Description:FIELD OF DISCLOSURE
[0001] The present disclosure relates generally relates to automated educational feedback, more specifically, relates to artificial intelligence powered personalized feedback generation system and method thereof.
BACKGROUND OF THE DISCLOSURE
[0002] This invention provides individualized feedback tailored to each student's response, helping them understand their strengths and areas for improvement. Unlike generic feedback methods, it ensures that students receive specific guidance that enhances their learning process and fosters continuous improvement.
[0003] Traditional feedback methods require significant manual effort from educators, making it difficult to provide timely responses, especially for large classes. This system automates the feedback process, reducing the workload on teachers while ensuring that students receive instant, consistent, and well-structured feedback.
[0004] Whether in traditional classrooms, online learning platforms, or large-scale educational programs, this system can be seamlessly integrated. It adapts to different subjects and academic levels, making it a versatile tool for enhancing education across various institutions and learning setups.
[0005] Many existing systems provide one-size-fits-all feedback that does not address the specific strengths and weaknesses of individual students. This lack of personalization makes it difficult for students to understand how to improve their responses effectively
[0006] Traditional feedback methods rely heavily on manual evaluation by educators, which can be slow and inconsistent, especially in large classrooms or online learning platforms. This delay in feedback can hinder student progress and engagement.
[0007] Existing solutions often struggle to adjust to different subjects, learning styles, and academic levels. They may work well for certain types of responses but fail to provide meaningful feedback across a diverse range of topics and educational settings.
[0008] Thus, in light of the above-stated discussion, there exists a need for an artificial intelligence powered personalized feedback generation system and method thereof.
SUMMARY OF THE DISCLOSURE
[0009] The following is a summary description of illustrative embodiments of the invention. It is provided as a preface to assist those skilled in the art to more rapidly assimilate the detailed design discussion which ensues and is not intended in any way to limit the scope of the claims which are appended hereto in order to particularly point out the invention.
[0010] According to illustrative embodiments, the present disclosure focuses on an artificial intelligence powered personalized feedback generation system and method thereof which overcomes the above-mentioned disadvantages or provide the users with a useful or commercial choice.
[0011] An objective of the present disclosure is to provide a system and method for generating personalized feedback for students based on their responses, ensuring that feedback is specific, meaningful, and tailored to individual learning needs.
[0012] Another objective of the present disclosure is to reduce the dependency on manual evaluation by automating the feedback process, thereby saving time and effort for educators while maintaining consistency in feedback quality.
[0013] Another objective of the present disclosure is to enhance the learning experience by providing instant feedback, allowing students to understand their mistakes and improve their responses without delays.
[0014] Another objective of the present disclosure is to create a scalable solution that can be applied to various educational settings, including classrooms, online learning platforms, and large-scale academic assessments.
[0015] Another objective of the present disclosure is to ensure that feedback is contextually relevant, helping students grasp complex concepts more effectively by receiving guidance that aligns with their specific responses.
[0016] Another objective of the present disclosure is to provide a feedback system that can adapt to different subjects and academic disciplines, making it suitable for a wide range of educational applications.
[0017] Another objective of the present disclosure is to improve student engagement and motivation by offering constructive and actionable feedback that encourages continuous learning and improvement.
[0018] Another objective of the present disclosure is to provide a reliable and unbiased feedback mechanism that eliminates human errors and inconsistencies commonly found in manual grading and assessment.
[0019] Another objective of the present disclosure is to integrate seamlessly with existing educational technologies and platforms, ensuring ease of adoption without disrupting traditional teaching methods.
[0020] Yet another objective of the present disclosure is to support educators by providing data-driven insights into student performance, helping them identify learning gaps and tailor their teaching strategies accordingly.
