Human-Computer Interaction at CMU Jodi Forlizzi Jason Hong

Why are these hard to use?

HCI at CMU Our mission is to create and evaluate effective, useful, and enjoyable experiences with technology, through engagement with the disciplines of computer science, social science, cognitive science, design, and engineering.

HCI at CMU Multidisciplinary research areas: Enabling technology Human assistance and well-being Interdisciplinary centers Learning science and technology Social computing Research through design ~20 faculty, ~40 PhD students

HCI at CMU Our approach User-centered design and development Qualitative and quantitative study Active role of prototyping Labs and naturalistic settings With the people who will use the product

HCI at CMU Multidisciplinary research areas: Enabling technology Human assistance and well-being Interdisciplinary centers Learning science and technology Social computing Research through design

Enabling Technology Natural Programming Alice: Making Programming Accessible Automatic calibration of projectors Human-Robotic interaction / Assistive robots Context-aware computing Human modeling Models of attention Tools for creating web-based mashups Usable Privacy and Security

Making Mashups with Marmite

Anti-Phishing Training

Anti-Phishing Phil

Web Browser Warnings How effective are browser warnings?

Privacy and Security for Pervasive Computing

Social Computing Footprints, use social nets vs global warming Designing online communities HomeNet Mobile social computing Information displays MOVE Impact Glanceable displays My agent as myself or another I can briefly mention the greyed out ones, and then transition to you

Social computing Our research is developing social, context- aware applications that: Sense and provide information about people and their physical and social context Improve decision-making by creating applications that present information in appropriate ways Inspire positive changes in people’s behavior

Information display Create visualizations that: Build on the science of warnings Integrate multiple pieces of data Show trends and relationships Avoid information overload

A Science of Warnings See the warning? Understand? Believe it? Motivated? Planning on refining this model for computer warnings

Information display Design variables we have explored Abstraction Symbolism Complexity Social representation R

MOVE Maps Optimized for the Vehicular Environment Goal: use cognitive resources efficiently in the context of in-car navigation Approach: systematic study of navigation activities, information design techniques, information optimization techniques Fivefold improvement over static maps

IMPACT Improving and Motivating Physical Activity Using Context Goal: allow people to see opportunities for increasing daily activity, motivate them to change their behavior to do so Approach: ethnographic study with those who have set and reached or failed to reach goals to become more active, system design and evaluation To date, users increase awareness and motivation for increasing physical activity

Glanceable displays How to design glanceable visuals for contexts of divided attention? Simple visuals are not preferred over complex ones Colored representations can be successfully interpreted peripherally Impact on performance small, impact on preference large

My agent as myself or another How would users interact with agents that resembled the user? Agents that look like the self are rated highly for credibility Seen as more credible when someone else creates the faces Self-esteem may play a role in the adoption of an agent that looks like the user or is simply familiar Design implications for agents that maintain social relationships with users

Conclusions Multidisciplinary research areas: Enabling technology Human assistance and well-being Interdisciplinary centers Learning science and technology Social computing Research through design