We have prepared eight different use cases for generative AI, specifically large language models, such as ChatGPT or Bing Chat. Explore the versatility of generative AI in education across three spheres:

1. Independent student use
2. Independent educator use
3. Teaching activities involving both students and educators

We have crafted use cases for each educational sphere, featuring a consistent structure. Each case begins with a brief description followed by insights into its didactic advantages. A suggested prompt for the activity is provided, along with an updated version of Bloom’s taxonomy to illustrate its position. Finally, we unfold how each use case aligns with the principles for digitally supported PBL.

What is generative AI?

“Generative AI, short for Generative Artificial Intelligence, refers to a class of artificial intelligence algorithms and models that have the capability to generate new content, such as text, images, audio, or even video. Unlike traditional AI systems that rely on rule-based programming or supervised learning, generative AI is designed to produce novel outputs by learning patterns and structures from existing data.

One of the key technologies behind generative AI is deep learning, particularly a type of neural network called a generative model. These models learn to understand and mimic the patterns in the data they are trained on, allowing them to generate content that is often indistinguishable from what they have seen before.” (OpenAI. (2023). ChatGPT (November 15th version) ChatGPT 3.5.) 

If you are interested in knowing more about how generative AI works, we encourage you to access and complete the AAU micro course introduction to generative AI and ChatGPT where researchers from Aalborg University teach you about what generative AI is, how it works and how it can be used in an academic practice. The course is open to both staff and students at AAU.

Students at Aalborg University can access the following website to find answers regarding the regulations on generative AI, how they can and may use generative AI and what to be aware of when using AI at AAU.

Furthermore, Aalborg University Library has made a theme page about generative AI where you can find information about different aspects of using generative AI as a student.

While the above sources are primarily targeted at students, they are still relevant to you as an educator at the university.

At AAU we have a long history of grounding our education and research within specific core PBL principles. The PBL principles at Aalborg University allow local adoption and configuration which results in them being practised and performed differently locally at the university. At AAU a working group has developed a set of principles for digitally supported learning, which aims to supplement, challenge, and expand the existing principles when working with digitalization of PBL. The ambition is that Aalborg University takes a value-based approach when working with the opportunities that new digital technology has. The four principles are:

1. VARIATION

Variation as a principle challenges and extends existing PBL praxis. Variation, understood as both within and beyond the different levels of the programmes, aims to ensure that students experience a wide variation in teaching and project work throughout the programme. The principle proposes that heads of study, study boards, coordinators, lecturers and students consider variation as an essential principle in the organisation and practice of education and teaching. Students are motivated and learn in different ways, so variation can benefit all students through multiple approaches to learning, teaching and education. In this context, the digital can help widen the scope of educational opportunities and support variation as a principle at multiple levels of practice.

2. COLLABORATION AND OPENNESS

Digital technologies enable new types of collaborations that go beyond the individual course or project work, and where it is easier to open up for other stakeholders, a larger number of participants, create cross-collaborations or incorporate entirely new forms of collaboration.

3. CO-DETERMINATION AND EMPOWERMENT

Co-determination and empowerment are central parts of the educational model in a PBL-based educational institution and can be understood from the student's perspective as involvement and engagement in their own educational choices and processes. In problem-oriented project work, this is realised through the fundamental principle of participatory project management. Digital technologies can support empowerment and help increase student participation beyond project work. They can enable larger and more active learning communities, involving both students and lecturers within each educational programme, but also across programmes.

Participation in such learning communities has the potential to provide students with what is perceived as a more engaging study environment, where shared academic interests take centre stage and where closer communication and relationships between lecturers and students can enhance student empowerment.

4. INCLUSION

Inclusive study environments with an equal, appreciative and respectful culture are a high priority and play an essential role in good student life. Digital technologies can help to support inclusion efforts through a conscious focus on accessibility, diversity and flexibility. The principle aims to make learning and teaching activities accessible to all students, but also to ensure that each student feels able to participate according to their individual abilities.

A conscious focus on meeting different needs in the course teaching itself, in a semester or in the organisation of a programme can help to create better participation opportunities for students with diverse backgrounds.

You can find more information about the principles on the website for the principles for digitally supported PBL.

Principles for digitally supported PBL and generative AI

When considering whether and how to implement generative AI into your teaching practice, we strongly encourage you to consider how this aligns with the principles. In the use cases presented, you will find a description of how we envision a link to the principles, but you might be able to draw more connections to the principles.

You will find that variation as a principle is recurring in the inspirations since this principle cannot be fulfilled by a single learning activity but can only be fulfilled by variation in a course, semester and programme.

Currently Aalborg University is not having a data processing agreement with any generative AI platform, which means that as an educator, you cannot mandate the students to access a generative AI, but you may encourage and inspire the students to do so – however, this cannot be a required part of the curriculum. The restriction of usage is not unique to generative AI platforms but exist on every platform which does not have a data processing agreement with Aalborg University. You can read more about the regulations on students’ usage of generative AI at the university on this page. If you want to work with generative AI and large language models in classroom teaching, it will be possible to do so by preparing material prior to the teaching so that it will not be required for students to access and use the generative AI models.”

If you as an educator have experience using generative AI at Aalborg University and want to share an interesting use case with your colleagues, then we would like to hear from you so we can disseminate it. Please fill out this form and allow us to contact you.