[0021] In light of the above, in one aspect of the present disclosure, a artificial intelligence powered personalized feedback generation system is disclosed herein. The system comprises a processing unit operably coupled to various hardware components, wherein the processing unit comprises; a processing module, configured to remove unwanted characters, tokenize the student response, and normalize textual input for uniformity, a text embedding module, configured to convert processed textual input into numerical representations for contextual analysis, a feedback generation module, configured to process embedded input using a transformer-based encoder-decoder architecture to generate meaningful feedback, a validation module, configured to compare generated feedback against predefined evaluation metrics, ensuring contextual accuracy and coherence. The system also includes a memory unit, operably coupled to the processing unit, configured to store predefined training data, student responses, generated feedback, and evaluation models. The system includes a communication network, operably coupled to the input unit and the processing unit, configured to facilitate data transmission between the input unit and the processing unit for real-time interaction. The system also includes an input unit, operably coupled to the processing unit via the communication network, configured to receive student responses from external interfaces or educational platforms. The system also includes a display unit, operably coupled to the processing unit, configured to present the generated personalized feedback to students or educators.
[0022] In one embodiment, the processing module further comprises a noise reduction sub-module, configured to filter out irrelevant data such as stop words, punctuation, and special symbols to enhance the accuracy of feedback generation.
[0023] In one embodiment, the text embedding module further comprises a context-awareness sub-module, configured to analyse sentence-level relationships and maintain contextual coherence between different parts of the student response.
[0024] In one embodiment, the feedback generation module further comprises a multi-layer transformer-based encoder, configured to process student responses by assigning importance to different textual elements using self-attention mechanisms.
[0025] In one embodiment, the feedback generation module further comprises a decoder with dynamic attention, configured to generate personalized feedback by attending to both input responses and previously generated feedback tokens for improved contextual accuracy.
[0026] In one embodiment, the validation module further comprises a scoring sub-module, configured to evaluate the generated feedback using predefined metrics.
[0027] In one embodiment, the memory unit further comprises a feedback optimization module, configured to adaptively refine feedback generation models by continuously updating parameters based on historical student interactions.
[0028] In one embodiment, the display unit further comprises an interactive user interface, configured to provide students with explanatory insights into their feedback by visualizing strengths, weaknesses, and improvement areas.
[0029] In one embodiment, the system further comprises a multilingual adaptation module, configured to process and generate feedback in multiple languages by dynamically adjusting linguistic embeddings and syntax structures.
[0030] In light of the above, in one aspect of the present disclosure, artificial intelligence powered personalized feedback generation system is disclosed herein. The method comprises receiving a student response via an input unit from an educational platform. The method includes processing the student response using a processing module, wherein the processing includes removing unwanted characters, tokenizing the response, and normalizing the textual input for uniformity. The method also includes converting the processed textual input into numerical representation using a text embedding module, contextual embeddings are granted for semantic analysis. The method also includes analysing the embedded input using a transformer-based encoder-decoder architecture, an encoder processes the numerical representation using self-attention mechanisms to extract contextual meaning, and a decoder generates personalized feedback by aligning the processed representation with pre-trained linguistic patterns. The method also includes validating the generated feedback using a validation module. Further, the validation module compares feedback, output against predefined evaluation metrics to ensure contextual accuracy and coherence. The method also includes storing the student response, generated feedback, and evaluation results in a memory unit for adaptive learning and improvement, transmitting the validated personalized feedback to a student for educator via a display unit of external communication platform, ensuring real-time and automated feedback generation.
[0031] These and other advantages will be apparent from the present application of the embodiments described herein.
[0032] The preceding is a simplified summary to provide an understanding of some embodiments of the present invention. This summary is neither an extensive nor exhaustive overview of the present invention and its various embodiments. The summary presents selected concepts of the embodiments of the present invention in a simplified form as an introduction to the more detailed description presented below. As will be appreciated, other embodiments of the present invention are possible utilizing, alone or in combination, one or more of the features set forth above or described in detail below.
[0033] These elements, together with the other aspects of the present disclosure and various features are pointed out with particularity in the claims annexed hereto and form a part of the present disclosure. For a better understanding of the present disclosure, its operating advantages, and the specified object attained by its uses, reference should be made to the accompanying drawings and descriptive matter in which there are illustrated exemplary embodiments of the present disclosure.
