In-home, place-based, conversational agents have exploded in popularity over the past three years. In particular, Amazon's conversational agent, Alexa, now dominates the market and is in millions of homes. This paper presents two complementary studies investigating the experience of households living with a conversational agent over an extended period of time. First, we gathered the history logs of 75 Alexa participants and quantitatively analyzed over 278,000 commands. Second, we performed seven in-home, contextual interviews of Alexa owners focusing on how their household interacts with Alexa. Our findings give the first glimpse of how households integrate Alexa into their lives. We found interesting behaviors around purchasing and acclimating to Alexa, in the number and physical placement of devices, and in daily use patterns. Participants also uniformly described interactions between children and Alexa. We conclude with suggestions for future improvement for intelligent conversational agents.

Machine learning (ML) plays an increasingly important role in improving a user's experience. However, most UX practitioners face challenges in understanding ML's capabilities or envisioning what it might be. We interviewed 13 designers who had many years of experience designing the UX of ML-enhanced products and services. We probed them to characterize their practices. They shared they do not view themselves as ML experts, nor do they think learning more about ML would make them better designers. Instead, our participants appeared to be the most successful when they engaged in ongoing collaboration with data scientists to help envision what to make and when they embraced a data-centric culture. We discuss the implications of these findings in terms of UX education and as opportunities for additional design research in support of UX designers working with ML.

Peer feedback is a central activity for project-based design education. The prevalence of devices carried by students and the emergence of novel peer feedback systems enables the possibility of collecting and sharing feedback immediately between students during class. However, pen and paper is thought to be more familiar, less distracting for students, and easier for instructors to implement and manage. To evaluate the efficacy of in-class digital feedback systems, we conducted a within-subjects study with 73 students during two weeks of a game design course. After short student presentations, while instructors provided verbal feedback, peers provided feedback either on paper or through a device. The study found that both methods yielded comments of similar quality and quantity, but the digital approach provided additional ways for students to participate and required less effort from the instructors. While both methods produced similar behaviors, students held inaccurate perceptions about their behavior with each method. We discuss design implications for technologies to support in-class feedback exchange.

As people increasingly turn to digital channels to share, store, and reflect on their lives and experiences, the processes by which they manage the diverse collection of information generated over the course of their lives are changing. These processes, once a matter of hands-on curation and personal meaning making, are now deeply rooted in interactions with digital systems. In this work, we drew from prior research from personalization, memory, and information management to create four interactive, provocative systems. Through sessions with 12 adults from Pittsburgh, PA we used a combination of these systems and interviews to examine how systems might play a role in the near and long term resurfacing of personal and familial digital information. Findings point to an opportunity to create systems that can openly mediate the curation and transmission of digital content, and ways to draw meaning from the differences between how systems and people recall and represent their experiences.