, enabling targeted practice on identified weak bridges while maintaining engagement through achievable challenge level.
[0069] In an embodiment, the diagnostic agent may be configured for classifying error type, scoring severity, analysing root cause of failure and tracing prerequisites. Further, the diagnostic agent may be configured to identify the exact cognitive failure point. Further, in an exemplary embodiment, the diagnostics agent may be configured to perform root cause analysis using chain-of-thought reasoning and error pattern matching against repository.
[0070] In an embodiment, the processor (202) may be configured to execute the methods on an edge computing device to reduce assessment latency. In an exemplary embodiment, the processor (202) may comprise a GPU-accelerated edge computing processor. Further, in an embodiment, ninety percent of assessment computation is executed locally on the edge computing device. Further, the processor (202) may be configured to predict the user’s difficulty in an attempted subject domain prior to the user’s interaction with that subject domain. Further, the user’s difficulty may be predicted by correlating the plurality of evaluated ordered cognitive bridges with structurally equivalent cognitive bridges across multiple subject domains using a cross-domain structural equivalence engine. In an exemplary embodiment, the processor (202) may be configured to distinguish higher-order thinkers from rote learners even when final answers are identical.
[0071] A person skilled in the art will understand that the scope of the disclosure should not be limited to the educational field and using the aforementioned techniques. Further, the examples provided in supra are for illustrative purposes and should not be construed to limit the scope of the disclosure.
[0072] Referring to FIG. 3, a flowchart that illustrates a method (300) for learning pathway assessment of the user, in accordance with at least one embodiment of the present subject matter. The flowchart is described in conjunction with Figure 1 and Figure 2. The method (300) may be implemented by the one or more portable devices (108) including the one or more processors and the memory (204) configured to store processor-executable programmed instructions, causing the processor (202) to perform the following steps.
[0073] At step (302), the method (300) involves receiving the one or more responses of the user corresponding to the one or more educational problems.
[0074] At step (304), the method (300) involves identifying the problem-solving pattern, associated with the one or more educational problems, in the one or more responses of the user.
[0075] At step (306), the method (300) involves decomposing the problem-solving pattern into the plurality of ordered cognitive bridges. In an embodiment, each of the plurality of ordered cognitive bridges may represent the non-trivial cognitive transition between successive problem-solving steps.
[0076] At step (308), the method (300) involves evaluating each of the plurality of ordered cognitive bridges by comparing the corresponding one or more responses of the user against the expected bridge-specific cognitive solutions stored in the bridge repository.
[0077] At step (310), the method (300) involves generating the bridge level learning pathway assessment of the user based on the evaluation.
[0078] Let us delve into a detailed working example of the present disclosure.
[0079] Example 01: Assessment of Maths Exam.
[0080] Question asked in the exam.
Find the positive roots of x² - 5x + 6 = 0.
[0081] Solution provided by the student.
x² - 5x + 6 = 0
(x - 2) (x - 3) = 0
x - 2 = 0 or x - 3 = 0
x = 2 x = 3
[0082] Further, this response corresponding to the question is received by the system through camera photo (3024x4032 pixels, ~8MB JPEG). Further, the system identifies the problem-solving pattern, associated with the one or more educational problems, in the one or more responses of the user.
Query to Vector DB: “x^2 - 5x + 6 = 0, quadratic equation, factoring”
RAG Retrieval Results:
• Rank 1: Pattern P_089 “Quadratic Factoring” (similarity: 0.94)
• Rank 2: Pattern P_091 “Quadratic Formula” (similarity: 0.71)
• Rank 3: Pattern P_093 “Completing Square” (similarity: 0.68)
Selected Pattern: P_089 (Quadratic Factoring)
Bridge Sequence Retrieved: [B_201, B_202, B_203, B_204, B_205]
[0083] Further, the problem-solving patterns are decomposed into a plurality of ordered cognitive bridges.
Pattern P_089: Quadratic Factoring
Associated Bridges:
• B_201: Recognize standard quadratic form (ax² + bx + c = 0)
• B_202: Identify factoring possibility (check discrimination, find factors)
• B_203: Factor into (x - p) (x - q) form
• B_204: Apply zero product property
• B_205: Solve linear equations and verify solutions
Here, each of the plurality of ordered cognitive bridges represents a non-trivial cognitive transition between successive problem-solving steps.
[0084] Further, each of the plurality of ordered cognitive bridges are evaluated by comparing the corresponding one or more responses of the user against an expected bridge-specific cognitive solution stored in a bridge repository.
Bridge B_201 Evaluation:
• Student Work: "x² - 5x + 6 = 0"
• Expected: Recognize ax² + bx + c = 0 with a=1, b=-5, c=6
