DESC:FIELD OF THE INVENTION

The present disclosure relates to the field of educational technology and specifically to an interactive learning system designed for commerce education. This system encompasses various tools and modules that facilitate the teaching and learning of commerce-related subjects, including financial accounting, statutory filings, and real-time client interactions. Utilizing advanced technologies such as artificial intelligence, machine learning, and adaptive learning engines, the system aims to enhance the educational experience by providing personalized, interactive, and practical learning opportunities for students. The invention is particularly relevant for educational institutions, educators, and students engaged in commerce and business studies, offering a comprehensive platform for both teaching and assessment.

BACKGROUND OF THE INVENTION

The most relevant prior art in educational technology includes existing learning management systems and online educational platforms. While these systems have significantly contributed to the digitization of education and provided access to a wide range of educational resources, they often lack comprehensive integration of various essential components tailored specifically for the commerce education also, these systems often lack comprehensive integration of simulated tools, interactive elements, computation tools, open book case studies, and diverse evaluation methods tailored specifically for commerce and education sectors. The existing methods short in providing a truly immersive and engaging learning experience, in the field of commerce and accounting learning. Moreover, the existing systems also lack diversity in evaluation methods other than traditional assessment methods.

The deficiencies in the prior art highlight the need for a unified platform that effectively combines these essential components to provide a holistic learning and assessment experience tailored specifically for commerce education. The present invention, addresses these shortcomings by offering a comprehensive solution that integrates simulated tools, interactive elements, computation tools, open book case studies, and diverse evaluation methods into a single platform for commerce and accounting learning.

The present invention also provides access to learners and users to computation tools for conducting analysis, enabling to develop practical skills relevant, assessment and to enhance the learning experience by challenging users to think critically and creatively, preparing and real time application them for real-world challenges.

Overall, the N Practicum tool represents a significant advancement in educational technology for accounting & commerce education, offering a unified platform that addresses the deficiencies in prior art and provides students with a comprehensive and engaging learning experience.

The existing state-of-the-art in educational technology for commerce and education sectors often falls short in providing comprehensive integration, customization, and adaptability, resulting in limited interactivity, insufficient real-world simulation, and a lack of diverse assessment methods.

Traditional pedagogical approaches often prioritize theoretical knowledge over practical application, hindering students' ability to translate concepts into real-world scenarios. Commerce education including accounting subjects faces numerous challenges, beginning with the inherent complexity of accounting principles and standards. This complexity often proves daunting for beginners, serving as a barrier to effective learning and teaching. Traditional commerce subjects teaching includes accounting exacerbates this issue by focusing predominantly on theoretical concepts, neglecting practical application. As a result, students struggle to bridge the gap between theory and real-world scenarios, hindering their ability to apply knowledge flexibly. Moreover, in some educational settings, accounting is taught through rote memorization rather than critical thinking and problem-solving skills development, further impeding students' understanding of underlying principles. In current system. The failure to integrate technology effectively can lead to outdated skills among students, rendering them ill-prepared for the demands of modern accounting practices.

Addressing these drawbacks, the present invention provides a unified platform that seamlessly combines simulated tools, interactive elements, computation tools, open book case studies, and varied evaluation methods. This integration fosters enhanced engagement through active learning, facilitates practical application of knowledge with real-world scenarios, and ensures thorough assessment of students' understanding and skills. By overcoming the deficiencies of existing solutions, the present invention significantly improves learning outcomes, better preparing students and users for success in commerce and accounting areas.

In view of the foregoing discussion, it is portrayed that there is a need to have an interactive learning system for commerce education.

SUMMARY OF THE INVENTION

The present disclosure seeks to provide an interactive learning system for commerce education. It integrates simulated tools, interactive elements, computation tools, open book case studies, and diverse evaluation methods into a unified platform tailored specifically for commerce and education sectors. By offering a dynamic and engaging learning experience, bridging the gap between theory and practice, and providing effective assessment tools, the present invention aims to enhance learning outcomes and better prepare students for success in their respective fields.

In an embodiment, an interactive learning system for commerce education is disclosed. The system includes a user interface allowing students to input interactive lessons, quizzes, and multimedia content tailored for commerce education, process, and evaluate responses for statutory and financial processes, wherein the user interface includes tools for performing financial accounting tasks selected from journal entries, ledger postings, trial balances, and financial statement preparation.
The system further includes, a simulated government platform replicating actual statutory filing systems, including interfaces for tax returns, GST filings, and income tax submissions.
The system further includes, a chatbot module configured to simulate client interactions and provide scenario-specific guidance in real-time, wherein the chatbot module comprises an adaptive learning engine, employing artificial intelligence to customize educational content based on a learner's performance, pace, and preferences, wherein the adaptive learning engine utilizes a combination of machine learning and analytics to identify knowledge gaps and recommend appropriate remedial actions.
The system further includes, an adaptive feedback mechanism to analyze user actions, detect errors, and provide targeted, dynamic feedback to track student progress, manage assignments, and provide analytics on performance metrics.
The system further includes, a performance tracking dashboard for monitoring and analyzing metrics selected from accuracy, task completion, and learning milestones.
The system further includes, a customizable simulation platform to adjust task complexity and feedback detail based on user skill levels.
The system further includes, a teacher’s dashboard for evaluating student progress and providing personalized feedback.

In another embodiment, a method for interactive learning of commerce education is disclosed. The method includes simulating statutory processes using a simulated government platform that replicates government portal workflows, wherein the simulation includes hands-on tasks for tax filings, GST registrations, and financial statement preparation.
The method further includes guiding users through processes with a chatbot module providing step-by-step instructions and facilitating collaboration through live sessions, group discussions, and peer reviews using a collaborative learning module.
The method further includes evaluating user performance via a tracking dashboard upon tracking user progress using a tracking mechanism and generating personalized insights using a learning management module.
The method further includes dynamically customizing simulation parameters selected from difficulty and task scenarios to align with individual learning needs.
The method further includes providing adaptive learning content to identify and address user-specific errors.

An object of the present disclosure is to enhance the learning experiences in commerce and education by integrating simulated tools, interactive elements, computation aids, open book case studies, and diverse evaluation methods, thereby making the educational process more engaging and effective.
Another object of the present disclosure is to bridge the gap between theoretical knowledge and practical application by replicating real-world scenarios, allowing students to gain hands-on experience and better prepare for real-life challenges in their respective fields.
Yet another object of the present invention is to deliver an expeditious and cost-effective customizable learning platform that can be tailored to the specific needs of the commerce and education sectors, allowing educators to adjust content and task complexity based on user skill levels.

To further clarify the advantages and features of the present disclosure, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which are illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail in the accompanying drawings.

