DESC:TECHNICAL FIELD
[0001] The present disclosure relates to digital credential management and verification systems. In particular, the present disclosure relates to a system and a method for securely managing of academic credentials of one or more students using blockchain and artificial intelligence.

BACKGROUND
[0002] The current educational landscape faces numerous challenges in providing personalized and effective guidance to students throughout their academic journey. Traditional systems for tracking and supporting student development rely heavily on limited datasets, such as grades and participation in extracurricular activities. This narrow focus fails to account for a student’s holistic growth, including critical aspects such as learning patterns, emotional well-being, soft skills, and evolving interests. Consequently, the guidance provided is often generic, lacking the depth required to truly cater to individual student needs.
[0003] Career counselors, widely available in schools, often base their recommendations on surface-level data, leading to a one-size-fits-all approach. This method overlooks the unique challenges, strengths, and developmental trajectories of each student. As a result, many students receive guidance that may not align with their aspirations or potential, hindering their ability to make informed decisions about their future.
[0004] While clinical counsellors and psychologists are available in some schools, their services depend on students actively seeking help, a step many are reluctant to take due to discomfort, lack of awareness, or fear of stigma. This hesitancy leaves a significant portion of students without the necessary emotional or psychological support. Furthermore, even when these services are utilized, follow-up care is often inconsistent, limiting the ability to provide comprehensive and sustained support for student well-being.
[0005] The university application process introduces additional hurdles. Students typically submit digital copies of their records and achievements, which must undergo extensive verification to ensure authenticity. This process, often manual, places a significant burden on university admissions departments, consuming time and resources. The inefficiency of this system creates unnecessary delays and complications for both students and institutions.
[0006] Moreover, the integration of advanced technologies like AI and blockchain into education remains fragmented. While blockchain has demonstrated its potential for secure data storage in some educational setups, it is not widely adopted, especially at the school level. Furthermore, existing solutions often focus on either blockchain or AI without integrating the two to address the broader challenges of personalization and collaboration in learning pathways.
[0007] Therefore, in view of the above-mentioned problems, it is desirable to provide a system and a method that may eliminate, or at least mitigate one or more of the above-mentioned problems associated with the existing solutions.

SUMMARY
[0008] This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the present disclosure. This summary is neither intended to identify key or essential inventive concepts of the present disclosure and nor is it intended for determining the scope of the present disclosure.
[0009] In an embodiment, the present disclosure provides a method for securely managing of academic credentials of one or more students. The method includes generating, by a platform, a digital wallet for the one or more students based on registration of the one or more students on the platform. The digital wallet comprises a public key and a private key. The method further includes receiving, by the platform, one or more academic certificates uploaded by the one or more students. The method further includes verifying, by a verifying module associated with the platform, the one or more academic certificates received by the platform. The verifying module receives input from one or more mentors. The method further includes recording, by a blockchain module, the one or more verified academic certificates on a blockchain. The one or more verified academic certificates are tokenized and associated with a unique transaction hash. The unique transaction hash is stored in the digital wallet associated with the one or more students. The method further includes integrating, by the platform, the blockchain module with a pre-trained artificial intelligence (AI) model configured to analyze data associated with the one or more students in real time.
[0010] In another embodiment, the present disclosure provides a system for securely managing of academic credentials of one or more students. The system includes a memory and at least one processor in communication with the memory. The at least one processor is configured to generate, by a platform, a digital wallet for the one or more students based on registration of the one or more students on the platform. The digital wallet comprises a public key and a private key. The at least one processor is configured to receive, by the platform, one or more academic certificates uploaded by the one or more students. The at least one processor is configured to verify, by a verifying module associated with the platform, the one or more academic certificates received by the platform. The verifying module receives input from one or more mentors. The at least one processor is further configured to record, by a blockchain module, the one or more verified academic certificates on a blockchain. The one or more verified academic certificates are tokenized and associated with a unique transaction hash. The unique transaction hash is stored in the digital wallet associated with the one or more students. The at least one processor is further configured to integrate, by the platform, the blockchain module with a pre-trained artificial intelligence (AI) model configured to analyze data associated with the one or more students in real time.
[0011] To further clarify the advantages and features of the present disclosure, a more particular description of the disclosure will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the disclosure and are therefore not to be considered limiting of its scope.

BRIEF DESCRIPTION OF THE DRAWINGS
[0012] These and other features, aspects, and advantages of the present disclosure will become better understood when the following detailed description is read with reference to the accompanying drawings in which like characters represent like parts throughout the drawings, wherein:
[0013] FIG. 1 illustrates an environment of a system for securely managing of academic credentials of one or more students, according to an embodiment of the present disclosure;
[0014] FIG. 2 illustrates a block diagram of a system for securely managing of academic credentials of one or more students, according to an embodiment of the present disclosure; and
[0015] FIG. 3 illustrates a flowchart depicting a method for securely managing of academic credentials of one or more students, according to an embodiment of the present disclosure.
