Description:TECHNICAL FIELD
[0001] The present disclosure generally relates to an educational technology and Artificial Intelligence (AI) integration. In particular, the present disclosure relates to an AI-powered system optimizes the evaluation of students’ scientific project videos within educational settings through a seamless workflow that relies on cutting-edge technologies.

BACKGROUND
[0002] Background description includes information that may be useful in understanding the present disclosure. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed disclosure, or that any publication specifically or implicitly referenced is prior art.
[0003] Education has witnessed a paradigm shift with the advent of digital technology, moving from traditional classroom teaching to more interactive and technology-driven learning methods. This transformation has led to an increased emphasis on the use of multimedia in the education sector, with student-generated videos becoming a popular tool for demonstrating understanding and creativity in scientific projects. However, the process of evaluating these videos and ensuring their adherence to educational standards has remained largely manual, time-consuming, and subjective.
[0004] Furthermore, the safe storage and dissemination of these videos, and their accessibility to various stakeholders like parents, educators, and government entities, presents another significant challenge. The advent of cloud-based storage has provided a solution to some extent, but the need for a more efficient and automated process for video submission, evaluation, and distribution remained unmet.
[0005] The inception of this invention arose from the imperative to tackle these obstacles and refine the procedure of video-based learning within educational establishments. The proposed AI-driven system harnesses state-of-the-art technologies such as automated speech recognition, comprehensive language model training, intricate video processing, and cloud storage to streamline the submission, evaluation, and dissemination of student-generated scientific project videos. In doing so, the system not only accelerates the assessment process but also ensures compliance with educational standards and amplifies stakeholder engagement in the learning journey.
[0006] There is, therefore, a need to provide an AI-powered system optimizes the evaluation of students’ scientific project videos within educational settings through a seamless workflow that relies on cutting-edge technologies

OBJECTS OF THE PRESENT DISCLOSURE
[0007] Some of the objects of the present disclosure, which at least one embodiment herein satisfies are as listed herein below.
[0008] An object of the present disclosure is to provide automated speech recognition for automating the process of transcribing and Natural Language Processing (NLP) , generating various similarity like keyword similarity, semnatic similarity based on the student content and model answer content usig LLM techniques for evaluating video content, thereby saving time and resources.
[0009] Another object of the present disclosure is to utilize secure cloud-based storage for video submissions, addressing data privacy and security concerns.
[0010] Another object of the present disclosure is to exploit an AI and machine learning techniques for sophisticated video processing, analysis, and transcription, thereby enhancing the efficiency and effectiveness of the system.
[0011] Yet another object of the present disclosure is to implement the orthogonal learning to provide a more holistic and multidimensional educational experience.
[0012] Another object of the present disclosure is to immediate distribution of approved videos to parents and other stakeholders via a Real-Time Messaging Protocol (RTMP) Subsystem.

SUMMARY
[0013] This section is provided to introduce certain objects and aspects of the present disclosure in a simplified form that are further described below in the detailed description. This summary is not intended to identify the key features or the scope of the claimed subject matter.
[0014] In an aspect, the present disclosure relates to an AI-powered system for automating management of students scientific project videos within educational institutions, the AI system including a server may be configured to optimize evaluation of students’ scientific project videos within educational settings through a seamless workflow that relies on cutting-edge technologies; where the server includes one or more processors and a memory operatively coupled to the server, where the memory includes processor-executable instructions, which on execution, cause the one or more processors to upload project videos securely to a designated portal, with data encryption safeguarding sensitive information by the student, wherein the uploaded videos configured to store in a cloud-based platform; utilize state-of-art speech recognition technologies to convert transcribes video audio content of the uploaded videos into written text.
[0015] Furthermore, the one or more processors may be configured to analyze transcribed text using Natural Language Processing (NLP) techniques comprising any or a combination of, stop word removal, lemmatization, and semantic similarity using LLM techniques; process videos that meet a specified similarity score threshold based on the text analysis results; and distribute approved videos promptly to parents and stakeholders using a Real-Time Messaging Protocol (RTMP) subsystem.
[0016] In an aspect, the video processing technologies may be configured to employ tools any or a combination of Moviepy and OpenCV to edit and format videos, preparing the videos for the distribution.
[0017] In an aspect, the distributed videos are transcoded and repackaged to suit diverse on one or more parental device comprises any or a combination of mobiles, laptops, desktop, and personal computer.
[0018] In an aspect, the cloud-based platform for storing uploaded videos configured to provide secure and easy access to facilitate efficient management and retrieval of video content within educational institutions.
[0019] In an aspect, the seamless workflow facilitated by the server optimizes administrative tasks related to video management within educational institutions.
[0020] In an aspect, the distribution of approved videos to parents and stakeholders via the RTMP subsystem comprises personalized notifications and updates, fostering increased parental involvement and support for students’ educational endeavors.
[0021] In an aspect, the method comprising integrating orthogonal learning methodologies into the system’s framework.
[0022] Various objects, features, aspects, and advantages of the inventive subject matter will become more apparent from the following detailed description of preferred embodiments, along with the accompanying drawing figures in which like numerals represent like components.