BRIEF DESCRIPTION OF THE DRAWINGS
[0034] To describe the technical solutions in the embodiments of the present disclosure or in the prior art more clearly, the following briefly describes the accompanying drawings required for describing the embodiments or the prior art. Apparently, the accompanying drawings in the following description merely show some embodiments of the present disclosure, and a person of ordinary skill in the art can derive other implementations from these accompanying drawings without creative efforts. All of the embodiments or the implementations shall fall within the protection scope of the present disclosure.
[0035] The advantages and features of the present disclosure will become better understood with reference to the following detailed description taken in conjunction with the accompanying drawing, in which:
[0036] FIG. 1 illustrates a block diagram of an artificial intelligence powered personalized feedback generation system and method thereof in accordance with an exemplary embodiment of the present disclosure;
[0037] FIG. 2 illustrates a flowchart of a artificial intelligence powered personalized feedback generation system in accordance with an exemplary embodiment of the present disclosure;
[0038] FIG. 3 illustrates a flowchart of a Artificial intelligence powered personalized feedback generation method in accordance with an exemplary embodiment of the present disclosure.
[0039] Like reference, numerals refer to like parts throughout the description of several views of the drawing.
[0040] The artificial intelligence powered personalized feedback generation system and method thereof is illustrated in the accompanying drawings, which like reference letters indicate corresponding parts in the various figures. It should be noted that the accompanying figure is intended to present illustrations of exemplary embodiments of the present disclosure. This figure is not intended to limit the scope of the present disclosure. It should also be noted that the accompanying figure is not necessarily drawn to scale.
DETAILED DESCRIPTION OF THE DISCLOSURE
[0041] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to communicate the disclosure. However, the amount of detail offered is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present disclosure.
[0042] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the present disclosure. It may be apparent to one skilled in the art that embodiments of the present disclosure may be practiced without some of these specific details.
[0043] Various terms as used herein are shown below. To the extent a term is used, it should be given the broadest definition persons in the pertinent art have given that term as reflected in printed publications and issued patents at the time of filing.
[0044] The terms “a” and “an” herein do not denote a limitation of quantity, but rather denote the presence of at least one of the referenced items.
[0045] The terms “having”, “comprising”, “including”, and variations thereof signify the presence of a component.
[0046] Referring now to FIG. 1 to FIG. 3 to describe various exemplary embodiments of the present disclosure. FIG. 1 illustrates a block diagram of an artificial intelligence powered personalized feedback generation system, in accordance with an exemplary embodiment of the present disclosure.
[0047] The system 100 may include a a processing unit 102 operably coupled to various hardware components, wherein the processing unit comprises: a processing module 104 configured to remove unwanted characters, tokenize the student response, and normalize textual input for uniformity a text embedding module 106 configured to convert processed textual input into numerical representations for contextual analysis; a feedback generation module 108 configured to process embedded input using a transformer-based encoder-decoder architecture to generate meaningful feedback a validation module 110 configured to compare generated feedback against predefined evaluation metrics, ensuring contextual accuracy and coherence. The system 100 may also include a memory unit 112 operably coupled to the processing unit, configured to store predefined training data, student responses, generated feedback, and evaluation models. The system 100 may also include a communication network 116, operably coupled to the input unit 114 and the processing unit 102, configured to facilitate data transmission between the input unit 114 and the processing unit 102 for real-time interaction. The system 100 may also include an input unit 114, operably coupled to the processing unit via the communication network 116, configured to receive student responses from external interfaces or educational platforms. The system 100 may also include a display unit 118 operably coupled to the processing unit, configured to present the generated personalized feedback to students or educators.
[0048] The processing module 104 further comprises a noise reduction sub-module, configured to filter out irrelevant data such as stop words, punctuation, and special symbols to enhance the accuracy of feedback generation.
[0049] The text embedding module 106 further comprises a context-awareness sub-module, configured to analyse sentence-level relationships and maintain contextual coherence between different parts of the student response.
[0050] The feedback generation module 108 further comprises a multi-layer transformer-based encoder, configured to process student responses by assigning importance to different textual elements using self-attention mechanisms.
[0051] The feedback generation module 108 further comprises a decoder with dynamic attention, configured to generate personalized feedback by attending to both input responses and previously generated feedback tokens for improved contextual accuracy.
