The use of innovative technology in the field of Speech and Language Therapy (SLT) has gained significant attention nowadays. Despite being a promising research area, Socially Assistive Robots (SARs) have not been thoroughly studied and used in SLT. This paper makes two main contributions: firstly, providing a comprehensive review of existing research on the use of SARs to enhance communication skills in children and adolescents. Secondly, organizing the information into tables that categorize the interactive play scenarios described in the surveyed papers. The inclusion criteria for play scenarios in the tables are based only on their effectiveness for SLT proven by experimental findings. The data, systematically presented in a table format, allow readers to easily find relevant information based on various factors, such as disorder type, age, treatment technique, robot type, etc. The study concludes that the despite limited research on the use of social robots for children and adolescents with communication disorders (CD), promising outcomes have been reported. The authors discuss the methodological, technical, and ethical limitations related to the use of SARs for SLT in clinical or home environments, as well as the huge potential of conversational Artificial Intelligence (AI) as a secondary assistive technology to facilitate speech and language interventions.

The consortium of “The Future is in Applied Artificial Intelligence” Project designed the first competency-based applied artificial intelligence curriculum at the higher-education institution level. The development was based on advanced system research on existing artificial intelligence-related resources and surveying target groups of teachers, information technology students, and employers, which should enhance the performance of implementing artificial intelligence education. A review of applied artificial intelligence was prepared in the form of keyword clustering. The initial data were collected with the help of surveying by identifying job offers, existing artificial intelligence training courses, scientific projects, and real cases. A synthetic analysis of the textual information from the studies was conducted using the word clouds technique. A tensor-based approach was used for the presentation of the competency-based course. The specific numerical requirements for the course in the form of priorities followed from the solution to decision-making problems using the analytic hierarchy process technique. Based on a comprehensive study of surveys, educational experience, scientific projects, and business requirements, and a meta-analysis of the recent references, we specified the criteria for a training course in the form of a tensor-based representation of competencies in relation to content and educational modules.

Automated bibliographic metadata processing is a key component of intelligent library information systems. However, the use of large language models (LLMs) for bibliographic record generation is associated with factual errors, bibliographic hallucinations, and structurally incorrect metadata. This study proposes a retrieval-constrained workflow in which generative AI operates only on previously extracted and validated information from authoritative bibliographic sources. The workflow integrates deterministic metadata extraction, hierarchical source selection, normalization, multi-stage validation, and specialized AI agents for metadata structuring, Universal Decimal Classification (UDC) assignment, and MARC-formatted record generation. The architecture was evaluated on 200 real ISBN-identified bibliographic records representing 12 languages. The experimental design distinguishes ISBN-only diagnostic conditions, an external Google Books bibliographic metadata baseline, a retrieval-only internal architectural ablation, and the full specialized AI Library workflow. Google Books returned exact ISBN-matched records for 48 of the 200 submitted identifiers (24.0% coverage), resulting in a macro-average manually verified field-level accuracy of 14.8% over the complete dataset. In the controlled GPT-4o comparison, the specialized AI Library workflow increased the macro-average manually verified accuracy of the principal bibliographic fields from 65.0% to 83.2%. It also increased the UDC Recognition Rate from 85.0% to 99.5% and COMARC/B record-generation coverage from 87.5% to 97.5%, while mean Exact+Partial correspondence with the COBISS reference records increased from 62.1% to 73.6%. Overall, the results demonstrate consistent improvements in bibliographic metadata accuracy, UDC assignment coverage, and COMARC/B record-level correspondence.