Abstract: A method for generating a set of concepts related to a target concept includes accessing a set of candidate concepts, embedding the target concept and the set of candidate concepts in a semantic vector space, selecting one or more intermediate concepts from the set of candidate concepts in response to determining whether each embedded candidate concept in the set of embedded candidate concepts satisfies a predetermined relationship with the embedded target concept, and filtering the one or more intermediate concepts to yield the set of concepts related to the target concept. The method may further include generating a multiple-choice question in which the target concept corresponds to a correct answer choice and the set of concepts related to the target concept correspond to distractors.

Abstract: Learning systems and methods of the present disclosure include generating a text document based on a digital file, tokenizing the text document, generating a semantic model based on the tokenized text document using an unsupervised machine learning algorithm, assigning a plurality of passage scores to a corresponding plurality of passages of the tokenized text document, selecting one or more candidate knowledge items from the tokenized text document based on the plurality of passage scores, filtering the one or more candidate knowledge items based on user data, generating one or more structured learning assets based on the one or more filtered candidate knowledge items, generating an interaction based at least on the one or more structured learning assets, and transmitting the interaction to a user device. Each passage score is assigned based on a relationship between a corresponding passage and the semantic model.

Abstract: Learning systems and methods of the present disclosure include generating a text document based on a digital file, tokenizing the text document, generating a semantic model based on the tokenized text document using an unsupervised machine learning algorithm, assigning a plurality of passage scores to a corresponding plurality of passages of the tokenized text document, selecting one or more candidate knowledge items from the tokenized text document based on the plurality of passage scores, filtering the one or more candidate knowledge items based on user data, generating one or more structured learning assets based on the one or more filtered candidate knowledge items, generating an interaction based at least on the one or more structured learning assets, and transmitting the interaction to a user device. Each passage score is assigned based on a relationship between a corresponding passage and the semantic model.

Abstract: Learning systems and methods of the present disclosure include generating a text document based on a digital file, tokenizing the text document, generating a semantic model based on the tokenized text document using an unsupervised machine learning algorithm, assigning a plurality of passage scores to a corresponding plurality of passages of the tokenized text document, selecting one or more candidate knowledge items from the tokenized text document based on the plurality of passage scores, filtering the one or more candidate knowledge items based on user data, generating one or more structured learning assets based on the one or more filtered candidate knowledge items, generating an interaction based at least on the one or more structured learning assets, and transmitting the interaction to a user device. Each passage score is assigned based on a relationship between a corresponding passage and the semantic model.

Abstract: A system, method and apparatus for maximizing the effectiveness and efficiency of learning, retaining and retrieving knowledge and skills includes a main engine having a Learn Module, a Review Module and a Test Module. Each of the Learn, Review and Test Modules are capable of operating independently but are preferably arranged to operate interactively such that operation of each of the Learn, Review and Test Modules are changed based on a user's past performance within one or more of the three modules. In addition, the main engine may also include a Schedule Module for flexibly scheduling learning, reviewing and retrieving knowledge and skills based on various factors and input information. The main engine also may include a Progress Module which monitors a user's performance on any of the Learn, Review and Test Modules and changes the future operation of each module based on the monitored performance.

Abstract: A system, method and apparatus for maximizing the effectiveness and efficiency of learning, retaining and retrieving knowledge and skills includes a learning engine that includes a novel model of human learning that adaptively determines a memory indicator for each item to be learned for each user during all phases of learning, including a short active phase of learning in which items are actively recalled and a long passive phase of learning in which items are passively forgotten. The memory indicator is determined based on a user's actual memory performance during the short-term active phase of learning and is accurately predicted based on mathematical modeling during the long-term passive phase of learning. The learning model makes use of a target level and an alert level of memory performance for each item of information for each user and the learning engine schedules presentation of items for review or study based on the user's performance with respect to the target and alert levels.