• Assessment: ✓ PASS (correctly identified standard form)
• Confidence: 0.99
• Processing Time: 180ms
Bridge B_202 Evaluation:
• Student Work: [Implicit - student proceeded to factoring]
• Expected: Check if factorable (discriminant b²-4ac = 25-24 = 1, perfect square)
Find factors of c=6 that sum to b=-5: (-2) + (-3) = -5 ✓
• Assessment: ✓ PASS (correctly determined factoring approach)
• Confidence: 0.87 (implicit reasoning, inferred from correct factorization)
• Processing Time: 340ms
Bridge B_203 Evaluation:
• Student Work: "(x - 2) (x - 3) = 0"
• Expected: Factor as (x - 2) (x - 3) = 0
• Assessment: ✓ PASS (correct factorization)
• Verification: Expand (x-2) (x-3) = x² - 3x - 2x + 6 = x² - 5x + 6 ✓
• Confidence: 0.98
• Processing Time: 420ms
Bridge B_204 Evaluation:
• Student Work: "x - 2 = 0 or x - 3 = 0"
• Expected: Apply zero product property (if AB=0 then A=0 or B=0)
• Assessment: ✓ PASS (correctly applied)
• Confidence: 0.99
• Processing Time: 290ms
Bridge B_205 Evaluation:
• Student Work: "x = 2 x = 3" + verification steps
• Expected: Solve x-2=0 → x=2 and x-3=0 → x=3, verify by substitution
• Assessment: ✓ PASS (correct solutions with verification)
• Confidence: 0.99
• Processing Time: 450ms
OVERALL ASSESSMENT:
• All Bridges: PASS (5/5)
• Total Processing Time: 2340ms (including overhead)
• Bridge-Level Profile: [1.0, 1.0, 1.0, 1.0, 1.0] (perfect execution)
[0085] Further, the plurality of evaluated ordered cognitive bridges are corelated with structurally equivalent cognitive bridges across multiple subject domains using a cross-domain structural equivalence engine.
Cross-Domain Correlation Query:
Pattern P_089 (Quadratic Factoring) correlated with:
- Pattern P_347 (Physics): Projectile motion time-of-flight equations
- Pattern P_512 (Chemistry): Equilibrium concentration calculations
- Pattern P_156 (Mathematics): Polynomial roots
[0086] Further, the system generates a bridge level learning pathway assessment of the user based on the evaluation.
Proactive Flag: Student has mastered quadratic factoring →
• Predicted SUCCESS in physics projectile problems requiring factorization
• Predicted SUCCESS in chemistry quadratic equilibrium expressions
[0087] Example 02: Projectile Motion (Physics) - Cross-Domain Application.
[0088] Question asked in the exam.
A ball is thrown vertically upward with initial velocity 20 m/s from the ground. Find the time when the ball reaches maximum height and the maximum height achieved. (g = 10 m/s²).
[0089] Handwritten solution provided by the student.
Given: u = 20 m/s, g = 10 m/s², v = 0 (at max height)
Using v = u - gt
0 = 20 - 10t
10t = 20
t = 2 seconds ✓
For maximum height:
v² = u² - 2gh
0 = (20)² - 2(10)h
0 = 400 - 20h
20h = 400
h = 400/20 = 25 m ✗
[Crossed out: h = 25 m]
[Corrected to: h = 20 m] ✓.
[0090] Further, this response corresponding to the question is received by the system. Further, the system identifies the problem-solving pattern, associated with the one or more educational problems, in the one or more responses of the user.
Query: “projectile motion vertical upward maximum height time”
Selected Pattern: P_347 (Vertical Projectile Motion)
Bridge Sequence: [B_511, B_512, B_513, B_514, B_515]
Cross-Domain Link Identified:
- Bridge B_513 (Solve quadratic for time): Structurally equivalent to
- Bridge B_203 (Factor quadratic): Same cognitive operation!
Cross-Domain Prediction:
- Student passed B_203 in Example 1 (Math) →
- Predicted to pass B_513 (Physics) with 89% confidence.
[0091] Further, the system performs bridge-by-bridge evaluation.
Bridge B_511: Identify Known Quantities
• Student Work: "u = 20 m/s, g = 10 m/s², v = 0"
• Assessment: ✓ PASS (correctly identified all known values)
Bridge B_512: Select Appropriate Kinematic Equation
• Student Work: "v = u - gt" (for time), "v² = u² - 2gh" (for height)
• Assessment: ✓ PASS (correctly selected equations)
• Bridge B_513: Solve for Time
• Student Work: "0 = 20 - 10t→ t = 2 s"
• Assessment: ✓ PASS (correct algebra and solution)
Bridge B_514: Solve for Maximum Height
• Student Work: "0 = 400 - 20h→ h = 400/20 = 25 m" [INITIAL ERROR]
• Assessment: ✗ FAIL → Arithmetic error (400/20 = 20, not 25)
• Error Pattern: EP_073 (Division calculation error)
• Detected Correction: Student self-corrected to h = 20 m ✓
Bridge B_515: Verification
• Student Work: [No verification shown]
• Assessment: ⚠ PARTIAL (solution correct after correction, but no verification)
OVERALL ASSESSMENT:
- Bridges Passed: 4/5 (B_515 partial)
- Error Detected: Arithmetic mistake in division (self-corrected)
- Processing Time: 2.89 seconds
[0092] Further, the system performs diagnostic analysis.
Error Type: Arithmetic calculation error (EP_073)
Root Cause: Division 400/20 incorrectly computed as 25
Self-Correction: Student identified and corrected error (positive indicator)
Recommendation:
- student has STRONG conceptual understanding (correct approach)
- student has WEAK computational fluency (arithmetic errors)
- Intervention: Deliberate practice on mental math / calculator discipline
Cross-Domain Insight:
Despite arithmetic weakness, students demonstrate STRONG higher-order thinking:
- ✓ Correct pattern selection
- ✓ Correct bridge sequence execution
- ✓ Self-monitoring and error correction
This would be INVISIBLE in traditional scoring (final answer eventually correct)

