Method and system for interactive, role-playing, experiential career exploration and upskilling through games

Abstract

A system for experiential career exploration and upskilling, the system comprising: a hardware processor and a memory device on which instructions are encoded to cause the hardware processor to perform the operations of: receiving input data associated with day-in-the-life scenarios associated with a plurality of careers; generating multi-modal day-in-the life scenarios tailored to a particular demographic; and generating multimodal game content comprising at least one of text, audio and video based on the multi-modal day-in-the life scenarios.

Description

METHOD AND SYSTEM FOR INTERACTIVE, ROLE-PLAYING, EXPERIENTIAL CAREER EXPLORATION AND UPSKILLING THROUGH GAMESFIELD

[0001] The present disclosure relates to methods and systems for experiential career exploration and upskilling.BACKGROUND

[0002] Immersive learning platforms are gaining in popularity, and are often used to assist users, such as students or job seekers, in selecting an appropriate or a fulfilling career. However, these platforms are not tailored to the individual users. For instance, these platforms are not able to tailor the courses to the user interest, age group, or ensure diversity in the instructional and day-in-the-life scenarios. Furthermore, these platforms do not provide a future outlook on possible career trajectories based on the initial user interest and current career. This is very challenging due to the propensity of these interests to change over time depending on individual circumstances, environments, and life incidents especially when young.SUMMARY

[0003] In one of its aspects, a system for experiential career exploration and upskilling, the system comprising: a hardware processor and a memory device on which instructions are encoded to cause the hardware processor to perform the operations of: receiving input data associated with day-in-the-life scenarios associated with a plurality of careers; generating multi-modal day-in-the life scenarios tailored to a particular demographic; and generating multimodal game content comprising at least one of text, audio and video based on the multi-modal day-in-the life scenarios.

[0004] In another aspect, a method for experiential career exploration and upskilling, the method comprising the steps of:with processing circuitry, execute instructions stored in a memory device, receiving input data associated with day-in-the-life scenarios for various careers; generate multi-modal day-in-the life scenarios tailored to a particular demographic; and generate multimodal game content comprising at least one of text, audio and video based on the multi-modal day-in-the life scenarios, wherein the multimodal game content comprises a simulated environment in which a user can assume different roles pertaining to a chosen career.

[0005] In another aspect, a computer readable medium storing instructions executable by a processor to carry out the operations comprising: receiving input data associated with day-in-the-life scenarios for various careers; generating multi-modal day-in-the life scenarios tailored to a particular demographic; generating multimodal game content comprising at least one of text, audio and video based on multi-modal day-in-the life scenarios, wherein the multimodal game content comprises a simulated environment in which a user can assume different roles pertaining to a chosen career.

[0006] The methods and system described herein provide a means to evaluate and improve learning outcomes on dynamic and ambiguous tasks such as tailoring student future career interests / prospects to their interests. Additionally, using the scores on the learning tasks for the various fields and the student interests in these fields (these can be tracked by how often they take similar courses in certain fields), the potential of these students within their chosen fields can be tracked and compared to their actual real-life future career preferences overtime.

[0007] In addition, the methods and system described herein enable users to map their career paths, or enable career professionals to switch careers, or find new job opportunities, and enables companies reduce employee chum by suggesting alternative career paths tailored to their employees and opportunities within their organization.BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 shows a top-level diagram of an overall system architecture for experiential career exploration and upskilling;

[0009] Figure 2 shows a flow chart with example steps for experiential career exploration and upskilling for a user;

[0010] Figure 3 shows a flowchart with example steps for predicting a future career path; and

[0011] Figures 4 shows an architecture of a computing device configurable to implement aspects of the processes described herein.DETAILED DESCRIPTION

[0012] The following detailed description refers to the accompanying drawings. Wherever possible, the same reference numbers are used in the drawings and the following description to refer to the same or similar elements. While embodiments of the disclosure may be described, modifications, adaptations, and other implementations are possible. For example, substitutions, additions, or modifications may be made to the elements illustrated in the drawings, and the methods described herein may be modified by substituting, reordering, or adding stages to the disclosed methods. Accordingly, the following detailed description does not limit the disclosure. Instead, the proper scope of the disclosure is defined by the appended claims.

