## 5 Results
### Study of Academics  
This research study conducted an extensive survey to evaluate the current state of AAI education among academics. A survey collected and analyzed questionnaires from 80 lecturers across five countries. The findings provide valuable insights into the teaching practices, competencies, and areas for improvement in AAI education.  
**The AI background of the academics.** The results revealed that a significant number of respondents were beginners in the field of AAI, while most of the respondents claimed intermediate-level skills. Advanced-level proficiency was reported by 23.75% of the participants, with only 7.50% considering themselves experts. Therefore, the survey presents a comprehensive overview, rather than a narrow expert perspective.  
One noteworthy finding was that more than half of the teachers were self-taught in AI, followed by those who received AI lessons during their university degrees. Less than a fifth had taken specialized AI courses. Regarding preferred activities for knowledge expansion, thematic courses were the most popular choice, while participation in conferences was half as popular. Commercial projects, open-source projects, engagement with public scientific groups, and getting to know the results of research conducted at universities garnered similar levels of support (about 50 percent).  
**AI teaching experience.** The study highlighted that a significant number of teachers lacked experience in teaching AI and had limited publication and research participation in applied AI (nearly forty percent had never published an article on applied AI matters, and only half had participated in AAI research). This emphasizes the need to involve more colleagues in AI projects, considering their evident interest. However, more than 50% of the teachers had at least one year of teaching experience, and almost one-fifth possessed over five years of teaching experience, positioning them as experts. Merely 15% of the teachers expressed disinterest in AI teaching.  
**Academics' involvement in AI projects.** Interestingly, the majority of the teachers had not participated in commercial AI projects but expressed a positive inclination toward involving external AI experts from industries (somewhat agree at 37.50%, agree at 42.50%, and no one was against experts from industries).  
**The AAI competencies important for academics.** There was strong consensus among the respondents regarding the importance of including AAI competencies in the curriculum, such as major AI areas, including the context of applications, recognizing the breadth and utility of ML methods, and their practical implementation. This focus includes the need to compare ML methods and select appropriate ones, together with related training and testing. However, advanced topics like overfitting, the curse of dimensionality, performance metrics, and algorithmic and data bias were considered less critical.  
The significance of discussing the potential ramifications of decision making arising from machine learning (ML) received relatively limited attention from the respondents, suggesting that it was not considered highly crucial. Similar results were observed regarding ethical considerations surrounding AI systems. It is likely that the teachers perceived the acquisition of comprehensive knowledge of the methods themselves as the primary focus for students, as the debate surrounding usage and ethics would be futile without a solid understanding of the functioning of these methods. Furthermore, the importance of logic and probabilistic formalism, along with their reasoning, was also deemed relatively less significant.