Provided are a device and method for recommending educational content. The method includes acquiring a user's learning data, wherein the learning data includes at least one of the user's first learning ability information at a first time point, the user's second learning ability information at a second time point, and the user's question answering information, acquiring the user's target learning ability information on the basis of the learning data, determining a neural network model on the basis of the target learning ability information, distributing resources corresponding to the determined neural network model, and acquiring educational content to be recommended to the user through the determined neural network model.

Provided are a device and method for recommending educational content. The method includes acquiring a user's learning data, wherein the learning data includes at least one of the user's first learning ability information at a first time point, the user's second learning ability information at a second time point, and the user's question answering information, acquiring the user's target learning ability information on the basis of the learning data, determining a neural network model on the basis of the target learning ability information, distributing resources corresponding to the determined neural network model, and acquiring educational content to be recommended to the user through the determined neural network model.

A computer-implemented method of providing virtual reality (VR) or Augmented Reality (AR) training includes adapting the VR/AR training based on feedback on the user's biometric data, which may include electroencephalogram (EEG) data and other biometric data. Associations are determined between the biometric data and psychological/neurological factors related to learning, such as a cognitive load, attention, anxiety, and motivation. In one implementation, predictive analytics are used to adapt the VR/AR training to maintain the user with an optimal learning zone during the training.

A computer-implemented method of providing virtual reality (VR) or Augmented Reality (AR) training includes adapting the VR/AR training based on feedback on the user's biometric data, which may include electroencephalogram (EEG) data and other biometric data. Associations are determined between the biometric data and psychological/neurological factors related to learning, such as a cognitive load, attention, anxiety, and motivation. In one implementation, predictive analytics are used to adapt the VR/AR training to maintain the user with an optimal learning zone during the training.

A method and apparatus for providing learning content. According to one embodiment of the present disclosure, the experienced difficulty analysis method and apparatus are provided for determining at least one current item parceling for providing learning to the learner using the learner knowledge map; generating a learning set based on all or part of items included in the current item parceling; and providing an item to a learner interface based on the learning set.

A method and apparatus for providing learning content. According to one embodiment of the present disclosure, the experienced difficulty analysis method and apparatus are provided for determining at least one current item parceling for providing learning to the learner using the learner knowledge map; generating a learning set based on all or part of items included in the current item parceling; and providing an item to a learner interface based on the learning set.

A computer-implemented method and a smart-learning and knowledge retrieval system (SLKRS) are provided for imparting adaptive and personalized e-learning based on continually artificially learned unique characteristics of a knowledge seeker. A knowledge concept graph is generated for the knowledge seeker continually based on each of the received query and the received feedback by the smart-learning and knowledge retrieval system, thereby artificially learning unique characteristics of the knowledge seeker for measuring an ability of the knowledge seeker to learn and to show continued interest in an e-learning course. The knowledge concept graph is a cognitive blueprint of the knowledge seeker in a domain of knowledge at a point in time. The knowledge concept graph displays levels of granularity comprising one or more of interconnected concepts, categories of concepts, sub-categories of concepts, granular concepts, micro concepts, and macro concepts.

A computer-implemented method and a smart-learning and knowledge retrieval system (SLKRS) are provided for imparting adaptive and personalized e-learning based on continually artificially learned unique characteristics of a knowledge seeker. A knowledge concept graph is generated for the knowledge seeker continually based on each of the received query and the received feedback by the smart-learning and knowledge retrieval system, thereby artificially learning unique characteristics of the knowledge seeker for measuring an ability of the knowledge seeker to learn and to show continued interest in an e-learning course. The knowledge concept graph is a cognitive blueprint of the knowledge seeker in a domain of knowledge at a point in time. The knowledge concept graph displays levels of granularity comprising one or more of interconnected concepts, categories of concepts, sub-categories of concepts, granular concepts, micro concepts, and macro concepts.

A computer-implemented method and a smart-learning and knowledge retrieval system (SLKRS) are provided for imparting adaptive and personalized e-learning based on continually artificially learned unique characteristics of a knowledge seeker. The SLKRS ingests data in multiple formats from multiple sources, merges the data into a knowledge base based on computed strengths of terms in the sources, and assimilates the merged data to generate experiences. The SLKRS receives feedback from the knowledge seeker and computes a score based on the feedback and the query to artificially learn unique characteristics of the knowledge seeker. The SLKRS generates a learning path for the knowledge seeker on a graphical output, wherein the learning path's state transition points lead to a projected learning path determined by the knowledge seekers performance over one or more of subtopics, topics, and lessons.

A computer-implemented method and a smart-learning and knowledge retrieval system (SLKRS) are provided for imparting adaptive and personalized e-learning based on continually artificially learned unique characteristics of a knowledge seeker. The SLKRS ingests data in multiple formats from multiple sources, merges the data into a knowledge base based on computed strengths of terms in the sources, and assimilates the merged data to generate experiences. The SLKRS receives feedback from the knowledge seeker and computes a score based on the feedback and the query to artificially learn unique characteristics of the knowledge seeker. The SLKRS generates a learning path for the knowledge seeker on a graphical output, wherein the learning path's state transition points lead to a projected learning path determined by the knowledge seekers performance over one or more of subtopics, topics, and lessons.