Five-day seminar at the Chitralada Technology Institute, Bangkok

How can AI be used to sustainably support professions in the food value chain – and what challenges does this pose for educational processes?

From August 4 to 8, Dr. Michael Scharp from the BilRess Project will give a five-day seminar at the Chitralada Technology Institute. The seminar will focus on three topics: resources, education, sustainable nutrition, and artificial intelligence. It is planned that 30 bachelor’s and master’s students from various disciplines will participate in the seminar.

Day 1 – Artificial Intelligence and Nutrition
A 4-hour session introducing the five-day seminar, clarifying key terms and exploring AI’s relevance for the nutrition value chain. A welcome segment allows participants to share goals and interests via pinboards, followed by an overview of course schedule and benefits. The trainer presents foundational AI: definitions, types (generative, visual, voice-controlled), and applications in general education and nutrition. Participants reflect on their understanding of AI and its relevance. After a break, they research where AI is already in use—both globally and locally—particularly in education and nutrition’s stages: agriculture, processing, trade, catering, households. Findings are documented using tools like Miro or Google Forms and discussed: key AI trends, structuring AI usage, risks, and potential advantages for nutrition.
Homework asks participants to define what sustainability means for nutrition across four dimensions—ecology, economy of producers, societal impact, and private life—delivering their reflections via digital formats recorded in Google Forms.

Day 2 – Sustainability in Nutrition-Related Professions
Over 4 hours, participants examine sustainability through vocational contexts. They compare Germany’s and Thailand’s systems, focusing on professions like system catering specialists. Group work explores sustainability via identified SDGs (1, 2, 3), using pinboards and research to compare national frameworks. The trainer provides insights on vocational training regulations, occupational profiles, and how nutrition professions address environmental protection. Students synthesize learnings, operationalize sustainability for their own profiles, and prepare homework via Google Forms or short presentations.

Day 3 – Large Language Models (LLMs) and Sustainability
This day deepens the connection between AI and sustainability in food systems. Participants evaluate different LLMs (e.g., ChatGPT, Gemini, Claude), practice writing prompts, and engage debates on ethical considerations like truth, bias, and trust. Group presentations cover topics such as sustainable food choices, children’s nutrition in Thailand, and health impacts of pesticides. Tasks include vocational education relevance, identifying necessary trainee skills, and documenting via Google Classroom. Homework includes comparing LLM outputs and consolidating findings in shared digital spaces.

Day 4 – Creating Your Own RAG for Sustainability
Participants learn about Retrieval-Augmented Generation (RAG)—its simple implementation via ChatGPT and more complex setups involving databases, embeddings, APIs, feedback systems and deployment. Activities include testing the KEECZ RAG, comparing results across LLMs, and creating menu plans based on German Nutrition Society guidelines for schools. Participants form groups, generate plans via various models, justify choices, and compare plans from Thai and German contexts. Initial homework focuses on sourcing materials to build a simple job-specific RAG database.

Day 5 – Sustainable Value for Nutrition & AI Applications
On the final day, participants explore sustainability indicators for out-of-home catering and further develop RAG tools to create healthy nutrition plans. Tasks include bundling resources (PDFs), prompt refinement, offline and online RAG testing against verified literature, and ethical discussions on AI’s privacy and data-use implications in Thailand and Europe. A feedback round, reflection, and closing ceremony conclude the seminar.