[0052] The validation module 110 further comprises a scoring sub-module, configured to evaluate the generated feedback using predefined metrics
[0053] The memory unit 112 further comprises a feedback optimization module, configured to adaptively refine feedback generation models by continuously updating parameters based on historical student interactions.
[0054] The display unit 118 further comprises an interactive user interface, configured to provide students with explanatory insights into their feedback by visualizing strengths, weaknesses, and improvement areas.
[0055] The system further comprises a multilingual adaptation module, configured to process and generate feedback in multiple languages by dynamically adjusting linguistic embeddings and syntax structures.
[0056] The method 100 may include a receiving a student response via an input unit 114 from an educational platform. The method may also include a processing the student response using a processing module 104 wherein the processing includes removing unwanted characters, tokenizing the response, and normalizing the textual input for uniformity. The method 100 may also include a converting the processed textual input into numerical representations using a text embedding module 106 wherein contextual embeddings are generated for semantic analysis. The method 100 may also include analysing the embedded input using a transformer-based encoder-decoder architecture, wherein an encoder processes the numerical representation using self-attention mechanisms to extract contextual meaning, and a decoder generates personalized feedback by aligning the processed representation with pre-trained linguistic patterns. The method 100 may also include. validating the generated feedback using a validation module 110 wherein the validation module compares feedback output against predefined evaluation metrics to ensure contextual accuracy and coherence. The method 100 may also include storing the student response, generated feedback, and evaluation results in a memory unit 112 for adaptive learning and improvement. The method 100 may also include transmitting the validated personalized feedback to a student or educator via a display unit 118 or external communication platform, ensuring real-time and automated feedback generation.
[0057] The processing unit 102 handles the core operations of the artificial intelligence powered personalized feedback generation system 100. The processing unit 102 executes all computations necessary for text processing, embedding generation, feedback creation, validation, and communication with external systems. The processing unit 102 ensures efficient coordination between various components, managing the flow of data from input to output. The processing unit 102 retrieves student responses from the input unit 114 and directs them to the processing module 104 for further refinement. The processing unit 102 interacts with the memory unit 112 to fetch pre-existing training data, past student responses, and stored evaluation models to enhance feedback accuracy. The processing unit 102 also facilitates communication with the communication network 116, allowing real-time interaction with educational platforms. The processing unit 102 consistently updates stored parameters in the memory unit 112 based on received feedback, ensuring that future responses align with continuously evolving learning patterns. The processing unit 102 regulates the integration of multiple sub-modules within the feedback generation module 108, validation module 110, and text embedding module 106, ensuring that each process occurs in a structured and sequential manner without data loss or inconsistency.
[0058] The processing module 104 is responsible for cleaning and preparing student responses for further analysis. The processing module 104 eliminates unnecessary characters, removes noise, and ensures uniform formatting to standardize input. The processing module 104 breaks down text into smaller tokens, enabling structured analysis by the text embedding module 106. The processing module 104 normalizes words by converting variations of the same word into a single base form, preventing inconsistencies that may arise from different word usages. The processing module 104 ensures that all responses adhere to a standardized format, preventing variations in sentence structure from affecting feedback accuracy. The processing module 104 efficiently processes both short and long responses, ensuring optimal compatibility with subsequent modules.
[0059] The text embedding module 106 converts refined student responses into numerical representations, making them interpretable for artificial intelligence models. The text embedding module 106 preserves contextual relationships within sentences, ensuring that the meaning of words is not lost in conversion. The text embedding module 106 assigns specific numerical values to each word or phrase, allowing the feedback generation module 108 to analyse responses effectively. The text embedding module 106 utilizes pre-trained models stored in the memory unit 112, ensuring that linguistic patterns and contextual nuances are accurately captured. The text embedding module 106 ensures that synonyms and related words maintain their intended meaning, preventing misinterpretation during feedback generation.