[0013] Moreover, it should be appreciated that the particular implementations shown and described herein are illustrative of the invention and are not intended to otherwise limit the scope of the invention in any way. Indeed, for the sake of brevity, certain sub-components of the individual operating components, and other functional aspects of the systems may not be described in detail herein. Furthermore, the connecting lines shown in the various figures contained herein are intended to represent exemplary functional relationships and / or physical couplings between the various elements. It should be noted that many alternative or additional functional relationships or physical connections may be present in a practical system.

[0014] Figure 1 shows an overall system architecture 10 comprising a user device 12 and a machine or apparatus 14, such as a computing device e.g. back-end processing server, with processing circuitry or processor 16, memory 18 and storage backend 20. In one example, memory 18 is capable of storing data 21, machineexecutable instructions 22, including data models and process models. Storage backend 20 is coupled to the computing device 14 and stores pre-processed data, model output data and audit data. Further, the processor 16 is capable of executing the instructions 22 stored in the memory 18 to implement aspects of processes described herein. For example, the machine 14 comprises instructions 22 executable by processor 16, wherein the software instructions 22 may specifically configure the processor 16 to perform algorithms and / or operations described herein when the software instructions are executed. Alternatively, the processor 16 may execute hard- coded functionality. For example, memory device 18 comprise several modules with instructions 22 stored therein which are executable by the processing circuitry 16. The modules may include a data preparation module 23; a large language model module 24; a scenario generation module 25, a training module 26, prediction module 27; and a resume module 28 with executable instructions for generating resumes; and a ranking module 29 with executable instructions for generating ranking careers or scenarios, and a career progression module 30 with executable instructions for future prediction of a user’s career progression. External processing server 33 may be coupled to the computing device 14 for additional storage and processing.

[0015] Figure 2 shows a flow chart 100 with example steps for generating interactive, evidence-based content tailored to assist the user in exploring a chosen career. In step 102, machine or apparatus 14 receives or gathers day-in-the-life scenarios for various careers and provides real-time training on various skills, topics, and tools that professionals in the career would typically use. In one example, day-in- the-life scenarios may be obtained via web scrapping from available online profiles. For example, input training data includes career paths extracted from online platforms such as Linkedln. The data is then pre-processed before being input to the training module 26 to remove unintended symbols and irrelevant text and then embedded into vectors (tensors) for training the model. The various career paths that make up the input data at different times instances are also masked to prevent the model from memorizing the pattern rather it the system is forced to leam the patterns. The output of the training module 26 is a tensor that is converted into text using the initially embedded vector space obtained during pre-processing.

[0016] In step 104, the machine or apparatus 14 generates a plurality of multimodal day-in-the life scenarios that are tailored to the user interests, age group e.g., the day-in-the-life scenarios for high school students and for young professionals looking to change careers will be different. In one example, the multi-modal day-in- the life scenarios comprise audio, image and video data that is automatically generated from text-based prompts or voice-based prompts that are understood by a large language model (LLM) module 23 associated with the the machine or apparatus 14. In one example, the user inputs text, profde (age, career interest, previous experience) and the scenario generation module 25 determines what types of scenarios to generate. Alternatively, a user may input text of the career day-in-the-life scenario associated with the career of interest, and if that particular scenario not already stored on the machine or apparatus 14, then the text prompt is provided to the LLM module 23 to generate the input data which is fed into the scenario generation module 25 to generate the scenario. Accordingly, the input data and scenarios are stored in memory device 18 or storage backend 20 for future use.

[0017] In step 106, the multi-modal day-in-the life scenarios are deployed to an external processing server 33, such as cloud-based servers e.g. Heroku or AWS cloud computing platforms. In another example, the multi-modal day-in-the life scenarios are stored on storage backend 20.

[0018] In step 108, multimodal game content comprising text, audio and video is generated from the multi-modal day-in-the life scenarios. The multimodal game content comprises a simulated environment where the user can assume different roles pertaining to the chosen career. Specifically, the algorithms rely on a combination of machine learning (for user performance monitoring and categorization of user profiles) and generative Al techniques (for multi-model content generation) which are used to create realistic and immersive scenarios, personalize the user experience, and provide real-time feedback, and takes on various roles in the gamified experience and their responses to the game tasks are scored against expected / taught responses.

