A generative ai based system and method for providing personlized and scalable educational experience through a digital avatar

Abstract

The present disclosure provides a system and method for generating an Intelligent Educational Assistant (IEA) using generative AI to provide personalized and scalable educational experiences through digital avatars. The system comprises a central server, an IEA engine, plurality user devices, and communication network. Users, such as faculty, students, and administrators, transmit data though user devices. The education institution data is transformed into an integrated format on the central server, where the IEA is generated by an IEA engine using the integrated data and generative AI. The IEA delivers various outputs, including career counseling, personalized course content, doubt clearance, and multilingual content. These outputs are presented as interactive digital avatars on user devices. Continuous feedback is incorporated to refine and validate the performance of the generative AI.

Description

A GENERATIVE Al BASED SYSTEM AND METHOD FOR PROVIDING PERSONLIZED AND SCALABLE EDUCATIONAL EXPERIENCE THROUGH A DIGITAL AVATAR FIELDThe embodiment herein generally relates to an education technology system. Specifically, the embodiment herein relates to a generative Al based system and method for providing personalized and scalable educational experience to students by using a digital avatar.BACKGROUND ART

[0001] The digital revolution has dramatically transformed the educational landscape, with the proliferation of online courses, educational apps, and interactive learning platforms. These technologies have made education more accessible, allowing students to engage with learning materials and instructors across the globe. However, while digital platforms offer convenience and scalability, they often fail to provide the personalized and human-centric experience critical to effective learning. Current systems typically deliver standardized content, which does not adapt well to individual student needs or learning styles.

[0002] Educators today face an overwhelming administrative burden. Studies show that teachers spend up to 50% of their time on non-teaching tasks such as grading, reporting, and managing student inquiries. These administrative responsibilities leave them with limited time to engage meaningfully with students, negatively impacting both teaching effectiveness and student learning outcomes.The lack of personalized interaction between educators and students on digital platforms contributes to lower student engagement and higher dropout rates.

[0003] Existing educational technologies, while innovative, often fall short in addressing the critical need for real-time adaptability and personalization. They primarily focus on static content delivery, advanced reporting, and analytics, which do not fully replicate the interactive and adaptive nature of in-person teaching. Additionally, the impersonal nature of digital learning can lead to reduced student motivation, limiting the effectiveness of these platforms.

[0004] Moreover, many current educational platforms fail to streamline workflows for educators. Despite technological advancements, educators continue to struggle with inefficiencies in managing routine tasks, which exacerbates the problem of administrative overload. As a result, education technology companies often face operational challenges, leading to low student engagement and financial instability.

[0005] Additionally, while education technology promises to streamline workflows, many tools fail to ease educators' burdens or improve operational efficiency. This leads to both teacher frustration and the underperformance of educational platforms, with some companies facing financial instability as a result of ineffective implementation.

[0006] Consequently, there is a pressing need for a scalable system and method that not only enhances personalized learning for students but also provides educators with effective tools to reduce administrative burdens and engage students more effectively. Such a system would enable real-time adaptation to each student'slearning preferences, automate routine tasks, and offer enhanced engagement through intelligent digital interactions.OBJECTS

[0007] Some of the objects of the present disclosure are described herein below:

[0008] A main object of the present disclosure is to a generative Al-based system and method for creating an Intelligent Educational Assistant (IEA) that delivers personalized, scalable educational experiences by using a digital avatar representing an educator.

[0009] Another object of the present disclosure is to provide a system and method to create a highly accurate digital avatar of an educator, capable of emulating their teaching methodologies, communication style, and subject matter expertise, which can autonomously interact with students.

[0010] Yet another objective of the present disclosure is to provide a system and method to facilitate seamless and continuous interaction between students and the digital avatar, offering personalized support, doubt resolution, and continuous learning opportunities around the clock.

[0011] Still another objective of the present disclosure is to provide a system and method to generate personalized educational content tailored to each student's unique learning style, preferences, and performance metrics.

[0012] The other objects and advantages of the present disclosure will be apparent from the following description when read in conjunction with the accompanying drawings, which are incorporated for illustration of preferred embodiments of the present disclosure and are not intended to limit the scope thereof.SUMMARY

[0013] In view of the foregoing, embodiments herein provide a generative Al based system and method for providing personalized and scalable educational experiences to students using a digital avatar, wherein the system generates an Intelligent Educational Assistant (IEA) to interact with users in real time.