BRIEF DESCRIPTION OF DRAWINGS
[0023] The accompanying drawings are included to provide a further understanding of the present disclosure, and are incorporated in, and constitute a part of this specification. The drawings illustrate exemplary embodiments of the present disclosure, and together with the description, serve to explain the principles of the present disclosure.
[0024] In the figures, similar components, and/or features may have the same reference label. Further, various components of the same type may be distinguished by following the reference label with a second label that distinguishes among the similar components. If only the first reference label is used in the specification, the description is applicable to any one of the similar components having the same first reference label irrespective of the second reference label.
[0025] FIG. 1 illustrates an exemplary block diagram of an AI-powered system optimizes the evaluation of students’ scientific project videos, in accordance with an embodiment of the present disclosure.
[0026] FIG. 2 illustrates an exemplary architecture of module diagram of a proposed AI-powered system optimizes the evaluation of students’ scientific project videos, in accordance with an embodiment of the present disclosure.
[0027] FIG. 3 illustrates an exemplary view of a flow diagram of proposed method for automating management of students’ scientific project videos within educational institutions, in accordance with some embodiments of the present disclosure.
[0028] The foregoing shall be more apparent from the following more detailed description of the disclosure.
DETAILED DESCRIPTION
[0029] In the following description, for the purposes of explanation, various specific details are set forth in order to provide a thorough understanding of embodiments of the present disclosure. It will be apparent, however, that embodiments of the present disclosure may be practiced without these specific details. Several features described hereafter can each be used independently of one another or with any combination of other features. An individual feature may not address all of the problems discussed above or might address only some of the problems discussed above. Some of the problems discussed above might not be fully addressed by any of the features described herein.
[0030] The ensuing description provides exemplary embodiments only, and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the ensuing description of the exemplary embodiments will provide those skilled in the art with an enabling description for implementing an exemplary embodiment. It should be understood that various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the disclosure as set forth.
[0031] Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it will be understood by one of ordinary skill in the art that the embodiments may be practiced without these specific details. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail in order to avoid obscuring the embodiments.
[0032] Also, it is noted that individual embodiments may be described as a process which is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed but could have additional steps not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function.
[0033] The word “exemplary” and/or “demonstrative” is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as “exemplary” and/or “demonstrative” is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. Furthermore, to the extent that the terms “includes,” “has,” “contains,” and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive—in a manner similar to the term “comprising” as an open transition word—without precluding any additional or other elements.
[0034] Reference throughout this specification to “one embodiment” or “an embodiment” or “an instance” or “one instance” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, the appearances of the phrases “in one embodiment” or “in an embodiment” in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
[0035] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. As used herein, the term “and/or” includes any and all combinations of one or more of the associated listed items.
[0036] An embodiment of the present disclosure relates to an AI-powered system for automating management of students scientific project videos within educational institutions, the AI system including a server may be configured to optimize evaluation of students’ scientific project videos within educational settings through a seamless workflow that relies on cutting-edge technologies; where the server includes one or more processors and a memory operatively coupled to the server, where the memory includes processor-executable instructions, which on execution, cause the one or more processors to upload project videos securely to a designated portal, with data encryption safeguarding sensitive information by the student, wherein the uploaded videos configured to store in a cloud-based platform; utilize state-of-art speech recognition technologies to convert transcribes video audio content of the uploaded videos into written text.
[0037] Furthermore, the one or more processors may be configured to analyze transcribed text using Natural Language Processing (NLP) techniques comprising any or a combination of, stop word removal, lemmatization, and semantic analysis using LLM ; process videos that meet a specified similarity score threshold based on the text analysis results; and distribute approved videos promptly to parents and stakeholders using a Real-Time Messaging Protocol (RTMP) subsystem.
[0038] The manner in which the proposed system works is described in further details in conjunction with FIGs. 1 to 3. It may be noted that these figures are only illustrative, and should not be construed to limit the scope of the subject matter in any manner.
[0039] FIG. 1 illustrates an exemplary block diagram of an AI-powered system optimizes the evaluation of students’ scientific project videos, in accordance with an embodiment of the present disclosure.
[0040] As illustrated in FIG. 1, an AI-powered system 100 (interchangeably referred to as a system 100, hereafter) may be configured to develop the assessment process of students’ scientific project videos within educational environments using state-of-the-art technologies. The system 100 may initiate by securely uploading student-generated content to a cloud-based platform. The system 100 may be configured to employ advanced speech recognition; the system can convert the project videos into text, followed by analysis through Natural Language Processing (NPL). The analysis incorporate the system 100 any or a combination of, but not limited to, stop word removal and lemmatization, leveraging transformer models like Large Language Model (LLM) to gauge content alignment with educational objectives.
[0041] Furthermore, the system 100 may involve a video processing technologies further scrutinize content using tools like Moviepy and OpenCV to edit and format videos for distribution. The approved videos are shared with parents via RTMP, facilitating timely communication and boosting parental engagement. Additionally, the videos are adapted for different devices, enhancing accessibility. Integrating orthogonal learning methodologies into the system’s 100 framework could enrich student assessments, fostering comprehensive educational growth. The system 100 may approach leverages diverse learning vectors to offer a more complete understanding of student capabilities and progress.