[0019] In step 110, the generated multi-modal scenarios are audited for ethical and diversity considerations and the performance of these scenarios in teaching / introducing users to the various aspects of the career is tracked over timefrom various user metrics, e.g., including user interactions, performance metrics, and learning progress.

[0020] In step 112, the generated multi-modal content is stored and published for interaction with users.

[0021] In step 114, the multi-modal scenarios are validated by industry experts and their ratings used to improve the Al-pre-screening models for better performance. In one example, the training data for these algorithms are sourced from expert input, user interactions, and educational research, to ensure that they accurately simulate real-world career situations and support meaningful learning outcomes.

[0022] In step 116, the multi-modal game content is validated by selected endusers (e.g., high school students) within the desired age-brackets using metrics such as ease of understanding, how well the areas are presented etc. System validation and testing may be performed through pilot studies with target user groups to gather feedback and refine the tool, as well as conducting reliability analyses (monitoring user feedback and interaction) to ensure that the tool consistently delivers accurate and meaningful results. Additionally, collaboration with researchers and experts in the field of learning science may be beneficial to validate the effectiveness of the methods described herein in achieving its intended impact on student learning and career exploration.

[0023] The user roles are obtained using multi-modal data (audio / video / text) depending on what is most appropriate for the various tasks. For each role the user plays, feedback in terms of required skills / responses for the role that are not adequately captured in the user response are provided to the user, and the user will have the option of going back to the required sections to leam more about their missing skills / required learnings. Hence, this can also serve to tailor the gamified experience to the users with different knowledge of the careers. That is, the user can play roles before going through the cours e / program and based on their responses and scores, they can be directed to areas where they need improvement for that role, while areas where they are already well grounded are not shown to them. Furthermore, the efficacy of the tool and user improvement can be obtained by comparing the user scores on various tasks with previous scores over time. The user may also be asked torate the platform after each learning task in terms of various metrics including ethical considerations such as diversity and inclusion of the characters, how easy the ideas were to understand, etc. Thus, the user experience is monitored over time and used to improv e / support better learning outcomes.

[0024] In one example, suppose a user has chosen a career as a dental assistant, one day-in-the life scenario may be that of a patient coming in for cavity cleansing. Given this scenario, a simulated experience (video, audio and text prompts) is generated where the user welcomes a patient in, and is expected to take the patient’s details and do preliminary assessment / information gathering for the patient in preparation for the dentist.

[0025] The user, role-playing as a dental assistant, is presented with a video on a GUI 31 that shows a dental room and a reception that takes in patients. The GUI 31 may comprise a dialog box 32 with text-based instructions or information. In one scene, the dentist receives a notification that a patient is here to see them. As such, the video may include questions and tasks for training and assessment. An example question might be “What is the first thing the dentist / assistant must do when a new patient visits a dental clinic? Answer: Take record of the patient’s dental history. A possible task maybe: “In the following scenario, Talle is a patient coming for her first appointment, and you are the Dental Assistant. Please check in Talle to see Dr. Jones.” Accordingly, the dental assistant takes the patients personal data, health history, etc., and tells the patient that the dentist will see them shortly. Once the dentist comes in, the dentist will review the collected patient information, and proceed to find out what the patient wants, does a check on the patient’s teeth, gums, etc. and if necessary, books an immediate session or a session for another day.

[0026] In one scenario, the user finds out the patient has a serious gum infection, and so has to recommend immediate treatment or a follow up session. In on example, the system may flag inappropriate content or language. For example, user commentary or opinions on a patient’s looks, use of racially suggestive terms or asking racially biased questions.

[0027] In one example, the career progression module 30 comprises instructions executable by the processor to develop career paths and expected outcomes usingtrained recurrent neural networks on masked data of various career paths. This ensures the user can achieve a holistic career exploration experience e.g., a future prediction of a person’s career progression and possible career transitions would be presented based on the chosen career. For example, a future career progression algorithm uses training data from existing career paths of experienced professional e.g., scrapped data and the predicted path would be tailored to user interests and personalities.

