## 2 Research on Evaluation and Analysis of Criteria for GenAI Tools Based on Multi-Criteria Methods
Psychological concerns (anxiety about technological competence, cognitive overload, weak self-efficacy, social isolation, resistance to change). The paper addresses the problem of GenAI implementation, not GenAI selection. AHP is used. The relationships between criteria are not analyzed. Papers like [16; 17] propose hybrid decision-making frameworks such as fuzzy TOPSIS or AHP + CoCoSo in commercial or industrial contexts (chatbot selection or telecom), but again, they do not target the academic research domain nor analyze interdependencies among evaluation criteria. In the paper [16], a fuzzy multi-attribute decision-making framework to assist in selecting GenAI chatbots based on key features and performance is presented. It introduces a modified TOPSIS method adapted for an interval-valued hesitant Fermatean fuzzy (IVHFF) environment, improving the handling of uncertainty and hesitation in decision-making. The framework evaluates GenAI chatbots both statically and dynamically using four criteria: conversational ability, user experience, integration capability, and price. A case study comparing ChatGPT, Copilot (Microsoft Copilot, Redmond, WA, USA), Gemini (Google DeepMind, London, UK), Claude (Anthropic, San Francisco, CA, USA), and Perplexity demonstrates its effectiveness. Recommendations are provided for selecting and implementing chatbots in various applications. This paper does not analyze the relationships between criteria. It is not oriented towards academic research. GenAI chatbots are selected. The paper [17] introduces a decision-making framework for selecting chatbots in the telecommunication industry, leveraging AI advancements to enhance customer support. The proposed method combines the Analytic Hierarchy Process (AHP) for attribute weighting and the Combined Compromise Solution (CoCoSo) for ranking chatbots within a single-valued neutrosophic set (SVNS) environment, effectively handling uncertainty. A case study validates the model's efficiency and applicability, with sensitivity analysis confirming its robustness. The findings suggest that the AHP-CoCoSo approach yields more realistic chatbot selection outcomes under uncertain conditions, offering a practical tool for telecom decision-makers. In this paper, the problem is about selecting chatbots in the telecommunication industry. The relationships between criteria are not analyzed. It is not oriented towards academic research. Only a few studies [18; 19; 20] employ advanced methods such as DEMATEL, F-DEMATEL, or grey-DEMATEL to analyze causal relationships between criteria. Nevertheless, these are not focused on the selection of GenAI tools and not on academic research. Instead, they address issues related to the integration of AI in government services, energy management, and general barriers to GenAI adoption. The study [18] explores the integration of ChatGPT-type models into government services, highlighting both their potential to improve efficiency and the risks they pose. To understand public acceptance, the researchers used Latent Dirichlet Allocation (LDA) to identify 15 key influencing factors and applied grey-DEMATEL and TAISM to analyze their causal relationships. The criteria considered are as follows: Data layer (Data capacity, Data quality). Technology layer (own technology, technology maturity, technology ethics). User layer (information literacy, perceived risk, trust). Service layer (meeting demands, perceived ease of use, relative advantage), environment layer (old and new parallel, oversight and accountability, security guarantee, public officials' literacy). This paper addresses the problem of integration of ChatGPT-type models, not of the selection of a GenAI. It is not oriented towards academic research. The paper [19] explores the role of GenAI in enhancing energy efficiency amid growing demand for sustainable energy solutions.