Introduction to Applied AI for Researchers

Aims

This course offers doctoral candidates a structured introduction to the use of artificial intelligence in research, with an emphasis placed on AI-augmented methods rather than any particular set of applications or platforms. By the end of the course, participants will understand how generative large language models are built and why this shapes their behaviour, be able to deploy AI models as "tool-building tools" capable of handling bespoke research tasks, possess a working overview of the features offered by the major AI platforms, and be equipped with a basic framework for critically evaluating and benchmarking AI-generated outputs. The aim throughout is to leave participants with transferable methodological competencies that remain useful to them as researchers even as the underlying models and applications continue to rapidly change.

Content

The course is organised into four thematic blocks delivered across two half-day sessions. It opens with a block on the fundamentals of large language models (c. 2 hours), examining how contemporary LLMs are pretrained and aligned to specific goals, what their architectures imply for their behaviour, and what methodological limitations follow. The centrepiece is a longer block on applied AI methods (c. 3-4 hours), focused on the use of AI models as “tool-building tools” for tasks geared towards specific research questions including the analysis of large datasets. A third block (c. 1-2 hours) surveys the major AI platforms and their respective affordances for research workflows. The course closes with a block (c. 1 hour) on the critical evaluation and benchmarking of AI-generated outputs, introducing approaches for assessing reliability, accuracy, and transparency.

Methods

The course is taught in person across two half-day sessions and combines presentations with live demonstrations and short, hands-on exercises. Participants work on their own laptops using free accounts set up in advance with multiple AI model providers, so that the methods and platforms under discussion can be tried out directly during and after class. Voluntary preparatory readings and follow-up exercises will be provided for participants who wish to extend and consolidate their learning, but no additional work outside the classroom is required to complete the course.

Target Group

All Doctoral Candidates & Postdocs

Requirements

Participants are expected work from their own laptops during class. Prior to the first day of class, students will receive instructions on how to register for (free) user accounts with several commercial and open source AI providers.

About the Trainer

Arno Bosse leads the Research and Infrastructure Support (RISE) unit at the University of Basel (https://rise.unibas.ch). His academic background is in Comparative Literature, Cinema Media Studies, and Art History. Prior to coming to the University of Basel in 2025 he held a variety of digital humanities roles at the University of Chicago, Göttingen State and University Library, Oxford University, and the Royal Netherlands Academy of Arts and Sciences.

Workload

Course attendance: 8h
Optional readings and exercises: 4h

Feature

Once registration is open, applications will be collected for 24 hours and course places allocated by lot. All registrations received after the initial 24h period will be put on a waiting list and assigned on a first come, first served basis.

Course places/places on the waiting list will be confirmed by e-mail. Course registrations can only be canceled before the registration period ends (send an e-mail to grace@unibas.ch). Full course attendance is mandatory. Participants who fail to attend a course without prior notification or withdraw after the registration deadline are subject to a fee of CHF 30. In addition, participants who cancel their course registration at a later point in time, are absent without an excuse or do not attend the entire course will, for reasons of fairness, not be considered for course registration in the following semester and will be removed from other courses offered in the same semester. Please find the detailed regulations on the Transferable Skills Homepage.