WO-2026090751-A1 - 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.

Inventors

• AGBONLAHOR, EHIZOGIE MARYMARTHA
• AGBONLAHOR, OSAZUWA GABRIEL

Assignees

• 14047591 Canada Inc.

Dates

Publication Date

20260507

Application Date

20251031

Priority Date

20241101

Claims (20)

1. 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. 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. 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. 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. 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. 6. The system of claim 5, wherein the at least one recurrent neural network is trained on masked data of various career paths.
7. 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. 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. 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. 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. 11. The system of claim 9, wherein the data preparation module pre-processes the data to remove unintended symbols and irrelevant text.
12. 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. 13. The system of claim 12, further comprising an application program interface (API) for capturing user activities to dynamically generate career predictions.
14. 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. 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. 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. 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. 18. The method of claim 17, wherein the multimodal game content comprises immersive multi-modal day-in-the life scenarios.
19. 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. 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.

Description

METHOD AND SYSTEM FOR INTERACTIVE, ROLE-PLAYING, EXPERIENTIAL CAREER EXPLORATION AND UPSKILLING THROUGH GAMES FIELD [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