Category: Teacher training

A5.4: Teacher training on AAI

A5.4: Teacher training on AAI

Teacher Training A5.4 in Trnava, Slovakia

Dates: May 15-19, 2024
Location: University of Ss. Cyril and Methodius in Trnava, Slovakia

 

 

The A5.4 Teacher Training event, held from May 15-19, 2024, in Trnava, Slovakia, brought together educators from five partner institutions to develop comprehensive teaching materials and guidelines for Artificial Intelligence (AI) and Machine Learning (ML) education. Organized by the University of Ss. Cyril and Methodius in Trnava, this training focused on equipping teachers with practical knowledge, resources, and frameworks to support and enhance AI education within various academic environments.

Participating Institutions:

  1. University of Bielsko-Biala, Poland (UBB)
  2. University of Library Studies and IT, Bulgaria (ULSIT)
  3. The University of Nis, Serbia (UNI)
  4. The University of Ss. Cyril and Methodius in Trnava, Slovakia (UCMT)
  5. University of Montenegro, Montenegro (UoM)

Training Overview and Agenda Highlights

Across five days, participants worked collaboratively to establish practical resources, including guidelines and educational materials for AI instruction. This training not only focused on content development but also provided a forum for discussing and refining methodologies, curriculum design, and teaching strategies for AI-related courses.

Day 1: Opening and Good Practice Guidelines

The training commenced with a welcome address by UCMT, which introduced participants to each other and set the tone for the week’s collaborative work. ULSIT led the first session on Good Practice in Developing Guidelines, sharing methods for structuring effective teaching resources. This session offered practical insights into creating comprehensive yet adaptable teaching materials, critical for AI education across diverse institutional settings.

 

Day 2: Developing the AAI Teacher Guide

UBB led a focused session on the development of the AAI Teacher Guide, which provides educators with foundational AI concepts, teaching strategies, and adaptable resources for classroom use. Following this, teachers collaborated in team sessions to expand and refine the guide with input from UCMT, ensuring that it would be accessible and relevant across different educational contexts.

Day 3: Creating the AAI Student Guide

ULSIT took the lead on Day 3 to develop the AAI Student Guide. This guide aims to provide students with clear, foundational information on AI principles and practical skills, tailored to varying levels of AI knowledge. The guide also includes interactive activities and project-based learning recommendations, supporting a hands-on approach to AI education.

Day 4: Building the AAI Business Guide

Day 4 shifted focus toward practical applications of AI in business, with UNI presenting the initial framework for the AAI Business Guide. This resource is designed to bridge the gap between academic AI education and industry applications, preparing students with the knowledge and skills they’ll need to understand and apply AI solutions in professional settings. UoM and UNI facilitated collaborative team sessions, encouraging participants to integrate business-relevant AI scenarios and case studies.

Day 5: Certification and Round Table on Future Improvements

On the final day, all participants received certificates of completion as recognition for their involvement in developing these essential AI resources. This was followed by a round-table discussion on enhancing the AAI framework, including potential revisions to methodology, curriculum, and content to align with emerging trends and requirements in AI and ML. Institutional coordinators concluded with a summary of training outcomes and an outline of future project steps to ensure continued progress and support.

Conclusion

The A5.4 Teacher Training in Trnava served as a productive and impactful event, allowing educators to collaboratively build and refine resources that will enhance AI and ML education for students, teachers, and industry professionals alike. By developing these guides, the training has contributed to a stronger foundation for AI learning across participating institutions, ensuring that both educators and students are well-prepared for the demands of the AI field. As the FAAI project progresses, these resources will play a crucial role in supporting an informed, skilled generation of AI practitioners.

 

 

       

 

FAAI: Teacher training 4.5

FAAI: Teacher training 4.5

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.

Learning materials shared before the training are here

Learning Materials for Teacher Training A4.5

Learning Materials for Teacher Training A4.5

The link to the entire document is  here.     This document outlines a comprehensive framework for developing learning materials tailored to training teachers in Applied Artificial Intelligence (AAI). It emphasizes structured methodologies, interactive teaching strategies, and innovative assessment approaches to foster competence-based education.

The content is divided into six main sections. The first section introduces the methodology for training in AAI, outlining objectives, core principles, and a phased framework. These include the foundational phase for theoretical knowledge, the applied learning phase for practical skills, and the pedagogical integration phase to prepare teachers for classroom delivery. Essential tools, resources, and feedback mechanisms are also discussed to enhance the learning process.

Subsequent sections delve into specific instructional strategies, such as preparing lectures in a question-and-answer format to engage learners actively and designing competence-based classes that integrate theoretical and practical elements. Special emphasis is placed on developing use cases and collaborative projects, fostering teamwork and real-world problem-solving skills.

Methodologies for competence examination are highlighted, providing detailed guidelines for structuring foundational and application-level assessments. This includes examples of quizzes, case studies, and team-based projects, along with best practices for evaluation and feedback. The document also explores how to design learning trajectories, focusing on linear and branching scenarios to cater to diverse learning needs. Activities and resources are recommended to enhance the learning experience, ensuring adaptability and relevance.

The framework prioritizes continuous improvement through feedback and performance analysis, ensuring that the materials remain dynamic and aligned with advancements in AI technologies. By equipping teachers with robust strategies and tools, this document aims to transform AAI education, preparing educators to lead dynamic, engaging, and impactful learning experiences.

Annex

Links to the presentations

Introduction_TargetGroups

FAAI_StateOfTheArt

Research 1 and 2

Research 5

Research 6

Preparing_methodology_for_competence_examination

AI_modules_4_9

Module5_presentation_AAI_External_modules

Module8_presentation_AAI_Healthcare

How_to_develop_learning_trajectories