BRIEF DESCRIPTION OF FIGURES

These and other features, aspects, and advantages of the present disclosure will become better understood when the following detailed description is read concerning the accompanying drawings in which like characters represent like parts throughout the drawings, wherein:

Figure 1 illustrates a block diagram of an interactive learning system for commerce education in accordance with an embodiment of the present disclosure;
Figure 2 illustrates a flow chart of a method for interactive learning of commerce education in accordance with an embodiment of the present disclosure;
Figure 3 illustrates a block diagram of an interactive learning system;
Figure 4 illustrates splits integration;
Figure 5 illustrates IPEL-FAS (interactive experiential learning of financial accounting systems);
Figure 6 illustrates the evaluation method;
Figure 7 illustrates an architecture of the unified platform; and
Figure 8 illustrates a flow chart of a computer-implemented method for interactive learning in commerce education in in accordance with another embodiment of the present disclosure.

Further, skilled artisans will appreciate those elements in the drawings are illustrated for simplicity and may not have necessarily been drawn to scale. For example, the flow charts illustrate the method in terms of the most prominent steps involved to help to improve understanding of aspects of the present disclosure. Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the drawings by conventional symbols, and the drawings may show only those specific details that are pertinent to understanding the embodiments of the present disclosure so as not to obscure the drawings with details that will be readily apparent to those of ordinary skill in the art having the benefit of the description herein.

DETAILED DESCRIPTION:

To promote an understanding of the principles of the invention, reference will now be made to the embodiment illustrated in the drawings and specific language will be used to describe the same. It will nevertheless be understood that no limitation of the scope of the invention is thereby intended, such alterations and further modifications in the illustrated system, and such further applications of the principles of the invention as illustrated therein being contemplated as would normally occur to one skilled in the art to which the invention relates.

It will be understood by those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the invention and are not intended to be restrictive thereof.

Reference throughout this specification to “an aspect”, “another aspect” or similar language 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, appearances of the phrase “in an embodiment”, “in another embodiment” and similar language throughout this specification may, but do not necessarily, all refer to the same embodiment.

The terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process or method that comprises a list of steps does not include only those steps but may include other steps not expressly listed or inherent to such process or method. Similarly, one or more devices or sub-systems or elements or structures or components proceeded by "comprises...a" does not, without more constraints, preclude the existence of other devices or other sub-systems or other elements or other structures or other components or additional devices or additional sub-systems or additional elements or additional structures or additional components.

Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The system, methods, and examples provided herein are illustrative only and not intended to be limiting.

Embodiments of the present disclosure will be described below in detail concerning the accompanying drawings.

Referring to Figure 1, a block diagram of an interactive learning system for commerce education is illustrated in accordance with an embodiment of the present disclosure. The system (100) includes a user interface (102) allowing students to input interactive lessons, quizzes, and multimedia content tailored for commerce education, process, and evaluate responses for statutory and financial processes, wherein the user interface (102) includes tools for performing financial accounting tasks selected from journal entries, ledger postings, trial balances, and financial statement preparation.

In an embodiment, a simulated government platform (104) replicating actual statutory filing systems, including interfaces for tax returns, GST filings, and income tax submissions.

In an embodiment, a chatbot module (106) is configured to simulate client interactions and provide scenario-specific guidance in real-time, wherein the chatbot module (106) comprises an adaptive learning engine, employing artificial intelligence to customize educational content based on a learner's performance, pace, and preferences, wherein the adaptive learning engine utilizes a combination of machine learning and analytics to identify knowledge gaps and recommend appropriate remedial actions.

In an embodiment, an adaptive feedback mechanism (108) is used to analyze user actions, detect errors, and provide targeted, dynamic feedback to track student progress, manage assignments, and provide analytics on performance metrics.

In an embodiment, a performance tracking dashboard (110) is used for monitoring and analyzing metrics selected from accuracy, task completion, and learning milestones.

In an embodiment, a customizable simulation platform (112) is used to adjust task complexity and feedback detail based on user skill levels.

In an embodiment, a teacher’s dashboard (114) is used for evaluating student progress and providing personalized feedback.

In another embodiment, the adaptive feedback mechanism (108) includes a real-time error detection module to detect real-time error and generate error pattern; a processor to generate a tailored guidance based on user error patterns; and a progressive difficulty scaling system aligned with a user’s learning curve.

In a further embodiment, the performance tracking dashboard (110) provides detailed analytics on accuracy, error frequency, and time spent on tasks, and visualizations of learning progress using graphs and milestone trackers.

In one of the embodiments, the customizable simulation platform (112) integrates tasks for Income tax processes including PAN registration and tax submissions, GST filings such as GSTR-1, GSTR-3B, and e-way bill generation, and Corporate compliance tasks including company registration and director appointment filings.

