Programme

12:00–12:10
Jarmo Mikkola, Director, School of ICT and Business
Welcome and workshop objectives

--- KEYNOTE ---

12:10–12:40
The Future of Responsible AI in Society and Higher Education
Daniel Molnar (Brantner): Innovation-Driven Responsible AI in Waste Management
Artificial intelligence is a critical enabler for the circular economy, bringing unprecedented precision to waste management. This talk explores the practical application of computer vision through three real-world sorting and recycling use cases: Störstoffscanner (organic waste), Qualitätsscanner (glass), and Loopit (mattresses). Alongside insights from the field, the session discusses the operational reality of implementing "Responsible AI" in an industrial setting — where academic theory gives way to practical benchmarks like rugged reliability, safety, and minimising the environmental footprint of the technology itself.

--- RESPONSIBLE AI IN EDUCATION: CASE SESSION ---

12:40–13:00
Case 1 - Jukka Nevalainen: AI in Education: Beyond Automation
Programming courses have long relied on automated tests that provide students with scores but limited learning support. This presentation demonstrates how AI-assisted feedback can help transform assessment from simple grading into a more supportive learning experience — even in large student groups.

13:00–13:20
Case 2 - Chibuzor Udokwu: Automating Compliance for Trustworthy AI Development and Verification
Trustworthy AI applications are secure, high-performing, ethical, and compliant: resistant to prompt injection and data leaks, accurate and stable over time, trained on unbiased and explainable data, and compliant with regulations such as GDPR, the EU AI Act, and standards like ISO 42001. This presentation introduces early results from a framework offering reusable controls for developing trustworthy AI applications and automating compliance verification.

13:20–13:40
Case 3 - Bogdan Chiriță: AI in Engineering Education and Technical Training
This talk explores how artificial intelligence can be responsibly integrated into engineering education and technical training — considering its impact on teaching, learning, and assessment, while highlighting the importance of practical skill development, academic rigour, and human professional judgement in preparing future engineers and technical specialists.

--- BREAK 13:40–13:50 ---

--- RESPONSIBLE AI IN RESEARCH & DEVELOPMENT: CASE SESSION ---

13:50–14:10
Case 4 - Sami Koski: Artificial Intelligence as a Support for Learning: Toward Teaching and Assessment That Emphasize the Thought Process
Generative AI is reshaping education by making information and solutions readily available, challenging traditional assessment methods that focus on final outputs. This presentation argues that teaching and assessment should increasingly emphasise students' reasoning, problem-solving process, and ability to explain their decisions — using AI to support reflection rather than restricting it, so that assessment makes students' thinking visible instead of evaluating AI-generated results alone.

14:10–14:30
Case 5 - Rubén Ruiz Torrubiano: Future Skills for Experts in Responsible AI: Bridging the Gap
This talk motivates the need for specific educational approaches for AI experts in a rapidly changing world, focusing on tertiary education. It presents curricula developed at IMC Krems designed to bridge the gap between current AI-intensive bachelor's and master's programmes and societal needs — including industry partners and environmental considerations. A key insight: AI professionals grounded in sustainable practice and ethical/environmental responsibility will be essential to delivering value to society and addressing pressing global problems.

14:30–14:50
Case 6 - Ioana Pleșcău: AI in Entrepreneurship, Innovation and Project-Based Learning
This talk focuses on the responsible use of artificial intelligence in entrepreneurship education, innovation activities, and project-based learning — with attention to creativity, project development, ethical awareness, sustainability, and responsible decision-making.

--- GROUP DISCUSSIONS ---

14:50–15:30 Parallel Group Discussions (6 themes)

1. Designing responsible AI guidelines for teaching
How can we design practical responsible AI guidelines that support teachers and students in everyday teaching, learning, and assessment?

2. AI competence development for teaching staff
What AI-related skills and support do teaching staff need to use AI responsibly and confidently in their work?

3. AI best practices for R&D projects
What good practices are needed to ensure that AI is used responsibly, securely, and effectively in R&D projects?

4. From responsible AI principles to practical implementation
How can responsible AI move from guidelines and theory into everyday teaching, R&D, and organisational practice?

5. AI-supported learning and assessment: making thinking visible
How can AI be used to support feedback, reflection, and reasoning rather than only automate grading or produce final answers?

6. International collaboration in responsible AI education and R&D
What joint courses, BIP/COIL activities, projects, or shared learning materials could partners develop after the workshop?

7. Future skills for responsible AI professionals
What skills do students, teachers, and experts need in the AI era, especially regarding sustainability, ethics, technical competence, and professional judgement?

15:30–16:00 Group Discussion Results

16:00 Closing
Key takeaways, next steps, and collaboration opportunities