[0042] In an exemplary embodiment, a system 100 may be configured to include a server 102 where the server 102 may be configured to include one or more processor 104 (interchangeably referred to as a processor 104, hereinafter) and a memory 106 storing a set of instructions, which upon being executed cause the processor 104. The processor 104 includes any or a combination of suitable logic, circuitry, and/or interfaces that are operable to execute one or more instructions stored in the memory 106 to perform pre-determined operations. The memory 106 may be operable to store the one or more instructions. The processor 104 may be implemented using one or more processor technologies known in the art. Examples of the processor 104 include but are not limited to, an x86 processor, a RISC processor, an ASIC processor, a CISC processor, or any other processor.
[0043] In an embodiment, the system 100 may be configured to include a processor 104, either singular or multiple, interconnected with a memory 106 component. The memory 106 can serve as a storage repository for sets of instructions that dictate the system’s 100 operations and behaviors. When these instructions are activated and processed by the one or more processors 104 (interchangeably referred to as a processor 104, hereinafter), they initiate specific actions within the system 100, guiding its functionality and behavior. This arrangement facilitates the system’s 100 ability to perform tasks and execute processes according to the programmed instructions stored in memory 106.
[0044] In an exemplary embodiment, the system 100 may be configured to include a network 108 can include, but are not limited to, a Wireless Fidelity (Wi-Fi) network, a Wide Area Network (WAN), a Local Area Network (LAN), or a Metropolitan Area Network (MAN). Various devices in the system 100 can connect to the network 180 in accordance with the various wired and wireless communication protocols such as Transmission Control Protocol and Internet Protocol (TCP/IP), User Datagram Protocol (UDP), and 2G, 3G, and 4G communication protocols.
[0045] In an exemplary embodiment, the system 100 may be configured to include one or more parental devices (110-1, 110-2…110-N) (interchangeably referred to as a parental device 110, hereinafter) for transcoding and repackaging videos involve converting the original video files into different formats or resolutions to ensure compatibility with various devices the parents may use, any or a combination of, but not limited to, mobile phones, laptops, tablets, etc.
[0046] FIG. 2 illustrates an exemplary architecture of module diagram of a proposed AI-powered system optimizes the evaluation of students’ scientific project videos, in accordance with an embodiment of the present disclosure.
[0047] In an exemplary embodiment, referring to FIG. 2, a module diagram 200 of the system 100 may comprise one or more processor(s) 104 (interchangeably referred to as a processor 104, hereinafter). The processor 104 may be implemented as one or more microprocessors, microcomputers, microcontrollers, edge or fog microcontrollers, digital signal processors, central processing units, logic circuitries, and/or any devices that process data based on operational instructions. Among other capabilities, the processor 104 may be configured to fetch and execute computer-readable instructions stored in a memory 106 of the system 100. The memory 106 may be configured to store one or more computer-readable instructions or routines in a non-transitory computer readable storage medium, which may be fetched and executed to create or share data packets over a network service. The memory 106 may comprise any non-transitory storage device including, for example, volatile memory such as Random Access Memory (RAM), or non-volatile memory such as Erasable Programmable Read-Only Memory (EPROM), flash memory, and the like.
[0048] The system 100 may include an interface(s) 208. The interface(s) 208 may comprise a variety of interfaces, for example, interfaces for data input and output devices, referred to as I/O devices, storage devices, and the like. The interface(s) 208 may facilitate communication to/from the system 100. The interface(s) 208 may also provide a communication pathway for one or more components of the system 100. Examples of such components include but are not limited to, processing unit/engine(s) 210 and a database 202.
[0049] In an embodiment, the processing unit/engine(s) 210 may be implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the processing engine(s) 210. In examples described herein, such combinations of hardware and programming may be implemented in several different ways. For example, the programming for the processing engine(s) 210 may be processor-executable instructions stored on a non-transitory machine-readable storage medium and the hardware for the processing engine(s) 210 may comprise a processing resource (for example, one or more processors), to execute such instructions.
[0050] In the present examples, the machine-readable storage medium may store instructions that, when executed by the processing resource, implement the processing engine(s) 210. In such examples, the system 100 may include the machine-readable storage medium storing the instructions and the processing resource to execute the instructions, or the machine-readable storage medium may be separate but accessible to the system 100 and the processing resource. In other examples, the processing engine(s) 210 may be implemented by electronic circuitry.
[0051] In an embodiment, the database 202 may include data that may be either stored or generated as a result of functionalities implemented by any of the components of the processor 102 or the processing engine 210. In an embodiment, the database 202 may be separate from the system 100. The database 202 may be configured to include a cloud database may be provide scalable and reliable storage for the uploaded videos, ensuring the securely stored and easily accessible to authorized users from any location with internet access.
[0052] In an exemplary embodiment, the processing engine 210 may include one or more engines selected from any of a video storage engine 212, a transcription engine 214, an artificial intelligence (AI) engine 216, a video processing engine 218, an orthogonal learning engine 220 and a stackholder engagement engine 222.
[0053] In an exemplary embodiment, the video storage engine 212 may be configured to indicate the system 100 can employ a secure cloud-based storage solution to handle video submissions. By utilizing such storage, the system 100 may ensure the uploaded videos are stored safely and accessibly. The approach addresses concerns related to data privacy and security, as cloud-based storage typically offers robust encryption and access control mechanisms to protect sensitive information.
[0054] Additionally, using a cloud-based solution can provide scalability and reliability, ensuring that the system 100 can accommodate growing volumes of video submissions while maintaining high levels of security. Overall, the utilization of secure cloud-based storage enhances the integrity and confidentiality of the video content within the system.
[0055] In an exemplary embodiment, the transcription engine 214 may be configured to utilize cutting-edge technologies any or a combination of, the automated speech recognition and Natural Language Processing (NLP), the system 100 streamlines the transcription and evaluation of the video content, resulting in significant time and resource savings.