[0028] For the prediction of future career progression, the methods employed by the system 10 are reproducible i.e. different users starting with the same initial career should have similar career paths, robust i.e. the methods are applicable to most careers and accommodate synonyms e.g., data analyst and data intelligence analyst should yield similar paths, and also collect the required data for training the model. Also, the models are trained on data with career progressions such that the models learn the patterns rather than memorize the patterns. Hence the models are regularized in order to achieve this outcome.

[0029] Looking at Figure 3, there is shown a flowchart 200 with example steps for predicting a future career path using the prediction module 27. In step 202, machine or apparatus 14 receives or gathers career progression data. In one example, career progression data is obtained via web scrapping from available online profdes. For example, input training data includes career paths extracted from online platforms such as Linkedln®.

[0030] In step 204, the data is then pre-processed before being input to the training module 26 to remove unintended symbols and irrelevant text and then embedded into vectors (tensors) for training the model. The various career paths that make up the input data at different times instances are also masked to prevent the model from memorizing the pattern rather the system is forced to learn the patterns. The output of the training module 26 is a tensor that is converted into text using the initially embedded vector space obtained during pre-processing.

[0031] In step 206, a machine learning model training phase is initiated, and comprises evaluating various ML models and modelling parameters using algorithms such as GridSearch, Naive Bayes, neural networks and XG Boost. The training module 26 may be configured to train various models. Generally, the training data setand the feature vectors are used to fully train one or more predictive models. In one example, different machine learning classifiers or algorithms are used for building the predictive models, such as, supervised learning algorithms, unsupervised learning algorithms and reinforcement learning algorithms. Examples of supervised learning algorithm systems include support vector machine, decision tree, linear regression, logistic regression, naive Bayes, ^-nearest neighbor, random forest, AdaBoost, XGBoost, and neural network methods. Examples of unsupervised learning algorithm systems include K-means, mean shift, affinity propagation, hierarchical clustering, DBSCAN (density-based spatial clustering of applications with noise), Gaussian mixture modeling, Markov random fields, ISODATA (iterative selforganizing data), and fuzzy C-means systems. Examples of reinforcement learning algorithm systems include Maj a and Teaching-Box systems. Generally, training the predictive models involves optimizing the parameters of a predictive system to minimize the loss function. In addition to the training step, the predictive models also undergo validation using test datasets.

[0032] As such, in one example, the XGBoost regressor model is trained to use the best hyperparameters obtained. The trained model is then saved to the file system for future use, especially for making predictions on new data. The evaluation phase starts with making predictions on the validation and test sets. The model's performance is evaluated using various metrics, including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Pearson Correlation, RA2, and Concordance Correlation Coefficient (CCC). These metrics provide different lenses through which the model's predictive performance can be assessed. As an example, the XGBoost algorithm is able to automatically handle missing data values, and therefore it is sparse aware, includes block structure to support the parallelization of tree construction, and can further boost an already fitted model on new data i.e. continued training. For example, different ML models may be developed, which include parameters including accuracy, precision, recall, and Fl score functionality. Accordingly, the models are evaluated for their accuracy, precision, recall, and Fl -score.

[0033] In one example, the system 10 uses a recurrent neural network (RNN), with Long-Short Term Memory (LSTM). The RNN uses cells with feedback loopsand a hidden state to give the RNN a memory. The tanh activation function is used to overcome challenges with vanishing gradients in the RNN. The various layers also use regularization functions to force the model to learn the patterns including bath normalization and drop out. The architecture uses a cross-entropy loss function and the algorithms are optimized by an Adam Optimizer. Hyperparameters that are used in the training process may comprise epoch, number of neurons, learning rate, number of layers, batch size, sequence length, dropout rate, weight initialization method, etc.

[0034] In step 208, an application program interface (API) is created for career prediction using the trained models. The methods and system described herein use detailed data collection algorithm (using the web and mobile app) that is tailored to capture a wide range of user activities, and integrate this data collection with various machine learning models and APIs at various stages of the data collection to facilitate accurate and dynamic career predictions tailored to the user, as well possible job matches.