[0014] In accordance with an embodiment, the system includes a plurality of user devices, a communication network, and a central server hosting the IEA engine. In an embodiment, the users such as faculty, students and administrators access the system through respective user devices to transmit educational institutional data including lesson plans, teaching styles, articles written by teachers, interactions with students, individual student performance, test results, behavioural patterns, sales and marketing data, learning management systems (LMS) data and other audio, video and textual information.

[0015] In an embodiment, the plurality of user devices is connected to the central server through the communication network, wherein the user devices transmit the collected data to the central server for storage and processing.

[0016] In accordance with an embodiment, the central server includes an IEA engine, processor, memory and database, wherein the server processes the received data to transform it into an integrated format. Using generative Al techniques, the IEA engine generates the Intelligent Educational Assistant (IEA), which is capable of analyzing educational data, generating output based on specific use cases and delivering the results in the form of a digital avatar on user devices. The use cases include but are not limited to career counseling, doubt clearance, personalized course content, automated administrative tasks, webinars, real-time feedback and multilingual content.

[0017] In an embodiment, the IEA engine further includes multiple modules including data acquisition, data processing, feature extraction, entity identification,attribute definition, model generation, data population and feedback, wherein each module performs individual operations to build and refine the IEA and its outputs.

[0018] In accordance with another embodiment, the method includes steps of receiving educational institutional data from users, transforming the received data into integrated format, detecting key entities from the integrated data, defining relevant attributes for the identified entities, generating models representing behaviour and interactions of the entities, populating the IEA with the processed data, generating personalized output based on the specific use cases and displaying the output as a digital avatar through user devices, and finally assimilating continuous feedback to refine and validate the performance of the generative Al engine.

[0019] In an embodiment, the digital avatar presented to each user device allows immersive interaction with the IEA, wherein students receive learning materials and personalized guidance, faculty can upload and manage content, and administrators can oversee system operations including managing accounts and institutional workflows.

[0020] These and other aspects of the embodiments herein will be better appreciated and understood when considered in conjunction with the following description and the accompanying drawings. It should be understood, however, that the following descriptions, while indicating preferred embodiments and numerous specific details thereof, are given by way of illustration and not of limitation. Many changes and modifications may be made within the scope of the embodiments herein without departing from the spirit thereof, and the embodiments herein include all such modifications.BRIEF DESCRIPTION OF DRAWINGS

[0021] The detailed description is set forth with reference to the accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The use of the same reference numbers in different figures indicates similar or identical items.

[0022] Fig. 1 illustrates an architecture of a generative Al based system and method for providing personalized and scalable educational experiences to students by using a digital avatar, according to an embodiment herein;

[0023] Fig. 2 illustrates a block diagram of a central server of the system, according to an embodiment herein;

[0024] Fig. 3 illustrates a block diagram of an Intelligent Education Assistant (IEA) engine, according to an embodiment herein;

[0025] Fig. 4 illustrates a block diagram of user device of the system, according to an embodiment herein;

[0026] Fig. 5 illustrates a flowchart of the generative Al based method for providing personalized and scalable educational experiences to students using digital avatar, according to an embodiment herein; and

[0027] Fig 6 illustrates another block diagram of the generative Al based method for providing personalized and scalable educational experiences to students by using the digital avatar, according to an embodiment herein.DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0028] The embodiments herein and the various features and advantageous details thereof are explained more fully with reference to the non-limiting embodiments and detailed in the following description. Descriptions of well-known components and processing techniques are omitted so as to not unnecessarily obscure the embodiments herein. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein may bepracticed and to further enable those of skill in the art to practice the embodiments herein. Accordingly, the examples should not be construed as limiting the scope of the embodiments herein.

[0029] As mentioned above, there is a need for an Intelligent Educational Assistant (IEA) that can enable the creation of personalized learning paths for students and offer valuable resources to faculty. Specifically, there is a need for an IEA that can revolutionize education by providing personalized learning experiences, streamlining administrative tasks, and supporting diverse student populations. The IEA must be adaptable to student needs, capable of creating personalized content, and able to automate routine tasks, allowing educators to focus on teaching and student interaction. The embodiments herein achieve this by providing a system and method for generating an IEA employing generative Al to deliver personalized and scalable educational experiences to students through digital avatars.. Referring now to the drawings, Fig. 1 through Fig.6, where similar reference characters denote corresponding features consistently throughout the figures, there are shown preferred embodiments.