The system further comprising a real-time error detection module (116) that provides immediate feedback on user errors, including notification sensitivity adjustments based on user expertise levels
.
In an embodiment, the adaptive learning engine identifies knowledge gaps and recommends remedial actions by: extracting user performance data in real time from a structured event-driven logging system, wherein each user action—including incorrect responses, response latency, and repeated attempts—is stored in a time-series database indexed by task type, accuracy percentage, and error classification; generating a weighted error profile by applying a matrix-based scoring function that assigns error severity scores to individual mistakes, wherein errors occurring in fundamental financial concepts are weighted higher than secondary computational errors, allowing the system to prioritize remediation of critical weaknesses; identifying knowledge gaps through a decision-tree-based error clustering, wherein the system recursively analyzes user mistakes, groups them into logical categories (e.g., misclassification of liabilities, incorrect GST slab selection), and generates an adaptive learning path by selecting additional exercises from a pre-trained knowledge graph that maps financial concepts to learning modules; recommending remedial actions dynamically, wherein upon detecting a recurring pattern of incorrect ledger postings, the system triggers a structured remediation sequence by: automatically adjusting the next learning module to reinforce foundational concepts before allowing progression to advanced tasks; generating interactive correction guides that overlay common mistakes with explanations directly on the user interface, requiring user confirmation before proceeding; triggering chatbot-assisted correction walkthroughs, wherein the chatbot breaks down the user's mistake step by step, prompting user inputs at each correction stage to ensure active learning engagement; and continuously refining remedial recommendations using a feedback-based learning loop, wherein after each remedial session, the system re-evaluates the user’s corrected responses, adjusts the difficulty scaling for future exercises, and dynamically reorders pending lessons based on updated proficiency scores.
In this embodiment, the adaptive learning engine plays a crucial role in identifying knowledge gaps and providing dynamic remediation based on real-time user performance. The system extracts detailed performance data from a structured event-driven logging system that records every user interaction, including incorrect responses, response latency, and repeated attempts. This data is stored in a time-series database, which is indexed by task type, accuracy percentage, and error classification. For instance, if a user repeatedly struggles with certain financial calculations, the system logs these actions and associates them with the relevant task type, such as tax filing or ledger entry, and the user's accuracy percentage. This enables the system to analyze trends in the user's learning progress and identify recurring issues in specific areas, such as misclassifying liabilities or selecting the wrong GST slab.
The next step involves the generation of a weighted error profile using a matrix-based scoring function. This function assigns severity scores to individual errors based on their impact on the overall learning objectives. For example, mistakes related to fundamental financial concepts, such as incorrectly posting journal entries or misapplying tax codes, are given a higher weight compared to computational errors, such as simple arithmetic mistakes. This prioritization allows the system to focus on remediating more critical weaknesses in the user’s understanding first, rather than addressing secondary issues that do not significantly impact the user’s grasp of core concepts.
Once errors are logged, the system uses a decision-tree-based error clustering technique to group related mistakes into logical categories. By recursively analyzing user mistakes, the system identifies patterns of misunderstanding in specific areas, such as incorrect ledger postings or faulty tax filing procedures. Based on these clusters, the system generates an adaptive learning path by selecting appropriate exercises from a pre-trained knowledge graph that links financial concepts to relevant learning modules. For example, if the system detects that a user consistently struggles with GST calculations, it can automatically select additional exercises focused on GST slab selection, thereby reinforcing the user’s understanding of that specific topic.
Remedial actions are then recommended dynamically by the system. When a recurring pattern of mistakes, such as repeated incorrect ledger postings, is detected, the system triggers a structured remediation sequence. This sequence first adjusts the next learning module to focus on reinforcing foundational concepts before progressing to more advanced tasks. The system generates interactive correction guides that overlay common mistakes on the user interface, providing explanations for each mistake and requiring the user to confirm their understanding before continuing. This approach encourages active learning and ensures that the user is fully engaged with the remediation process.
Additionally, chatbot-assisted correction walkthroughs are triggered when a user makes an error. The chatbot guides the user through a step-by-step correction process, prompting the user for inputs at each stage to ensure that the learning process is interactive and engaging. For example, if the user makes an error in a tax filing process, the chatbot might walk them through each step of the filing procedure, asking them to make the necessary corrections and explaining the rationale behind each step.
The system continuously refines its remedial recommendations using a feedback-based learning loop. After each remedial session, the system evaluates the user’s corrected responses, adjusts the difficulty scaling for future exercises, and dynamically reorders pending lessons based on updated proficiency scores. For instance, if the user successfully corrects their mistakes during the remediation session, the system may increase the difficulty level of subsequent exercises to further challenge the user. Conversely, if the user struggles to understand the corrections, the system might reduce the complexity of future tasks to ensure that the user can build their understanding incrementally.
In an embodiment, the real-time error detection module operates by: intercepting user actions at the interface level using event-driven hooks, wherein a background event listener monitors keystrokes, button clicks, and input field modifications, logging each user interaction in a structured event queue that retains timestamps and contextual metadata; performing inline validation on financial transactions by implementing a two-pass verification process, wherein in the first pass, each numerical input undergoes format validation using a regular expression parser to detect incorrect syntax, and in the second pass, a rules engine compares the input against predefined tax codes and accounting principles, triggering a validation event if mismatches are found; generating automated ledger correction suggestions by parsing transactional discrepancies using hash-matching, wherein incorrect debit-credit mappings are detected by computing hash signatures of the user's ledger entries and comparing them against an indexed repository of verified accounting templates; and executing rollback and auto-correction for detected errors, wherein when a tax calculation inconsistency is identified, the system dynamically reconstructs the sequence of calculations leading to the error by replaying stored event logs, allowing the user to either manually correct the issue or apply an automated fix generated by a pre-trained financial correction model.
In this embodiment, the real-time error detection module is designed to identify and address errors in financial transactions as they occur, ensuring a seamless and error-free user experience. The module begins by intercepting user actions at the interface level through event-driven hooks. These hooks are integrated into the user interface, where a background event listener continuously monitors key user actions such as keystrokes, button clicks, and modifications to input fields. Every user interaction is logged in a structured event queue, capturing essential metadata such as timestamps, input values, and the contextual information surrounding each action. This logging mechanism not only ensures that each action is recorded in real time but also allows for a detailed analysis of user behavior, which can be used to detect errors as they occur.
Once user inputs are captured, the system performs inline validation on the financial transactions entered. This validation is conducted through a two-pass verification process. The first pass uses a regular expression parser to validate the format of numerical inputs. For example, if the user enters an amount, the system ensures that the input follows the correct syntax, such as decimal places and the appropriate number format. This prevents basic errors, such as entering alphabetic characters in a numerical field or misplacing the decimal point. The second pass involves a more complex validation process where a rules engine compares the user’s inputs against predefined tax codes and accounting principles. The rules engine checks whether the values entered by the user conform to the expected financial norms, such as proper tax slab selection or valid ledger account assignments. If mismatches are detected during this process, a validation event is triggered, notifying the user of the inconsistency and providing an opportunity for correction before proceeding further.
In addition to inline validation, the system is capable of generating automated ledger correction suggestions for detected discrepancies. This is achieved through the use of hash-matching techniques, where the system computes hash signatures of the user’s ledger entries and compares them against an indexed repository of verified accounting templates. If discrepancies are found in the debit-credit mappings, the system identifies the mismatch by comparing the hash values of the user’s entries with those of correct entries stored in the repository. This enables the system to pinpoint where the mistake occurred, whether it's a misclassified transaction or a missing entry, and automatically suggest the appropriate corrections to the user.
If an error is detected, particularly in more complex calculations such as tax computations, the system executes rollback and auto-correction procedures. For instance, if the system detects a tax calculation inconsistency, it dynamically reconstructs the sequence of calculations leading to the error. By replaying the stored event logs, the system can trace the steps that led to the mistake, identifying the exact point of failure. This enables the user to either manually correct the error or apply an automated fix generated by a pre-trained financial correction model. The model, trained on historical data and tax rules, suggests the most appropriate corrections based on the nature of the discrepancy. This level of automation reduces the chances of user error and accelerates the correction process, ultimately improving the overall efficiency of the financial transaction process.
By capturing user actions in real time, performing two-pass validation, generating automated correction suggestions, and enabling rollback and auto-correction, the system ensures that errors are promptly identified and addressed, reducing the risk of inaccuracies and ensuring that users can complete financial tasks with confidence. The combination of real-time monitoring, intelligent validation, and automated correction represents a sophisticated approach to maintaining accuracy and consistency in complex financial processes.
In embodiment, the adaptive feedback mechanism is configured to: track user mistakes at a granular level using a stateful session monitor, wherein each interaction, including at least one of tax calculation, journal posting, is assigned a session ID, and all associated errors are logged in an indexed session table that maintains contextual snapshots of user actions to enable precise reconstruction of error scenarios; Implement dynamic error flagging through an execution tracer, wherein the system tracks real-time input flows by injecting trace points into critical functions responsible for financial processing, allowing immediate identification of computational errors by comparing intermediary calculations against expected financial models; apply a progressive hint mechanism for repeated errors, wherein after the first incorrect attempt, the system displays a contextual tooltip highlighting the affected field, on the second attempt it overlays a semi-transparent correction guide demonstrating the correct process, and on the third incorrect attempt, the system prompts a step-by-step interactive walkthrough forcing the user to confirm each calculation before submission; and modify future learning paths based on past errors by maintaining an error-weighted difficulty adjustment system, wherein frequent mistakes in GST filing tasks result in the system queuing additional, slightly varied tax filing exercises, while proficiency in an area triggers a transition to more advanced financial topics.
In this embodiment, the adaptive feedback mechanism is designed to provide a personalized learning experience by tracking user mistakes at a granular level, ensuring that the system adapts to each user's needs and offers targeted guidance for improvement. The first aspect of this mechanism is the stateful session monitor, which assigns a unique session ID to each user interaction, including complex tasks like tax calculation or journal posting. Every action, such as entering financial values, making an incorrect entry, or correcting a mistake, is logged into an indexed session table that stores contextual snapshots of the user’s actions. These logs capture detailed information about each step of the user’s interaction, which enables the system to reconstruct the error scenarios accurately. This granular tracking provides a precise understanding of where the user went wrong and the context in which the mistake occurred, allowing for more effective and focused remediation.
The system also incorporates dynamic error flagging through an execution tracer, which plays a crucial role in identifying computational errors in real time. By injecting trace points into critical functions responsible for financial processing, the system can track the flow of inputs as they are processed by the system. Each intermediate calculation is monitored and compared against expected financial models, enabling the system to flag discrepancies immediately. For instance, if a user incorrectly calculates a tax obligation or makes an erroneous journal entry, the execution tracer detects the inconsistency by comparing the real-time calculations to predefined rules or financial templates. This allows for prompt identification and correction of computational errors, ensuring that the user receives immediate feedback and guidance.
To further assist the user in overcoming repeated mistakes, the system applies a progressive hint mechanism. This mechanism introduces a series of escalating hints to guide the user through their mistakes. On the first incorrect attempt, the system displays a contextual tooltip that highlights the affected field, providing a subtle hint about the error. If the mistake is repeated, the system escalates the assistance by overlaying a semi-transparent correction guide, which visually demonstrates the correct process, allowing the user to see the proper steps. On the third attempt, the system takes a more active approach by initiating a step-by-step interactive walkthrough. This walkthrough forces the user to confirm each calculation and ensure they understand the correction before moving on, encouraging active learning and reinforcing the correct approach.
The adaptive feedback mechanism also ensures that the system continues to tailor the learning experience based on past errors. This is achieved through an error-weighted difficulty adjustment system, which dynamically adjusts the complexity of future tasks based on the user’s performance. For example, if the system detects frequent mistakes in a specific area, such as GST filing tasks, it queues additional exercises with slight variations to reinforce the learning of that topic. On the other hand, if the user demonstrates proficiency in an area, the system transitions them to more advanced financial topics, ensuring that the user is always challenged at the appropriate level. This adaptive approach allows the system to maintain an optimal learning trajectory for each user, preventing them from becoming overwhelmed by tasks that are too difficult or disengaged by tasks that are too easy.
By tracking errors at a granular level, providing real-time error flagging, offering escalating hints for repeated mistakes, and adjusting the difficulty of future tasks based on past performance, the system ensures that users receive the right level of support and challenge throughout their learning journey. This personalized approach enhances learning outcomes by promoting active engagement, reinforcing correct concepts, and guiding users toward mastery of complex financial tasks.
In an embodiment, the chatbot module provides personalized real-time assistance, wherein said chatbot module is configured to: parse and structure user queries dynamically, wherein when a user asks about tax deductions, the chatbot tokenizes the input, extracts relevant financial terms, and performs a domain-specific classification that assigns a confidence score to potential tax categories before generating a response; retrieve and assemble responses using a hierarchical data structure, wherein upon detecting a query regarding financial statement preparation, the chatbot dynamically constructs its response by navigating a decision tree that breaks the process into modular steps, allowing the user to interactively complete each stage before proceeding; provide interactive financial simulations in response to user errors, wherein if a user repeatedly miscalculates GST liability, the chatbot generates a real-time mock filing session by dynamically injecting incorrect and correct values into a replicated tax form, prompting the user to identify and correct errors using guided interactions; and adjust its response format based on user proficiency, wherein for novice users, responses are broken into structured explanations with visual aids, whereas for advanced users, direct numerical breakdowns and regulatory citations are provided, ensuring that chatbot complexity scales with the learner’s expertise.