[0093] Further, the system generates a bridge level learning pathway assessment of the user based on the evaluation, here the bridge level learning pathway assessment includes targeted remedial content specific to the failed cognitive bridge.
Targeted Content: “Practice problems focusing on arithmetic in physics context”
Generated Problems:
1. If v² = 144, find v (practice: √144)
2. Solve 2gh = 200 where g=10 (practice: 200/20)
3. Calculate 500/25 in projectile context
Metacognitive Prompt:
- “You correctly set up the equations but made a calculation error.
- Try using a calculator for final numerical computation or practice mental arithmetic for common divisions.”
[0094] The above working examples, clearly demonstrate the practical application of the present disclosure and the technical effect being brought about by the present disclosure.
[0095] FIG. 4 illustrates a block diagram (400) of an exemplary computer system (401) for implementing embodiments consistent with the present disclosure.
[0096] Variations of computer system (401) may be used for learning pathway assessment of the user. The computer system (401) may comprise a central processing unit (“CPU” or “processor”) (402). The processor (402) may comprise at least one data processor for executing program components for executing user or system generated requests. A user may include a person, a person using a device such as those included in this disclosure, or such a device itself. Additionally, the processor (402) may include specialized processing units such as integrated system (bus) controllers, memory management control units, floating point units, graphics processing units, digital signal processing units, or the like. In various implementations the processor (402) may include a microprocessor, such as AMD Athlon, Duron or Opteron, ARM’s application, embedded or secure processors, IBM PowerPC, Intel’s Core, Itanium, Xeon, Celeron or other line of processors, for example. Accordingly, the processor (402) may be implemented using mainframe, distributed processor, multi-core, parallel, grid, or other architectures. Some embodiments may utilize embedded technologies like application-specific integrated circuits (ASICs), digital signal processors (DSPs), or Field Programmable Gate Arrays (FPGAs), for example. The processing elements can be a single CPU or multitude of CPUs, a GPU ( Graphical Processing Unit) or a bank of GPUs, a single TPU (Tensor Processing Unit) or a multitude of TPUs, a single edge GPU ( such as Jetson Orion, Jetson AGX, or similar) or a multitude of edge GPUs. These high end computing processors are required to compute using the various AI models. The GPUs and TPUs require a separate VRAM ( Video RAM) . These are high speed GPU RAM which will host the AI model weights, input data and the intermediate calculation values.
[0097] Processor (402) may be disposed in communication with one or more input/output (I/O) devices via I/O interface (403). Accordingly, the I/O interface (403) may employ communication protocols/methods such as, without limitation, audio, analog, digital, monoaural, RCA, stereo, IEEE-1394, serial bus, universal serial bus (USB), infrared, PS/2, BNC, coaxial, component, composite, digital visual interface (DVI), high-definition multimedia interface (HDMI), RF antennas, S-Video, VGA, IEEE 802.n /b/g/n/x, Bluetooth, cellular (e.g., code-division multiple access (CDMA), high-speed packet access (HSPA+), global system for mobile communications (GSM), long-term evolution (LTE), WiMAX, or the like, for example.
[0098] Using the I/O interface (403), the computer system (401) may communicate with one or more I/O devices. For example, the input device (404) may be an antenna, keyboard, mouse, joystick, (infrared) remote control, camera, card reader, fax machine, dongle, biometric reader, microphone, touch screen, touchpad, trackball, sensor (e.g., accelerometer, light sensor, GPS, gyroscope, proximity sensor, or the like), stylus, scanner, storage device, transceiver, video device/source, or visors, for example. Likewise, an output device (405) may be a user’s smartphone, tablet, cell phone, laptop, printer, computer desktop, fax machine, video display (e.g., cathode ray tube (CRT), liquid crystal display (LCD), light- emitting diode (LED), plasma, or the like), or audio speaker, for example. In some embodiments, a transceiver (406) may be disposed in connection with the processor (402). The transceiver (406) may facilitate various types of wireless transmission or reception. For example, the transceiver (406) may include an antenna operatively connected to a transceiver chip (example devices include the Texas Instruments® WiLink WL1283, Broadcom® BCM4750IUB8, Infineon Technologies® X-Gold 618-PMB9800, or the like), providing IEEE 802.11a/b/g/n, Bluetooth, FM, global positioning system (GPS), and/or 2G/3G/5G/6G HSDPA/HSUPA communications, for example.
[0099] In some embodiments, the processor (402) may be disposed in communication with a communication network (408) via a network interface (407). The network interface (407) is adapted to communicate with the communication network (408). The network interface (407) may employ connection protocols including, without limitation, direct connect, Ethernet (e.g., twisted pair 10/100/1000 Base T), transmission control protocol/internet protocol (TCP/IP), token ring, or IEEE 802.11a/b/g/n/x, for example. The communication network (408) may include, without limitation, a direct interconnection, local area network (LAN), wide area network (WAN), wireless network (e.g., using Wireless Application Protocol), or the Internet, for example. Using the network interface (407) and the communication network (408), the computer system (401) may communicate with devices such as shown as a laptop (409) or a mobile/cellular phone (410). Other exemplary devices may include, without limitation, personal computer(s), server(s), fax machines, printers, scanners, various mobile devices such as cellular telephones, smartphones (e.g., Apple iPhone, Blackberry, Android-based phones, etc.), tablet computers, desktop computers, eBook readers (Amazon Kindle, Nook, etc.), laptop computers, notebooks, gaming consoles (Microsoft Xbox, Nintendo DS, Sony PlayStation, etc.), or the like. In some embodiments, the computer system (401) may itself embody one or more of these devices.