[0016] Further, skilled artisans will appreciate that elements in the drawings are illustrated for simplicity and may not have necessarily been drawn to scale. For example, the flow charts illustrate the method in terms of the most prominent steps involved to help to improve understanding of aspects of the present invention. Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the drawings by conventional symbols, and the drawings may show only those specific details that are pertinent to understanding the embodiments of the present invention so as not to obscure the drawings with details that will be readily apparent to those of ordinary skill in the art having benefit of the description herein.

DETAILED DESCRIPTION
[0017] For the purpose of promoting an understanding of the principles of the invention, reference will now be made to the embodiment illustrated in the drawings and specific language will be used to describe the same. It will nevertheless be understood that no limitation of the scope of the invention is thereby intended, such alterations and further modifications in the illustrated system, and such further applications of the principles of the invention as illustrated therein being contemplated as would normally occur to one skilled in the art to which the invention relates. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skilled in the art to which this invention belongs. The system, methods, and examples provided herein are illustrative only and not intended to be limiting.
[0018] For example, the term “some” as used herein may be understood as “none” or “one” or “more than one” or “all.” Therefore, the terms “none,” “one,” “more than one,” “more than one, but not all” or “all” would fall under the definition of “some.” It should be appreciated by a person skilled in the art that the terminology and structure employed herein is for describing, teaching, and illuminating some embodiments and their specific features and elements and therefore, should not be construed to limit, restrict, or reduce the spirit and scope of the present disclosure in any way.
[0019] For example, any terms used herein such as, “includes,” “comprises,” “has,” “consists,” and similar grammatical variants do not specify an exact limitation or restriction, and certainly do not exclude the possible addition of one or more features or elements, unless otherwise stated. Further, such terms must not be taken to exclude the possible removal of one or more of the listed features and elements, unless otherwise stated, for example, by using the limiting language including, but not limited to, “must comprise” or “needs to include.”
[0020] Whether or not a certain feature or element was limited to being used only once, it may still be referred to as “one or more features” or “one or more elements” or “at least one feature” or “at least one element.” Furthermore, the use of the terms “one or more” or “at least one” feature or element does not preclude there being none of that feature or element, unless otherwise specified by limiting language including, but not limited to, “there needs to be one or more...” or “one or more element is required.”
[0021] Unless otherwise defined, all terms and especially any technical and/or scientific terms, used herein may be taken to have the same meaning as commonly understood by a person ordinarily skilled in the art.
[0022] Reference is made herein to some “embodiments.” It should be understood that an embodiment is an example of a possible implementation of any features and/or elements of the present disclosure. Some embodiments have been described for the purpose of explaining one or more of the potential ways in which the specific features and/or elements of the proposed disclosure fulfil the requirements of uniqueness, utility, and non-obviousness.
[0023] Use of the phrases and/or terms including, but not limited to, “a first embodiment,” “a further embodiment,” “an alternate embodiment,” “one embodiment,” “an embodiment,” “multiple embodiments,” “some embodiments,” “other embodiments,” “further embodiment”, “furthermore embodiment”, “additional embodiment” or other variants thereof do not necessarily refer to the same embodiments. Unless otherwise specified, one or more particular features and/or elements described in connection with one or more embodiments may be found in one embodiment, or may be found in more than one embodiment, or may be found in all embodiments, or may be found in no embodiments. Although one or more features and/or elements may be described herein in the context of only a single embodiment, or in the context of more than one embodiment, or in the context of all embodiments, the features and/or elements may instead be provided separately or in any appropriate combination or not at all. Conversely, any features and/or elements described in the context of separate embodiments may alternatively be realized as existing together in the context of a single embodiment.
[0024] Any particular and all details set forth herein are used in the context of some embodiments and therefore should not necessarily be taken as limiting factors to the proposed disclosure.
[0025] Embodiments of the present invention will be described below in detail with reference to the accompanying drawings.
[0026] Throughout, the present disclosure, the term “system” may refer to the overall messaging system or platform where the present disclosure is implemented. It includes all the components necessary for sending, receiving, and managing messages.
[0027] FIG. 1 illustrates an environment 100 of a system for securely managing of academic credentials of one or more students, according to an embodiment of the present disclosure.
