Artificial Intelligence (AI) has been enhancing data analysis efficiency and accuracy during plant phenotyping, which is vital for tackling global agricultural and environmental challenges. Designing a reliable AI system to assist precise plant phenotyping begins with high-quality phenotypic feature annotation, which usually involves collaboration between plant scientists and AI specialists. However, due to the high level of diversity in these researchers’ backgrounds, it is likely that they have differing user needs from a fine-grained plant feature annotation system. We conducted semi-structured interviews with eight experienced annotators from diverse backgrounds, and observed how they interact with their preferred annotation system, to elucidate the challenges faced when annotating plant features and identify user needs. We collected qualitative responses to the interview questions, and conducted a quantitative evaluation of the agreement of their annotations on the given images. By analyzing the participants’ behaviors and the collected data, we identified common user needs and derived implications for the design of an AI-assisted annotation system, including providing a range of annotation options, the flexibility to adapt annotations, and functions to help addressing uncertainty. Our research contributes to the design of systems that make annotations efficient and reliable, not only benefiting plant phenotyping, but also other interdisciplinary fields that rely on user-driven annotations.

Contextualization in education connects learning material to students' experiences and background knowledge to make it relevant and meaningful. It involves placing content in a real-world context to show students the practical applications and relevance of the concepts they are learning. Contextualizing is an important pedagogical strategy. In order to support teachers in this effort, it is necessary to understand what contexts to provide for teachers to make relevant instruction in certain situations. In this paper, we aim to explore: In what context do teachers need students' everyday life experiences to make the class more relevant to students? We conducted focus groups with teachers who shared their contextualizing experiences. The context for contextualizing refers to the information used to characterize the situation in the classroom and the experiences of the students. The results of this research include the three categories of context information, time, activity, and location, that are relevant to the lesson and the students' prior experiences related to these factors. Design implications are discussed in this paper to illustrate how adaptive teacher support systems can provide relevant and meaningful information to the teachers by considering context information and students' prior experiences.

With advances in areas such as sensors and machine learning, wearable technologies will have increased potential to support our daily lives. Even though today’s landscape of smart wearable devices is highly varied, the real-world adoption of wearables has remained lukewarm. We propose that a key reason is that we currently only have a surface-level understanding of people’s interaction behaviors with wearable devices. A deeper understanding of user behaviors toward different wearable devices will help to inform wearable design for more seamless user experiences. We present an empirical study with 50 participants that explore people’s micro-behaviors toward five types of smart wearable devices (wristband, ring, clip, necklace, glasses) in a lab-based information-gathering context. A micro-analysis of participants’ session videos and interviews showed that people have different behaviors and attitudes in terms of affordances and functionality for different forms of wearables giving rise to a variety of design implications.

It is becoming increasingly common for intelligent systems, such as recommender systems, to provide explanations for their generated recommendations to the users. However, we still do not have a good understanding of what types of explanations work and what factors affect the effectiveness of different types of explanations. Our work focuses on explanations for movie recommender systems. This paper presents a mixed study where we hypothesize that the type of explanation, as well as user motivation for watching movies, will affect how users respond to recommendation system explanations. Our study compares three types of explanations: i) neighbor-ratings, ii) profile-based, and iii) event-based, as well as three types of user movie-watching motivations: i) hedonic (fun and relaxation), ii) eudaimonic (inspiration and meaningfulness), and iii) educational (learning new content). We discuss the implications of the study results for the design of explanations for movie recommender systems, and future novel research directions that the study results uncover.