Implementing

Project-Based Workshop Structure

The workshop was organized around weekly projects each of which focused on a particular research method. The participants formed small groups, and each group completed a project of their choice with each method.

Weekly, a new method was introduced, after which the small groups conceived, designed, and executed a small research project using that method. The following week, the groups shared their projects in detail, after which the next method was introduced, and so on. The workshop covered five different research methods and a visit to an archive.

Addressing AI Ethics and Tools at the Outset

At the beginning of the workshop, I discussed the use of AI from the perspectives of data security and privacy. At Tampere University, Microsoft Copilot and Scopus AI are in use. These were the only AI that I could recommend to the participants. Even with the university endorsed AI – like with anything – data security and privacy should always be considered. These aspects were easy to incorporate as part of general research ethics discussion.

Emphasizing AI as a Learning Journey

I also emphasized that the workshop’s point was that everyone, including the teacher, was here to learn about using AI. By highlighting AI competency as something that will probably be an advantage, I motivated the participants. By underlining that the overall idea is to “learn by doing,” part of which is trial and error, the participants understood that they need not “get it right” the first time.

Encouraging Reflection and Iteration

The workshop structure re-enforced this “learning by doing” pedagogy. The projects did not need to be perfect each week. Rather, each week the groups shared their projects, experiences, and challenges and in the meetings, they received questions and comments from the teacher and the other groups. Those comments were used to finish their projects, or even re-do their whole project, if a group wanted. The real deadline for the projects was the portfolio deadline, but even the portfolios were first handed in as drafts to get feedback on them.

Use of AI by students in the course

• AI for Brainstorming and Idea Generation: to exemplify how AI was used – without the teacher instructing them to do any of these – the most common AI use was related to brainstorming and idea-development. This ranged from ideas for a group name to project ideas and to project details. For instance, AI was used to make questions for surveys and theme interviews. Although the questions were then edited, it was quick to have AI generate a pool of questions from which the most suitable were chosen.
• Enhancing Efficiency Through AI: AI was used many ways to improve efficiency: for planning, for searching information, for re-writing notes, for summarizing, for formatting, and for editing texts. The participants succeeded in delegating various manual or mechanical tasks to AI, thus leaving them more time to focus on the substance.
• Creative and Analytical Use of Generative AI: one example of AI use, which I as the teacher did not consider, included speech-to-text recognition during interviews. This saved a lot of time when conducting interviews. Generative AI uses included also the making of group logos and graphics, translations, data analysis, and the compilation of multiple ethnographic voices into one coherent narrative.
• AI as a Tutor and Research Companion: AI worked also as a tutor. Some used AI for inspiration. Others asked AI for advice and for example what kinds of things to consider or practical advice in a particular project. Some also used the AI to historically contextualize their archival research project.