[0028] The environment 100 includes one or more students 102, a student device 104, one or more mentors 106, a mentor device 108, and a server 110. In an embodiment, the system 112 may be implemented in the server 110. The student device 104, the mentor device 108, and the server 110 may be connected to each other through a network 112. The student device 104 and the mentor device 108 may further include respective user interfaces (not shown).
[0029] In one embodiment, the one or more students 102 may be represented as a first student 102a, a second student 102b, a third student 102c, and upto an nth student 102n.
[0030] In operation, the one or more students 102 (alternatively referred to as the student 102) may begin the operation by registering on the platform using the student device 104.
[0031] In an embodiment, the system 112 may be configured to receive student registration data. In an embodiment, the student registration data may include at least one of a name, a date of birth, an institutional affiliation, and a contact information of the one or more students 102.
[0032] In an embodiment, the digital wallet is used as a secure container for managing verified academic credentials, including academic certificates and video submissions, as well as for associating tokenized data recorded on a blockchain with a unique transaction hash. The digital wallet also enables real-time interaction with a journaling module and an emotional artificial intelligence (AI) agent as part of the student’s academic journey on the platform.
[0033] In an exemplary scenario, the first student 102a initiates a registration process using a student device 104, such as a smartphone or laptop, to access the platform associated with the system 112. The system 112 receives student registration data submitted by the student for instance named as “Riya Sharma”. The student registration data includes Riya’s full name ("Riya Sharma"), date of birth ("15 August 2003"), institutional affiliation ("Delhi Institute of Technology"), and contact information.
[0034] Further, based on the received student registration data, the system 112 generates a unique student identifier for Riya Sharma. The unique student identifier is then used to register Riya Sharma on the platform. Upon successful registration, the system 112 generates the digital wallet for the student. The digital wallet comprises the public key and the private key uniquely associated with the student 102.
[0035] Further, the system 112 may be configured to generate a unique student identifier for each student based on the received registration data.
[0036] Further, the system 112 may be configured to register the one or more students 102 on the platform based on generating the unique student identifier for each student.
[0037] Upon registration, the system 112 may be configured to generate a digital wallet for the one or more students 102 (alternatively referred to as students, for the sake of brevity) based on registration of the one or more students 102. In an embodiment, the digital wallet may include a public key and a private key.
[0038] Further, the system 112 may be configured to receive one or more academic certificates uploaded by the one or more students 102.
[0039] In an example, upon receiving the student registration data submitted by the first student 102a, the system 112 may be configured to process the data to generate the unique student identifier. In an embodiment, the unique student identifier may be a system-generated alphanumeric code derived from a hash of the student registration data, such as "RS-DIT-2003-001".
[0040] Based on the generation of the unique student identifier, the system 112 registers student 102. The registration enables the one or more students 102 to access the features of the platform, including uploading academic certificates, submitting video content for verification, and accessing the digital wallet.
[0041] Further, the system 112 may be further configured to verify, using a verifying module the one or more academic certificates. In an embodiment, the verifying module receives input from the one or more mentors 106.
[0042] In an example, upon receiving the academic certificate using the student device 104. The system 112 may be configured to verify the uploaded academic certificate of the student 102a. The verifying module receives input from the one or more mentors 106 affiliated with the first student named Riya Sharma’s institution, who confirm the authenticity of the certificate through a secure interface.
[0043] Further, the system 112 may be further configured to record the one or more verified academic certificates on a blockchain using a blockchain module. In an embodiment, the one or more verified academic certificates are tokenized and associated with a unique transaction hash. In an embodiment, the unique transaction hash is stored in the digital wallet associated with the one or more students 102.
[0044] For example, upon successful verification, the system 112 records the verified academic certificate of the student 102a, Riya Sharma, on the blockchain using the blockchain module. The verified academic certificate is tokenized and associated with the unique transaction hash, for example, "0xA1B2C3D4...". The unique transaction hash is stored in the digital wallet associated with Riya Sharma for secure and immutable reference.
[0045] Further, the system 112 may be configured to integrate the blockchain module with a pre-trained artificial intelligence (AI) model configured to analyze data associated with the one or more students 102 in real time.
[0046] In an example, the system 112 integrates the blockchain module with the pre-trained artificial intelligence (AI) model. The pre-trained AI model is configured to analyze, in real time, data associated with the student 102a, Riya Sharma, including verified academic certificates recorded on the blockchain, academic performance data, and related metadata stored in the digital wallet.
[0047] In an embodiment, the system 112 may be configured to receive one or more video submissions uploaded by the one or more students 102.
[0048] Further, the system 112 may be configured to verify each of the one or more video submissions based on receipt of the one or more video submissions using the verifying module.
[0049] Further, the system 112 may be configured to associate the one or more verified video submissions with the digital wallet of the one or more students 102.
