Oct 2026· Izvestia Journal of the Union of Scientists - Varna Economic Sciences Series· 0 citations
Abstract
The rapid expansion of artificial intelligence (AI) in education and scientific research presents both significant opportunities and substantial challenges for higher education institutions (HEIs). Despite widespread adoption, a large proportion of AI initiatives fail to deliver sustainable value, underscoring the need for structured, ethical, and human- centered implementation models. This paper presents the modern achievements of the University of Economics – Varna in the application of AI across education, scientific research, and institutional processes. The study outlines an institutional framework for AI integration based on the Plan–Do–Check–Act (PDCA) cycle, emphasizing strategic vision,stakeholder collaboration, ethical governance, and continuous improvement. The presented experience contributes
practical insights into the transition from AI toward hybrid intelligence, where technological advancement is balanced with human values, trust, and institutional responsibility
Investigating how experienced developers use agents in building software, including their motivations, strategies, task suitability, and sentiments finds that while experienced developers value agents as a productivity boost, they retain their agency in software design and implementation out of insistence on fundamental software quality attributes.
This systematic review evaluates 55 studies from 2017 to 2023 on the application of machine learning techniques to ASD, highlighting key challenges and opportunities, particularly the need for models that can integrate complex data to improve diagnostic accuracy and treatment outcomes.
Rafael Muñoz-Terol, Jesús Peral, Sandra Amador et al.· Heliyon· 4 citations· ⚡1
This paper identifies two different routes through which models can acquire geometrically separable features: they can learn them from complementary co-occurrence signals in general language data, including text-number co-occurrence and cross-number interaction, or from multi-token addition problems.
A novel quantum generative model for synthesizing tabular data by proposing a quantum generative adversarial network architecture with flexible data encoding and a novel quantum circuit ansatz for effectively modeling tabular data is introduced.
P. Bhardwaj, Caitlin Jones, Lasse Dierich et al.· Scientific Reports· 2 citations
This survey model agent state as a dynamic graph, where memories, tools, skills, workflows, and inter-agent relations are represented as typed nodes, edges, and subgraphs updated through schema-constrained rewrites to provide a compact structural lens for designing and governing self-evolving agents.
Yuanyuan Xu, Wenjie Zhang, Yin Chen et al.· 2 citations
