Generation of Training Initial Generation Content

Rotkin, Vladimir; researcher, Independent

doi:10.15276/eltecs.32.108.2020.7

Cited by 2 publications

(2 citation statements)

References 5 publications

(5 reference statements)

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“…Another study [71] examines the impact of automated content creation on AI-based e-learning trends. Widely used content generators do not actually create new content, but modify content stored in databases.…”

Section: Education and Sciencementioning

confidence: 99%

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%

Configurable Intelligent Design Based on Hierarchical Imitation Models

et al. 2023

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“…Meanwhile, existing content generation systems, as a rule, modify the traditional content retrieved from databases but do not produce qualitatively new content. To obtain such content, a fundamentally different approach is needed, based, for example, on imitation-ontological modelling and the creation of intelligent generators of knowledge [20]- [24].…”

Section: Introductionmentioning

confidence: 99%

Trainable generator of educational content

Rotkin

2021

IJAAS

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<p>As the main problem of the research, the possibility of creating a universal educational platform that combines the possibilities of an online generation of educational content with the interface of the training process itself was considered. The methodology of the educational platform has been developed, in which the mass generation of content is carried out at random, based on simulation models of educational objects. A matrix interface is used, which allows performing custom operations by entering a sequence of typical operators. The system forms a reference base of operators, replenishing it from user solutions, which makes it possible to train and improve the system in order to provide methodological support to student users. An active demo layout of an educational content generator was created and tested, using the example of a specific problem from school mathematics. All methodological options function in the layout. There are three interface options: administrative, training and control. It was concluded that the approach based on the simulation of educational objects makes it possible to create a unified algorithmic platform that combines the functions of content generation with educational training. The system contains a unique option to teach yourself based on its interaction with students.</p>

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Generation of Training Initial Generation Content

Cited by 2 publications

References 5 publications

Configurable Intelligent Design Based on Hierarchical Imitation Models

Configurable Intelligent Design Based on Hierarchical Imitation Models

Trainable generator of educational content

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