[0035] In one example, the system 10 provides feedback on the user’s strengths, areas of opportunities and growth.

[0036] Figure 4 illustrates a block diagram of an example of a machine 14 upon which any one or more of the techniques (e.g., methodologies) discussed herein can perform. In alternative embodiments, the machine 14 can operate as a standalone device or are connected (e.g., networked) to other machines. In a networked deployment, the machine 14 can operate in the capacity of a server machine, a client machine, or both in server-client network environments. In an example, the machine 14 can act as a peer machine in peer-to-peer (P2P) (or other distributed) network environment. The machine 14 is a personal computer (PC), a tablet PC, a set-top box (STB), a personal digital assistant (PDA), a mobile telephone, a smart phone, a web appliance, a network router, switch or bridge, a server computer, a database, conference room equipment, or any machine capable of executing instructions (sequential or otherwise) that specify actions to be taken by that machine. In various embodiments, machine 14 can perform one or more of the processes described above. Further, while only a single machine is illustrated, the term “machine” shall also be taken to include any collection of machines that individually or jointly execute a set(or multiple sets) of instructions to perform any one or more of the methodologies discussed herein, such as cloud computing, software as a service (SaaS), other computer cluster configurations.

[0037] Examples, as described herein, can include, or can operate on, logic or a number of components, modules, or mechanisms (all referred to hereinafter as “modules”). Modules are tangible entities (e.g., hardware) capable of performing specified operations and is configured or arranged in a certain manner. In an example, circuits are arranged (e.g., internally or with respect to external entities such as other circuits) in a specified manner as a module. In an example, the whole or part of one or more computer systems (e.g., a standalone, client or server computer system) or one or more hardware processors are configured by firmware or software (e.g., instructions, an application portion, or an application) as a module that operates to perform specified operations. In an example, the software can reside on a non- transitory computer readable storage medium or other machine-readable medium. In an example, the software, when executed by the underlying hardware of the module, causes the hardware to perform the specified operations.

[0038] Accordingly, the term “module” is understood to encompass a tangible entity, be that an entity that is physically constructed, specifically configured (e.g., hardwired), or temporarily (e.g., transitorily) configured (e.g., programmed) to operate in a specified manner or to perform part or all of any operation described herein. Considering examples in which modules are temporarily configured, each of the modules need not be instantiated at any one moment in time. For example, where the modules comprise a general-purpose hardware processor configured using software, the general-purpose hardware processor is configured as respective different modules at different times. Software can accordingly configure a hardware processor, for example, to constitute a particular module at one instance of time and to constitute a different module at a different instance of time.

[0039] Machine 14 can include a hardware processor 16 (e.g., a central processing unit (CPU), a graphics processing unit (GPU), a hardware processor core, or any combination thereof), a main memory 18, and a static memory 306, some or all of which can communicate with each other via an interlink 308 (e.g., bus). The machine14 can further include a display unit 310, an alphanumeric input device 312 (e.g., a keyboard), and a user interface (UI) navigation device 314 (e.g., a mouse). In an example, the display unit 310, input device 312 and UI navigation device 314 are a touch screen display. The machine 14 can additionally include a storage device (e.g., drive unit) 316, a signal generation device 318 (e.g., a speaker), a network interface device 320, and one or more sensors 321, such as an accelerometer, or other sensor. The machine 14 can include an output controller 328, such as a serial (e.g., universal serial bus (USB), parallel, or other wired or wireless (e.g., infrared (IR), near field communication (NFC), etc.) connection to communicate or control one or more peripheral devices (e.g., a printer, card reader, etc.).

[0040] The storage device 316 can include a machine readable medium 322 on which is stored one or more sets of data structures or instructions 324 (e.g., software) embodying or utilized by any one or more of the techniques or functions described herein, such as algorithms 22. The instructions 324 can also reside, completely or at least partially, within the main memory 18, within static memory 306, or within the hardware processor 16 during execution thereof by the machine 14. In an example, one or any combination of the hardware processor 16, the main memory 18, the static memory 306, or the storage device 316 can constitute machine readable media. While the machine readable medium 322 is illustrated as a single medium, the term "machine readable medium" can include a single medium or multiple media (e.g., a centralized or distributed database, and / or associated caches and servers) configured to store the one or more instructions 324.

