An answer evaluation method that is executed by an answer evaluating system includes acquiring information of an answer that is an evaluation target corresponding to a question, and information of a comprehensive evaluation method, which is a method of determining a comprehensive evaluation of the answer based on an evaluation on the answer with respect to one evaluation item or each of two or more evaluation items for evaluating the answer represented by the information of the answer; and outputting information representing the comprehensive evaluation of the answer and acquired based on the answer represented by the acquired information of the answer and the comprehensive evaluation method represented by the acquired information of the comprehensive evaluation method.

A pegboard of the present disclosure includes: a main device including multiple unit modules; a board plate including multiple receiving portions; and multiple pegs to be inserted into the multiple receiving portions, and the multiple unit modules include multiple sensor modules configured to sense whether the multiple pegs are inserted into the multiple receiving portions and multiple light source modules configured to output light to the multiple receiving portions.

Provided is a method for controlling privileges for an intelligent industrial assistant. The method may include receiving a first input from a user. The first input may include a first natural language input and first biometric data. An identity of the user may be determined based on the first biometric data. A role associated with the user may be determined based on the identity of the user. A request of the user may be determined based on the natural language input. The request may be associated with at least one acceptable role. A determination may be made whether the role associated with the user matches at least one of the acceptable role(s). A system and computer program product are also disclosed.

Provided is a method for controlling privileges for an intelligent industrial assistant. The method may include receiving a first input from a user. The first input may include a first natural language input and first biometric data. An identity of the user may be determined based on the first biometric data. A role associated with the user may be determined based on the identity of the user. A request of the user may be determined based on the natural language input. The request may be associated with at least one acceptable role. A determination may be made whether the role associated with the user matches at least one of the acceptable role(s). A system and computer program product are also disclosed.

An educational system may include a contextualizer that accesses and processes content items and build a knowledge base including educational content and relationships between content items. The system may include a user assessor unit that determines user assessment variables based on user responses to previous educational content items and generate a user model based on the user assessment variables. The system may include a recommender that navigates the knowledge base and generates recommendations of new educational content based on the user model. The recommendation can assess the user's proficiency on a given concept based on the user model and maximize the user's probability of success around the concept of which the user shows proficiency, or improve the user's proficiency around the concept the user shows weakness. In some examples, the system may acquire an expert's knowledge about certain concepts to refine the user model, increasing the accuracy of the recommender.

A method for improving learning comprehension regarding a lesson includes issuing a first learning illustrative model to a set of learner computing entities. The method further includes obtaining a first comprehension evaluation associated with a first learner computing entity for the first learning illustrative model. The method further includes obtaining a second comprehension evaluation associated with a second learner computing entity for the first learning illustrative model. The method further includes modifying the fundamental illustrative model to illustrate a second set of learning assets of a second learning object based on at least one of the first and second comprehension evaluations to produce a second learning illustrative model. The method further includes sending the second learning illustrative model to the set of learner computing entities.

A method for improving learning comprehension regarding a lesson includes issuing a first learning illustrative model to a set of learner computing entities. The method further includes obtaining a first comprehension evaluation associated with a first learner computing entity for the first learning illustrative model. The method further includes obtaining a second comprehension evaluation associated with a second learner computing entity for the first learning illustrative model. The method further includes modifying the fundamental illustrative model to illustrate a second set of learning assets of a second learning object based on at least one of the first and second comprehension evaluations to produce a second learning illustrative model. The method further includes sending the second learning illustrative model to the set of learner computing entities.

The present disclosure relates to management of transparent glassboard representations, where a virtual glassboard is comprehensively integrated with presentation content. An exemplary transparent glassboard representation provides a layered configuration of content layers in an ordered arrangement to maximize integration of a virtual glassboard within presentation content. Some examples of the present disclosure comprise those where a graphical user interface (GUI) object is managed during the design of presentation content. In further examples, the present disclosure pertains to management for rendering of a transparent glassboard representation within a GUI of a host application/service endpoint during real-time (or near real-time) presentation. Further non-limiting examples apply trained artificial intelligence (AI) processing to efficiency automate management of transparent glassboard representations both in a design phase and presentation phase for managing presentation content. Representations of presentation content, comprising a transparent glassboard representation, may be synchronized through a plurality of different host applications/services.

The systems, methods and associated devices electronically collect, report and generate normalized educational outcome summaries of multiple different educational inputs, including didactic, experiential and problem solving events and/or assessments.

The systems, methods and associated devices electronically collect, report and generate normalized educational outcome summaries of multiple different educational inputs, including didactic, experiential and problem solving events and/or assessments.