Category: HEI

Collecting real cases of AAI

Collecting real cases of AAI

Project FAAI:2022-1-PL01-KA220-HED-000088359 “Future is in Applied Artificial Intelligence” (FAAI) under the Erasmus+ program started in September 2022. This project aims to bring together universities and business, and provide innovative solutions to develop artificial intelligence experts.

The project unites 5 partners from Central European and Eastern European universities: Poland, Slovakia, Serbia, Bulgaria and Montenegro.

In fulfillment of the goals set in the project, a case study with real application of AAI was conducted at stage WP2. The survey was conducted by the participants of this project.

Full paper can be found here: Research 8_EN_StateOfTheArt

 

Collecting IT specifications of good practices in AI

Collecting IT specifications of good practices in AI

The work presents the study of specifications of good practices in applied artificial intelligence (AAI). The analysis of 25 questionnaires from five partner institutions revealed key insights into the current state of artificial intelligence (AI) and machine learning (ML) projects. Training conducted in Serbia and Bulgaria, was signaling a need for expanded opportunities in EU countries. As a result of the study, we obtained that Deep ML prevails, particularly in Convolutional Neural Networks, while Gated Recurrent Unit is less common. Data volumes between 1 GB and 1 TB are typical, reflecting practical constraints. AI applications span diverse fields, with TensorFlow leading in libraries. Permissive licenses are most prevalent, databases are primary data sources, and texts/pictures dominate data characteristics. NoSQL databases are favored for storage. Security features and data processing tools vary. Dedicated servers and clusters are widely used, recommender systems are prominent, Python is the preferred language, and Apache Hadoop dominates ecosystems. Free datasets foster accessibility. Overall, the findings emphasize the dynamic nature of AI/ML projects, providing a foundation for future research in the rapidly advancing field.

Full paper can be found here: Research 7 StateOfTheArt_V3

 

Questionnaire for employers: Specifying graduate competencies in applied AI

Questionnaire for employers: Specifying graduate competencies in applied AI

FAAI is an ERASMUS+ project that aims to evaluate existing AI systems and tools and develop common EU competencies for skill-building systems that use AI capabilities in the SME sector. The project aims to increase the quality and relevance of students’ and graduates’ knowledge and skills in AI/ML-specific topics based on skills needed in the labor market. This survey was conducted in the context of the FAAI project to assess the needs of employers in graduate competencies in Artificial Intelligence, Machine Learning, and Data Science in general. The survey aimed to research the needs and expectations of employers and companies for the purpose of training specialists in the field of Applied AI. A total of 38 companies filled in the survey representing a good starting point for examination and analysis of their needs related to applied AI. The survey consisted of 31 questions, including questions on general competencies needed, the type of machine learning problems solved, and the AI libraries used in companies. The survey also included questions on soft skills required, additional competencies needed, employer satisfaction with the level of preparation of Master’s studies’ graduates in the area of AI, and views towards raising the qualification of current employees of organizations by letting them study AI at а master’s level.

Full paper can be found here: Research 6

Questionnaire for IT Students, Masters and Alumni in Information Systems and Technologies

Questionnaire for IT Students, Masters and Alumni in Information Systems and Technologies

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

Survey for Academics (lecturers) in the field of applied AI

Survey for Academics (lecturers) in the field of applied AI

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

Survey of scientific projects in applied AI

Survey of scientific projects in applied AI

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

 

Study of the labor market in the field of applied AI

Study of the labor market in the field of applied AI

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

Existing Training Courses in the Field of Applied AI

Existing Training Courses in the Field of Applied AI

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

Artificial Intelligence Learning Requirements

Artificial Intelligence Learning Requirements

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

Competence framework

Competence framework

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