Faculty

Background: Individuals living with advancing stages of dementia (persons with dementia, PWDs) or other cognitive disorders do not have the luxury of remembering how to perform basic day-to-day activities, which in turn makes them increasingly dependent on the assistance of caregivers. Dressing is one of the most common and stressful activities provided by caregivers because of its complexity and privacy challenges posed during the process. Objective: In preparation for in-home trials with PWDs, the aim of this study was to develop and evaluate a prototype intelligent system, the DRESS prototype, to assess its ability to provide automated assistance with dressing that can afford independence and privacy to individual PWDs and potentially provide additional freedom to their caregivers (family members and professionals). Methods: This laboratory study evaluated the DRESS prototype's capacity to detect dressing events. These events were engaged in by 11 healthy participants simulating common correct and incorrect dressing scenarios. The events ranged from donning a shirt and pants inside out or backwards to partial dressing-typical issues that challenge a PWD and their caregivers. Results: A set of expected detections for correct dressing was prepared via video analysis of all participants' dressing behaviors. In the initial phases of donning either shirts or pants, the DRESS prototype missed only 4 out of 388 expected detections. The prototype's ability to recognize other missing detections varied across conditions. There were also some unexpected detections such as detection of the inside of a shirt as it was being put on. Throughout the study, detection of dressing events was adversely affected by the relatively smaller effective size of the markers at greater distances. Although the DRESS prototype incorrectly identified 10 of 22 cases for shirts, the prototype preformed significantly better for pants, incorrectly identifying only 5 of 22 cases. Further analyses identified opportunities to improve the DRESS prototype's reliability, including increasing the size of markers, minimizing garment folding or occlusions, and optimal positioning of participants with respect to the DRESS prototype. Conclusions: This study demonstrates the ability to detect clothing orientation and position and infer current state of dressing using a combination of sensors, intelligent software, and barcode tracking. With improvements identified by this study, the DRESS prototype has the potential to provide a viable option to provide automated dressing support to assist PWDs in maintaining their independence and privacy, while potentially providing their caregivers with the much-needed respite.

This project aimed to improve students' learning and task performance using a non-cognitive learning companion in the context of both a tutor and a meta-tutor. The tutor taught students how to construct models of dynamic systems and the meta-tutor taught students a learning strategy. The non-cognitive learning companion was designed to increase students' effort and persistence in using the learning strategy. It decided when to intervene and what to say using both log data and affective state monitoring via a facial expression camera and a posture sensor. Experiments with high school students showed that the non-cognitive learning companion increased students' learning and performance. However, it had no effect on performance during a transfer phase in which the learning companion, meta-tutor, and tutor were all absent. The transfer phase null effect must be interpreted with caution due to low power, a possible floor effect, and other issues.

The objective of this integrative review was to describe current US trends for health technology-enabled adherence interventions among behaviorally HIV-infected youth (ages 13–29 years), and present the feasibility and efficacy of identified interventions. A comprehensive search was executed across five electronic databases (January 2005–March 2016). Of the 1911 identified studies, nine met the inclusion criteria of quantitative or mixed methods design, technology-enabled adherence and or retention intervention for US HIV-infected youth. The majority were small pilots. Intervention dose varied between studies applying similar technology platforms with more than half not informed by a theoretical framework. Retention in care was not a reported outcome, and operationalization of adherence was heterogeneous across studies. Despite these limitations, synthesized findings from this review demonstrate feasibility of computer-based interventions, and initial efficacy of SMS texting for adherence support among HIV-infected youth. Moving forward, there is a pressing need for the expansion of this evidence base.

We present results of a randomized controlled study that compared different types of affective support messages delivered by pedagogical agents. Results suggest that using a character that is empathic and emphasizes the malleability of intelligence and the importance of effort provides useful results in student learning, while reducing boredom and anxiety. Emphasizing success and failure ("That is correct/wrong") appears to be detrimental to learning and interest and promotes anxiety. We examine a variety of student affective, cognitive and engagement outcomes in an intelligent tutoring system for mathematics.

