Abstract: 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 chatbot platform with a chatbot interface provides for interaction between the knowledge seeker, a parent, a teacher, or another stakeholder. The chatbot platform allows multiple channels of engagement. The chatbot platform provides translation services comprising text to speech and speech to text service. The chatbot platform integrates third-party services into its responses to the user and queries from the user through the integration module. The chatbot platform performs pattern recognition and checks simplified and rephrased questions against a knowledge base. The chatbot platform uses conversation audits to train artificial intelligence and machine learning algorithms, to generate an appropriate response to the query of the knowledge seeker.

Abstract: 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. In response to a query received from the knowledge seeker, the SLKRS retrieves and sends in an immersive format one of the generated experiences or an experience created based on an artificially intelligent understanding of the received query. The SLKRS computes a coefficient of retention for the knowledge seeker based on a test of the ability of the knowledge seeker to recall a concept after the passage of a predetermined length of time, and after being exposed to a predetermined number of applications of the concept. The SLKRS generates interventions and improved experiences to provide adaptive and personalized e-learning to the knowledge seeker based on the computed coefficient of retention.

Abstract: 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.

Abstract: 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.

Abstract: 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. In response to a query received from the knowledge seeker, the SLKRS retrieves and sends in an immersive format one of the generated experiences or an experience created based on an artificially intelligent understanding of the received query. 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 interventions and improved experiences for the knowledge seeker based on the computed score.

Abstract: 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. In response to a query received from the knowledge seeker, the SLKRS retrieves and sends in an immersive format one of the generated experiences or an experience created based on an artificially intelligent understanding of the received query. 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 interventions and improved experiences for the knowledge seeker based on the computed score.