[0030] Fig. 1 illustrates an architecture 100 of a generative Al based system and method for providing personalized and scalable educational experiences to students using digital avatar, according to an embodiment. The system can include but not limited to plurality of user devices 104,105,106, and a central server 108, wherein the user devices 104,105,106 are connected to the central server 108 through a communication network 107.

[0031] In an embodiment, a user system (not shown) provided in the user device 104 can be configured to enable the faculty 101 to create, manage and upload educational content, assign tasks and monitor student progress in the system. The user system in the user device 104 can be configured to transmit the data obtainedfrom the faculty 101 through the communication network 107 to the central server 108 for storing the transmitted data.

[0032] In an embodiment, a user system (not shown) provided in the user device 105 can be configured to enable the students 102 for logging in to the system and interact with the digital avatar to access the educational materials, complete assignments, and receive personalized guidance. The user system in the user device 105 can be configured to transmit the data obtained from the students 102 through the communication network 107 to the central server 108 for storing the transmitted data.

[0033] In an embodiment, a user system (not shown) provided in the user device 106 can be configured to enable the administrator 103 to manage user accounts, overseeing the system’s operations, including managing user accounts, handling administrative tasks (for example attendance, report) and ensuring smooth communication. The user system in the user device 106 can be configured to transmit the data obtained from the administrators 103 through the communication network 107 to the central server 108 for storing the transmitted data.

[0034] In an embodiment, the central server 108 can include but not limited to an Intelligent Educational Assistant (IEA) engine 109. The central server 108 can be configured for storing and processing data, managing user interactions and running IEA engine. The IEA engine 109 can be configured to processes the data to generate personalized output.

[0035] Fig. 2 illustrates a block diagram 200 of the central server 108 of the system, according to an embodiment. The central server 108 is the core component of the system, which can be configured for coordinating and managing the overall operations in the system. The central server 108 can include the intelligent educational assistant (IEA) 109, a processor 201, a memory 202 and a database 203.

[0036] In an embodiment, the database 203 can store and organize the information related to educational institutional data including but not limited to lesson plans, student performance metrics, and behavioural data. In an embodiment, the memory 202 stores the data temporarily after it is retrieved from the database 203. The memory 202 is connected to both the database 203 and the processor 201, acting as an intermediary that passes data between them. In an embodiment, the processor 201 can be configured to process the received data and transform into integrated format, which enables to generating an IEA (herein also referred as digital avatar).

[0037] In an embodiment, the IEA engine 109 can be configured to process the integrated format using generative Al techniques to generate the IEA and generate outputs, including real-time feedback, personalized content, and automated administrative support. Further, the IEA engine 109 can be configured to make decision and create various outputs to specific use cases, wherein the use case includes career counseling, doubt clearance, personalized course content, webinars and multilingual content.

[0038] Fig. 3 illustrates a block diagram 300 of the Intelligent Educational Assistant (IEA) engine 109, according to an embodiment. The IEA engine 109 can include but not limited to a generative Al 301, a data acquisition module 302, a data processing module 303, a feature extraction module 304, an entity identification module 305, an attribute definition module 306, a model generation module 307, a data population module 308 and a feedback module 309.

[0039] In an embodiment, the data acquisition module 302 can be configured to collect various types of data (herein referred as education institution date) from the various entities of the educational institution. In an embodiment, the educationinstitutional data can include but not limited to textual data, audio / video recordings, and learning management systems (LMS).

[0040] For example, the education institutional data can include lesson plans and course materials, as well as audio and video recordings of lectures and classroom interactions received from the faculty 101. Additionally, the education institutional data can include learning management systems (LMS) and behavioural patterns received from the students 102. Moreover, the education institutional data can include sales and marketing data from administrators 103 to understand the institution's outreach efforts and performance.

[0041] In an embodiment, the data processing module 303 can be configured to processing the collected data from the data acquisition module 302 and transform into an integrated format.

[0042] In an embodiment, the feature extraction module 304 can be configured to extract relevant features from the integrated format that can be consumable by the IEA engine 109.

[0043] In an embodiment, the entity identification module 305 can be configured for identifying the key entities from the integrated data, which are then represented within the IEA engine 109.

