Gaming System Predicting Symbiotic Objectives via Machine Learning
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Solution Overview
Problem
Existing educational video games are limited in teaching real-life skills such as cooking, sewing, child care, and budgeting, leaving a gap for more comprehensive educational opportunities in gaming.
Innovation Solution
A computer gaming system that allows users to create a gaming profile associated with an entity, displays objectives, receives user selections, creates gaming actions, and provides digital remuneration for achieving objectives, which can include financial skills like saving, purchasing, and increasing credit score.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional educational video games are used, then remedial education is provided, but real-life skills such as cooking, sewing, child care, and budgeting cannot be taught
Solution Approach 1:
The gaming system is designed to perform multiple educational functions beyond traditional remedial education. It teaches real-life skills including cooking, sewing, child care, and budgeting through integrated gameplay mechanics. The system universally applies gamification principles across diverse educational domains, allowing a single platform to deliver varied educational content that was previously requiring separate specialized systems.
2Productivity
If gamification elements are added to educational games, then user engagement increases, but the educational effectiveness may be diluted
Solution Approach 1:
The system merges gamification elements directly with educational objectives rather than layering them separately. Game mechanics such as objectives, actions, and remuneration are integrated with real-life skill development. The gamification structure reinforces educational content through synchronized feedback loops where game rewards align with educational milestones, ensuring both engagement and effectiveness are enhanced simultaneously.
Solution Approach 2:
The system implements continuous feedback mechanisms where user actions in the game produce immediate educational feedback and gamification rewards. When users complete educational tasks like budgeting exercises or cooking simulations, they receive both educational confirmation and game-based remuneration. This dual feedback system validates both the educational effectiveness and maintains user engagement through rewarding progress.
3Ease of operation
If digital remuneration is provided for achieving objectives, then user motivation increases, but the system complexity increases
Solution Approach 1:
The system implements automated remuneration distribution where digital rewards are automatically granted when users achieve predefined objectives. The gaming system self-manages the complexity of tracking user progress, evaluating objective completion, and distributing remuneration without requiring manual intervention. This automation handles the system architecture complexity internally while presenting a simple motivational interface to users.
Data Source
AI summary
Systems and methods for achieving objectives include receiving, via a user device, user input requesting access to a gaming profile, receiving entity data from the user's gaming profile, collecting entity data from a plurality of user profiles associated with an entity, and extracting resource data from the entity data for the user and the plurality of users. In response to extracting the resource data, the system uses a machine learning dataset to predict an objective for the user. Based on the predicted objective, the system determines gaming actions capable of being performed via the user device and associated with the gaming profile that would further the objective. The system assigns a remuneration amount to each gaming action, monitors the gaming profile for completion of the gaming actions, and allocates to the gaming profile remuneration in the amount assigned to the gaming actions completed by the user.