## 2 Research on Evaluation and Analysis of Criteria for GenAI Tools Based on Multi-Criteria Methods
### Criteria for Selecting GenAI Tools  
Some criteria for selecting GenAI tools are:  
_Methodological and Ethical Considerations_: The integration of AI tools in academic research requires careful consideration of methodological and ethical implications. This includes addressing issues related to authorship, accountability, and the role of human researchers [11; 13; 17; 19; 21; 22]. The European Union, for example, has established frameworks to guide the responsible development and deployment of AI systems [8]. _Functionality_: The AI tools should be relevant to the research objectives and domain. AI tools should enhance research efficiency, accuracy, and quality across various stages of the research process, such as literature review, data analysis, and writing [22; 23]. They should also support diverse research methods, including quantitative and qualitative data analysis [23]. The selection of AI tools should be based on their performance and suitability for specific research tasks [6; 23; 24; 25]. AI tools designed for literature review must efficiently identify relevant sources [7]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**References** & **Problem** & **Multi-Criteria Method** & **Criteria** & **Domain** \\ \hline \multirow{6}{*}{[17]} & The barriers which & & & \\  & are impeding the & & & \\  & implementation of & & & \\  & Generative AI & & & \\  & Technologies, such as & & & \\  & ChatGPT in the & & & \\  & educational & & & \\  & landscape & & & \\ \hline \multirow{6}{*}{[18]} & Integrating the Chat & & & \\  & Generative & & & \\  & Pre-Trained & LDA & Data layer, technology layer, & \\  & Transformer-type & Grey-DEMATEL & user layer, service layer, & \\  & (ChatGPT-type) & method and TAISM & environment layer & \\  & model with & & & \\  & government services & & & \\ \hline \multirow{6}{*}{[19]} & GenAI a critical & & & \\  & solution for & & & \\  & improving energy & & & \\  & efficiency amid & & & \\  & increasing demand & & & \\  & for sustainable & & & \\  & energy at the & & & \\  & operational and & & & \\  & supply chain & & & \\  & management levels & & & \\ \hline \multirow{2}{*}{[20]} & Adoption of GenAI & F-DEMATEL and & Ethical, technological, & \\  & tools & FAHP & regulations and policies, cost, & \\  & & and human resources & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Cont. Transparency and Accountability:It is important to ensure transparency in the use of AI tools, emphasizing the importance of human intelligence and critical thinking in the research process. This helps maintain the authenticity and credibility of academic work [17; 18; 19; 21; 26]. Ease of Use:The tools should have an intuitive interface, should be user-friendly, allowing researchers without technical skills to utilize them effectively [10; 23; 27]. This enhances accessibility, especially for those without advanced technical skills [3] tools with quick responses and user-friendly interfaces are preferred [27]. Adaptability and Innovation:AI tools should be adaptable to various stages of the research process, from literature review to data analysis and writing. They should also foster innovation while balancing ethical considerations [16; 22; 28]. Scalability:AI tools must be able to handle increasing amounts of data and complexity as research projects grow. This ensures that the tools remain useful and relevant as research demands evolve [14; 16; 22]. Data Quality and Security:High-quality data input and output are critical for reliable research outcomes. AI tools should be capable of processing complex data accurately, which is particularly important in fields like healthcare and pharmaceutical research [29]. Consider the privacy and security features of the AI tool, especially when handling sensitive data [11; 12; 17; 18; 30]. Verify that the AI tool provides accurate information based on credible sources. Ensure that the tool adheres to data security standards and ethical research practices. Data quality is important for systematic reviews [22].