For whom?
For all students, BA2, BA3 and (pre-)Master's students, interested in exploring whether innovations related to generative AI should be integrated with education at the university level. Together we will dive into the ethical and philosophical questions that inevitable come up when discussing AI. Though you will be exposed to technical concepts like working with Generative AI models, it is not expected you have any background knowledge of coding.
What are you going to do?
This Honours Lab is designed to give all students, regardless of background, a practical understanding of Generative AI.
After meeting and aligning on learning objectives in the first session, the second session will be an all-day field trip to the Microsoft office at Schiphol. During that day, you’ll work hands-on with Generative AI models, exploring their application in chat-based tools to move beyond theory and gain a clear view of their current capabilities and limitations.
During the remainder of the course, you will gather insights from diverse groups to understand different perspectives on the implementation of AI. Finally, you will consolidate your findings and recommendations in a published manifesto, outlining your future vision for how universities should approach AI implementation.
To include a diverse set of perspectives before writing a manifesto on if and how AI should be adopted by universities, the following themes can be covered in this Honours Lab, depending on students’ backgrounds and interests:
- Building Generative AI: Understanding the fundamentals of generative AI, including capabilities, limitations, and ethical considerations for educational applications.
- Instructional design: Exploring methods to create effective educational content and learning experiences that align with AI-enhanced tools and pedagogical goals.
- User research with teachers, students, and other stakeholders: Conducting user research to gather insights directly from teachers and students, ensuring AI implementations meet genuine needs and enhance the learning experience.
- Data privacy and ethics: Examining the ethical implications and data privacy challenges associated with AI in education to ensure responsible and transparent use.
- AI governance and policy: Establishing frameworks for AI governance, setting guidelines, and creating policies to ensure ethical and sustainable AI usage within the university.
- Predicting future trends: Identifying and analyzing emerging trends in AI technology and educational innovation to anticipate future needs, challenges, and opportunities for universities.
- How to write a manifesto: Guidance on articulating a clear, compelling, and actionable manifesto, structured to outline principles, goals, and recommendations for future AI adoption.
Study load
2 hours per week and 8 meetings (around 3 hours per week).