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1. Social Boundaries for Personal Agents in the Interpersonal Space of the Home

   https://doi.org/10.1145/3313831.3376311  

   Luria, Michal ; Zheng, Rebecca ; Huffman, Bennett ; Huang, Shuangni ; Zimmerman, John ; Forlizzi, Jodi ( April 2020 , Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems)

   The presence of voice activated personal assistants (VAPAs) in people's homes rises each year [31]. Industry efforts are invested in making interactions with VAPAs more personal by leveraging information from messages and calendars, and by accessing user accounts for 3rd party services. However, the use of personal data becomes more complicated in interpersonal spaces, such as people's homes. Should a shared agent access the information of many users? If it does, how should it navigate issues of privacy and control? Designers currently lack guidelines to help them design appropriate agent behaviors. We used Speed Dating to explore inchoate social mores around agent actions within a home, including issues of proactivity, interpersonal conflict, and agent prevarication. Findings offer new insights on how more socially sophisticated agents might sense, make judgements about, and navigate social roles and individuals. We discuss how our findings might impact future research and future agent behaviors. 

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2. Youth invisible work: the sociocultural and collaborative processes of online search and brokering between adolescents and English-language learning families

   https://doi.org/10.1108/ILS-01-2022-0004  

   Yip, Jason ; Roldan, Wendy ; Gonzalez, Carmen ; Pina, Laura R. ; Ruiz, Maria ; Vanegas, Paola ( July 2022 , Information and Learning Sciences)

   This study aims to investigate the collaboration processes of immigrant families as they search for online information together. Immigrant English-language learning adults of lower socioeconomic status often work collaboratively with their children to search the internet. Family members rely on each other’s language and digital literacy skills in this collaborative process known as online search and brokering (OSB). While previous work has identified ecological factors that impact OSB, research has not yet distilled the specific learning processes behind such collaborations. Design/methodology/approach: For this study, the authors adhere to practices of a case study examination. This study’s participants included parents, grandparents and children aged 10–17 years. Most adults were born in Mexico, did not have a college-degree, worked in service industries and represented a lower-SES population. This study conducted two to three separate in-home family visits per family with interviews and online search tasks. Findings: From a case study analysis of three families, this paper explores the funds of knowledge, resilience, ecological support and challenges that children and parents face, as they engage in collaborative OSB experiences. This study demonstrates how in-home computer-supported collaborative processes are often informal, social, emotional and highly relevant to solving information challenges. Research limitations/implications: An intergenerational OSB process is different from collaborative online information problem-solving that happens between classroom peers or coworkers. This study’s research shows how both parents and children draw on their funds of knowledge, resilience and ecological support systems when they search collaboratively, with and for their family members, to problem solve. This is a case study of three families working in collaboration with each other. This case study informs analytical generalizations and theory-building rather than statistical generalizations about families. Practical implications: Designers need to recognize that children and youth are using the same tools as adults to seek high-level critical information. This study’s model suggests that if parents and children are negotiating information seeking with the same technology tools but different funds of knowledge, experience levels and skills, the presentation of information (e.g. online search results, information visualizations) needs to accommodate different levels of understanding. This study recommends designers work closely with marginalized communities through participatory design methods to better understand how interfaces and visuals can help accommodate youth invisible work. Social implications: The authors have demonstrated in this study that learning and engaging in family online searching is not only vital to the development of individual and digital literacy skills, it is a part of family learning. While community services, libraries and schools have a responsibility to support individual digital and information literacy development, this study’s model highlights the need to recognize funds of knowledge, family resiliency and asset-based learning. Schools and teachers should identify and harness youth invisible work as a form of learning at home. The authors believe educators can do this by highlighting the importance of information problem solving in homes and youth in their families. Libraries and community centers also play a critical role in supporting parents and adults for technical assistance (e.g. WiFi access) and information resources. Originality/value: This study’s work indicates new conditions fostering productive joint media engagement (JME) around OSB. This study contributes a generative understanding that promotes studying and designing for JME, where family responsibility is the focus.

    

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3. Intelligence Augmentation for Collaborative Learning

   Roschelle, Jeremy ( January 2021 , HCI International)

   null (Ed.)

   Today’s classrooms are remarkably different from those of yesteryear. In place of individual students responding to the teacher from neat rows of desks, one more typically finds students working in groups on projects, with a teacher circulating among groups. AI applications in learning have been slow to catch up, with most available technologies focusing on personalizing or adapting instruction to learners as isolated individuals. Meanwhile, an established science of Computer Supported Collaborative Learning has come to prominence, with clear implications for how collaborative learning could best be supported. In this contribution, I will consider how intelligence augmentation could evolve to support collaborative learning as well as three signature challenges of this work that could drive AI forward. In conceptualizing collaborative learning, Kirschner and Erkens (2013) provide a useful 3x3 framework in which there are three aspects of learning (cognitive, social and motivational), three levels (community, group/team, and individual) and three kinds of pedagogical supports (discourse-oriented, representation-oriented, and process-oriented). As they engage in this multiply complex space, teachers and learners are both learning to collaborate and collaborating to learn. Further, questions of equity arise as we consider who is able to participate and in which ways. Overall, this analysis helps us see the complexity of today’s classrooms and within this complexity, the opportunities for augmentation or “assistance to become important and even essential. An overarching design concept has emerged in the past 5 years in response to this complexity, the idea of intelligent augmentation for “orchestrating” classrooms (Dillenbourg, et al, 2013). As a metaphor, orchestration can suggest the need for a coordinated performance among many agents who are each playing different roles or voicing different ideas. Practically speaking, orchestration suggests that “intelligence augmentation” could help many smaller things go well, and in doing so, could enable the overall intention of the learning experience to succeed. Those smaller things could include helping the teacher stay aware of students or groups who need attention, supporting formation of groups or transitions from one activity to the next, facilitating productive social interactions in groups, suggesting learning resources that would support teamwork, and more. A recent panel of AI experts identified orchestration as an overarching concept that is an important focus for near-term research and development for intelligence augmentation (Roschelle, Lester & Fusco, 2020). Tackling this challenging area of collaborative learning could also be beneficial for advancing AI technologies overall. Building AI agents that better understand the social context of human activities has broad importance, as does designing AI agents that can appropriately interact within teamwork. Collaborative learning has trajectory over time, and designing AI systems that support teams not just with a short term recommendation or suggestion but in long-term developmental processes is important. Further, classrooms that are engaged in collaborative learning could become very interesting hybrid environments, with multiple human and AI agents present at once and addressing dual outcome goals of learning to collaborate and collaborating to learn; addressing a hybrid environment like this could lead to developing AI systems that more robustly help many types of realistic human activity. In conclusion, the opportunity to make a societal impact by attending to collaborative learning, the availability of growing science of computer-supported collaborative learning and the need to push new boundaries in AI together suggest collaborative learning as a challenge worth tackling in coming years. 