[0041] The term “machine readable medium” can include any medium that is capable of storing, encoding, or carrying instructions for execution by the machine 14 and that cause the machine 14 to perform any one or more of the techniques of the present disclosure, or that is capable of storing, encoding or carrying data structures used by or associated with such instructions. Nonlimiting machine-readable medium examples can include solid-state memories, and optical and magnetic media. Specific examples of machine-readable media can include: non-volatile memory, such as semiconductor memory devices (e.g., Electrically Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM)) andflash memory devices; magnetic disks, such as internal hard disks and removable disks; magneto-optical disks; Random Access Memory (RAM); Solid State Drives (SSD); and CD-ROM and DVD-ROM disks. In some examples, machine readable media can include non-transitory machine-readable media. In some examples, machine readable media can include machine readable media that is not a transitory propagating signal.

[0042] The instructions 324 can further be transmitted or received over a communications network 29 using a transmission medium via the network interface device 320. The machine 14 can communicate with one or more other machines utilizing any one of a number of transfer protocols (e.g., frame relay, internet protocol (IP), transmission control protocol (TCP), user datagram protocol (UDP), hypertext transfer protocol (HTTP), etc.). Example communication networks can include a local area network (LAN), a wide area network (WAN), a packet data network (e.g., the Internet), mobile telephone networks (e.g., cellular networks), Plain Old Telephone (POTS) networks, and wireless data networks (e.g., Institute of Electrical and Electronics Engineers (IEEE) 302.11 family of standards known as Wi-Fi®, IEEE 302.16 family of standards known as WiMax®), IEEE 302.15.4 family of standards, a Long Term Evolution (LTE) family of standards, a Universal Mobile Telecommunications System (UMTS) family of standards, peer-to-peer (P2P) networks, among others. In an example, the network interface device 320 can include one or more physical jacks (e.g., Ethernet, coaxial, or phonejacks) or one or more antennas to connect to the communications network 29. In an example, the network interface device 320 can include a plurality of antennas to wirelessly communicate using at least one of single-input multiple-output (SIMO), multiple-input multipleoutput (MIMO), or multiple-input single-output (MISO) techniques. In some examples, the network interface device 320 can wirelessly communicate using Multiple User MIMO techniques.

[0043] Examples, as described herein, can include, or can operate on, logic or a number of components, modules, or mechanisms. Modules are tangible entities (e.g., hardware) capable of performing specified operations and are configured or arranged in a certain manner. In an example, circuits are arranged (e.g., internally or withrespect to external entities such as other circuits) in a specified manner as a module. In an example, the whole or part of one or more computer systems (e.g., a standalone, client, or server computer system) or one or more hardware processors are configured by firmware or software (e.g., instructions, an application portion, or an application) as a module that operates to perform specified operations. In an example, the software can reside on a machine-readable medium. In an example, the software, when executed by the underlying hardware of the module, causes the hardware to perform the specified operations.

[0044] There may be any number of computers associated with, or external to, the system 10 and communicating over network 29. Further, the terms “client,” “user,” and other appropriate terminology may be used interchangeably, as appropriate, without departing from the scope of this disclosure.

[0045] In another implementation, system 10 follows a cloud computing model, by providing an on-demand network access to a shared pool of configurable computing resources (e.g., servers, storage, applications, and / or services) that can be rapidly provisioned and released with minimal or nor resource management effort, including interaction with a service provider, by a user (operator of a thin client).

[0046] The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and / or flowchart illustration, and combinations of blocks in the block diagrams and / or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hard-ware and computer instructions.

[0047] Benefits, other advantages, and solutions to problems have been described above with regard to specific embodiments. However, the benefits, advantages, solutions to problems, and any element(s) that may cause any benefit, advantage, or solution to occur or become more pronounced are not to be construed as critical, required, or essential features or elements of any or all the claims. As used herein, the terms "comprises," "comprising," or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Further, no element described herein is required for the practice of the invention unless expressly described as "essential" or "critical."