[00100] In some embodiments, the processor (402) may be disposed in communication with one or more memory devices (e.g., RAM 613, ROM 614, etc.) via a storage interface (412). The storage interface (412) may connect to memory devices including, without limitation, memory drives, removable disc drives, etc., employing connection protocols such as serial advanced technology attachment (SATA), integrated drive electronics (IDE), IEEE-1394, universal serial bus (USB), fiber channel, small computer systems interface (SCSI), etc. The memory drives may further include a drum, magnetic disc drive, magneto-optical drive, optical drive, redundant array of independent discs (RAID), solid-state memory devices, or solid-state drives, for example.
[00101] The memory devices may store a collection of program or database components, including, without limitation, an operating system (416), user interface application (417), web browser (418), mail client/server (419), user/application data (420) (e.g., any data variables or data records discussed in this disclosure) for example. The operating system (416) may facilitate resource management and operation of the computer system (401). Examples of operating systems include, without limitation, Apple Macintosh OS X, UNIX, Unix-like system distributions (e.g., Berkeley Software Distribution (BSD), FreeBSD, NetBSD, OpenBSD, etc.), Linux distributions (e.g., Red Hat, Ubuntu, Kubuntu, etc.), IBM OS/2, Microsoft Windows (XP, Vista/7/8, etc.), Apple iOS, Google Android, Blackberry OS, or the like.
[00102] The user interface (417) is for facilitating the display, execution, interaction, manipulation, or operation of program components through textual or graphical facilities. For example, user interfaces (417) may provide computer interaction interface elements on a display system operatively connected to the computer system (401), such as cursors, icons, check boxes, menus, scrollers, windows, or widgets, for example. Graphical user interfaces (GUIs) may be employed, including, without limitation, Apple Macintosh operating systems’ Aqua, IBM OS/2, Microsoft Windows (e.g., Aero, Metro, etc.), Unix X-Windows, or web interface libraries (e.g., ActiveX, Java, JavaScript, AJAX, HTML, Adobe Flash, etc.), for example.
[00103] In some embodiments, the computer system (401) may implement a web browser (418) stored program component. The web browser (418) may be a hypertext viewing application, such as Microsoft Internet Explorer, Google Chrome, Mozilla Firefox, Apple Safari, or Microsoft Edge, for example. Secure web browsing may be provided using HTTPS (secure hypertext transport protocol), secure sockets layer (SSL), Transport Layer Security (TLS), or the like. Web browsers may utilize facilities such as AJAX, DHTML, Adobe Flash, JavaScript, Java, or application programming interfaces (APIs), for example. In some embodiments the computer system (401) may implement a mail client/server (419) stored program component. The mail server (419) may be an Internet mail server such as Microsoft Exchange, or the like. The mail server may utilize facilities such as ASP, ActiveX, ANSI C++/C#, Microsoft .NET, CGI scripts, Java, JavaScript, PERL, PHP, Python, or WebObjects, for example. The mail server (419) may utilize communication protocols such as internet message access protocol (IMAP), messaging application programming interface (MAPI), Microsoft Exchange, post office protocol (POP), simple mail transfer protocol (SMTP), or the like. In some embodiments, the computer system (401) may implement a mail client (420) stored program component. The mail client (420) may be a mail viewing application, such as Apple Mail, Microsoft Entourage, Microsoft Outlook, or Mozilla Thunderbird.
[00104] In some embodiments, the computer system (401) may store user/application data (421), such as the data, variables, records, or the like as described in this disclosure. Such databases may be implemented as fault-tolerant, relational, scalable, secure databases such as Oracle or Sybase, for example. Alternatively, such databases may be implemented using standardized data structures, such as an array, hash, linked list, struct, structured text file (e.g., XML), table, or as object-oriented databases (e.g., using ObjectStore, Poet, Zope, etc.). Such databases may be consolidated or distributed, sometimes among the various computer systems discussed above in this disclosure. It is to be understood that the structure and operation of any computer or database component may be combined, consolidated, or distributed in any working combination.
[00105] Furthermore, one or more computer-readable storage media may be utilized in implementing embodiments consistent with the present invention. A computer-readable storage medium refers to any type of physical memory on which information or data readable by a processor may be stored. Thus, a computer-readable storage medium may store instructions for execution by one or more processors, including instructions for causing the processor(s) to perform steps or stages consistent with the embodiments described herein. The term “computer readable medium” should be understood to include tangible items and exclude carrier waves and transient signals, i.e., non-transitory. Examples include Random Access Memory (RAM), Read- Only Memory (ROM), volatile memory, non-volatile memory, hard drives, Compact Disc (CD) ROMs, Digital Video Disc (DVDs), flash drives, disks, and any other known physical storage media.