[0050] Further, the system 112 may be configured to store the associated one or more verified video submissions in a database.
[0051] In an exemplary embodiment, the second student 102b, Aarav Mehta, uploads a video submission through the student device 104 demonstrating a project presentation as part of a coursework requirement.
[0052] The system 112 verifies the received one or more video submissions using the verifying module, which confirms the authenticity of the uploaded video based on approval of the one or more mentors 106 and metadata consistency.
[0053] Further, the one or more verified video submissions are associated with the digital wallet of the second student 102b, Aarav Mehta. The system 112 stores the associated one or more verified video submissions in the secure database for future reference and analysis.
[0054] In an embodiment, the system 112 may be configured to receive input data using the pre-trained AI model. In an embodiment, the input data may include the one or more verified academic certificates recorded on the blockchain, academic performance data associated with the one or more students 102, and one or more verified video submissions associated with the digital wallet of the one or more students 102.
[0055] Further, the system 112 may be configured to generate one or more performance insights using the pre-trained AI model. In an embodiment, the performance insights indicate academic progress and non-academic competencies of the one or more students 102 based on multi-modal analysis of the input data.
[0056] For example, the system 112 may receive input data for the third student 102c, Meera Iyer, using the pre-trained AI model. The input data may include verified academic certificates recorded on the blockchain, academic performance data from institutional records, and the one or more verified video submissions linked to Meera Iyer’s digital wallet.
[0057] Using multi-modal analysis, the pre-trained AI model generates performance insights indicating Meera Iyer’s academic progress and non-academic competencies, such as communication skills and subject understanding, demonstrated in the one or more video submissions.
[0058] In an embodiment of the present disclosure, the system 112 may be configured to provide a journaling module accessible to the one or more students 102 through a chat-style interface.
[0059] Further, the system 112 may be configured to enable a real-time interaction between the one or more students 102 and the emotional artificial intelligence (AI) agent associated with the pre-trained AI model configured to process natural language input using the journaling module.
[0060] In an exemplary scenario, the system 112 may provide the journaling module accessible to the fourth student 102d, Neha Verma, through a chat-style interface available on the student device 104.
[0061] The journaling module enables real-time interaction between Neha Verma and the emotional artificial intelligence (AI) agent associated with the pre-trained AI model. The AI agent processes natural language input to offer personalized emotional support and reflective prompts.
[0062] In an embodiment, the system 112 may be configured to encrypt one or more journaling sessions associated with the journaling module using end-to-end encryption protocols.
[0063] In an example, the system 112 may encrypt journaling sessions of the furth student 102d, Neha Verma, using end-to-end encryption protocols to ensure that the content of the interactions with the AI agent remains confidential and securely stored.
[0064] FIG. 2 illustrates a block diagram of the system 112 for securely managing of academic credentials of one or more students 102, according to an embodiment of the present disclosure.
[0065] The system 112 may include, but is not limited to, one or more processors 202 (referred to as the “processor 202”), a memory 204, an input component 206, an output component 208, a communication interface 210, and one or more modules 212.
[0066] The one or more processors 202 may be a single processing unit or several units, all of which could include multiple computing units. The one or more processors 202 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the one or more processors 202 are adapted to fetch and execute computer-readable instructions and data stored in the memory 204.
[0067] The memory 204 may include suitable logic, circuitry, and interfaces that may be configured store historical data associated with the one or more students 102, the one or more verified academic certificates, outputs of the pre-trained artificial intelligence (AI) model 218, and other relevant data to facilitate secure credential management and real-time analysis using blockchain and AI technologies. Examples of the memory 204 may include, but are not limited to, a random-access memory (RAM), a read-only memory (ROM), a removable storage drive, a hard disk drive (HDD), a flash memory, a solid-state memory, or the like. It will be apparent to a person skilled in the art that the scope of the disclosure is not limited to realizing the memory 204 in the system 112, as described herein. In other embodiments, the memory 204 may be realized in the form of a database or a cloud storage working in conjunction with the processor 202, without deviating from the scope of the disclosure.
[0068] The input component 206 may be configured to receive information, such as user input. For example, the input component 206 may include, but not be limited to, a touchscreen display, a keyboard, a keypad, a mouse, a button, a switch, and/or a microphone associated with the system 112.
[0069] The output component 208 may be configured to display information from the system 112 to the user or other systems, utilizing a variety of devices and technologies tailored to specific application needs. The output component 208 may include visual output devices such as display screens (LCD, LED, OLED), projectors, and heads-up displays (HUDs) for presenting graphical or textual information. Additionally, auditory output through speakers and headphones provides audio feedback and alerts, while haptic output devices, like vibration motors in smartphones or game controllers, offer tactile feedback. Functionally, the output component 208 serves multiple roles, including displaying graphical user interface (GUI) elements for user interaction, delivering notifications and alerts through sound, visual indicators, or vibrations, and rendering complex data visualizations like charts and graphs for easier comprehension.
