School of Artificial Intelligence

Learning the core ideas of artificial intelligence opens the door to understanding how one of the most transformative technologies of our time actually works. Discovering the logic behind systems that learn from data, interpret images, generate language, and carry out complex tasks expands your sense of what becomes possible in your own field.

In this course, you will grasp how a model learns and how it can apply what it has learned to new situations. The foundations of machine learning and neural networks will help you make sense of both the successes and the failures of artificial intelligence. You will be able to assess which problems can be addressed with AI, how much confidence to place in a model’s outputs, and which decisions are required to develop a sound solution.

By becoming familiar with deep learning and transformer architectures, you will learn the ideas that make large language models and multimodal systems possible. Through reinforcement learning, you will explore how experience and reward signals can improve a system’s behaviour. This conceptual foundation will make it easier to understand new models you encounter, follow developments more critically, and deepen your knowledge in areas of interest.

Throughout the applications, you will use this knowledge to develop your own solutions. While working on models that generate predictions from data, assistants that draw on documents, and agents that use tools to carry out tasks, you will experience how an idea becomes a working prototype. In this way, you will see where artificial intelligence can add value in your research and professional work, choose appropriate methods, and gain a solid foundation for starting your own projects.

The Artificial Intelligence: Enabling Tools module is designed for participants who wish to use artificial intelligence and digital research tools effectively—especially in academic research, education, and knowledge production. The aim of the module is not to develop expertise in technical fields such as machine learning or AI engineering, but to introduce tools that researchers can use directly in their everyday academic work and to demonstrate, through practice, how these tools can be integrated into research processes.

Throughout the course, generative AI tools such as ChatGPT, Claude, and similar platforms will be used actively. Topics including large language models, prompt engineering, API use, and agent development will be addressed through hands-on applications. Participants will experience how these tools can support literature review, academic writing, text analysis, data processing, transcription and coding of interview data, presentation preparation, and the automation of various research workflows.

The module is not limited to tools based solely on large language models. Different software applications and digital tools developed to facilitate specific tasks in academic research will also be introduced in the relevant weeks. How these tools—used in areas such as data visualisation, transcription, coding, literature management, presentation, and design—address particular needs, and how they can be incorporated into the research process, will be examined in an applied manner.

The core goal of the module is not merely for participants to learn how to use specific programmes, but for them to select the right tool for different research needs, use these tools efficiently in combination, and conduct AI-supported work processes in ways that are ethical, secure, and consistent with academic principles.

Artificial intelligence is no longer merely a tool used by software developers; it is one of the new ways of turning ideas into working products. The Modern Software Development module is designed for anyone who wants to explore, in practice, how to design, build, validate, and transform an idea into a sustainable software product in the age of AI.

Whether you are a professional software developer, someone who has begun building applications through “vibe coding,” or a researcher, academic, entrepreneur, designer, or professional from another field seeking to solve a domain problem with software, this module will show you how to use artificial intelligence not merely as a helper that generates code, but as an active part of the development process.

The aim is not to write more code, but to build cleaner, more modular, testable, sustainable, and evolvable software systems. As construction phases accelerate with artificial intelligence, value in modern software development is shifting toward correctly defining the problem, clarifying requirements in detail, making architectural decisions early, and systematically validating outputs.

For this reason, while the module equips newcomers with an approach to building more robust products with AI, it also offers professional developers a perspective on adapting to the changing software lifecycle and managing shorter development cycles with stronger planning and engineering practices.

By the end of the module, the goal is to gain not merely the ability to use AI tools, but a mindset capable of designing, developing, and evaluating modern software systems together with artificial intelligence.