Trainable generator of educational content

Rotkin, Vladimir

doi:10.11591/ijaas.v10.i4.pp363-372

Cited by 1 publication

(2 citation statements)

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“…The study [72] proposes a universal educational platform that combines online content generation and the learning interface. It introduces a methodology using imitation models to generate educational content and a matrix interface for user actions.…”

Section: Education and Sciencementioning

confidence: 99%

“…Reference models can be further used both to build and study configurations, and to offer them to the users for smart support purposes. For example, whenever a student user enters a computational or logical formula when solving a problem online, the system identifies the relevant model [72] and offers (displays) suitable operators. The algorithmic system also offers supplementary features, such as image recognition, by correlating configuration variables.…”

Section: Structure and Functionsmentioning

confidence: 99%

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Configurable Intelligent Design Based on Hierarchical Imitation Models

et al. 2023

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The deterministic AI system under review is an alternative to neural-network-based machine learning. In its application fields, which are science, technology, engineering, and business, the implementation of rule-based AI systems leads to benefits such as accuracy and correctness of design, and personalization of the process itself and the results. An algorithmic AI suite is based on design and logical imitation models alone, without creating and/or using Big Data and knowledge bases. Excessive complexity of configuration and high design resource capacity, which are inherent in deterministic systems, are balanced by a special methodology. A hierarchical modeling approach gives a quasi-dynamic network effect, symmetric to the analogous effect in neural networks. System performance is improved by deterministic reference training capable of modifying imitation models in online interaction with users. Such training, which serves as an alternative to neural machine learning, can be implemented by means of experimental partially empirical algorithms and system–user dialogues to build reference model libraries (portfolios). Partially empirical algorithms based on experimental design methods and system user dialogues are used to create reference model libraries (portfolios) that form a deterministic training system, which can be an alternative to neural machine learning. Estimated resources can be saved by using modified optimization techniques and by controlling the computational complexity of the algorithms. Since the proposed system in the considered layout has no analogues, and the relevant research and practical knowledge are extremely limited, special methods are required to implement this project. A gradual, phased implementation process involves the step-by-step formation of sets of algorithms with verification tests at each stage. Each test is performed using an iteration method, and each test includes test, tweak, and modification cycles. Final testing should lead to the development of an AI algorithm package, including related methodological and working papers.

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Section: Education and Sciencementioning

confidence: 99%

Section: Structure and Functionsmentioning

confidence: 99%