In this embodiment, the chatbot module is designed to provide personalized, real-time assistance to users as they navigate complex financial tasks. The chatbot’s primary function is to offer context-sensitive, dynamic responses that are tailored to the user’s specific queries and proficiency level. The first key capability of the chatbot module is its ability to parse and structure user queries dynamically. When a user asks a question, such as one about tax deductions, the chatbot first tokenizes the input, breaking down the sentence into individual components to better understand the user's request. It then extracts relevant financial terms, such as "tax deductions," "deductions categories," or "eligible expenses," and uses these terms to perform a domain-specific classification. For instance, the chatbot may classify the query under the "taxation" category, applying a confidence score to potential tax-related terms, such as income tax or corporate tax. This classification process ensures that the chatbot understands the context and retrieves the most relevant information to generate a precise response.
Once the input is parsed and classified, the chatbot assembles its response using a hierarchical data structure. If the user inquires about something more complex, such as the preparation of financial statements, the chatbot dynamically constructs its answer by navigating a decision tree. The decision tree breaks down the financial statement preparation process into modular, sequential steps—such as gathering income and expense data, determining tax liabilities, and reviewing balance sheets—allowing the user to interactively complete each stage before proceeding to the next. This approach not only provides clear guidance but also fosters engagement by encouraging users to actively participate in the financial task rather than simply receiving a one-time response.
The chatbot module also provides interactive financial simulations to help users who may be struggling with particular tasks. For example, if a user repeatedly miscalculates GST liability, the chatbot can generate a real-time mock filing session. During this session, the chatbot dynamically injects both incorrect and correct values into a replicated tax form, simulating a real-world filing scenario. The user is then prompted to identify and correct the errors in the form through guided interactions. This simulation provides an immersive learning experience, helping users understand the correct steps and recognize where they went wrong, which in turn enhances their problem-solving skills and deepens their understanding of the subject matter.
Another significant feature of the chatbot module is its ability to adjust its response format based on the user's proficiency level. For novice users who may be new to financial concepts, the chatbot breaks down responses into structured, easy-to-understand explanations, often supplemented by visual aids such as diagrams, graphs, or flowcharts. This helps ensure that complex concepts are presented in a digestible format. On the other hand, for advanced users who are more familiar with financial principles, the chatbot shifts to providing direct numerical breakdowns, advanced calculations, and regulatory citations. This scalability ensures that the chatbot’s responses are always appropriate for the user’s level of expertise, providing enough detail to support further learning without overwhelming the user.
In an embodiment, the performance tracking dashboard is configured to: continuously aggregate user performance data from a distributed logging system, wherein each completed learning task, error occurrence, and correction attempt is serialized into a real-time data stream that feeds into a backend analytics engine for computation of accuracy trends and engagement metrics; dynamically update progress visualizations using event-driven rendering, wherein user progress graphs are not statically generated but instead updated in real time by subscribing to a data feed that triggers visual refreshes whenever a new performance metric is recorded; implement performance predictions using time-weighted scoring, wherein user accuracy rates and completion times are analyzed using a moving average function that assigns greater significance to recent activities, allowing the system to detect whether a student is improving or struggling based on weighted trends; and generate instructor alerts based on anomaly detection, wherein when a student’s performance deviates beyond a predefined threshold from their historical average, an alert is triggered that compiles a detailed diagnostic report containing error breakdowns, suggested interventions, and recommended remedial exercises.
In this embodiment, the performance tracking dashboard is a critical component of the system designed to monitor and optimize user learning by continuously tracking and analyzing their performance data. The dashboard aggregates user performance data from a distributed logging system, ensuring that every interaction—whether it’s the completion of a learning task, an error occurrence, or a correction attempt—is serialized into a real-time data stream. This data stream feeds into a backend analytics engine, which processes the raw data to compute key metrics such as accuracy trends and engagement levels. For example, each time a user completes a task or makes a correction, the system logs this data, helping to track the user's progress over time and identify any patterns in their performance. This continuous aggregation of data allows the system to provide a comprehensive and up-to-date view of the user's learning journey.
The performance tracking dashboard is designed to dynamically update visualizations based on the real-time data. Rather than having static progress graphs that need to be manually refreshed, the system employs event-driven rendering. This means that user progress graphs are automatically updated as new performance metrics are recorded. The system subscribes to a data feed that triggers visual refreshes whenever a new event, such as task completion or error correction, occurs. This ensures that the user and the instructor always have the most current information at their fingertips. For instance, as a student works through exercises, their progress in areas such as accuracy rate, task completion time, and error correction rate is immediately reflected in the dashboard. This dynamic updating fosters a more engaging and responsive learning environment.
The dashboard also implements performance predictions through the use of time-weighted scoring. This involves analyzing the user’s accuracy rates and completion times over a series of interactions using a moving average function. In this method, more recent activities are given greater weight in determining the user’s current performance trend. For example, if a user improves their accuracy in the most recent tasks, the system will reflect that improvement more heavily in the prediction, allowing the system to detect whether a student is improving or struggling based on recent activity. This time-weighted approach ensures that performance assessments are not skewed by older data, providing a more accurate representation of the user’s current abilities and trajectory.
To further support instructors in monitoring student progress, the system incorporates an anomaly detection mechanism that triggers alerts when a student’s performance deviates significantly from their historical average. This functionality is key to identifying students who may be struggling or encountering persistent issues. When an anomaly is detected, the system generates a detailed diagnostic report that includes an analysis of the student's errors, an explanation of the potential causes, and suggested interventions. The report may also recommend specific remedial exercises aimed at addressing the student’s weaknesses. For example, if a student repeatedly makes mistakes in tax calculation or journal entries, the system might alert the instructor and suggest targeted exercises to reinforce these skills. This proactive intervention helps to ensure that no student falls behind and that issues are addressed promptly. By aggregating real-time data, dynamically updating progress visualizations, making performance predictions based on recent activities, and generating instructor alerts for anomalies, the system ensures that learning is both adaptive and responsive. This enables users to track their own progress and allows instructors to offer timely and targeted support, ultimately enhancing the overall learning experience.
In an embodiment, the customizable simulation platform dynamically adjusts difficulty levels by: evaluate user input in real-time against predefined difficulty scaling parameters, wherein upon detecting rapid correct responses, the system automatically increases complexity by requiring manual calculations, removing tool-assisted guidance, or introducing real-world financial discrepancies such as rounding errors and tax adjustments; introducing time-based constraints to simulate real-world financial environments, wherein for advanced users, filing simulations impose time limits that replicate actual government deadlines, and failure to meet these constraints triggers an automated lesson on time management and financial accuracy;
customize learning paths, wherein upon successful completion of an initial tax filing scenario, the system generates a follow-up simulation with modified tax rates, additional deductions, or multi-step reconciliation tasks, ensuring learning extends beyond rote memorization; and integrate real-time market data into financial exercises, wherein to enhance realism, stock market data, interest rates, or current regulatory changes are dynamically fetched and incorporated into tax and accounting scenarios, requiring users to adapt calculations based on evolving financial parameters.
In this embodiment, the customizable simulation platform is designed to enhance the learning experience by dynamically adjusting the difficulty levels based on user performance, ensuring that the system provides challenges that are appropriately tailored to each user’s evolving skills. The platform evaluates user input in real-time against predefined difficulty scaling parameters. For instance, if the system detects that a user is consistently providing rapid and accurate responses, it automatically increases the complexity of the task. This could involve requiring the user to perform manual calculations, removing tool-assisted guidance, or introducing real-world financial discrepancies such as rounding errors or tax adjustments. These adjustments make the tasks more representative of real-world financial scenarios, where users often have to deal with unforeseen complications and perform detailed calculations without external aids.
To further simulate real-world financial environments, the platform introduces time-based constraints. For advanced users who have demonstrated proficiency in the core concepts, the system imposes time limits on filing simulations that replicate actual deadlines imposed by government or financial institutions. For example, users might be required to complete a tax filing within a set time frame, mirroring the pressure of real-world deadlines. If the user fails to meet these time constraints, the system triggers an automated lesson on time management and financial accuracy. This ensures that users not only learn the technical aspects of financial tasks but also gain experience in managing time efficiently—an essential skill in real-world finance.
The platform also customizes learning paths to ensure that users continue to progress beyond rote memorization. After a user successfully completes an initial tax filing simulation, for instance, the system generates a follow-up simulation that introduces new variables, such as modified tax rates, additional deductions, or multi-step reconciliation tasks. This adaptive learning approach ensures that the user is constantly challenged with increasingly complex scenarios, reinforcing their understanding of the concepts and encouraging critical thinking. By adjusting the learning path based on the user's prior performance, the system ensures that users are not just repeating the same tasks but are continually advancing to more sophisticated levels of financial understanding.
Additionally, the platform integrates real-time market data into financial exercises to further enhance realism. For example, stock market data, interest rates, or changes in regulatory policies are dynamically fetched and incorporated into tax and accounting scenarios. Users are required to adapt their calculations based on these evolving financial parameters. For instance, if the stock market sees a significant shift, the user may need to adjust their tax calculations or account for changes in investment values. This integration of real-world data makes the learning experience more immersive, providing users with a practical understanding of how dynamic financial environments impact decision-making and calculations.
In an embodiment, the teacher’s dashboard is configured to: provide real-time student performance insights through a dynamically updating metrics panel, wherein each student’s learning trajectory is visualized with a heatmap indicating strong and weak areas based on cumulative error frequencies; allow targeted intervention using an intelligent recommendation engine, wherein based on detected learning deficiencies, the system suggests customized assignments, quizzes, or one-on-one review sessions, all of which are queued in a teacher-controlled task manager; and facilitate remote monitoring through live session playback, wherein teachers can replay a student’s past interactions, including every keystroke, calculation step, and chatbot query, enabling precise diagnosis of misunderstandings.
In this embodiment, the teacher’s dashboard is a powerful tool designed to provide real-time insights into student performance, enabling educators to monitor, analyze, and support their students effectively throughout the learning process. The dashboard’s first key feature is its dynamically updating metrics panel, which visualizes each student’s learning trajectory in real time. This panel includes a heatmap that highlights both strong and weak areas of the student’s performance. The heatmap is generated based on cumulative error frequencies, meaning that areas where the student consistently struggles are visually represented with more intense colors, allowing the teacher to quickly identify where the student needs additional support. For example, if a student repeatedly makes mistakes in tax calculations or misclassifies transactions, these areas will be highlighted, enabling the teacher to focus intervention efforts precisely where they are needed.
Another important feature of the teacher’s dashboard is the intelligent recommendation engine. This engine analyzes student performance data to detect learning deficiencies, and based on these insights, the system suggests targeted interventions. For instance, if a student shows weaknesses in understanding tax codes or is consistently making errors in financial reporting, the system might recommend customized assignments, quizzes, or even one-on-one review sessions that focus on those specific areas. These recommendations are automatically queued in a teacher-controlled task manager, allowing the teacher to review and assign the suggested tasks with ease. The ability to target interventions based on a student’s individual needs ensures that learning is personalized, helping students address their weaknesses and progress effectively.
The teacher’s dashboard also facilitates remote monitoring through live session playback. This feature allows teachers to replay a student’s past interactions with the system, including every keystroke, calculation step, and chatbot query. By doing so, teachers can closely analyze how the student approached a particular task or concept, identifying exactly where misunderstandings or mistakes occurred. This level of detail gives teachers the ability to make precise diagnoses of student challenges, such as misinterpreting a financial concept or failing to follow a multi-step procedure correctly. For example, if a student has difficulty calculating tax deductions, the teacher can review the student’s session to pinpoint where the error originated—whether it was in entering data incorrectly, misapplying a rule, or simply overlooking a step. This capability allows for more informed and targeted teaching, as teachers can provide specific feedback based on the student’s actual performance and reasoning process. By visualizing strengths and weaknesses, recommending customized interventions, and allowing for precise analysis of past student interactions, the dashboard ensures that teachers can provide effective and personalized support, ultimately enhancing the learning experience for each student.
Figure 2 illustrates a flow chart of a method for interactive learning of commerce education in accordance with an embodiment of the present disclosure. At step 202, method 200 includes simulating statutory processes using a simulated government platform that replicates government portal workflows, wherein the simulation includes hands-on tasks for tax filings, GST registrations, and financial statement preparation.
At step 204, method 200 includes guiding users through processes with a chatbot module providing step-by-step instructions and facilitating collaboration through live sessions, group discussions, and peer reviews using a collaborative learning module.
At step 206, method 200 includes evaluating user performance via a tracking dashboard upon tracking user progress using a tracking mechanism and generating personalized insights using a learning management module.
At step 208, method 200 includes dynamically customizing simulation parameters selected from difficulty and task scenarios to align with individual learning needs.
At step 210, method 200 includes providing adaptive learning content to identify and address user-specific errors.
The method (200) further comprising the step of integrating multilingual support to provide localized educational content.
The method (200) further comprising the integration of gamification techniques, including rewarding task completion with badges or points; and fostering competitive and collaborative learning environments.
In another embodiment, the tracking mechanism generates detailed performance reports identifying recurring challenges and recommending targeted exercises for improvement.
Yet, in another embodiment, simulation parameters are selected from variable statutory process rules, task difficulties that scale progressively, and time constraints mimicking real-world scenarios.

