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.

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.

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.

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.

Implementations relate to determining a secondary language proficiency measure, for a user in a secondary language (i.e., a language other than a primary language specified for the user), where determining the secondary language proficiency measure is based on past interactions of the user that are related to the secondary language. Those implementations further relate to utilizing the determined secondary language proficiency measure to increase efficiency of user interaction(s), such as interaction(s) with a language learning application and/or an automated assistant. Some of those implementations utilize the secondary language proficiency measure in automatically setting value(s), biasing automatic speech recognition, and/or determining how to render natural language output.

A method of generating an educational output unit includes analyzing, using a machine learning module, content based on a logic tree, generating a plurality of blocks, associating tags with each block of the plurality of blocks, and assembling the plurality of blocks into an output unit based on one or more parameters and the tags. The logic tree comprises a structural hierarchy for the content.

A learning effect estimation apparatus includes a model storage memory storing a model that takes learning data as input, the learning data being data on learning results of users and being assigned with categories for different learning purposes. There is a correct answer probability generation unit that inputs the learning data to the model to generate the correct answer probability of each of the categories; a correct answer probability database that accumulates time-series data of the correct answer probability for each of the users; and a comprehension and reliability generation unit that acquires range data, the range data being data specifying a range of categories for estimating a learning effect for a specific user, and generates a comprehension.

A method for assessing learning comprehension regarding a topic includes modifying a fundamental illustrative model to illustrate a first set of assessment assets of a first learning object of learning objects to produce a first assessment illustrative model. The fundamental illustrative model is based on illustrative assets of a lesson that includes the learning objects. The method further includes obtaining a first assessment response for the first assessment illustrative model. When the first assessment response is favorable, the method further includes modifying the fundamental illustrative model to illustrate a second set of assessment assets of a second learning object of the learning objects to produce a second assessment illustrative model and obtaining a second assessment response for the second assessment illustrative model.

A method for assessing learning comprehension regarding a topic includes modifying a fundamental illustrative model to illustrate a first set of assessment assets of a first learning object of learning objects to produce a first assessment illustrative model. The fundamental illustrative model is based on illustrative assets of a lesson that includes the learning objects. The method further includes obtaining a first assessment response for the first assessment illustrative model. When the first assessment response is favorable, the method further includes modifying the fundamental illustrative model to illustrate a second set of assessment assets of a second learning object of the learning objects to produce a second assessment illustrative model and obtaining a second assessment response for the second assessment illustrative model.

The disclosure provides a calculation practicing method, a system, an electronic device and a computer readable storage medium, the calculation practicing method includes: providing a calculation question; identifying the type and content of the calculation question; generating an answer area according to the type and content of the calculation question; receiving an answering operation in which the user inputs the answer string for the calculation question in the answer area; identifying the answer string inputted by the user; and determining whether each of the answer characters in the answer string is correct, if there is an incorrect answer character, it will be marked, so that the calculation practice can be realized through the electronic device, which is convenient for students to carry out training.