[0070] In an embodiment, the output component 208 may be configured to receive processed data from the processor 202, which determines the information to be communicated, and the output component 208 may access the memory 204 to retrieve and display stored information such as documents, media files, or application states.
[0071] Furthermore, the output component 208 may be configured to meet the specific requirements of different applications, such as high-resolution visual output and immersive audio for gaming systems or clear and precise data visualization and alert mechanisms for industrial control systems. Through these varied output methods, the output component 208 ensures effective communication of information, enhancing both the system 112 functionality and user experience.
[0072] The communication interface 210 is a hardware and/or software component that may be configured to enable the system 112 to exchange data with other user devices or systems. The communication interface 210 may be configured to serve as the link for transmitting and receiving information, either within a local environment (e.g., between components of the same system) or across networks.
[0073] The one or more modules 212, amongst other things, include routines, programs, objects, components, data structures, etc., which perform particular tasks or implement data types. The one or more module(s) 212 may also be implemented as, signal processor(s), state machine(s), logic circuitries, and/or any other device or component that manipulates signals based on operational instructions.
[0074] Further, the one or more modules 212 may be implemented in hardware, instructions executed by a processing unit 202, or by a combination thereof. The processing unit 202 can comprise a computer, a processor, such as the one or more processor 202, a state machine, a logic array, or any other suitable devices capable of processing instructions. The processing unit 202 can be a general-purpose processor which executes instructions to cause the general-purpose processor to perform the required tasks or, the processing unit can be dedicated to performing the required functions. In another embodiment of the present disclosure, the one or more modules 212 may be machine-readable instructions (software) which, when executed by a processor/processing unit 202, perform any of the described functionalities.
[0075] In an embodiment, the processor 202 may be configured to configured to receive the student registration data. In an embodiment, the student registration data may include at least one of the name, the date of birth, the institutional affiliation, and the contact information of the one or more students 102.
[0076] In an embodiment, the digital wallet is used as a secure container for managing verified academic credentials, including academic certificates and video submissions, as well as for associating tokenized data recorded on the blockchain with a unique transaction hash. The digital wallet also enables real-time interaction with a journaling module and emotional artificial intelligence (AI) agent as part of the student’s academic journey on the platform.
[0077] Further, the processor 202 may be configured to generate the unique student identifier for each student based on the received registration data.
[0078] Further, the processor 202 may be configured to register the one or more students 102 based on generating the unique student identifier for each student.
[0079] Upon registration, the processor 202 may be configured to generate the digital wallet for the one or more students 102 based on the registration of the one or more students 102. In an embodiment, the digital wallet may include the public key and the private key.
[0080] Further, the processor 202 may be configured to receive the one or more academic certificates uploaded by the one or more students 102.
[0081] Based on the generation of the unique student identifier, the processor 202 registers student 102. The registration enables the student to access the features of the platform, including uploading academic certificates, submitting video content for verification, and accessing the digital wallet.
[0082] Further, the processor 202 may be further configured to verify, using the verifying module 214, the one or more academic certificates. In an embodiment, the verifying module 214 receives input from the one or more mentors 106.
[0083] Further, the processor 202 may be further configured to record the one or more verified academic certificates on the blockchain using the blockchain module 216. In an embodiment, the one or more verified academic certificates are tokenized and associated with the unique transaction hash. In an embodiment, the unique transaction hash is stored in the digital wallet associated with the one or more students 102.
[0084] Further, the system 112 may be configured to integrate the blockchain module 216 with the pre-trained AI model 218 configured to analyze data associated with the one or more students 102 in real time.
[0085] In an embodiment, the processor 202 may be configured to receive the one or more video submissions uploaded by the one or more students 102.
[0086] Further, the processor 202 may be configured to verify each of the one or more video submissions based on receipt of the one or more video submissions using the verifying module 214.
[0087] Further, the processor 202 may be configured to associate the one or more verified video submissions with the digital wallet of the one or more students 102.
[0088] Further, the processor 202 may be configured to store the associated one or more verified video submissions in the database.
[0089] The processor may be configured to verify the received one or more video submissions, using the verifying module 214, which confirms the authenticity of the uploaded video based on the approval of the one or more mentors 106 and metadata consistency.
[0090] In an embodiment, the processor 202 may be configured to receive input data using the pre-trained AI model 218. In an embodiment, the input data may include the one or more verified academic certificates recorded on the blockchain, academic performance data associated with the one or more students 102, and one or more verified video submissions associated with the digital wallet of the one or more students 102.