[00106] Various embodiments of the disclosure provide a non-transitory computer readable medium and/or storage medium, and/or a non-transitory machine-readable medium and/or storage medium having stored thereon, a machine code and/or a computer program having at least one code section executable by a machine and/or a computer for learning pathway assessment of the user. The at least one code section in the non-transitory computer readable medium causes the machine and/or computer (application server) including one or more processors to perform the steps, which includes receiving the one or more responses of the user corresponding to the one or more educational problems. Further, the processor (402) may be configured for identifying the problem-solving pattern, associated with the one or more educational problems, in the one or more responses of the user. Further, the processor (402) may be configured for decomposing the problem-solving pattern into the plurality of ordered cognitive bridges. In an embodiment, each of the plurality of ordered cognitive bridges may represents the non-trivial cognitive transition between successive problem-solving steps. Further, the processor (402) may be configured for evaluating each of the plurality of ordered cognitive bridges by comparing the corresponding one or more responses of the user against the expected bridge-specific cognitive solutions stored in the bridge repository. Further, the processor (402) may be configured for generating the bridge level learning pathway assessment of the user based on the evaluation.
[00107] Various embodiments of the disclosure encompass numerous advantages including the method and the system for learning pathway assessment of the user. The disclosed method and system have several technical advantages, but are not limited to the following:
• Cognitive bridge–level diagnostic granularity for higher assessment accuracy: By decomposing a problem-solving pattern into a plurality of ordered cognitive bridges representing non-trivial cognitive transitions, the system overcomes the limitations of topic-level and knowledge-point-level assessment and provides a high-resolution diagnostic view of learner reasoning at the level of internal cognitive operations.
• Subject-agnostic problem-solving pattern identification using retrieval-augmented generation: The use of retrieval-augmented generation over vectorized problem-solving pattern embeddings enables identification of structurally similar reasoning patterns across different subjects, overcoming domain-specific hardcoding and improving scalability, generalization, and diagnostic consistency across curricula.
• Bridge-specific evaluation that reduces false mastery signals: By evaluating intermediate cognitive transitions rather than relying solely on final answers or procedural correctness, the system distinguishes between superficial procedural success and genuine conceptual understanding, significantly reducing false positives in mastery detection common in conventional tutoring systems.
• Accurate diagnosis y analysis of intermediate: By analysing intermediate response steps corresponding to an educational problem rather than enforcing a predefined solution path, the system overcomes the limitations of procedural step-tracking systems and enables accurate diagnosis even when learners employ alternative or non-canonical reasoning paths.
• Hierarchical cognitive complexity classification for reasoning-level differentiation: The system classifies evaluated cognitive bridges into hierarchical cognitive complexity levels including pattern recognition, procedural execution, strategic analysis, and conceptual reasoning, enabling precise differentiation between lower-order and higher-order thinking that is not achievable in step-tracking or rule-based systems.
• Cross-domain correlation of structurally equivalent cognitive bridges: By correlating evaluated cognitive bridges using a cross-domain structural equivalence engine, the system identifies equivalence between cognitive bridges across mathematics, physics, and chemistry domains, enabling cross-domain cognitive inference that is not achievable in subject-specific assessment systems.
• Prediction of cognitive difficulty in unattempt subject domains: By leveraging cross-domain structural equivalence between cognitive bridges, the system predicts a user’s difficulty in an attempted subject domain prior to the user’s interaction with that subject domain, reducing reliance on repetitive assessment and trial-and-error remediation.
• Enhanced cognitive signal extraction: The system integrates handwritten input, digital pen stroke data, typed text, voice input, and diagrammatic images, along with time-based hesitation metrics, overcoming the limitations of single-modality input systems and improving robustness and reliability of cognitive inference under varied interaction conditions.
• Multi-agent architecture for scalable cognitive bridge evaluation: By executing the method using a multi-agent architecture comprising a coordination agent, assessment agent, diagnostics agent, and remediation agent, the system dynamically allocates computational resources based on the number and complexity of cognitive bridges, improving scalability and processing efficiency.
• Accurate differentiation between rote learning and higher-order reasoning: The system identifies higher-order thinkers based on successful execution of conceptual or strategic cognitive bridges independent of procedural correctness, overcoming the inability of conventional systems to distinguish rote memorization from genuine reasoning capability.
• Enhanced diagnostic reliability through bridge repository standardization: The use of predefined cognitive transition templates stored in a bridge repository ensures consistent evaluation across learners and problems, reducing variance introduced by ad hoc rule sets or loosely defined knowledge points.
• Implementation-agnostic learning pathway assessment framework: The system is compatible with rule-based engines, neural networks, multi-agent systems, or hybrid architectures, overcoming vendor- or model-specific constraints and enabling seamless integration into existing educational technology stacks.
• Edge-based execution for reduced assessment latency: By executing cognitive bridge evaluation on edge computing devices, the system reduces dependence on cloud-only processing, achieving lower latency, faster feedback loops, and improved responsiveness in real-time assessment environments.