Figure 3 illustrates a block diagram of an interactive learning system.
Commerce education, in its conventional form, primarily focuses on imparting theoretical knowledge through a linear process of teaching and assessment. While this approach equips students with a strong understanding of concepts, it falls short in preparing them for the practical challenges they will encounter in professional environments. This invention addresses this gap by introducing an interactive learning system that integrates theory-based education with practical skill development, creating a holistic and dynamic educational experience.
The present scenario in commerce education involves two core stages: theoretical learning and assessment. Students first gain knowledge of theory subjects, which is followed by evaluations typically in the form of written examinations or assignments. Though this process builds a strong academic foundation, it does not provide students with the skills needed to apply their knowledge in real-world contexts, leaving them inadequately prepared for professional roles.
The proposed system introduces a transformative approach by adding a Practicum component alongside traditional theory-based learning. The Practicum is designed to engage students in active learning through modularized topics (referred to as "splits"), interactive tools, and computational platforms. These components provide hands-on experiences and simulate real-world commerce scenarios, enabling students to practice and refine essential professional skills. For instance, students can perform financial analysis or process large datasets using advanced computational tools, bridging the gap between academic concepts and their practical applications.
To ensure a guided and effective learning process, the system incorporates a Teacher Dashboard. This dashboard allows educators to assign tasks, monitor student progress, and provide tailored feedback. By leveraging this feature, teachers can identify areas where students may need additional support and adjust the learning process accordingly. This interactive mechanism fosters greater engagement and personalized learning experiences, enhancing overall educational outcomes.
The assessment methods in the proposed system are also redesigned to comprehensively evaluate students’ theoretical understanding and practical competencies. Assessments include practical assignments, controlled examinations simulating real-world challenges, multiple-choice questions (MCQs) for quick theoretical evaluation, and open-book exams to encourage the application of knowledge rather than mere memorization. These diversified assessment strategies ensure that students are tested on both their academic knowledge and their ability to apply it in professional contexts.
The interactive learning system ensures that students graduate with a strong foundation in commerce theory and the practical skills needed to thrive in their careers. By integrating theory and practice, this system prepares students to face complex professional scenarios with confidence. Moreover, the modular and flexible nature of the Practicum allows students to learn at their own pace, making the system adaptable to diverse learning needs.
Figure 4 illustrates splits integration.