[0091] Further, the processor 202 may be configured to generate one or more performance insights using the pre-trained AI model 218. In an embodiment, the performance insights indicate academic progress and non-academic competencies of the one or more students 102 based on multi-modal analysis of the input data.
[0092] In an embodiment of the present disclosure, the processor 202 may be configured to provide the journaling module 220 accessible to the one or more students 102 through a chat-style interface.
[0093] Further, the processor 202 may be configured to enable the real-time interaction between the one or more students 102 and the emotional artificial intelligence (AI) agent associated with the pre-trained AI model 218 configured to process natural language input using the journaling module 220.
[0094] In an embodiment, the processor 202 may be configured to encrypt the one or more journaling sessions associated with the journaling module 220 using end-to-end encryption protocols.
[0095] In an embodiment, the one or more modules 212 may include the verifying module 214, the blockchain module 216, the pre-trained AI model 218, the journaling module 220, and a life-graph module 222.
[0096] In an embodiment, the verifying module, the blockchain module 216, the pre-trained AI model 218, the journaling module 220, and the life-graph module 222 may be connected to each other.
[0097] In an embodiment, the verifying module 214 is a software and/or hardware component configured to authenticate the one or more academic credentials submitted by the one or more students 102. The verifying module 214 may receive input from one or more mentors 106, who are authorized reviewers associated with academic institutions.
[0098] Upon receiving the one or more certificates or the one or more video submissions, the verifying module 214 may be configured to initiate the validation workflow, wherein the one or more mentors 106 may verify the authenticity and the relevance of the submitted materials through the secure interface.
[0099] Further, the verifying module 214 may be configured to update the verification status of submissions and trigger downstream operations such as blockchain recording. The verifying module 214 ensures institutional oversight while maintaining automation, accuracy, and auditability in the credential verification process.
[0100] In an embodiment, the blockchain module 216 is a decentralized, tamper-proof component configured to securely store and manage the verified credentials of the one or more students 102. Upon successful verification, the blockchain module 216 may be configured to tokenize each verified academic certificate or the one or more video submissions and associate the one or more verified academic certificates with the unique transaction hash.
[0101] In an embodiment, the tokenized data is recorded on the blockchain, and the unique transaction hash is stored in the digital wallet associated with the student 102.
[0102] Further, the blockchain module 216 may be configured to manage the public key and the private key corresponding to each student’s digital wallet, thereby ensuring secure ownership and verifiability of the credentials.
[0103] Additionally, the blockchain module 216 may be configured to enable external entities such as universities or employers to independently validate the credentials without manual intervention.
[0104] In an embodiment, the pre-trained (AI) model 218 may an intelligent processing unit integrated with the blockchain module 216. The pre-trained AI model may be configured to analyze student data in real time.
[0105] Further, the pre-trained AI model 218 may be configured to receive multi-modal input data, including the one or more verified academic certificates recorded on the blockchain, academic performance data, and the one or more verified video submissions linked to the digital wallet of the student 102.
[0106] In an embodiment, the pre-trained AI model 218 may be configured to generate the performance insights that indicate both academic progress and non-academic competencies such as communication skills, presentation abilities, and the emotional intelligence AI agent using advanced data analysis techniques.
[0107] In an embodiment, the performance insights are used for student development, institutional reporting, and external evaluation.
[0108] In an embodiment, the journaling module 220 is an interactive, chat-style interface accessible to the one or more students 102. Further, the journaling module 220 may be configured to facilitate reflective and emotional engagement.
[0109] Further, the journaling module 220 may be integrated with the emotional artificial intelligence (AI) agent associated with the pre-trained AI model 218. In an embodiment, the emotional AI agent may be configured to process the natural language input from the one or more students 102 in real time, providing supportive responses, feedback, or prompts to encourage personal reflection, stress relief, and emotional well-being.
[0110] Further, all journaling sessions are protected using end-to-end encryption protocols, ensuring the confidentiality and privacy of the student interactions and enabling continuous emotional support throughout their academic journey.
[0111] In an embodiment, the life-graph module 222 may be configured to enable the utility of traditional educational platforms by creating a continuous, lifelong record of a student's academic, personal, and professional journey. Unlike conventional systems that focus solely on the years spent in school, the life-graph module 222 may enable students to document and track their growth throughout their lives.
[0112] Further, the one or more students 102 may regularly input data about their evolving skills, interests, experiences, and career milestones, creating a dynamic and comprehensive repository of their achievements and personal development.