[0056] Once the video is uploaded, it undergoes speech-to-text conversion using state-of-the-art speech recognition technologies. This process involves converting the audio content of the video into written text, which is facilitated by a neural network-based system. For example, the spoken explanation in the biology experiment video is transcribed into text format.
[0057] In an exemplary embodiment, the artificial intelligence (AI) engine 216, the transcribed text is then analyzed using Natural Language Processing (NLP) techniques, including stop word removal and lemmatization. This is followed by keyword matching and semantic similarity algorithms powered by advanced language models like BERT. The system evaluates the educational relevance of the video content against predefined learning objectives. For instance, the system might check if the text content of the biology experiment video aligns with the learning objectives of the biology course.
[0058] In an exemplary embodiment, the Video Processing engine 218 based on the text analysis, videos that achieve a certain similarity score undergo further processing using Python libraries such as Moviepy and OpenCV for editing and formatting. For example, the biology experiment video might be edited to highlight key sections or formatted to fit a certain screen resolution.
[0059] In an exemplary embodiment, the orthogonal learning engine 220 may offer a comprehensive educational experience. The methodology prompts students to approach subjects from diverse viewpoints, enriching their comprehension and memory of intricate concepts. Integrating multidimensional learning into our AI-driven video assessment system 100 facilitates students in synthesizing ideas from one or more disciplines, promoting deeper understanding and stimulating creative problem-solving abilities.
[0060] In an exemplary embodiment, the stakeholder engagement engine 222 may be configured to encourage the individuals to engage more actively in the educational process. The involvement can take one or more forms, any or a combination of, parents watching the videos to stay informed about their child’s progress, discussing the content with their child to deepen understanding, or providing feedback to educators based on the child observes in the videos. Overall, by facilitating easy access to educational materials, the system 100 can help to strengthen the connection between the school, students, and their families, fostering a collaborative learning environment.
[0061] FIG. 3 illustrates an exemplary view of a flow diagram of proposed method for automating management of students’ scientific project videos within educational institutions, in accordance with some embodiments of the present disclosure.
[0062] As illustrated, a method 300 for an advanced technological solution designed to automate and streamline the submission, evaluation, and distribution of students’ scientific project videos within educational institutions. At step 302, the method 300 may involve the action of securely uploading project videos to a designated portal by processors 104 associated with a system 100. The method 300 may involve safeguarding sensitive information by the student through data encryption. Furthermore, the uploaded videos are configured to be stored on a cloud-based platform.
[0063] Continuing further, at step 304, the method 300 may involve utilization by the processors 100, of advanced speech recognition technologies for converting transcribed audio content of the uploaded videos into written text. At step 306, the method 300 may involve the analysis, performed by the processors 104, of transcribed text using Natural Language Processing (NLP) techniques. The techniques may encompass any combination of processes, including stop word removal, lemmatization, and semantic analysis. This is followed by the system leverages keyword matching and BERT-powered semantic similarity algorithms to assess video content's educational relevance against predefined learning objectives, ensuring alignment, such as verifying if a biology experiment video matches the course's learning goals.
[0064] Continuing further, at step 308, the method 300 may involve the processors 104 to analyze videos based on transcribed text, a process facilitated by Natural Language Processing (NLP) techniques like stop word removal, lemmatization, and semantic analysis. Following the text analysis, the system 100 can compute a similarity score for each video, indicating its alignment with predefined criteria or learning objectives. Videos exceeding a predetermined similarity score threshold are subject to further processing by the processors 104. The processing may involve tasks such as editing, formatting, or applying additional analysis methods. The purpose is to focus resources on videos closely aligned with educational goals, optimizing quality standards and resource allocation within the system.
[0065] Continuing further, at step 310, Videos with a certain similarity score are further processed using Python libraries like Moviepy and OpenCV for editing and formatting. For instance, a biology experiment video might be edited to highlight key sections or formatted to fit a specific screen resolution.
[0066] Continuing further, at step 312, the method 310 may be configured to facilitate the distribution of approved videos to parents and stakeholders in a timely manner using a Real-Time Messaging Protocol (RTMP) subsystem. The distribution process involves the efficient transmission of approved video content to designated recipients, such as parents and stakeholders, through a messaging protocol capable of real-time delivery.
[0067] In summary, the system’s workflow, students initiate by uploading their science project videos to a dedicated portal, ensuring data security through encryption before storage in a cloud-based system. Once uploaded, the videos undergo speech-to-text conversion using advanced speech recognition technology, transforming audio content into written text. This transcribed text then undergoes thorough analysis employing Natural Language Processing (NLP) techniques, including stop word removal and lemmatization, followed by keyword matching and semantic similarity algorithms.
[0068] Furthermore, the system evaluates the educational relevance of the content against predefined learning objectives. Videos meeting specific similarity score criteria undergo further processing, utilizing Python libraries such as Moviepy and OpenCV for editing and formatting purposes. Finally, approved videos are promptly distributed to parents and stakeholders through a Real-Time Messaging Protocol (RTMP) Subsystem, ensuring immediate accessibility across diverse parental devices. This seamless process enhances parental involvement and facilitates efficient communication between educators and guardians.
[0069] While the foregoing describes various embodiments of the disclosure, other and further embodiments of the disclosure may be devised without departing from the basic scope thereof. The scope of the disclosure is determined by the claims that follow. The disclosure is not limited to the described embodiments, versions or examples, which are included to enable those having ordinary skill in the art to make and use the disclosure when combined with information and knowledge available to those having ordinary skill in the art.