Figure 5 illustrates IPEL-FAS (interactive experiential learning of financial accounting systems).

Figure 6 illustrates the evaluation method.

Figure 7 illustrates an architecture of the unified platform.

Figure 8 illustrates a flow chart of a computer-implemented method for interactive learning in commerce education, the method comprising the steps of:
Step 802: receiving user input through an interactive interface configured to enable engagement with commerce-related tasks including financial accounting operations, tax filings, and compliance simulations, wherein each interaction is recorded in an event log with a timestamp and task metadata;
Step 804: executing real-time validation and error detection by capturing financial input values and structuring them into hierarchical data models, wherein individual fields are mapped to predefined statutory rulesets stored in a compliance database;
Step 806: performing multi-stage validation by executing a first-pass validation that applies a syntax parser to ensure numerical and textual accuracy, and a second-pass validation that applies a dependency-checking to detect logical inconsistencies including unbalanced journal entries, incorrect tax slab selection, and improper ledger allocations;
Step 808: generating a dynamic correction workflow by dynamically triggering an interactive guide upon detection of errors, wherein the interactive guide overlays step-by-step correction instructions directly on the input fields and requires the user to confirm each amendment before proceeding;
Step 810: logging detected errors in a structured knowledge repository, wherein user mistakes are classified based on frequency, severity, and conceptual impact, and wherein the logged errors are analyzed to refine future learning recommendations;
Step 812: adapting learning pathways dynamically based on user performance by analyzing error patterns and response times to adjust the complexity of future tasks, wherein the system applies a scoring function that assigns a proficiency score to each user action and dynamically increases or decreases task difficulty based on historical performance trends;
Step 814: implementing an adaptive feedback mechanism that monitors incorrect responses and initiates progressive remediation strategies, wherein repeated errors trigger automated guided explanations, real-time simulations, and interactive walkthroughs to reinforce conceptual understanding;
Step 816: adjusting remedial content in real-time by identifying recurring knowledge gaps and injecting targeted review exercises before advancing users to subsequent topics;
Step 818: simulating statutory and financial compliance processes in an interactive learning environment by generating real-time financial transactions, dynamically adjusting regulatory scenarios based on evolving tax laws and compliance rules, and integrating a guided tax filing module that requires users to input real-time financial data, validate deductions, and reconcile tax obligations based on dynamically updated statutory limits;
Step 820: enhancing user engagement with a chatbot-driven assistance module that interprets user queries, retrieves contextually relevant regulatory information, and generates personalized, step-by-step task walkthroughs, wherein chatbot responses adapt dynamically based on the complexity of user inquiries and past learning behavior; and
Step 822: tracking user performance metrics through a performance analytics engine that continuously aggregates user activity data, computes learning efficiency scores based on response accuracy and speed, and generates personalized insights for users and instructors.