[0113] The life-graph module 222 may be configured to serve as a reflective tool, enabling users to revisit their past accomplishments, challenges, and growth trajectories, fostering a deeper understanding of their progress.
[0114] In an embodiment, the system 112 may be configured to use the life-graph module 222 to provide personalized recommendations to guide future decisions of the one or more students 102, such as education, career planning, or skill development, by using the performance insights. This lifelong support system keeps students connected to their growth journey and transforms the system 112 into a resource for continuous self-improvement and professional advancement.
[0115] Further, the life-graph module 222 bridges the gap between education and lifelong learning, ensuring that students remain engaged with their personal and professional development long after their formal schooling ends.
[0116] FIG. 3 illustrates a flowchart depicting the method 300 for securely managing of academic credentials of the one or more students 102, according to an embodiment of the present disclosure.
[0117] At step 302, the method 300 may include generating the digital wallet for the one or more students 102 based on registration of the one or more students 102. The digital wallet may include the public key and the private key.
[0118] At step 304, the method 300 may include receiving the one or more academic certificates uploaded by the one or more students 102.
[0119] At step 306, the method 300 may include verifying, by the verifying module 214, the one or more academic certificates. In an embodiment, the verifying module 214 may receive the input from the one or more mentors 106.
[0120] At step 308, the method 300 may further include recording, by the blockchain module 216, the one or more verified academic certificates on the blockchain. In an embodiment, the one or more verified academic certificates are tokenized and associated with the unique transaction hash. The unique transaction hash is stored in the digital wallet associated with the one or more students 102.
[0121] At step 310, the method 300 may include integrating the blockchain module 216 with the pre-trained AI model 218 configured to analyze the data associated with the one or more students 102 in real time.
[0122] The present disclosure advantageously overcomes one or more technical problems associated with the existing systems, such as:
[0123] The present disclosure enables decentralized and tamper-proof storage of the verified academic credentials using the blockchain module 216. The present disclosure eliminates risks associated with centralized storage systems, such as single-point failures and data manipulation. The present disclosure ensures long-term verifiability and integrity of student 102 records, thereby establishing trust in academic data exchanges across institutions and employers.
[0124] The present disclosure provides blockchain-backed e-portfolios that consolidate academic certificates, the one or more video submissions, and extracurricular achievements into a unified and verifiable digital identity. The present disclosure simplifies third-party validation processes by enabling direct credential verification via the digital wallet associated with each student 102. The present disclosure reduces administrative overhead, eliminates manual authentication steps, and accelerates processes such as university admissions and employment screening.
[0125] The present disclosure integrates the pre-trained artificial AI model 218 with the blockchain module 216 for real-time analysis of academic performance data, verified credentials, and multimedia submissions. The present disclosure enables the generation of personalized performance insights that reflect both academic progress and non-academic competencies of the student 102. The present disclosure supports adaptive learning interventions, skills gap detection, and automated feedback mechanisms.
[0126] The present disclosure introduces a journaling module 220 coupled with the emotional artificial intelligence (AI) agent that processes natural language inputs from the student 102. The present disclosure enables continuous mental well-being assessment and personalized emotional insights based on journaling activity. The present disclosure provides an AI-enhanced alternative to traditional counseling systems, fostering emotional intelligence and student self-awareness through secure and private interactions.
[0127] The present disclosure enables continuous tracking of academic and personal development through a dynamic “Life-Graph” representation using the Life-Graph module 222. The present disclosure updates the digital learning profile of the student 102 in real time, capturing achievements, emotional growth, and skill evolution. The present disclosure provides tailored recommendations and visual progress indicators, thereby promoting lifelong learning engagement and self-directed improvement.
[0128] The present disclosure achieves a holistic educational solution by combining blockchain-based data permanence with AI-driven performance and emotional analytics. The present disclosure ensures secure, insightful, and privacy-preserving management of academic credentials and personal development data, enabling a transformative approach to student lifecycle management.
[0129] Further numerous advantages of the present disclosure include a user-centric approach, efficiency enhancement, communication optimization, adaptability to user behaviour, competitive advantage, alignment with industry trends, market differentiation, and future-proofing capabilities.
[0130] While specific language has been used to describe the present disclosure, any limitations arising on account thereto, are not intended. As would be apparent to a person in the art, various working modifications may be made to the method to implement the inventive concept as taught herein. The drawings and the foregoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment. ,CLAIMS:WE CLAIM:

1. A method (300) for securely managing of academic credentials of one or more students (102) using blockchain and artificial intelligence, comprising:
generating a digital wallet for the one or more students (102) based on registration of the one or more students (102), wherein the digital wallet comprises a public key and a private key;
receiving one or more academic certificates uploaded by the one or more students (102);
verifying, by a verifying module (214), the one or more academic certificates, wherein the verifying module (214) receives input from one or more mentors (106);
recording, by a blockchain module (216), the one or more verified academic certificates on a blockchain, wherein the one or more verified academic certificates are tokenized and associated with a unique transaction hash, and wherein the unique transaction hash is stored in the digital wallet associated with the one or more students (102); and
integrating the blockchain module (216) with a pre-trained artificial intelligence (AI) model (218) configured to analyze data associated with the one or more students (102) in real time.