• Predictive identification of at-risk cognitive bridges across domains: The system predicts structurally equivalent cognitive bridges likely to fail in other subject areas based on observed bridge-level performance, enabling proactive diagnostic insights rather than reactive, performance-only feedback.
• Targeted remediation generation at the cognitive operation level: By linking failed cognitive bridges to bridge-specific remedial content, the system overcomes broad and repetitive content remediation approaches and enables precise corrective intervention aligned to the exact reasoning failure, reducing redundant computation and learner interaction cycles.
[00108] In summary, these technical advantages overcome the limitations of existing AI-driven tutoring, adaptive assessment, and knowledge-point–based educational systems by providing fine-grained, reasoning-level diagnostic assessment at the level of internal cognitive operations rather than surface-level performance indicators. By decomposing problem-solving processes into ordered cognitive bridges and evaluating learner performance across these non-trivial cognitive transitions, the system accurately identifies the specific reasoning operations that succeed or fail during problem solving, independent of topic, subject, or prescribed procedural pathways. The system further enhances diagnostic precision and instructional effectiveness through cross-domain structural equivalence analysis of cognitive bridges and hierarchical cognitive complexity classification, to ensure reliable differentiation between procedural execution, strategic reasoning, and conceptual understanding. Unlike conventional educational assessment and tutoring systems that rely on topic-level correctness, predefined procedural steps, or isolated knowledge-point mastery, the system diagnoses learner difficulties at the level of abstract reasoning transitions, correlates structurally equivalent cognitive operations across domains, and generates targeted remediation directly aligned to the underlying cognitive failure. This approach significantly improves assessment accuracy and instructional relevance, reduces false mastery signals and inefficient repetitive remediation, and enables scalable, subject-agnostic learning pathway assessment across educational domains.
[00109] The claimed invention of a system for learning pathway assessment of the user involves tangible components, processes, and functionalities that interact to achieve specific technical outcomes. The system integrates various elements such as processors, memory, evaluation unit, and decomposition unit to generate the bridge level learning pathway assessment of the user based on the evaluation.
[00110] Furthermore, the invention involves a non-trivial combination of technologies and methodologies that provide a technical solution for a technical problem. The integration of all the different functional units into a comprehensive system for learning pathway assessment of the user brings about improvement and technical advancement in the educational field.
[00111] In light of the above-mentioned advantages and the technical advancements provided by the disclosed method and system, the claimed steps as discussed above are not routine, conventional, or well understood in the art, as the claimed steps enable the following solutions to the existing problems in conventional technologies. Further, the claimed steps clearly bring an improvement in the functioning of the device itself as the claimed steps provide a technical solution to a technical problem.
[00112] The present disclosure may be realized in hardware, or a combination of hardware and software. The present disclosure may be realized in a centralized fashion, in at least one computer system, or in a distributed fashion, where different elements may be spread across several interconnected computer systems. A computer system or other apparatus adapted for carrying out the methods described herein may be suited. A combination of hardware and software may be a general-purpose computer system with a computer program that, when loaded and executed, may control the computer system such that it carries out the methods described herein. The present disclosure may be realized in hardware that comprises a portion of an integrated circuit that also performs other functions.
[00113] A person with ordinary skills in the art will appreciate that the systems, modules, and sub-modules have been illustrated and explained to serve as examples and should not be considered limiting in any manner. It will be further appreciated that the variants of the above disclosed system elements, modules, and other features and functions, or alternatives thereof, may be combined to create other different systems or applications.
[00114] Those skilled in the art will appreciate that any of the aforementioned steps and/or system modules may be suitably replaced, reordered, or removed, and additional steps and/or system modules may be inserted, depending on the needs of a particular application. In addition, the systems of the aforementioned embodiments may be implemented using a wide variety of suitable processes and system modules, and are not limited to any particular computer hardware, software, middleware, firmware, microcode, and the like. The claims can encompass embodiments for hardware and software, or a combination thereof.
[00115] While the present disclosure has been described with reference to certain embodiments, it will be understood by those skilled in the art that various changes may be made, and equivalents may be substituted without departing from the scope of the present disclosure. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the present disclosure without departing from its scope. Therefore, it is intended that the present disclosure is not limited to the particular embodiment disclosed, but that the present disclosure will include all embodiments falling within the scope of the appended claims.
, Claims:WE CLAIM:

