This study is based on extensive survey conducted as a part of activities during the realization of the Erasmus+ project “Future is in Applied Artificial Intelligence”. Survey aimed to research the needs and expectations of IT graduates Masters and IT Alumni in Information Systems and Technologies regarding various Applied Artificial Intelligence topics with the goal to examine knowledge and attitude of students toward AI contents, current state of AI education and future directions of the transformation of education system toward competency-based education.

Full paper can be found here: Research 5

The questionnaires of 80 teachers from 5 countries were collected and analyzed, concerning artificial intelligence teaching. Among the more interesting results belongs the finding, that most of the teachers are self-educated regarding artificial intelligence, the majority of them never participated in a commercial project regarding artificial intelligence, but most of the teachers would welcome extended participation of experts from industry in teaching of students. From their recommendations can be selected e.g. advices:

Focus more on the free versions.

• Select proper computing language and libraries first
• Attention on Computer Vision, Explainable AI, Human-AI interaction
• Add more doing by examples activities
• Solving real AI cases at classes

Most of the answers were analyzed and visualized in a form of graphs.

Full paper can ce found here: Research 4 StateOfTheArt_teachers_Dirgova

The questionnaires about 63 projects collected by partner organizations from the 5 countries were collected and analyzed, concerning artificial intelligence teaching. The project coordinators were from 19 countries. Among the more interesting results belongs the finding, that more than half of the projects concerned deep neural networks learning modules, and most machine learning tasks which were solved, were image processing, classification, regression, clusterization and natural language processing. Among the used AI libraries dominated TensorFlow, Keras, scikit-learn and CUDA. The programming languages were Python and C++.

Most of the answers were analyzed and visualized in a form of graphs.

Full document can be found here: Research 3 StateOfTheArt_scientificprojects_Dirgova

 

This questionnaire is a part of the research in connection with the objectives of project 2022-1-PL01-KA220-HED-000088359 “The Future is in Applied Artificial Intelligence” (FAAI) under the Erasmus + program. This project aims to join together Universities, and businesses and provide innovative solutions to develop AI experts. The questions in this study aimed to research the needs and expectations of business organizations to propose training specialists in the field of Applied AI. Please review the offered training courses in Applied AI and describe them. All fields are mandatory.

Full paper can be found here: Research 2 SOA Labor market

This questionnaire that is subject of this document is a part of the research in connection with the objectives of project 2022-1-PL01-KA220-HED-000088359 “The Future is in Applied Artificial Intelligence” (FAAI) under the Erasmus + program. This project aims to join together Universities, and businesses and provide innovative solutions to develop AI experts. The questions in this study aimed to research the needs and expectations of business organizations to propose training specialists in the field of Applied AI. This paper presents a short review of the offered training courses in Applied AI and describes them. Despite existing training courses offered, there is a need of aggregating and inferring information on topics required and covered within the training courses in the field of AI.

Full paper can be found here: Research1

The consortium of The Future is in Applied Artificial Intelligence Project designed the first competency-based applied artificial intelligence curriculum at the high-education institution level. The development was based on advanced system research of existing artificial intelligence-related resources and surveying target groups of teachers, information technologies students, and employers which should enhance the performance of implementing artificial intelligence education. A review of applied artificial intelligence was prepared in the form of keyword clustering. The initial data was collected with the help of surveying, gathering job offers, existing artificial intelligence training courses, scientific projects, and real cases. Synthetic analysis of the textual information from the studies was using the word clouds technique. Tensor-based approach was used for the presentation of the competence-based course. The specific numerical requirements for the course in the form of priorities follow from the solution of decision-making problems using the analytic hierarchy process technique. Based on the comprehensive study of the surveys, educational experience, scientific projects, and business requirements, a meta-analysis of the recent references, we specified the criteria for a training course in the form of a tensor-based representation of competencies in relation to the content and educational modules.

Full paper can be found here: A3_3_LearningRequirements

This result outlines a learning initiative focused on developing a Competence framework within the field of AAI. The approach involves analyzing and incorporating various competency standards, such as ACM and IEEE. The content of the framework aims to encompass knowledge areas, specifying scope, competencies, and sub-domains. Sub-domains are further detailed through the inclusion of corresponding knowledge, skills, and dispositions. This comprehensive approach seeks to establish a robust foundation for competence development in the rapidly evolving field of AAI.

Full paper can be found here: A3_2 Competence_framework

 

The work is fulfilled within the framework of Erasmus+ project “The Future is in Applied Artificial Intelligence” (FAAI) and devoted to the development the methodology for collecting and analyzing good practices in the field of applied artificial intelligence (AAI) regarding the competences, training, existing solutions and real cases, which can be used for developing training courses of competence based education. Here we propose the definition of good practice in the field of AAI together with the corresponding criteria and features. The offered methodology uses system research based on the data gathered from existing training courses in AAI, labor market, surveys filled in by academics, students and employers, AAI use cases in science and industry.

Full paper under this link: Methodology_collection