[0060] The feedback generation module 108 processes embedded student responses using a transformer-based encoder-decoder model. The feedback generation module 108 generates feedback based on linguistic structure, context, and predefined evaluation criteria stored in the memory unit 112. The feedback generation module 108 utilizes self-attention mechanisms to prioritize key aspects of student responses, ensuring that the most relevant information is emphasized in generated feedback. The feedback generation module 108 aligns generated feedback with predefined linguistic patterns to ensure grammatical correctness and coherence. The feedback generation module 108 continuously refines generated feedback based on previously stored student interactions.
[0061] The validation module 110 ensures that generated feedback aligns with predefined evaluation metrics, maintaining accuracy, coherence, and relevance. The validation module 110 compares generated feedback against linguistic, grammatical, and contextual parameters stored in the memory unit 112 to ensure correctness. The validation module 110 prevents incorrect, misleading, or ambiguous feedback from being presented to students by filtering out responses that fail to meet established quality standards. The validation module 110 ensures that each piece of generated feedback undergoes rigorous verification before being displayed to students or educators. The validation module 110 utilizes rule-based and machine-learning-based evaluation methods to measure the accuracy of generated responses. The validation module 110 refines validation criteria based on feedback received from educators and real-world student interactions, ensuring adaptability and continuous improvement. The validation module 110 operates in real-time, ensuring that each generated response undergoes immediate validation without causing delays in feedback delivery. The validation module 110 assigns confidence scores to generated feedback, providing insights into reliability and consistency. The validation module 110 interacts with the feedback generation module 108 to request modifications or refinements to generated feedback when inconsistencies or inaccuracies are detected. The validation module 110 ensures that generated feedback remains contextually appropriate for students of varying proficiency levels by adapting evaluation parameters based on historical student interactions. The validation module 110 evaluates the structure of generated feedback, ensuring that feedback is logically sequenced and conveys clear, actionable insights. The validation module 110 ensures that feedback generated for different types of student responses remains consistent in tone and formatting, maintaining a standardized feedback structure across various educational contexts. The validation module 110 integrates with external educational standards and grading rubrics, ensuring compliance with academic guidelines. The validation module 110 identifies discrepancies in generated feedback and refines validation models by updating stored evaluation metrics. The validation module 110 prevents redundancy by ensuring that generated feedback does not contain unnecessary repetitions. The validation module 110 continuously adapts validation criteria based on performance analysis and student feedback patterns stored in the memory unit 112. The validation module 110 facilitates personalization by ensuring that feedback aligns with the unique learning needs of individual students. The validation module 110 enhances system reliability by eliminating errors and inconsistencies before feedback reaches students or educators.
[0062] The memory unit 112 stores predefined training data, student responses, generated feedback, and evaluation models. The memory unit 112 ensures that each feedback generation cycle benefits from previously stored interactions, improving response accuracy over time. The memory unit 112 retains historical student performance data, allowing the artificial intelligence powered personalized feedback generation system 100 to identify learning patterns and suggest improvements based on past responses. The memory unit 112 enables continuous refinement of text embedding models by preserving linguistic patterns and contextual nuances extracted from previous student interactions. The memory unit 112 ensures that processing unit 102 retrieves relevant data for real-time feedback generation without delays. The memory unit 112 organizes stored data into structured categories, optimizing retrieval efficiency and preventing unnecessary computational overhead. The memory unit 112 retains variations of student responses and corresponding feedback, allowing the artificial intelligence powered personalized feedback generation system 100 to generate contextually diverse feedback while maintaining accuracy. The memory unit 112 stores adaptive learning models, enabling refinement of generated feedback based on evolving educational trends and student engagement data. The memory unit 112 ensures that stored training data remains up to date by continuously updating linguistic embeddings, predefined evaluation metrics, and feedback generation templates. The memory unit 112 prevents data loss by implementing secure storage mechanisms that maintain integrity across multiple interactions. The memory unit 112 interacts with the validation module 110 to retrieve evaluation metrics and refine feedback validation criteria based on past performance analysis. The memory unit 112 optimizes storage space by categorizing responses, embeddings, and feedback data based on relevance and frequency of access. The memory unit 112 supports multilingual adaptation by storing linguistic embeddings and syntactic structures for multiple languages, ensuring accurate feedback generation across different linguistic contexts. The memory unit 112 retrieves relevant data for the text embedding module 106, enabling efficient transformation of student responses into numerical representations for contextual analysis. The memory unit 112 ensures that the artificial intelligence powered personalized feedback generation system 100 remains adaptable by allowing continuous refinement of stored parameters. The memory unit 112 enhances the efficiency of feedback generation by reducing the need for redundant computations. The memory unit 112 integrates seamlessly with external databases and cloud storage systems, ensuring scalability for handling large volumes of student responses and generated feedback.
