Augmented reality, virtual reality, and artificial intelligence to enhance programming comprehension

Despite their growing use, there remains a gap in applying these technologies to help students, particularly in engineering, understand abstract programming concepts. This thesis addresses this gap by exploring how AR, VR, Artificial Intelligence (AI), and Electroencephalography (EEG) data can enhance the learning of Python collection data types—a challenging yet fundamental topic for electrical and electronic engineering students.

The research questions focus on how AR, VR, and AI can improve comprehension of Python collection data types and whether EEG data can reveal a relationship between attention and engagement levels and learning outcomes. To investigate these questions, three user studies were conducted with 20, 39, and 48 participants, respectively. The structured methodology included developing a paper-based booklet, an AR application, and two versions of a VR application with added interactivity and AI features, such as a virtual assistant.