Research Design for AI

Course Objective

The goal of this course is to teach students fundamental scientific research skills and how to apply them to the planning and execution of their Bachelor's thesis in Artificial Intelligence.

Upon completion of the course, students will:

• demonstrate knowledge of AI research design principles, including formulation of research questions and selection of appropriate research methods;
• be able to conduct thorough literature reviews and position their work within relevant AI research discourse;
• understand different approaches to data collection and analysis according to various AI research methodologies;
• be able to identify and address ethical considerations relevant to AI research;
• demonstrate ability to interpret and report on results in a clear and scientific manner;
• learn how to critically reflect on the limitations and implications of their findings.

Course Content

This course teaches students how to design and conduct scientific research in the domain of artificial intelligence. They will be guided through the different stages of an AI research project, from formulating research questions and conducting literature reviews, to developing research designs, collecting and analysing data, and reporting on their findings in a clear and critical scientific manner.

The course will support students in achieving key thesis milestones including:

• identifying and formulating relevant AI research topics and questions;
• conducting comprehensive literature reviews in relevant fields;
• selecting appropriate research methodologies for AI projects;
• collecting, processing, and analysing data (in accordance with GDPR requirements);
• following ethical and open research practices;
• scientific writing and communication of technical concepts;
• critically reporting on study findings and limitations.

Students will apply these skills to progressively develop their Bachelor's project, receiving guidance and feedback at each milestone.

Additional Information Teaching Methods

Students can expect around 3.5 contact hours per week, on campus, for six weeks (excluding education-free week). Each week consists of a lecture and a working group. The first week has no working group, but attending the next four working group sessions is mandatory.

Method of Assessment

The grade for this course is solely determined by the students’ participation in four specific mandatory working group sessions, which take place on campus and are graded as pass/fail. No alternative means of assessment are available, nor is remote or hybrid participation. The final project grade will be given by the BSc AI Project supervisor.

You are expected to attend the working groups even if you are still looking for a supervisor or fall behind on proceeding to the next thesis stage, as the working groups are designed to accommodate students at varying completion stages.

Entry Requirements

The course will be given in period 5 and should be completed alongside the BSc AI Project course, as it will guide and support each student through the stages of their project completion. Students who will postpone their thesis for P5 in the following year should take the course then instead.

Students who will complete their thesis at a different time (i.e. not in P5, this year or next) should preferably take the course before they do their projects rather than after, as it will provide them with the tools they need to start preparing and managing their expectations for when the time comes.

Literature

All course materials will be made available on Canvas.

Additional Information Target Audience

BSc Artificial Intelligence (year 3) students who are about to start on their final projects.

Additional Information

Questions regarding this course should be directed to the lecturers in class, whereas any administrative thesis-related questions (e.g. finding a supervisor or navigating OnStage) should be directed to the coordinators of the BSc AI Project course instead.

In this course, generative AI (GenAI), such as ChatGPT or similar tools, may only be used for orientation and brainstorming, for example to explore ideas or clarify questions. GenAI may not be used to generate content that becomes part of the final submission. The final product must be based entirely on the student's own understanding, analysis, and wording. Incorrect use of GenAI is considered fraud. If established, the submitted work or assessment result will be declared invalid, and the Examination Board may impose further measures.

Custom Course Registration

The course will be given in period 5 and should be completed alongside the BSc AI Project course, as it will guide and support each student through the stages of their project completion. Students who will postpone their thesis for P5 in the following year should take the course then instead.

Students who will complete their thesis at a different time (i.e. not in P5, this year or next) should preferably take the course before they do their projects rather than after, as it will provide them with the tools they need to start preparing and managing their expectations for when the time comes.