[0063] The input unit 114 receives student responses from external interfaces or educational platforms and transmits the received student responses to the processing unit 102 through the communication network 116 for further processing. The input unit 114 ensures seamless integration with learning management systems, virtual classrooms, and online assessment platforms, enabling real-time data collection and transmission. The input unit 114 processes multiple types of input formats, including textual responses, structured assessments, and interactive submissions, ensuring compatibility with diverse educational environments. The input unit 114 filters incoming student responses to remove irrelevant data before transmitting the student responses to the processing module 104. The input unit 114 ensures that the student responses are securely transferred through the communication network 116 without data corruption or loss. The input unit 114 facilitates adaptive learning by enabling real-time feedback collection and response submission through the communication network 116. The input unit 114 prevents errors by validating the integrity of the received student responses before further processing within the processing unit 102. The input unit 114 interacts with the memory unit 112 through the processing unit 102 to retrieve relevant historical data and ensure that the submitted student responses align with stored learning patterns. The input unit 114 optimizes response handling by categorizing and structuring the incoming data for efficient processing within the processing unit 102. The input unit 114 supports multiple communication protocols to ensure compatibility with various educational systems and external platforms, enabling seamless connectivity through the communication network 116. The input unit 114 enhances the accessibility of the artificial intelligence powered personalized feedback generation system 100 by ensuring that students can submit student responses through multiple channels, allowing integration with diverse technological infrastructures.
[0064] The communication network 116 enables interaction between the input unit 114 and the processing unit 102, facilitating seamless data exchange for processing student responses and generating personalized feedback. The communication network 116 transmits the received student responses from the input unit 114 to the processing unit 102 and transmits the generated personalized feedback from the processing unit 102 to the display unit 118 for presentation to students and educators. The communication network 116 ensures secure transmission of the student responses and the generated personalized feedback by implementing encryption and authentication mechanisms, preventing unauthorized access or data breaches. The communication network 116 facilitates adaptive feedback refinement by enabling educators to provide input that refines feedback models stored within the memory unit 112. The communication network 116 supports automated notifications by transmitting alerts from the processing unit 102 to students and educators, ensuring timely feedback delivery. The communication network 116 interacts with external cloud-based storage systems to enable large-scale data management, ensuring that historical student responses and feedback models remain accessible for long-term refinement. The communication network 116 optimizes network utilization by ensuring efficient bandwidth allocation for real-time feedback transmission between the processing unit 102, the input unit 114, and the display unit 118. The communication network 116 facilitates continuous learning enhancements by enabling seamless integration with external educational analytics platforms, allowing real-time updates to feedback algorithms within the artificial intelligence powered personalized feedback generation system 100.
[0065] The display unit 118 presents generated personalized feedback to students or educators in a structured and visually accessible format. The display unit 118 ensures that feedback is formatted for clarity, highlighting key insights, strengths, weaknesses, and suggested improvements. The display unit 118 supports interactive visualization tools that enable students to explore feedback dynamically. The display unit 118 enhances engagement by ensuring that feedback is displayed in an intuitive and structured manner. The display unit 118 facilitates personalized learning by adapting feedback presentation formats based on individual student preferences. The display unit 118 ensures accessibility by supporting multiple display formats, including web interfaces, mobile applications, and desktop-based learning environments. The display unit 118 maintains consistency across various educational platforms, ensuring that feedback is presented uniformly regardless of the system used. The display unit 118 prevents ambiguity by ensuring that feedback is clearly segmented into actionable insights. The display unit 118 interacts with the validation module 110 to ensure that only verified feedback is displayed. The display unit 118 optimizes readability by structuring feedback into digestible sections, preventing information overload for students. The display unit 118 ensures real-time updates, allowing students to access feedback as soon as it is generated. The display unit 118 integrates with accessibility features such as text-to-speech conversion and adaptive font sizing, ensuring inclusivity for diverse learners. The display unit 118 enhances student engagement by incorporating visual indicators that highlight progress and areas requiring improvement. The display unit 118 prevents inconsistencies by ensuring that displayed feedback aligns with predefined educational guidelines and assessment criteria. The display unit 118 continuously refines its presentation structure based on student interactions and engagement patterns stored in the memory unit 112. The display unit 118 supports multilingual adaptation, allowing feedback to be displayed in different languages based on student preferences.
