A useful set of learning and reference resources on using GenAI for research is available to researchers and other educators for self-learning purposes. The resources are also relevant for users other than researchers as a reference set on matters relating to GenAI – from a basic understanding of GenAI through advanced aspects of the use of GenAI in research.
The set of resources forms part of a course presented at the University of Cape Town (UCT), intended in the first place for researchers. The course was developed by Prof. Jonathan Shock of the Shocklab, an AI-in-academia initiative at UCT. The face-to-face course consists of pre-class reading materials and videos, in-class activities and post-class activity (deeper reading and practical assignments). The pre-class materials are available for self-learning and for use by educators in their own academic activities; the rest of the activities form part of the paid course.
The course is intended in for researchers and postgraduate students. The focus is therefore on understanding the issues at stake and to enable participants to decide where and how to make choices for implementing GenAI in their research in a way they would be willing and able to defend. This focus differs from other courses available on the internet that might focus on specific functionalities of applications or platforms, or that give step-by-step instructions. At its core, intention with the course is as follows, in the words of the author:
“If there is a single thing this course has been trying to build, it is a disposition rather than a set of facts. The specific tools, capabilities, models and benchmarks we have looked at will look different in two years, and considerably different in five. The disposition we have been trying to build outlasts all of that, because it is about how you read the field rather than about what is currently in it.” (Week 12 of the course)
In the lesson on the current landscape of GenAI, a snapshot is provided that is updated through May 2026.
The course materials are organised in 12 weeks of study, of which week 12 is the integrative capstone project, based on the researcher’s own research area. There is an optional ‘advanced track’ on agentic research with Claude Code. For each week, there are various lessons, in which description and guidance is provided on the topic, as well as references to publications on that topic.
The course is pitched at NQF Level 9 (postgraduate). The assumption is that the self-learner will be engaged in some or other form of research to which the insights and skills can be applied. No prior background in machine learning, computer science or programming is assumed.
The following lessons (in the weeks mentioned in brackets) give an indication of some of the lessons that should be of interest to a wide range of researchers, lecturers and postgraduate students:
- Foundations of Generative AI (1)
- Current Generative AI Landscape (1)
- Transparency, Authorship and Integrity (4)
- The AI Literature Review Landscape (5)
- Research Ideation with AI (6)
- The Shifting Research Landscape: Policy, Peer Review, Integrity (11)
One week of the course is devoted to ethical frameworks for AI in research, with one lesson expanding the scope of ethics from perspectives prevalent in the Global North to considerations relevant to the Global South, and specifically also in Africa.
The course can be accessed at: https://shocklab.github.io/Generative-AI-in-research-course/index.html
The open access to the course materials for self-learners is confirmed in the Course Overview.
Walter Claassen
Acting SFA Lead: Digital Transformation
