This advanced lecture introduces various kinds of advanced technologies used to support human learning (e.g., in school classrooms). We will review and critically analyze different techniques, learning science theories and principles, “learning engineering” efforts, and technological features that are embedded in such systems. We will work on a group-based project in which students will conduct an end-to-end cycle of designing, implementing, and testing (via a small experiment or user study) a learning technology for a target goal/domain, guided by the instructor and teaching team. The goal of this lecture is to help you understand core technologies and approaches used in the design/development of learning technologies (that are used in practice) and to equip you with the skills of designing/developing a piece of learning technology and evaluation its effectiveness on learning, engagement, and on related constructs. The course welcomes students with any background (in terms of cultural, racial, disciplinary, and technological) — we are looking forward to building a community of learners with diverse perspective to engage in deep discussions and hands-on activities.
The rise of artificial intelligence (AI) is transforming everyday lives, including education, and it requires us a deep understanding of and research insights into its applications and implications in the field. This seminar aims to equip students with the knowledge and skills needed to critically analyze AI in education and contribute to this evolving field. The seminar is jointly taught by Prof. Tomohiro Nagashima in the CS department and Dr. Sarah Malone in the Education Science department, and it targets students in both departments, as well as those from other departments!
During the seminar, students will collaboratively design and develop a “Living AI-education Dashboard,” a dynamic resource that summarizes and visualizes current research, trends, and data on AI in education. Through project-based learning, students will gain hands-on experience in data visualization, dashboard development, dashboard design, and research methods (e.g., how to conduct systematic literature review). Students would also be testing the dashboard with “real” stakeholders. They will also develop interdisciplinary thinking by integrating concepts from both computer science and education science and through collaborations across the domains. The course is taught by an interdisciplinary team that encourages collaboration between departments and prepares students to tackle complex, real-world problems.
This seminar is designed for students who have a research idea, or even just a broad curiosity, and want to learn how to turn it into a concrete empirical study design in Human–AI Interaction. The goal is to help students move from a broad research interest to a clear, feasible, and well-justified study design that can serve as the basis for a master’s thesis or doctoral research proposal.
The seminar covers key stages of empirical research design, including identifying a research problem, conducting a critical literature review, formulating research questions, comparing methodological approaches, designing studies, and articulating expected contributions. The seminar will also touch on critical and creative thinking as important foundations for research design: how to evaluate existing work carefully, generate original ideas, and turn them into meaningful study designs.
This seminar will be highly interactive and activity-based. It will combine short teaching inputs, paper discussions, hands-on activities, peer feedback, and research proposal development workshops. Students will read and discuss selected papers from Human–AI Interaction and related HCI research, while gradually developing their own empirical research proposal throughout the semester.
By the end of the seminar, students are expected to have a structured proposal draft and a clearer understanding of how to design an empirical study from the initial idea to a concrete research plan.