Abstract: A system and method are disclosed for generating standardized performance metrics for a user based on educational data obtained from one or more educational platforms. The system and method involve collecting performance-related data using a data collector integrated within an educational activity to curriculum standard mapping module, where the data reflects the user's educational activities. A normalization module standardizes this data according to predefined teaching curriculum standards. The normalized data is then mapped by assigning weights and confidence scores to determine the user's mastery level over various curriculum standards. A data managing module organizes this mastery information for further analysis. Based on the mapped data, the educational activity to curriculum standard mapping module produces a comprehensive and standardized performance metric for the user.

Abstract: A method for transforming a graphical user interface into an integrated academic performance and dynamic gaming representation. The transformed graphical user interface provides an interactive and dynamic gaming environment where a game character's energy level is influenced by both user interaction and academic task completion. As the user engages with the game, the character's energy gradually depletes based on user inputs. To replenish energy level, the user is prompted with academic tasks displayed within the same gaming environment. Successful completion of these academic tasks results in a partial or full replenishment of the character's energy, which is visually represented in the game. This method creates a feedback loop that connects academic effort with in-game progress, encouraging sustained engagement and learning through interactive play.

Abstract: A method is provided for guiding an Artificial Intelligence (AI) engine to generate enriched educational content by contextualizing educational standards with additional information. The method includes accessing a curriculum database containing educational standards and defining multiple extended attribute types, each representing a category of contextual data relevant to the standards. Detailed extended attributes are associated with these types and linked to specific courses within the curriculum, enabling content generation that aligns with both the standards and their educational context. A prompt is generated to direct a Large Language Model (LLM) to map the extended attributes to the corresponding educational standards. This prompt is transferred to the AI engine, enabling it to recognize and apply the extended attribute types for generating contextually enriched educational content.

Abstract: A method is disclosed for guiding an Artificial Intelligence (AI) engine to unlock new features within an online gaming platform based on a user's academic progress. The method includes providing a user interface that enables interaction with the gaming platform and creating a base knowledge structure stored within the user's profile, representing skills and knowledge acquired. As the user achieves educational milestones, the knowledge structure is incrementally increased in height, with each level signifying a corresponding advancement in learning. A prompt is generated to guide the AI engine in unlocking new game features when the knowledge structure reaches predefined thresholds. The prompt is transmitted to the AI engine, which then activates the new features within the gaming environment during gameplay. This approach incentivizes academic achievement by linking educational progress to enhanced in-game experiences, thereby promoting learning through interactive gaming.

Abstract: A method for guiding and constraining an Artificial Intelligence (AI) engine to deliver personalized learning recommendations based on a user's performance and behavior across online learning platforms. The method includes integrating a framework to enable communication between platforms and a learning system, collecting assessment and session data such as scores, time spent, answer choices, and navigation behavior. A data collection module parses this information to identify learning patterns, difficulties, and unproductive behaviors. Based on the analysis, a prompt is generated to guide the AI engine in producing personalized, actionable recommendations. These recommendations are presented to the user in real time via a popup window within the learning platform, providing adaptive, context-aware support during learning session.

Abstract: Aspects of the present disclosure relate to systems and method for providing a managed user experience for consumer products. A base station may detect a tag associated with a consumer product, which may be used to retrieve a managed user experience cached locally, or stored at a remote device associated with an experience provider. The managed user experience may include gameplay, content, and/or instructions to perform specified functionality. In an example, content and/or instructions may be transmitted to the consumer product and/or one or more intermediary devices in order to generate the managed user experience. In some examples, information may be received from the consumer product by way of the tag and used to provide the managed user experience. In other examples, the tag may be retrieve and/or provide aspects of a managed user experience from an experience provider without use of the base station or intermediary device.