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4. Predicting Sequential Design Decisions Using the Function-Behavior-Structure Design Process Model and Recurrent Neural Networks

   https://doi.org/10.1115/1.4049971  

   Rahman, Molla Hafizur ; Xie, Charles ; Sha, Zhenghui ( August 2021 , Journal of Mechanical Design)

   null (Ed.)

   Abstract In engineering systems design, designers iteratively go back and forth between different design stages to explore the design space and search for the best design solution that satisfies all design constraints. For complex design problems, human has shown surprising capability in effectively reducing the dimensionality of design space and quickly converging it to a reasonable range for algorithms to step in and continue the search process. Therefore, modeling how human designers make decisions in such a sequential design process can help discover beneficial design patterns, strategies, and heuristics, which are essential to the development of new algorithms embedded with human intelligence to augment the computational design. In this paper, we develop a deep learning-based approach to model and predict designers’ sequential decisions in the systems design context. The core of this approach is an integration of the function-behavior-structure (FBS) model for design process characterization and the long short-term memory unit (LSTM) model for deep leaning. This approach is demonstrated in two case studies on solar energy system design, and its prediction accuracy is evaluated benchmarking on several commonly used models for sequential design decisions, such as the Markov Chain model, the Hidden Markov Chain model, and the random sequence generation model. The results indicate that the proposed approach outperforms the other traditional models. This implies that during a system design task, designers are very likely to rely on both short-term and long-term memory of past design decisions in guiding their future decision-making in the design process. Our approach can support human–computer interactions in design and is general to be applied in other design contexts as long as the sequential data of design actions are available. 

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5. Creating online supports for at home making and STEM projects during COVID-19 (Work in Progress)

   Maltese, Adam V. ; Paul, Kelli ; Simpson, Amber ; Zych, Ariel ( July 2022 , Zone 1 Conference of the American Society for Engineering Education)

   Our NSF-funded project, CoBuild19, sought to address the large-scale shift to at-home learning based on nationwide school closures that occurred during COVID-19 through creating making/STEM activities for families with children in grades K-6. Representing multiple organizations, our CoBuild19 project team developed approximately 60 STEM activities that make use of items readily available in most households. From March through June 2020, we produced and shared videos and activity guides, averaging 3+ new activities per week. Initially, the activities consisted of whatever team members could pull together, but we soon created weekly themes with associated activities, including Design and Prototype Week, Textiles Week, Social and Emotional Learning Week, and one week which highlighted kids sharing cooking and baking recipes for other kids. All activities were delivered fully online. To do so, our team started a Facebook group on March 13, 2020. Membership grew to 3490 followers by April 1st, to 4245 by May 1st, and leveled off at approximately 5100 members since June 2020. To date, 22 of our videos have over 1000 views, with the highest garnering 23K views. However, we had very little participation in the form of submitted videos, images, or text from families sharing what they were creating, limiting our possible analyses. While we had some initial participation by members, as the FB group grew, substantive evidence of participation faded. To better understand this drop, we polled FB group members about their use of the activities. Responses (n = 101) were dominated by the option, "We are glad to know the ideas are available, but we are not using much" (49%), followed by, "We occasionally do activities" (35%). At this point, we had no data about home participation, so we decided to experiment with different approaches. Our next efforts focused on conducting virtual maker/STEM camps. Leveraging the content produced in the first months of CoBuild19, we hosted two rounds of Camp CoBuild by the end of July, serving close to 100 campers. The camps generated richer data in the form of recorded Zoom camp sessions where campers made synchronously with educators and youth-created Flipgrid videos where campers shared their process and products for each activity. We also collected post-camp surveys and some caregiver interviews. Preliminary analyses have focused on the range of participant engagement and which malleable factors may be associated with deeper engagement. Initial feedback from caregivers indicated that their children gained confidence to experiment with simple materials through engaging in these activities. This project sought to fill what we perceived as a developing need in the community at a large scale (e.g., across the US). Although we have not achieved the level of success we expected, the project achieved quick growth that took us in a different direction than we originally intended. Overall, we created content that educators and families can use to engage kids with minimal materials. Additionally, we have a few models of extended engagement (e.g., Camp CoBuild) that we can develop further into future offerings. 

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