2. The method (300) as claimed in claim 1, wherein registering the one or more students (102) comprises:
receiving student registration data comprising at least one of a name, a date of birth, an institutional affiliation, and a contact information of the one or more students (102);
generating a unique student identifier for each student based on the received registration data; and
registering the one or more students (102) based on generating the unique student identifier for each student.

3. The method (300) as claimed in claim 1, comprising:
receiving one or more video submissions uploaded by the one or more students (102);
verifying, by the verifying module (214), each of the one or more video submissions based on receipt of the one or more video submissions;
associating the one or more verified video submissions with the digital wallet of the one or more students (102); and
storing the associated one or more verified video submissions in a database.

4. The method (300) as claimed in claim 1, comprising:
receiving, by the pre-trained AI model (218), input data comprising the one or more verified academic certificates recorded on the blockchain, academic performance data associated with the one or more students (102), and one or more verified video submissions associated with the digital wallet of the one or more students (102); and
generating, by the pre-trained AI model (218), one or more performance insights indicating academic progress and non-academic competencies of the one or more students (102) based on multi-modal analysis of the input data.

5. The method as claimed in claim 1, comprising:
providing a journaling module (220) accessible to the one or more students (102) through a chat-style interface; and
enabling, by the journaling module (220), a real-time interaction between the one or more students (102) and an emotional artificial intelligence (AI) agent associated with the pre-trained AI model (218) configured to process natural language input.

6. The method as claimed in claim 5, comprising:
encrypting one or more journaling sessions associated with the journaling module (220) using end-to-end encryption protocols.

7. A system (112) for securely managing of academic credentials of one or more students (102) using blockchain and artificial intelligence, the system (112) comprising:
a memory (204);
at least one processor (202) in communication with the memory (204) is configured to:
generate a digital wallet for the one or more students (102) based on registration of the one or more students (102), wherein the digital wallet comprises a public key and a private key;
receive one or more academic certificates uploaded by the one or more students (102);
verify, by a verifying module (214), the one or more academic certificates received, wherein the verifying module (214) receives input from one or more mentors (106);
record, by a blockchain module (216), the one or more verified academic certificates on a blockchain, wherein the one or more verified academic certificates are tokenized and associated with a unique transaction hash, and wherein the unique transaction hash is stored in the digital wallet associated with the one or more students (102); and
integrate the blockchain module (216) with a pre-trained artificial intelligence (AI) model (218) configured to analyze data associated with the one or more students (102) in real time.
8. The system (112) as claimed in claim 7, wherein registering the one or more students (102), the at least one processor (202) is configured to:
receive student registration data comprising at least one of a name, a date of birth, an institutional affiliation, and a contact information of the one or more students (102);
generate a unique student identifier for each student based on the received registration data; and
register the one or more students (102) on the platform based on generating the unique student identifier for each student.

9. The system (112) as claimed in claim 1, the at least one processor (202) is configured to:
receive one or more video submissions uploaded by the one or more students (102);
verify, by the verifying module (214), each of the one or more video submissions based on receipt of the one or more video submissions;
associate the one or more verified video submissions with the digital wallet of the one or more students (102); and
store the associated one or more verified video submissions in a database.

10. The system (112) as claimed in claim 7, the at least one processor (202) is configured to:
receive input data comprising the one or more verified academic certificates recorded on the blockchain, academic performance data associated with the one or more students (102), and one or more verified video submissions associated with the digital wallet of the one or more students (102) using the pre-trained AI model (218); and
generate one or more performance insights indicating academic progress and non-academic competencies of the one or more students (102) based on multi-modal analysis of the input data using the pre-trained AI model (218), .

11. The system (112) as claimed in claim 7, the at least one processor (202) is configured to:
provide a journaling module (220) accessible to the one or more students (102) through a chat-style interface; and
enable a real-time interaction between the one or more students (102) and an emotional artificial intelligence (AI) agent associated with the pre-trained AI model (218) configured to process natural language input using the journaling module (220).

12. The system (112) as claimed in claim 11, the at least one processor (202) is configured to:
encrypt one or more journaling sessions associated with the journaling module (220) using end-to-end encryption protocols.