TOY - Trainers Online for Youth
This is a reference for Laimonas Ragauskas
Objectives:
- Equip participants with the skills to critically evaluate various AI tools and platforms, enabling them to select the most appropriate technologies for their specific training needs.
- Increasing competencies in designing AI-based mini-projects, supporting the implementation of training activities in the field of youth.
- Foster an understanding of ethical considerations related to the AI use in youth work training context.
- Support networking and international cooperation of trainers working in the field of youth.
23 Trainers working internationally in the field of youth work from Armenia, Austria, Belgium, Croatia, Denmark, France, Greece, Hungary, Italy, Netherlands, North Macedonia, Norway, Poland, Serbia, Spain, and Switzerland.
It was international team of trainers from Lithuania and Georgia.
Key methods included experiential group tasks, learning-by-doing with AI tools, guided experimentation, creative production, peer learning, and continuous reflection.
Participants actively explored generative AI through practical challenges such as prompt redesign, tool stations, visual and video creation, and the design of custom or agentic AI assistants.
These activities were complemented by study visits, showcasing sessions, and iterative testing with peer feedback, reinforcing learning through action and concrete experience.
Reflection was embedded daily through structured reflection groups, buddy systems, and plenary debriefings, supporting self-recognition of learning and alignment with the Youthpass process. The programme also used participatory methods such as open space elements, gallery walks, fishbowl discussions on ethics, statement exercises, and needs-and-contributions mapping, ensuring learners co-shape both content and process.
The training activity strengthened participants’ competences to critically and creatively utilise Generative AI in training contexts.
Participants developed practical skills in prompting, selecting and combining AI tools, producing AI-supported learning materials (text, visuals, video), and designing initial concepts for custom or agentic AI assistants aligned with real training needs. Importantly trainers actively applied skills to their own professional practice, resulting in a range of concrete AI-based solutions, prototypes, and training products developed during the course.
The success of the training can be seen in several ways. Participants demonstrated increasing confidence and autonomy in using Generative AI tools, moving from experimentation to purposeful application. This was evident in the quality of the AI-based outputs presented during showcasing sessions, as well as in participants’ ability to articulate ethical, pedagogical, and sustainability considerations linked to AI use.
Continuous reflection, peer feedback, and mid-term evaluation results showed high levels of engagement, motivation, and perceived relevance of the learning process.
Participants also reported feeling better equipped to transfer their learning into future training activities and to support others in responsible AI use.
Overall, the training achieved its objectives by combining competence development with tangible outcomes, critical reflection, and peer learning, demonstrating both learning impact and practical value for participants’ professional contexts.
Within the training team, my main role was to co-design, facilitate, and hold the learning process together with my colleage Gvantsa Mezvrishvili.
I was responsible for co-shaping the learning flow, ensuring that non-formal learning principles were applied consistently, and that participants could move from understanding Generative AI concepts to practical application in their own training contexts.
I facilitated sessions related to the essentials of Generative AI, including introductory inputs, structured exploration of AI tools for text generation, translation, data analysis, and multimodal use, as well as sessions on agentic and custom AI solutions. I guided participants through practical exploration phases, supported group work, and facilitated debriefings to help participants reflect on their experiences and connect them to their professional training contexts.
I facilitated ethical discussions on the use of AI in training contexts, addressing issues such as transparency, authorship, data protection, bias, sustainability, and responsible use through participatory methods. In addition, I supported mid-term and final evaluation processes, helping to gather feedback, assess learning progress, and adjust the programme in response to participants’ needs.
Throughout the training, I worked closely with the team to ensure a learner-centred, flexible, and non-formal learning approach, adapting sessions based on group dynamics, feedback, and emerging learning needs, while maintaining a clear focus on learning quality and applicability to participants’ training practice.