[0044] In an embodiment, the attribute definition module 306 can be configured for defining the attributes of the key entities based on the integrated format / For example, a teacher entity would have attributes like teaching style, subject expertise, and student performance.

[0045] In an embodiment, the model generation module 307 can be configured to build representation models based on the behavior and interactions of the defined key entities from the attribute definition module 307.

[0046] In an embodiment, the data population module 308 can be configured to populate the IEA engine 109 with the data that involves assigning vales to the attributes of the key entity based on the educational institutional data.

[0047] In an embodiment, the generative AI 301 is the core component in the IEA engine 109 that can be configured for generating new context based on the integrated format and interacts with the various modules to create outputs based on the specific use cases, (please write the complete functionality of the gen AI 301).

[0048] In an embodiment, the feedback module 309, can be configured to assimilate continuous feedback to refine and validate generative AI 301 performance in the IEA engine 109.

[0049] Fig. 4 illustrates a block diagram 400 of user devices 104, 105, 106 of the system. In an embodiment, the users / entities such as faculty 101, students 102 and administrators 103 can interact with the system through user devices 104, 105, 106. The user devices 104, 105, 106 can include but not limited to a user system, a digital avatar 401, a processor 402, a memory 403 and a database 404.

[0050] In an embodiment, the database 405 can include the attributes of the key entities from the integrated format to transmit to the users / entities from the central server 108 through user device 104, 105, 106. In an embodiment, the database 404 is connected to the memory 403 wherein the memory 403 includes the attributes of the key entities from the database 404 and other data received from the users,wherein the memory 403 is connected to the processor 402. In an embodiment, the processor 402 can be configured for executing the stored data from the memory 403 to generate output. In an embodiment, the digital avatar 401 can be configured to collect the processed data and provide various outputs for the specific use cases to faculty 101, students 102 and administrator 103 through user device 104, 105, 106.

[0051] Fig. 5 illustrates a flowchart 500 of the generative AI based method for providing personalized and scalable educational experiences to students using digital avatar. In an embodiment, the method includes but not limited to the following steps. First, receiving educational institutional data from the users such as faculty 101, students 102 and administrator 103 respectively through user device (104, 105, 106). Next, the received data is then combined to form integrated format using the IEA engine 109 and storing the integrated format in the central server’s database 203. Then, sing the integrated data, the generative AI 301 generates the Intelligent Educational Assistant (IEA), which identifies key entities and assigns attributes. Next, the key entities are detected from the integrated format and are processed by the IEA engine 109. Then, specifying relevant attributes from the key entities using the IEA engine 109 and storing it in the database 203. Then, the IEA engine 109 generates models representing the behaviour and interactions of the defined key entities from the integrated format. Next, populating IEA with the key entities based on educational institutional data using the IEA engine 109. Then, the IEA generates personalised output based on the specific use case and displaying the output in the form of a digital avatar 402 through user device 104, 105, 106. Particularly, The IEA generates personalized learning content, career advice, real¬ time feedback, and other outputs through the digital avatar (401), which are displayed via the user interfaces. Then, assimilating continuous feedback to refineand validate the generative Al 301 performance and adapt to changing educational needs.

[0052] Fig. 6 illustrates a block diagram 600 of the generative AI based method for providing personalized and scalable educational experiences to students using the digital avatar. In an embodiment, the user device 104, 105, 106 can be configured to collect the data inputs from respective users for example teaching styles, articles written by teachers, interactions with the students, individual student performance, test results, current customer management database, reach out style and any other audio, video and textual related data,

[0053] In an embodiment, the generative AI based algorithm receives the collected data from the user device 104, 105, 106, process the data and transform into integrated format to which it leads to form intelligent educational assistant (IEA).

[0054] In an embodiment, IEA provides various outputs based on processed data through digital avatar, for example interactive workshops, real-time product demos, interactive sales presentations, live problem-solving presentations, instant messaging support, tailored video tutorials, customized study materials and other administrative tasks.

[0055] A main advantage of the present disclosure is that the present generative AI-based system and method for creating an Intelligent Educational Assistant (IEA) that enables to delivers personalized, scalable educational experiences by using a digital avatar representing an educator.

[0056] Another advantage of the present disclosure is that the present system and method enables to create a highly accurate digital avatar of an educator, capable of emulating their teaching methodologies, communication style, and subject matter expertise, which can autonomously interact with students.