ADVANTAGES OF THE INVENTION
[0070] The present disclosure is to provide an AI-driven system for automating the submission, evaluation, and distribution of student-generated scientific project videos within educational institutions.
[0071] The present disclosure is to provide a system and a method for securing cloud-based storage for video submissions, ensuring data privacy and easy accessibility.
[0072] The present disclosure is to provide a system and a method for implementing of state-of-the-art speech recognition technologies for converting video content into transcribed text, facilitating subsequent analysis.
[0073] The present disclosure is to provide an application of Natural Language Processing (NLP) techniques, including stop word removal and lemmatization, for efficient text analysis.
[0074] The present disclosure is to provide the use of advanced language models like BERT for evaluating the educational relevance of video content against predefined learning objectives.
[0075] The present disclosure is to provide the use of video processing for editing and formatting using various Python libraries such as Moviepy , OpenCV etc for the approved videos
[0076] The present disclosure is to provide orthogonal learning methods enhance student assessments and understanding, fostering multi-dimensional educational growth.
[0077] The present disclosure is to provide System enables immediate distribution of educational videos to parents via RTMP, improving communication and accessibility.
, Claims:1. An AI-powered system (100) for automating management of students scientific project videos within educational institutions, the AI system (100) comprising:
a server (102) configured to optimize evaluation of students’ scientific project videos within educational settings through a seamless workflow that relies on cutting-edge technologies; wherein the server comprises:
one or more processors (104); and
a memory (106) operatively coupled to the server (102), wherein the memory (106) comprises processor-executable instructions, which on execution, cause the one or more processors (104) to:
upload project videos securely to a designated portal, with data encryption safeguarding sensitive information by the student, wherein the uploaded videos configured to store in a cloud-based platform;
utilize state-of-art speech recognition technologies to convert transcribes video audio content of the uploaded videos into written text;
analyze transcribed text using Natural Language Processing (NLP) techniques comprising any or a combination of, stop word removal, lemmatization, and semantic analysis using LLM;
process videos that meet a specified similarity score threshold based on the text analysis results; and
distribute approved videos promptly to parents and stakeholders using a Real-Time Messaging Protocol (RTMP) subsystem.