1. A method (300) for learning pathway assessment of a user, wherein the method (300) comprises:
receiving (302), via a processor (202), one or more responses of the user corresponding to one or more educational problems;
identifying (304), via the processor (202), a problem-solving pattern, associated with the one or more educational problems, in the one or more responses of the user;
decomposing (306), via the processor (202), the problem-solving pattern into a plurality of ordered cognitive bridges, wherein each of the plurality of ordered cognitive bridges represents a non-trivial cognitive transition between successive problem-solving steps;
evaluating (308), via the processor (202), each of the plurality of ordered cognitive bridges by comparing the corresponding one or more responses of the user against an expected bridge-specific cognitive solutions stored in a bridge repository; and
generating (310), via the processor (202), a bridge level learning pathway assessment of the user based on the evaluation.

2. The method (300) as claimed in claim 1, wherein the one or more responses comprise at least one of handwritten input, typed input, voice input, digital pen stroke data, image-based diagrammatic input, or a combination thereof; wherein one or more time-based hesitation metrics are derived from the digital pen stroke data.

3. The method (300) as claimed in claim 1, wherein the problem-solving pattern is identified by performing retrieval-augmented generation (RAG) over a vector database storing one or more pattern embeddings; wherein the problem-solving pattern is a subject agnostic problem-solving pattern.

