Learning Outcomes
(What) Students will go through an end-to-end Human-AI interaction lifecycle. (How) Students achieve this via project-based work by processing multimodal user inputs, engineering or fine-tuning AI models to interpret and generate responses, and integrating these loops into an interactive AI application. (Why) To enable students to build perceptive, proactive media systems and become creators of intelligent interface experiences.
(What) Students will be able to critically analyze advanced, peer-reviewed international Human-AI Interaction literature and transfer these technical insights to an audience of peers. (How) By reviewing and discussing papers from top-tier venues (e.g., ACM CHI, IUI) in a seminar-style format, delivering structured scientific presentations, and participating in peer-feedback sessions. (Why) This enables students to critically judge new Human-AI concepts, extract core methodologies, and strengthen their professional and academic communication skills.
Content
This module addresses selected and advanced topics in Human-AI Interaction (HAI) based on recent literature from venues such as the ACM CHI and IUI conferences. Example focus areas include Embodied AI (both physical and virtual), Affective Computing, Multimodal Interaction, and Inclusive Interaction. Within these domains, the seminar addresses both direct human-AI interaction and AI-mediated human-human interaction. Students explore these advanced interaction domains through paper analyses, presentations, and debates. Throughout the semester, emphasis is placed on empirical research methodologies and academic literacy. In multiple sessions, students evaluate how researchers design and validate intelligent interactive systems. The curriculum balances collaborative training and peer feedback.
To bridge theoretical research with the practice of building, students will engage in a semester-long, hands-on development project guided by a series of assignments. Moving through an end-to-end Human-AI interaction lifecycle, teams will design and implement an interactive AI application. They achieve this by processing multimodal user inputs, engineering or fine-tuning AI models to interpret and generate responses, and integrating these loops into a functional system.
This practical work connects to the final individual oral examination, where students present an assigned research paper and use its empirical frameworks to reflect on their own project.
Methods
- Faculty Coaching
- Student-Led Seminars
- Peer-to-Peer Feedback
- Project work
- Independent Scientific Inquiry