AI-Assisted Teaching of Data Analysis for Humanities Students: From Lectures to Interactive Practice

This presentation describes a work-in-progress teaching model for an undergraduate data analysis course designed for humanities and social-science students. The main research question is how generative AI and simple computational tools can help students move from passive listening to active understanding and practice. The course combines recorded lectures, classroom demonstrations, Orange-based data analysis, structured quizzes, short written summaries, and AI-supported interactive learning pages. Special attention is given to topics that are difficult for non-technical students, such as correlation, classification, logistic regression, and text analysis. The presentation discusses the design of the learning environment, examples of student tasks, and the role of AI in supporting explanation, practice, feedback, and project work. I also discuss practical challenges: avoiding superficial AI use, encouraging independent thinking, and helping students understand methods rather than only follow technical steps. The contribution is a practical, scalable model for AI-supported digital pedagogy.