Month: May 2023

Local Multiplier Event (activity A3.4): Precisely Software

Local Multiplier Event (activity A3.4): Precisely Software

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The event took place in Precisely Software, Dworkowa 2, 43-300 Bielsko-Biala, Poland.

The event kicked off with a warm welcome from Vasyl Martsenyuk, the Head of the Department of Computer Science at the University of Bielsko-Biala. His opening remarks set the stage for a day of deep exploration into the world of Applied Artificial Intelligence.

Marcin Bernaś took the floor to discuss the pressing challenges posed by Big Data in the context of modern technology. His presentation provided valuable insights into the complexities and opportunities associated with managing and leveraging vast amounts of data.

Aleksandra Klos-Witkowska introduced the FAAI project, providing an in-depth overview of its objectives, key partners, and identified target groups. Her presentation laid the foundation for understanding the project’s potential impact on various stakeholders.

Learning Requirements for Applied Artificial Intelligence (AAI)
The following sessions focused on gathering insights and requirements for AAI from various perspectives:
• Requirements from Job Market on AAI (Presenter: Tomasz Gancarczyk)
• Requirements from Employers (Presenter: Marcin Bernaś)
• Requirements from Students (Presenter: Aleksandra Klos-Witkowska)
• Requirements for Academics (Presenter: Vasyl Martsenyuk)
• Synthetic Analysis of Surveys (Presenter: Marcin Bernaś)
• Analytic Hierarchy Process (AHP) Analysis of Tensor Relation “Competence-Content-Module” (Presenter: Tomasz Gancarczyk)
• Tensor-Based Course Representation (Presenter: Marcin Bernaś)
These sessions provided a comprehensive view of the diverse requirements and perspectives that shape the learning landscape for Applied Artificial Intelligence.

Expert Panel
The event continued with an insightful Expert Panel discussion, featuring
distinguished experts who shared their perspectives on the learning
requirements for AAI. Their collective wisdom enriched the discourse.

Conclusions and Meeting Evaluation
In the final session, the event organizers summarized the key findings and conclusions drawn from the day’s discussions. Participants were encouraged to share their feedback, ensuring ongoing refinement for future events.

The Multiplier Event “The Future is in Applied Artificial Intelligence” within the framework of Work Package n°3 (activity A3.4) was an illuminating and productive gathering. It not only highlighted the critical learning requirements for AAI but also facilitated valuable exchanges among stakeholders.

Our heartfelt thanks go out to all the speakers, participants, and organizers for their invaluable contributions. We anticipate continued progress and success for the FAAI project.

 

FAAI: Teacher training 4.5

FAAI: Teacher training 4.5

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During May 15-19, 2023 teacher training activity was hold at Podgorica (Montenegro) within Erasmus+ Project No. 2022-1-PL01-KA220-HED-000088359 “The Future is in Applied Artificial Intelligence” (FAAI)

The training was hosted by University of Montenegro as the hosting organization. The representatives of 5 HEIs were trained, prepared educational materials, guides, and reported for 5 days (35 hours). Totally 25 participants were trained within the framework of the project, namely from University of Bielsko-Biala (UBB), University of Library Studies and Information Technologies (ULSIT), University of Nis (UNi), University of Ss Cyril and Methodius in Trnava (OSCM), University of Montenegro (UoM).

The goal of the joint staff training was to transfer and share the knowledge regarding the developed “Artificial Intelligence Competency Framework”, “Artificial Intelligence Learning Requirements” and “Мain content and topics of the curriculum”.  The given training activity was organized within a framework of the work package 4, as a result of which there should be developed learning methodology and learning materials for training teachers.

More information can be found here.

A 4.2: Learning Methodology

A 4.2: Learning Methodology

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The Learning Methodology developed in Work Package 4 establishes a competency-based framework for the Applied Artificial Intelligence (AAI) course, emphasizing practical applications in real-world scenarios. Covering essential AI topics such as data management, machine learning, ethical considerations, and applications in healthcare, smart cities, and robotics, the methodology aims to develop both technical and adaptive skills. Each module combines lectures, hands-on tasks, case-based studies, and discussions, aligning with required competencies like data handling, machine learning implementation, and critical thinking. Soft skills in collaboration and communication are also integrated. This active learning approach, with continuous assessment and feedback, prepares students to be adaptable AI practitioners equipped to solve industry-specific challenges.

You can download the document below

A4_2_Learning_methodology