Youth Climate LAB 2 | Third meeting's summary and recording

On 18th April 2024, we participated in the third session on AI and climate change in learning communities continuing the second session on knowledge formation.

In particular, during this session, we had the opportunity to engage around foresight with Erica Bol and Tommi Asikainen, working on this topic at the Joint Research Centre of the European Commission. 

Listening to the interventions of the two experts on the topic, we delved into the concerns and the potential of using AI tools while imagining future scenarios. The capacity to envision and predict future trends and developments is essential to best prepare for future challenges and adapt our world to them.

During this meeting, we also engaged in a fruitful discussion concerning the application of AI in learning scenarios. In particular, we covered the topic bearing in mind our end goals for this LAB; and we came up to the following considerations.

For teachers

• AI tools can enhance quality facilitating the creation of engaging materials such as presentations and flashcards, thereby capturing students' interests.
• They can aid in discovering innovative teaching methods, fostering dynamic learning experiences through quizzes, games and storytelling.
• AI tools can assist in exam creation, and in streamlining the assessment process for educators.

For students

• AI tools can shorten the research process by providing quick access to relevant papers and sources, aiding in comprehension of complex topics and in a limited time.
• AI tools can contribute to the improvements of writing skills and grammar correction.
• AI can also support exam preparation through the simulation of different questions and scenarios and the provision of multiple-choice questions. 

We also deliberated on optimizing our utilization of AI tools and methodologies, resulting in reminding ourselves the considerations made when focusing on disinformation. In particular:

• Vigilantly verifying the accuracy of AI-generated outcomes and requesting transparency regarding their sources. 
• Tailoring AI outputs to suit individual needs and preferences.
• Providing detailed instructions and information when asking AI systems to ensure precise results.
• Offering corrective feedback to adaptive AI systems to refine their performance and achieve desired outcomes effectively.