The present invention aims to enhance learning experiences in commerce and education by integrating simulated tools, interactive elements, computation aids, open book case studies, and diverse evaluation methods. It bridges the gap between theory and practice by replicating real-world scenarios and offers educators multiple assessment options, ultimately fostering critical thinking and preparing students for success in their respective fields.
The present invention surpasses existing educational technology solutions with its comprehensive integration of simulated tools, interactive elements, computation tools, open book case studies, and diverse evaluation methods. This holistic approach enhances engagement, promotes practical application of knowledge, and ensures thorough assessment. Its adaptability allows tailored content for commerce and education sectors, bridging the gap between theory and real-world scenarios to better prepare students. Overall, it represents a significant advancement in promoting effective learning and skill development in both sectors.

The drawings and the forgoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, orders of processes described herein may be changed and are not limited to the manner described herein. Moreover, the actions of any flow diagram need not be implemented in the order shown; nor do all of the acts necessarily need to be performed. Also, those acts that are not dependent on other acts may be performed in parallel with the other acts. The scope of embodiments is by no means limited by these specific examples. Numerous variations, whether explicitly given in the specification or not, such as differences in structure, dimension, and use of material, are possible. The scope of embodiments is at least as broad as given by the following claims.

Benefits, other advantages, and solutions to problems have been described above about specific embodiments. However, the benefits, advantages, solutions to problems, and any component(s) that may cause any benefit, advantage, or solution to occur or become more pronounced are not to be construed as a critical, required, or essential feature or component of any or all the claims. ,CLAIMS:1. An interactive learning system for commerce education, comprising:
a user interface allowing students to input interactive lessons, quizzes, and multimedia content tailored for commerce education, process, and evaluate responses for statutory and financial processes, wherein the user interface includes tools for performing financial accounting tasks selected from journal entries, ledger postings, trial balances, and financial statement preparation;
a simulated government platform replicating actual statutory filing systems, including interfaces for tax returns, GST filings, and income tax submissions;
a chatbot module configured to simulate client interactions and provide scenario-specific guidance in real-time, wherein the chatbot module comprises an adaptive learning engine, employing artificial intelligence to customize educational content based on a learner's performance, pace, and preferences, wherein the adaptive learning engine utilizes a combination of machine learning techniques and analytics to identify knowledge gaps and recommend appropriate remedial actions;
an adaptive feedback mechanism to analyze user actions, detect errors, and provide targeted, dynamic feedback to track student progress, manage assignments, and provide analytics on performance metrics;
a performance tracking dashboard for monitoring and analyzing metrics selected from accuracy, task completion, and learning milestones;
a customizable simulation platform to adjust task complexity and feedback detail based on user skill levels; and
a teacher’s dashboard for evaluating student progress and providing personalized feedback.

2. The system as claimed in claim 1, wherein the adaptive feedback mechanism includes:
a real-time error detection module to detect real-time error and generate error pattern;
a processor to generate a tailored guidance based on user error patterns; and
a progressive difficulty scaling system aligned with a user’s learning curve, wherein the performance tracking dashboard provides detailed analytics on accuracy, error frequency, and time spent on tasks, and visualizations of learning progress using graphs and milestone trackers, and wherein the customizable simulation platform integrates tasks relating to tax processes, and corporate compliance tasks including company registration and director appointment filings, and wherein a real-time error detection module is provided that is configured to provide immediate feedback on user errors, including notification sensitivity adjustments based on user expertise levels.

3. The system as claimed in claim 1, wherein the adaptive learning engine identifies knowledge gaps and recommends remedial actions by:
extracting user performance data in real time from a structured event-driven logging system, wherein each user action—including incorrect responses, response latency, and repeated attempts—is stored in a time-series database indexed by task type, accuracy percentage, and error classification;
generating a weighted error profile by applying a matrix-based scoring function that assigns error severity scores to individual mistakes, wherein errors occurring in fundamental financial concepts are weighted higher than secondary computational errors, allowing the system to prioritize remediation of critical weaknesses;
identifying knowledge gaps through a decision-tree-based error clustering, wherein the system recursively analyzes user mistakes, groups them into logical categories (e.g., misclassification of liabilities, incorrect GST slab selection), and generates an adaptive learning path by selecting additional exercises from a pre-trained knowledge graph that maps financial concepts to learning modules;
recommending remedial actions dynamically, wherein upon detecting a recurring pattern of incorrect ledger postings, the system triggers a structured remediation sequence by:
automatically adjusting the next learning module to reinforce foundational concepts before allowing progression to advanced tasks;
generating interactive correction guides that overlay common mistakes with explanations directly on the user interface, requiring user confirmation before proceeding;
triggering chatbot-assisted correction walkthroughs, wherein the chatbot breaks down the user's mistake step by step, prompting user inputs at each correction stage to ensure active learning engagement; and
continuously refining remedial recommendations using a feedback-based learning loop, wherein after each remedial session, the system re-evaluates the user’s corrected responses, adjusts the difficulty scaling for future exercises, and dynamically reorders pending lessons based on updated proficiency scores.

4. The system as claimed in claim 2, wherein the real-time error detection module operates by:
intercepting user actions at the interface level using event-driven hooks, wherein a background event listener monitors keystrokes, button clicks, and input field modifications, logging each user interaction in a structured event queue that retains timestamps and contextual metadata;
performing inline validation on financial transactions by implementing a two-pass verification process, wherein in the first pass, each numerical input undergoes format validation using a regular expression parser to detect incorrect syntax, and in the second pass, a rules engine compares the input against predefined tax codes and accounting principles, triggering a validation event if mismatches are found;
generating automated ledger correction suggestions by parsing transactional discrepancies using hash-matching, wherein incorrect debit-credit mappings are detected by computing hash signatures of the user's ledger entries and comparing them against an indexed repository of verified accounting templates; and
executing rollback and auto-correction for detected errors, wherein when a tax calculation inconsistency is identified, the system dynamically reconstructs the sequence of calculations leading to the error by replaying stored event logs, allowing the user to either manually correct the issue or apply an automated fix generated by a pre-trained financial correction model.

