2hr Learning, Inc.

A virtual character identification system for guiding an AI engine to identify a virtual character based on AI-generated dialogues and images on an online learning platform is disclosed. The virtual character identification method involves collecting input data from user profiles and data models. The collected data is analyzed to generate insights that are used for prompt creation. These prompts are then used to guide the AI engine to generate text dialogues, which include an introduction, hints, and learning content. The AI engine also converts these dialogues into speech and dynamically generates images of the virtual character, where the image is progressively revealed based on user interaction. Furthermore, interactive audio-visual feedback, including educational videos with the virtual character, is provided to enhance the learning experience, allowing real-time engagement between the user and the platform.

An avatar generation system and method generate dynamic and interactive three-dimensional avatars for educational interaction and a personalized virtual assistant. The avatar generation system utilizes a combination of 3D modeling, face reenactment, and text-to-speech module to produce 3D avatars with realistic movements and interactions. The avatar generation system utilizes prompts to guide an artificial intelligence (AI) engine in generating the avatars. The avatar generation system improves the scalability and personalization of avatars. Furthermore, the avatar generation system aims to provide a more efficient and quality-effective way for the generation of avatars. The use of algorithms for the automatic generation of movements and behaviors is introduced to produce more realistic and personalized animations.

A curriculum graph database environment includes a curriculum graph database providing access to granular concepts covered in one or more curriculum. A user accesses the graph database via a user interface of an online learning platform. The graph database includes a curriculum graph generator that parses one or more curriculum data to identify a plurality of concepts, where each concept represents a concept node. The curriculum graph generator maps one or more learning resources included in the graph database to one or more related concept nodes. The user provides his inputs to the graph database, via the user interface, to retrieve a learning path related to a selected learning topic that he wants to master. The graph database includes a learning path generator that generates a learning path including list of concept nodes and associated learning resources to be completed by the user to attain mastery in the selected topic.

The real-time tutor generation system using Artificial Intelligence for adaptive learning includes an artificial intelligence (AI) engine to generate a virtual character for adaptive and personalized learning experiences. The method involves processors that perform operations such as accessing a virtual character from a library via a user interface integrated within an online learning platform. Communication initialization between the user and the virtual character begins by receiving real-time speech input, converted to text using a speech-to-text converter. A prompt generator generates prompts for the AI engine, based on the user input. The AI engine utilizes a pre-trained Large Language Model (LLM) to match the behavior and speech patterns of specific figures, including historical, fictional, animation, and cartoon characters. The generated audio response is converted into a video featuring the virtual character speaking, enhancing the user's learning experience by integrating video with the selected character.

A method to guide and constrain an Artificial Intelligence (AI) engine in generating and distributing notifications aimed at re-engaging users of an online learning platform is disclosed. It begins by collecting user data incorporating user profile details, user engagement data, and user performance data such as study goals and interaction logs. After analyzing the user data, notification timings are determined to align with each user's educational objectives. The process involves generating prompts to direct the AI engine in creating personalized video messages featuring virtual characters in correspondence to users' previous learning sessions. These prompts are then utilized to integrate the virtual character into video notifications, which are shared with users. The notifications include real-time interactions with the virtual character, designed to motivate and encourage users, thereby enhancing their ongoing participation and retention within the online learning environment.

An adaptive test generation system based on varying mastery levels of the user on educational standards to guide and constrain an AI engine in selecting educational standards for adaptive testing and updating user mastery levels based on real-time responses is disclosed. The method involves receiving a list of eligible standards, the user's knowledge graph, and dependencies between standards. A prompt is generated to direct AI engine in choosing the next educational standard for questioning. The AI engine selects a standard based on current knowledge graph and previous user responses, presents a question, and receives the user's answer. The knowledge graph is updated to reflect user's mastery levels of related standards. The AI engine then selects next standard, considering the updated knowledge graph and interdependencies among standards. This iterative process determines difficulty of subsequent questions and continues until the adaptive test concludes, based on the states of the educational standards involved.

An educational fact generation system and method integrates programmatic control and a guided and constrained an Artificial Intelligence (AI) engine to generate educational facts aligned with user-specific educational standards on an online learning platform is disclosed. It involves accessing multiple databases to retrieve educational standards, curriculum data, virtual character details, and user engagement data. The collected data is analyzed to provide insights for creating a prompt structure, which is then used to generate prompts that guide the AI engine. These prompts guide and constrain the AI engine to generate educational facts that correspond to the educational standards and integrate these facts with the dialogue and persona of a virtual character. Using deep learning techniques, an educational video is created, featuring the virtual character presenting the educational facts. The video aligns with the user's educational standards and is displayed to the user, aiming to enhance engagement and learning through the online learning platform.

The content generation system and content generation process utilizes a prompt to guide an Artificial Intelligence (AI) engine for dynamically generating educational content, for creating an educational matching game content. The method and system utilizes an educational curriculum database to receive input, including educational standards and course details. The input is used to retrieve information for a historical figure relevant to the educational standard from the curriculum database, which includes the historical figure's image and voice. Additionally, a AI engine generates facts for the educational standard associated with the educational matching game content to ensure the educational content is rich and comprehensive. The system generates video response to present the educational content using the historical figure, adding an engaging multimedia element to the learning experience. A prompt is generated to guide and constrain the AI engine in analyzing the educational content and generating key-value pairs.

The system and method combine programmatic control and a guided and constrained Artificial Intelligence (AI) engine to deliver educational content to users through a social media-style user interface is disclosed. The personalized learning system includes one or more processors and memory operatively coupled to the processors, executing code to perform various operations. The personalized learning system integrates a social media style user interface within an online learning platform, featuring swipeable vertically browsing content and interactive buttons like likes, dislikes, comments, shares, and bookmarks to enhance user engagement. The personalized learning system collects user profile details and engagement data based on which a prompt is generated for an AI engine. Under the control of programmatic logic, the AI engine uses these prompts to generate customized learning paths and content feeds, prioritizing content with the highest engagement scores. Personalized content feed is then displayed via the user interface, maintaining high user engagement.

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.