4. The method (300) as claimed in claim 1, wherein the plurality of ordered cognitive bridges is decomposed corresponding to a single education problem.

5. The method (300) as claimed in claim 1, wherein evaluating the each of the plurality of ordered cognitive bridges corresponds to analysing intermediate response step corresponding to an educational problem rather than only a final answer; wherein the bridge repository is configured to store predefined cognitive transition templates.

6. The method (300) as claimed in claim 1, comprises classifying each of the evaluated cognitive bridges into a hierarchical cognitive complexity level distinguishing lower-order cognitive operations from higher-order cognitive operations; wherein classifying the user as a higher-order thinker requires successful execution of at least one higher-order cognitive bridge independent of procedural correctness; wherein the hierarchical cognitive complexity level is configured to distinguish procedural correctness from conceptual understanding; wherein the hierarchical cognitive complexity level comprises at least one of pattern recognition, procedural execution, strategic analysis, and conceptual reasoning.

7. The method (300) as claimed in claim 1, comprises correlating the plurality of evaluated ordered cognitive bridges with structurally equivalent cognitive bridges across multiple subject domains using a cross-domain structural equivalence engine; wherein the cross-domain structural equivalence engine is configured to identify equivalence between cognitive bridges from at least one of mathematics, physics, chemistry domains, or a combination thereof.

8. The method (300) as claimed in claim 1, comprises determining at least one failed cognitive bridge from the plurality of ordered cognitive bridges, where the user’s reasoning diverges from an expected cognitive transition.

9. The method (300) as claimed in claim 8, wherein the bridge level learning pathway assessment comprises at least one of the failed cognitive bridge, an associated cognitive error type, at least one predicted at-risk cognitive bridge in a different subject domain; wherein the bridge level learning pathway assessment further comprises generating targeted remedial content specific to the failed cognitive bridge.

10. The method (300) as claimed in claim 1, wherein the method is executed using a multi-agent architecture comprising a coordination agent, an assessment agent, a diagnostics agent, and a remediation agent; wherein the coordination agent dynamically allocates computational resources based on the number and complexity of cognitive bridges.

11. The method (300) as claimed in claim 1, wherein the method is executed on an edge computing device to reduce assessment latency.

12. The method (300) as claimed in claim 1, wherein the method predicts user’s difficulty in an attempted subject domain prior to user’s interaction with that subject domain.

13. A system (100) for learning pathway assessment of a user, wherein the system (100) comprises:
a processor (202);
a memory (204) coupled with the processor (202), wherein the memory (204) is configured to store programmed instructions that cause the processor (202) to:
receive (302) one or more responses of the user corresponding to one or more educational problems;
identify (304) a problem-solving pattern, associated with the one or more educational problems, in the one or more responses of the user;
decompose (306) the problem-solving pattern into a plurality of ordered cognitive bridges, wherein each of the plurality of ordered cognitive bridges represents a non-trivial cognitive transition between successive problem-solving steps;
evaluate (308) each of the plurality of ordered cognitive bridges by comparing the corresponding one or more responses of the user against an expected bridge-specific cognitive solutions stored in a bridge repository; and
generate (310) a bridge level learning pathway assessment of the user based on the evaluation.

14. A non-transitory computer-readable storage medium having stored thereon, a set of computer-executable instructions causing a computer comprising one or more processors to perform steps comprising:
receiving (302) one or more responses of the user corresponding to one or more educational problems;
identifying (304) a problem-solving pattern, associated with the one or more educational problems, in the one or more responses of the user;
decomposing (306), the problem-solving pattern into a plurality of ordered cognitive bridges, wherein each of the plurality of ordered cognitive bridges represents a non-trivial cognitive transition between successive problem-solving steps;
evaluating (308) each of the plurality of ordered cognitive bridges by comparing the corresponding one or more responses of the user against an expected bridge-specific cognitive solutions stored in a bridge repository; and
generating (310) a bridge level learning pathway assessment of the user based on the evaluation.

Dated this 13th Day of January 2026

PRIYANK GUPTA
IN/PA- 1454
AGENT FOR THE APPLICANT