AI in chemistry for deep learning and professional practice
The project investigates how AI can be integrated into education, including a potential Master’s course covering theory, model development, ethics and applications. It also examines how AI can be used pedagogically to enhance student learning and prepare them for future professional practice.
Project context
It is easy to associate knowledge acquisition through time-consuming problem-solving and fact-finding with understanding and competence. During fact-finding, natural learning takes place, and knowledge is broadened and deepened within a subject or question the more one searches and reads. Despite the advantages of having to look up information in this traditional way, we cannot avoid the development of artificial intelligence (AI) and the opportunities it offers. We, as teachers and students, need to make use of the positive potential of AI, but how, when, and why?
The importance of AI in both academia and industry is increasing rapidly. It is therefore very important that we give students as much knowledge about AI as possible. For a higher education institution, it becomes important to understand how we can teach our students about AI, with a focus on giving them knowledge of how they can use AI in their future working lives.
At the same time, AI can be used for many things, and in the wrong way, during education in ways that do not generate understanding or long-term deep learning. The possibilities of including AI in teaching in a way that creates curiosity and deeper learning and understanding also need to be investigated.
Purpose (outcome)
The purpose of the project is to investigate and identify proposals for both what and how in relation to AI education and AI use in our programmes.
The intended outcomes (which benefit the whole of KTH) are:
- Students are better equipped to use AI in their future working lives.
- More teachers become aware of the opportunities and are inspired to make use of the positive potential of AI.
Project results (output)
The project will deliver the following concrete results:
- Proposal for AI education design (format and content): The format could, for example, be a new Master’s course. Examples of content could include understanding basic AI and machine learning theory (linear algebra, statistics, regression analysis, algorithms in, for example, Python), design, training and evaluation of different models, reasoning about ethics, limitations, bias and risk in the use of AI, understanding how AI is used in industry, applying AI to real problems related to the programme, and communicating results.
- Proposal for guidance for teachers: how we as teachers can include AI in teaching and allow students to use AI in a way that increases their knowledge, curiosity, and deepens their learning.
Time plan
Start date: 2026-03-01
End date: 2027-02-15
Project documentation
If you have a KTH ID, you can read the documents when logged in:
- Poster: The project’s first poster (in Swedish) will be available at the ”Storträffen” meetup autumn 2026
- Project directive (Swedish): Projektdirektiv_Fru26_2611-CBH_AIFörDjuplärande.pdf