Tuesday 24 November

A beginner-level introduction to the landscape of Artificial Intelligence (AI) techniques. This course will help researchers build a foundational understanding of key areas within AI, including machine learning, deep learning, and large language models (LLMs) including what they are, how they work, and how they relate to one another within the broader AI ecosystem. Designed for researchers with little or no prior knowledge of AI, this course provides clear, accessible explanations without requiring any coding or statistics experience.

Tuesday 24 November

What questions should I ask before adopting an AI tool in my research workflow? What ethical responsibilities do I have as a researcher using AI? How do I handle transparency and reproducibility when AI has been part of my methodology?

Naturally following on from the AI Fundamentals for Researchers session, in this session we hear from 3 experts who will explore various AI considerations, including Environmental Cost, Privacy and Data Governance, and Cognitive Offloading. After 3 x 15 minute presentations, the experts will form a panel to have a discussion around these considerations and answer questions from the audience.

Meet the chair and panellists:

Aleksandra Nenadic (chair)

Aleksandra Nenadic is a research software training lead and community engagement specialist at the Institute for Research Software. She leads initiatives focused on open, reproducible, and sustainable research practices, with expertise in research software, digital skills training, and community building through programmes such as The Carpentries. Aleksandra has extensive experience in developing inclusive training programmes, supporting instructor development, and advancing collaborative approaches to research software and data skills across the UK and internationally.

Dr Anna-Stiina Wallinheimo

Dr Anna-Stiina Wallinheimo is an Experimental Cognitive Psychologist and a Research Fellow at the University of Southampton, UK. Her research focuses on human-centred AI, decision-making under uncertainty, cognitive workload, and human performance in complex high-stakes environments. Drawing on her expertise in experimental cognitive psychology and distributed cognition, she explores how AI systems affect human judgment, performance, and decision-making.

Kirsty Pringle

Kirsty Pringle is a Research Fellow at the Institute for Research Software, based at EPCC (University of Edinburgh). Before joining the Institute, she was a researcher and Research Software Engineer (RSE) specialising in climate and atmospheric science. She now combines her background in climate science with her interest in research software to address the environmental impacts of digital research. Kirsty is a NetDRIVE Champion for the UKRI-funded project NetDRIVE (Network for sustainable Digital Research Infrastructure Vision and Expertise) project, in her role as a Champion, Kirsty focuses on empowering individuals and communities to take practical action toward sustainability. She also founded the Green RSE Special Interest Group, which promotes best practices in sustainable research software. Additionally, she co-authored the open-access Advocacy for Sustainable Research course to help researchers build institutional support for these initiatives.

Kamilla Kopec-Harding

Kamilla Kopec-Harding is a Senior Research Data Scientist at the University of Birmingham  where she manages the Research Data Science team. Our team provides practical AI and data science support to researchers across campus, from all disciplines. She is working to facilitate responsible integration of AI coding tools into RSE & RDSci projects and to support researchers adopting AI in their workflows. She has experience of using AI in project work and can offer a personal perspective on the benefits and risks of AI coding.

Thursday 26 November

A practical introduction to using AI to support coding in research. This course will help researchers understand how to use AI to help them write code effectively and responsibly. This course is designed for researchers with little to no experience coding. The course provides clear, hands-on guidance for using AI to write, debug, and understand code, while addressing key ethical, security, and reliability considerations in research contexts.

Monday 30 November

When Green Does Not Mean Right: Specifying, Verifying and Scaling Evaluation of AI-Assisted Research Software

This talk explores why validation of AI-assisted research software has to stay anchored in the science. The reason is simple. AI tools tend to make the signal go away rather than fix the problem, so explicit scientific invariants matter: they give the AI something it can't silently change.

It then turns to specifying behaviour separately from implementation. Executable specifications preserve what matters through regeneration and refactoring - if and only if the checks come from a person or a different system. The AI should not mark its own homework.

Finally, it considers how evaluation must scale with AI intensity. Line-by-line review gives way to invariants, reproducible checks and adversarial review, yet designing those checks still rests on human expertise. Manual practice at different levels of AI autonomy remains a key part of the evaluation infrastructure.

This session is also part of a series of seminars organised by the Research Software Alliance.

Tuesday 1 December

An intermediate-level course on integrating Artificial Intelligence (AI) tools into research software development. Building on foundational knowledge of large language models and AI coding assistants, this course focuses on using these tools effectively within integrated development environments. Researchers will develop practical skills to enhance productivity, improve code quality, and apply AI responsibly in real-world research projects.

Pre-requisites:

• You have a basic knowledge of programming in Python (using variables, lists, conditional statements, functions and importing external libraries)
• You have previously written Python scripts or
•  iPython/Jupyter notebooks to accomplish tasks in your domain of work

Thursday 3 December

An advanced-level practical course on integrating Artificial Intelligence (AI) tools into research software development. This course explores task-based and process-based methods of using AI within the software development lifecycle while maintaining good software development practices. This course is for you if you have used GitHub Copilot in the past to improve your existing code, but would like to explore it further to plan and develop software from scratch using agents based on a defined set of project requirements.

Pre-requisites: 

• You have a basic knowledge of programming in Python (using variables, lists, conditional statements, functions and importing external libraries)
• You have previously written Python scripts or iPython/Jupyter notebooks to accomplish tasks in your domain of work
• You have previously used Microsoft Visual Studio Code to develop code in any language (opening workspaces, editing files, installing extensions)
• You have previously used GitHub Copilot to obtain suggestions to improve your code.
• It is also helpful but not necessary to have some basic knowledge of Git version control (cloning repositories, making commits, pushing to a remote repository).