The research problem is defined – the need for automating the narration and visualization of the complicated business processes for people with different types of sensory learning. The research methodology is created and discussed; a literature review is made to help find possible solution; diagram sources are chosen; a conceptual solution is discussed and proposed. A prototype experiment that uses LLMs is set up as proof of concept. The diagrams are analyzed using the prototype and the results are discussed. Conclusions on the applicability of this type of technology are made, and it is found that there is benefit from using the chosen or similar approaches for these purposes. Future directions for related research are proposed.
Graded logic is a human-centric continuum-valued propositional logic of natural human commonsense logical reasoning and decision making. It is a seamless generalization of the classical bivalent Boolean logic, fuzzy logic propositional calculus, and non-classical continuum-valued logics. This paper is an extended abstract which summarizes basic properties of Graded Logic and its applications in decision-support systems.
In this paper we suggest a flexible model for software support system that is able to automate data collection and analysis of academic and research management information. We follow on the experimental implementation, tested at the Plovdiv University to assess the strengths and challenges that face introduction of intelligent data processing and application of LLMs in support for higher education. Usability and quality of various support tools is also assessed, based on feedback and use statistics collected over the complete experiment period.
Strategic implementation of artificial intelligence (AI) in higher education (HE) is becoming essential as institutions seek to remain relevant and effective in a rapidly evolving digital landscape. The expansion of AI tools presents both transformative opportunities and significant challenges to conventional educational paradigms. This paper investigates how AI can be strategically integrated within HE by using the PDCA cycle as a part of decision support systems (DSS). Beginning with an overview of DSS, the study examines its practical applications in higher education institutions (HEIs) through different models for AI adoption. Using a bibliometric analysis of literature on "artificial intelligence", "strategy", and "university," and grounded in a conceptual framework developed from a systematic review, the research highlights key strategic priorities. These include the necessity of AI adoption to follow phased roadmaps and aligns with institutional mission, often requiring dedicated leadership, planning, and governance structures as well as institutional change management.
In the Bulgarian school education system, the use of electronic grade-books has become widespread, yet the full analytical potential of the data they collect remains largely untapped. Despite significant technological advancements, educational institutions still underutilize automated tools and artificial intelligence (AI) for educational diagnostics and administrative decision-making. This paper presents an approach based on Intuitionistic Fuzzy Evaluations (IFE) to assess student academic performance and detect early signs of learning difficulties.