[0066] FIG. 2 illustrates a flowchart of an artificial intelligence powered personalized feedback generation system in accordance with an exemplary embodiment of the present disclosure.
[0067] At 202, acquire and preprocess student responses from an input source for analysis.
[0068] At 204, perform text cleaning, normalization, and tokenization to enhance processing efficiency.
[0069] At 206, transform the refined text into structured numerical data for artificial intelligence driven interpretation.
[0070] At 208, utilize a transformer-based model to generate context-aware, personalized feedback.
[0071] At 210, assess the generated feedback by comparing it against predefined evaluation criteria.
[0072] At 212, store and update processed feedback data to refine future feedback accuracy.
[0073] At 214, deliver the finalized personalized feedback to students or educators via an output interface.
[0074] FIG. 3 illustrates a flowchart of a Artificial intelligence powered personalized feedback generation method in accordance with an exemplary embodiment of the present disclosure.
[0075] At 302, receiving a student response via an input unit from an educational platform.
[0076] At 304, processing the student response using a processing module, wherein the processing includes removing unwanted characters, tokenizing the response, and normalizing the textual input for uniformity.
[0077] At 306, converting the processed textual input into numerical representations using a text embedding module, wherein contextual embeddings are generated for semantic analysis.
[0078] At 308, analysing the embedded input using a transformer-based encoder-decoder architecture, wherein: an encoder processes the numerical representation using self-attention mechanisms to extract contextual meaning, and a decoder generates personalized feedback by aligning the processed representation with pre-trained linguistic patterns.
[0079] At 310, validating the generated feedback using a validation module, wherein the validation module compares feedback output against predefined evaluation metrics to ensure contextual accuracy and coherence.
[0080] At 312, storing the student response, generated feedback, and evaluation results in a memory unit for adaptive learning and improvement.
[0081] At 314, transmitting the validated personalized feedback to a student or educator via a display unit or external communication platform, ensuring real-time and automated feedback generation.
[0082] While the invention has been described in connection with what is presently considered to be the most practical and various embodiments, it will be understood that the invention is not to be limited to the disclosed embodiments, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the scope of the appended claims.
[0083] A person of ordinary skill in the art may be aware that, in combination with the examples described in the embodiments disclosed in this specification, units and algorithm steps may be implemented by electronic hardware, computer software, or a combination thereof.
[0084] The foregoing descriptions of specific embodiments of the present disclosure have been presented for purposes of illustration and description. They are not intended to be exhaustive or to limit the present disclosure to the precise forms disclosed, and many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described to best explain the principles of the present disclosure and its practical application, and to thereby enable others skilled in the art to best utilize the present disclosure and various embodiments with various modifications as are suited to the particular use contemplated. It is understood that various omissions and substitutions of equivalents are contemplated as circumstances may suggest or render expedient, but such omissions and substitutions are intended to cover the application or implementation without departing from the scope of the present disclosure.
[0085] Disjunctive language such as the phrase “at least one of X, Y, Z,” unless specifically stated otherwise, is otherwise understood with the context as used in general to present that an item, term, etc., may be either X, Y, or Z, or any combination thereof (e.g., X, Y, and/or Z). Thus, such disjunctive language is not generally intended to, and should not, imply that certain embodiments require at least one of X, at least one of Y, or at least one of Z to each be present.