[0048] The preceding detailed description of example embodiments of the invention makes reference to the accompanying drawings, which show the example embodiment by way of illustration. While these example embodiments are described in sufficient detail to enable those skilled in the art to practice the invention, it should be understood that other embodiments may be realized, and that logical and mechanical changes may be made without departing from the spirit and scope of the invention. For example, the steps recited in any of the method or process claims may be executed in any order and are not limited to the order presented. Thus, the preceding detailed description is presented for purposes of illustration only and not of limitation, and the scope of the invention is defined by the preceding description, and with respect to the attached claims.

Claims

CLAIMS:

1. A system for experiential career exploration and upskilling, the system comprising: a hardware processor and a memory device on which instructions are encoded to cause the hardware processor to perform the operations of: receiving input data associated with day-in-the-life scenarios associated with a plurality of careers; generating multi-modal day-in-the life scenarios tailored to a particular demographic; and generating multimodal game content comprising at least one of text, audio and video based on the multi-modal day-in-the life scenarios.

2. The system of claim 1, wherein the multimodal game content comprises a simulated environment in which a user can assume different roles pertaining to a chosen career.

3. The system of claim 2, further comprising at least one of a data preparation module; a large language model (LLM) module; a scenario generation module, a training module, and a prediction module.

4. The system of claim 3, further comprising at least one of a resume module with executable instructions for generating resumes; and a ranking module with executable instructions for generating ranking careers or scenarios, and a career progression module with executable instructions for future prediction of a user’s career progression.

5. The system of claim 4, wherein the career progression module comprises instructions executable by the processor to generate at least one career path and predict progression along the at least one career path and predict an expected outcome using at least one recurrent neural network.

6. The system of claim 5, wherein the at least one recurrent neural network is trained on masked data of various career paths.

7. The system of claim 6, wherein the masked data comprises scrapped data associated with experienced professionals in the at least one career path.

8. The system of claim 7, wherein the at least one career path is tailored to at least one of a user’s interests and personality.

9. The system of claim 3, wherein the multi-modal day-in-the life scenarios comprise at least one of audio, image and video data.

10. The system of claim 9, wherein the at least one of audio, image and video data is automatically generated from text-based prompts or voice-based prompts processed by the large language model (LLM) module.

11. The system of claim 9, wherein the data preparation module pre-processes the data to remove unintended symbols and irrelevant text.

12. The system of claim 10, wherein the data preparation module embeds the pre-processed data into vectors or tensors for training a model.

13. The system of claim 12, further comprising an application program interface (API) for capturing user activities to dynamically generate career predictions.

14. The system of claim 1, wherein the instructions are executable by the hardware processor to identify skills missing in a user profde and generate course content targeted at addressing said missing skills.

15. The system of claim 12, wherein the model is trained on data with career progressions, whereby the model is regularized to learn patterns rather than memorize the patterns.

16. The system of claim 12, wherein the recurrent neural network (RNN) comprises at least one layer employing regularization functions to force the model to learn the patterns comprising bath normalization and drop out.

17. A method for experiential career exploration and upskilling, the method comprising the steps of: with processing circuitry, execute instructions stored in a memory device, receiving input data associated with day-in-the-life scenarios for various careers; generate multi-modal day-in-the life scenarios tailored to a particular demographic; generate multimodal game content comprising at least one of text, audio and video based on the multi-modal day-in-the life scenarios, wherein the multimodal game content comprises a simulated environment in which a user can assume different roles pertaining to a chosen career.

18. The method of claim 17, wherein the multimodal game content comprises immersive multi-modal day-in-the life scenarios.

19. The method of claim 18, wherein the multimodal game content comprises tasks executable by a user, whereby performance of the tasks is evaluated, and whereby a user provides feedback.

20. A computer-readable medium storing instructions executable by a processor to carry out the operations comprising: receiving input data associated with day-in-the-life scenarios for various careers;generating multi-modal day-in-the life scenarios tailored to a particular demographic; generating multimodal game content comprising at least one of text, audio and video based on the multi-modal day-in-the life scenarios, wherein the multimodal game content comprises a simulated environment in which a user can assume different roles pertaining to a chosen career.

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