5. The system as claimed in claim 1, wherein the adaptive feedback mechanism is configured to:
track user mistakes at a granular level using a stateful session monitor, wherein each interaction, including at least one of tax calculation, journal posting, is assigned a session ID, and all associated errors are logged in an indexed session table that maintains contextual snapshots of user actions to enable precise reconstruction of error scenarios;
Implement dynamic error flagging through an execution tracer, wherein the system tracks real-time input flows by injecting trace points into critical functions responsible for financial processing, allowing immediate identification of computational errors by comparing intermediary calculations against expected financial models;
apply a progressive hint mechanism for repeated errors, wherein after the first incorrect attempt, the system displays a contextual tooltip highlighting the affected field, on the second attempt it overlays a semi-transparent correction guide demonstrating the correct process, and on the third incorrect attempt, the system prompts a step-by-step interactive walkthrough forcing the user to confirm each calculation before submission; and
modify future learning paths based on past errors by maintaining an error-weighted difficulty adjustment system, wherein frequent mistakes in GST filing tasks result in the system queuing additional, slightly varied tax filing exercises, while proficiency in an area triggers a transition to more advanced financial topics.

6. The system as claimed in claim 1, wherein the chatbot module provides personalized real-time assistance, wherein said chatbot module is configured to:
parse and structure user queries dynamically, wherein when a user asks about tax deductions, the chatbot tokenizes the input, extracts relevant financial terms, and performs a domain-specific classification that assigns a confidence score to potential tax categories before generating a response;
retrieve and assemble responses using a hierarchical data structure, wherein upon detecting a query regarding financial statement preparation, the chatbot dynamically constructs its response by navigating a decision tree that breaks the process into modular steps, allowing the user to interactively complete each stage before proceeding;
provide interactive financial simulations in response to user errors, wherein if a user repeatedly miscalculates GST liability, the chatbot generates a real-time mock filing session by dynamically injecting incorrect and correct values into a replicated tax form, prompting the user to identify and correct errors using guided interactions; and
adjust its response format based on user proficiency, wherein for novice users, responses are broken into structured explanations with visual aids, whereas for advanced users, direct numerical breakdowns and regulatory citations are provided, ensuring that chatbot complexity scales with the learner’s expertise.

7. The system as claimed in claim 1, wherein the performance tracking dashboard is configured to:
continuously aggregate user performance data from a distributed logging system, wherein each completed learning task, error occurrence, and correction attempt is serialized into a real-time data stream that feeds into a backend analytics engine for computation of accuracy trends and engagement metrics;
dynamically update progress visualizations using event-driven rendering, wherein user progress graphs are not statically generated but instead updated in real time by subscribing to a data feed that triggers visual refreshes whenever a new performance metric is recorded;
implement performance predictions using time-weighted scoring, wherein user accuracy rates and completion times are analyzed using a moving average function that assigns greater significance to recent activities, allowing the system to detect whether a student is improving or struggling based on weighted trends; and
generate instructor alerts based on anomaly detection, wherein when a student’s performance deviates beyond a predefined threshold from their historical average, an alert is triggered that compiles a detailed diagnostic report containing error breakdowns, suggested interventions, and recommended remedial exercises.

8. The system as claimed in claim 1, wherein the customizable simulation platform dynamically adjusts difficulty levels by:
evaluate user input in real-time against predefined difficulty scaling parameters, wherein upon detecting rapid correct responses, the system automatically increases complexity by requiring manual calculations, removing tool-assisted guidance, or introducing real-world financial discrepancies such as rounding errors and tax adjustments;
introducing time-based constraints to simulate real-world financial environments, wherein for advanced users, filing simulations impose time limits that replicate actual government deadlines, and failure to meet these constraints triggers an automated lesson on time management and financial accuracy;
customize learning paths, wherein upon successful completion of an initial tax filing scenario, the system generates a follow-up simulation with modified tax rates, additional deductions, or multi-step reconciliation tasks, ensuring learning extends beyond rote memorization; and
integrate real-time market data into financial exercises, wherein to enhance realism, stock market data, interest rates, or current regulatory changes are dynamically fetched and incorporated into tax and accounting scenarios, requiring users to adapt calculations based on evolving financial parameters.

9. The system as claimed in claim 1, wherein the teacher’s dashboard is configured to: provide real-time student performance insights through a dynamically updating metrics panel, wherein each student’s learning trajectory is visualized with a heatmap indicating strong and weak areas based on cumulative error frequencies;
allow targeted intervention using an intelligent recommendation engine, wherein based on detected learning deficiencies, the system suggests customized assignments, quizzes, or one-on-one review sessions, all of which are queued in a teacher-controlled task manager; and
facilitate remote monitoring through live session playback, wherein teachers can replay a student’s past interactions, including every keystroke, calculation step, and chatbot query, enabling precise diagnosis of misunderstandings.

10. A computer-implemented method for interactive learning in commerce education, the method comprising:
receiving user input through an interactive interface configured to enable engagement with commerce-related tasks including financial accounting operations, tax filings, and compliance simulations, wherein each interaction is recorded in an event log with a timestamp and task metadata;
executing real-time validation and error detection by capturing financial input values and structuring them into hierarchical data models, wherein individual fields are mapped to predefined statutory rulesets stored in a compliance database;
performing multi-stage validation by executing a first-pass validation that applies a syntax parser to ensure numerical and textual accuracy, and a second-pass validation that applies a dependency-checking to detect logical inconsistencies including unbalanced journal entries, incorrect tax slab selection, and improper ledger allocations;
generating a dynamic correction workflow by dynamically triggering an interactive guide upon detection of errors, wherein the interactive guide overlays step-by-step correction instructions directly on the input fields and requires the user to confirm each amendment before proceeding;
logging detected errors in a structured knowledge repository, wherein user mistakes are classified based on frequency, severity, and conceptual impact, and wherein the logged errors are analyzed to refine future learning recommendations;
adapting learning pathways dynamically based on user performance by analyzing error patterns and response times to adjust the complexity of future tasks, wherein the system applies a scoring function that assigns a proficiency score to each user action and dynamically increases or decreases task difficulty based on historical performance trends;
implementing an adaptive feedback mechanism that monitors incorrect responses and initiates progressive remediation strategies, wherein repeated errors trigger automated guided explanations, real-time simulations, and interactive walkthroughs to reinforce conceptual understanding;
adjusting remedial content in real-time by identifying recurring knowledge gaps and injecting targeted review exercises before advancing users to subsequent topics;
simulating statutory and financial compliance processes in an interactive learning environment by generating real-time financial transactions, dynamically adjusting regulatory scenarios based on evolving tax laws and compliance rules, and integrating a guided tax filing module that requires users to input real-time financial data, validate deductions, and reconcile tax obligations based on dynamically updated statutory limits;
enhancing user engagement with a chatbot-driven assistance module that interprets user queries, retrieves contextually relevant regulatory information, and generates personalized, step-by-step task walkthroughs, wherein chatbot responses adapt dynamically based on the complexity of user inquiries and past learning behavior; and
tracking user performance metrics through a performance analytics engine that continuously aggregates user activity data, computes learning efficiency scores based on response accuracy and speed, and generates personalized insights for users and instructors.