[0086] In a case that no conflict occurs, the embodiments in the present disclosure and the features in the embodiments may be mutually combined. The foregoing descriptions are merely specific implementations of the present disclosure, but are not intended to limit the protection scope of the present disclosure. Any variation or replacement readily figured out by a person skilled in the art within the technical scope disclosed in the present disclosure shall fall within the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure shall be subject to the protection scope of the claims.
, Claims:I/We Claim:
1. An artificial intelligence powered personalized feedback generation system (100) comprising:
a processing unit (102), operably coupled to various hardware components, wherein the processing unit comprises:
a processing module (104), configured to remove unwanted characters, tokenize the student response, and normalize textual input for uniformity;
a text embedding module (106), configured to convert processed textual input into numerical representations for contextual analysis;
a feedback generation module (108), configured to process embedded input using a transformer-based encoder-decoder architecture to generate meaningful feedback;
a validation module (110), configured to compare generated feedback against predefined evaluation metrics, ensuring contextual accuracy and coherence;
a memory unit (112), operably coupled to the processing unit, configured to store predefined training data, student responses, generated feedback, and evaluation models;
a communication network (116), operably coupled to the input unit (114) and the processing unit (102), configured to facilitate data transmission between the input unit (114) and the processing unit (102) for real-time interaction;
an input unit (114), operably coupled to the processing unit via the communication network (116), configured to receive student responses from external interfaces or educational platforms; and
a display unit (118), operably coupled to the processing unit, configured to present the generated personalized feedback to students or educators.
2. The system (100) as claimed in claim 1, wherein the processing module (104) further comprises a noise reduction sub-module, configured to filter out irrelevant data such as stop words, punctuation, and special symbols to enhance the accuracy of feedback generation.
3. The system (100) as claimed in claim 1, wherein the text embedding module (106) further comprises a context-awareness sub-module, configured to analyse sentence-level relationships and maintain contextual coherence between different parts of the student response.
4. The system (100) as claimed in claim 1, wherein the feedback generation module (108) further comprises a multi-layer transformer-based encoder, configured to process student responses by assigning importance to different textual elements using self-attention mechanisms.
5. The system (100) as claimed in claim 1, wherein the feedback generation module (108) further comprises a decoder with dynamic attention, configured to generate personalized feedback by attending to both input responses and previously generated feedback tokens for improved contextual accuracy.
6. The system (100) as claimed in claim 1, wherein the validation module (110) further comprises a scoring sub-module, configured to evaluate the generated feedback using predefined metrics.
7. The system (100) as claimed in claim 1, wherein the memory unit (112) further comprises a feedback optimization module, configured to adaptively refine feedback generation models by continuously updating parameters based on historical student interactions.
8. The system (100) as claimed in claim 1, wherein the display unit (118) further comprises an interactive user interface, configured to provide students with explanatory insights into their feedback by visualizing strengths, weaknesses, and improvement areas.
9. The system (100) as claimed in claim 1, wherein the system further comprises a multilingual adaptation module, configured to process and generate feedback in multiple languages by dynamically adjusting linguistic embeddings and syntax structures.
10. An artificial intelligence powered personalized feedback generation method (100) comprising:
receiving a student response via an input unit (114) from an educational platform;
processing the student response using a processing module (104), wherein the processing includes removing unwanted characters, tokenizing the response, and normalizing the textual input for uniformity;
converting the processed textual input into numerical representations using a text embedding module (106), wherein contextual embeddings are generated for semantic analysis;
analysing the embedded input using a transformer-based encoder-decoder architecture, wherein an encoder processes the numerical representation using self-attention mechanisms to extract contextual meaning, and a decoder generates personalized feedback by aligning the processed representation with pre-trained linguistic patterns;
validating the generated feedback using a validation module (110), wherein the validation module compares feedback output against predefined evaluation metrics to ensure contextual accuracy and coherence;
storing the student response, generated feedback, and evaluation results in a memory unit (112) for adaptive learning and improvement;
transmitting the validated personalized feedback to a student or educator via a display unit (118) or external communication platform, ensuring real-time and automated feedback generation.