[0057] Yet another advantage of the present disclosure is that the present system and method enables to facilitate seamless and continuous interaction between students and the digital avatar, offering personalized support, doubt resolution, and continuous learning opportunities around the clock.

[0058] Still another objective of the present disclosure is that the present system and method enables to generate personalized educational content tailored to each student's unique learning style, preferences, and performance metrics

[0059] The foregoing description of the specific embodiments will so fully reveal the general nature of the embodiments herein that others can, by applying current knowledge, readily modify and / or adapt for various applications such specific embodiments without departing from the generic concept, and, therefore, such adaptations and modifications should and are intended to be comprehended within the meaning and range of equivalents of the disclosed embodiments. It is to be understood that the phraseology or terminology employed herein is for the purpose of description and not of limitation. Therefore, while the embodiments herein have been described in terms of preferred embodiments, those skilled in the art will recognize that the embodiments herein can be practiced with modification within the spirit and scope of the embodiments as described herein.

Claims

1. We Claim1. A system for generating an Intelligent Educational Assistant (IEA) using generative AI, the system comprising:a plurality of user devices (104, 105, 106) connected to a central server(108) through a communication network (107), comprising:- the user device (104) includes a user system configured to enable faculty (101) to create, manage and upload educational content, assign tasks and monitor student progress and transmit data obtained from the faculty (101) to the central server (108); - the user device (105) includes a user system configured to enable students (102) to log in, interact with the digital avatar, access educational materials, complete assignments and receive personalized guidance and transmit data obtained from the students (102) to the central server (108);- the user device (106) includes a user system configured to enable administrators (103) to manage user accounts, oversee system operations and ensure smooth communication and transmit data obtained from the administrators (103) to the central server (108);the central server (108) includes an Intelligent Educational Assistant (IEA) engine (109), a processor (201), a memory (202) and a database (203), and is configured to store and process data, manage user interactions and run the IEA engine (109); andwherein the IEA engine (109) is configured to process integrated educational institutional data using generative AI (301) techniques to generate personalized output through the digital avatar (401).

2. The system of claim 1, wherein the database (203) is configured to store lesson plans, student performance metrics, behavioural data and othereducational institutional data obtained from the user devices (104, 105, 106).

3. The system of claim 1, wherein the memory (202) is configured to temporarily store the educational institutional data retrieved from the database (203) and communicate the stored data to the processor (201).

4. The system of claim 1, wherein the processor (201) is configured to process received educational institutional data and transform the data into integrated format for generating the IEA.

5. The system of claim 1, wherein the IEA engine (109) comprises a data acquisition module (302), a data processing module (303), a feature extraction module (304), an entity identification module (305), an attribute definition module (306), a model generation module (307), a data population module (308) and a feedback module (309).

6. The system of claim 1, wherein the digital avatar (401) provided in the user devices (104, 105, 106) is configured to collect processed data from the processor (402) and provide outputs for specific use cases to faculty (101), students (102) and administrators (103).

7. The system of claim 1, wherein the IEA engine (109) is configured to generate outputs selected from at least one of real-time feedback, personalized content, automated administrative support, career counseling, doubt clearance, personalized course content, webinars and multilingual content.

8. A method for generating an Intelligent Educational Assistant (IEA) system using generative AI, comprising the steps of:receiving educational institutional data from faculty, students and administrators through corresponding user devices;combining the received data to form integrated format using an Intelligent Educational Assistant (IEA) engine and storing the integrated format in a database;generating an Intelligent Educational Assistant (IEA) using generative Al based on the integrated data, identifying key entities and assigning attributes;detecting the key entities and specifying relevant attributes from the integrated format using the IEA engine and storing the attributes in the database;generating models representing behaviour and interactions of the defined key entities from the integrated format using the IEA engine; populating the IEA with the key entities based on educational institutional data;generating personalized output based on specific use cases and displaying the output in the form of a digital avatar through user devices; and assimilating continuous feedback to refine and validate generative AI performance and adapt to changing educational needs.

9. The method of claim 8, wherein the educational institutional data includes textual data, audio / video recordings and learning management system (LMS) data collected from the faculty, students and administrators.

10. The method of claim 8, wherein the personalized output generated by the IEA includes interactive workshops, real-time product demos, interactive sales presentations, instant messaging support, tailored video tutorials, customized study materials and administrative tasks displayed through the digital avatar.

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