2. The AI-powered system (100) as claimed in claim 1, wherein the cloud-based platform for storing uploaded videos configured to provide secure and easy access to facilitate efficient management and retrieval of video content within educational institutions.

3. The AI-powered system (100) as claimed in claim 1, wherein the implementation of state-of-the-art speech recognition technologies for converting video content into transcribed text, facilitating subsequent analysis.The application of Natural Language Processing (NLP) techniques, including stop word removal and lemmatization, for efficient text analysis.

4. The AI-powered system (100) as claimed in claim 1, wherein the use of advanced language models like BERT for evaluating the educational relevance of video content against predefined learning objectives.

5. The AI-powered system (100) as claimed in claim 1, wherein the video processing technologies, configured to employ tools any or a combination of Moviepy and OpenCV to edit and format videos, preparing the videos for the distribution.

6. The AI-powered system (100) as claimed in claim 1, wherein the approved videos configured to share with the parents through Real-Time Messaging Protocol (RTMP).

7. The AI-powered system (100) as claimed in claim 1, wherein the distributed videos are transcoded and repackaged to suit diverse on one or more parental device comprises any or a combination of mobiles, laptops, desktop, and personal computer.

8. The AI-powered system (100) as claimed in claim 1, wherein the seamless workflow facilitated by the server (102) optimizes administrative tasks related to video management within educational institutions.
9. The AI-powered system (100) as claimed in claim 1, integrating orthogonal learning methodologies into the system’s framework.