2hr Learning, Inc.

A system and method combine programmatic control and a guided and constrained Artificial Intelligence (AI) engine provide real-time tutors in an online learning platform to enable the AI tutors to interact with the user and relevantly respond to user queries. The method includes receiving a request from the user for guidance during an online learning session or practice test via an interactive button on the user interface. The AI engine then accesses the user profile and educational database using a collector, gathering details of ongoing and past sessions, performance data, and interaction data. Utilizing this information, a prompt generator creates prompts to direct the AI engine, which then produces a detailed, personalized response. This response is generated in correlation to the context of the learning session and user data, explains concepts, corrects misunderstandings, and provides guidance, enhancing the educational experience for the user.

A personalized coaching advice generation system and method collects media stream data and user interaction data. Machine learning algorithms analyze media stream data and user interaction data to identify specific learning patterns, which are then classified into positive (posi-patterns) and negative (anti-patterns) categories. The personalized coaching advice generation system and method generate coaching prompts based on these patterns and a predefined prompt structure created by the prompt engineer. An AI engine uses these prompts to create personalized coaching advice, which is then displayed to the user through an online learning platform. The personalized coaching advice addresses specific behaviors detected during the learning session and suggests corrective actions to improve learning efficiency. The personalized coaching advice generation system and method also include features for calculating learning time wastage.

A fill-in-the-blank generation system and method integrates programmatic control and a guided and constrained AI engine to generate a fill-in-the-blank (FITB) question aligned with the educational curriculum of the user. The study mode delivery system accesses the data from the educational standards database and historical database. The study mode delivery system receives a standard description and set of key terms from the data accessed from the databases. The analyzer integrated within the study mode delivery system analyzes the course, standard description, and key terms relevant to the educational curriculum and standards.

A comic generation system based on user profile details, proficiency and educational standard selects a learning topic for the comic generation by accessing the user profile details and one or more curriculum graphs stored in a curriculum database. One or more learning topics are generated that are relevant to the user profile details and the one or more curriculum graphs. The user profile details include one or more user interests obtained through the interaction between the user and a chatbot and user details stored in the user profile. The one or more curriculum graphs are generated based on curriculum obtained from the one or more educational standards.

A system and method for guiding and constraining an Artificial Intelligence (AI) engine to provide personalized educational support to a user on an online learning platform using a multi-assistant framework is disclosed. The system and method access curriculum database and grading rubrics. User interaction data including user responses and selected units or topics is received. A free-response questions (FRQs) are generated using algorithms. The user responses to the FRQs are graded by utilizing the grading rubrics and providing projected score using FRQ grader assistant. The FRQ grader provides feedback aligned with scoring guidelines based on grading rubrics, and delivers projected score. A prompt is generated and transferred to AI engine to generate an assessment corresponding to the user performance based on a grading result on the user responses to FRQs. The generated FRQs, graded user responses, assessment, and projected scores are provided to the user on the online learning platform.

A programmatically controlled and guided and constrained Artificial Intelligence (AI) engine reading passage generation system and method for guiding the AI engine in generating personalized reading passages for users using an online learning platform is disclosed. The method involves collecting user details, including interests, reading level, academic standards, and optional fictional attributes, from the user's profile. Based on the user's genre preference and reading level, the reading passage generation system selects an appropriate prompt structure from a repository. The collected data is then analyzed to generate insights, which are used to populate the selected prompt structure. The resulting prompts are transferred to the AI engine, guiding it to generate a reading passage that aligns with the user's interests, reading level, and age appropriateness. The final output includes not only the personalized passage but also multiple-choice questions (MCQs), a title, and an associated image, enhancing the overall educational experience.

The system and method for guiding an Artificial Intelligence (AI) engine to generate a set of key terms relevant to educational standards. The system and method involve receiving input data including educational standards and key term types from educational curriculum guidelines, configuring a quality bar comprising metrics such as standard relevance, category fit, curriculum alignment, and correctness, and generating a prompt to guide the AI engine for generating key terms. An algorithm is then applied involving iterative prompt refinement and selection of key terms based on the quality bar metrics. This includes generating initial key terms using the AI engine, evaluating the generated key terms against the quality bar metrics, and refining the prompts iteratively based on the evaluation to improve the relevance and accuracy of the key terms. Furthermore, the prompt is transferred to the AI engine to generate a final set of key terms aligned with the educational standards.

A personalized content generation method and system to guide and constrain an AI engine to generate personalized educational content for accelerating test preparation of a user based on the performance of a user in a mock test on an online learning platform is disclosed. The method starts with presenting a mock test related to a specific curriculum. User performance data, including mastery levels on various topics, is collected and analyzed. The data is mapped to historical exam data, identifying weak areas of the user. The system then determines the importance of these weak topics based on their frequency in past exams and their relevance to curriculum standards. The system generates prompts for the AI engine, guiding and constraining to create personalized educational content focused on these areas. The personalized content is delivered to the user in real-time, targeting topics where user's mastery level is low but is significant for exam.

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 guiding an artificial intelligence (AI) engine to generate and deliver educational content through an AI-generated tutor on an online learning platform. The method involves collecting input data from multiple sources, including educational, historical, image, and voice databases. Based on this data, prompts are generated to direct the AI engine in producing curriculum-aligned scripts. A digital AI tutor is created using the collected data, including synthesizing a voice that mimics the tutor and generating a corresponding visual representation. The script, voice, and visual elements are integrated into an animated video of the AI tutor. The video is then delivered and displayed to users on the learning platform at a scheduled time to facilitate personalized, standards-based instruction.