This essay imagines the role that artificial intelligence innovations play in the integrated living, learning and research environments of 2041. Here, in 2041, in the context of increasingly complex wicked challenges, whose solutions by their very nature continue to evade even the most capable experts, society and technology have co-evolved to embrace cyberlearning as an essential tool for envisioning and refining utopias-non-existent societies described in considerable detail. Our society appreciates that evolving these utopias is critical to creating and resolving wicked challenges and to better understanding how to create a world in which we are actively "learning to be" - deeply engaged in intrinsically motivating experiences that empower each of us to reach our full potential. Since 2015, Artificial Intelligence in Education (AIED) has transitioned from what was primarily a research endeavour, with educational impact involving millions of user/learners, to serving, now, as a core contributor to democratizing learning (Dewey 2004) and active citizenship for all (billions of learners throughout their lives). An expansive experiential super computing cyberlearning environment, we affectionately call the "Holodeck," supports transdisciplinary collaboration and integrated education, research, and innovation, providing a networked software/hardware infrastructure that synthesizes visual, audio, physical, social, and societal components. The Holodeck's large-scale integration of learning, research, and innovation, through real-world problem solving and teaching others what you have learned, effectively creates a global meritocratic network with the potential to resolve society's wicked challenges while empowering every citizen to realize her or his full potential.

Successful aging, independence and capacity for aging in place involves the maintenance and preservation of individuals' physical, mental and social well-being. Elderly people need to maintain the capacity to perform both activities of daily living (ADL) and instrumental activities of daily living (IADL). Advances in Smart Home technologies are increasingly able to provide embedded assessments of an individual's functional ability in his/her home on a momentto- moment, daily, and longitudinal basis. To date, in-situ functional assessment systems and research have focused to a greater extent on the advancement of technologies rather than the multi-faceted needs and experiences of users, however the success of any technology depends more on the users than the technology itself. This paper presents strategies for user-centric approaches to identify the technical and design challenges of developing, deploying, and using functional assessment systems in homes occupied by senior citizens. Case studies involved 4 healthy older adults (aged 65+), and examined the home deployment of smart home systems and interfaces aimed at assessment of a combination of ADL and IADL activities. Pre- and post-activity interviews were used to better understand issues related to desire, privacy, technological acceptance, suitability, and need fulfillment/support. The results inform strategies for usercentered functional assessment and assistive technology design and implementation, providing information capture, analysis, and delivery of in-home functional assessment that has the potential to support aging in place.

While creativity is essential for developing students' broad expertise in Science, Technology, Engineering, and Math (STEM) fields, many students struggle with various aspects of being creative. Digital technologies have the unique opportunity to support the creative process by (1) recognizing elements of students' creativity, such as when creativity is lacking (modeling step), and (2) providing tailored scaffolding based on that information (intervention step). However, to date little work exists on either of these aspects. Here, we focus on the modeling step. Specifically, we explore the utility of various sensing devices, including an eye tracker, a skin conductance bracelet, and an EEG sensor, for modeling creativity during an educational activity, namely geometry proof generation. We found reliable differences in sensor features characterizing low vs. high creativity students. We then applied machine learning to build classifiers that achieved good accuracy in distinguishing these two student groups, providing evidence that sensor features are valuable for modeling creativity.

Modelling is an important skill to acquire, but it is not an easy one for students to learn. Existing instructional technology has had limited success in teaching modelling. We have applied a recently developed technology, meta-tutoring, to address the important problem of teaching model construction. More specifically, we have developed and evaluated a system that has two parts, a tutor and a meta-tutor. The tutor is a simple step-based tutoring system that can give correct/incorrect feedback on student's steps and can demonstrate steps for students when asked. Because deep modelling requires difficult analyses of the quantitative relationships in a given system, we expected, and found, that students tended to avoid deep modelling by abusing the tutor's help. In order to increase the frequency of deep modelling, we added a meta-tutor that coached students to follow a learning strategy that decomposed the overall modelling problem into a series of "atomic" modelling problems. We conducted three experiments to test the effectiveness of the meta-tutor. The results indicate that students who studied with meta-tutor did indeed engage in more deep modelling practices. However, when the meta-tutor and tutor were turned off, students tended to revert to shallow modelling. Thus, the next stage of the research is to add an affective agent that will try to persuade students to persist in using the taught strategies even when the meta-tutoring and tutoring have ceased.

Teachable agents foster student learning by employing the learning by teaching paradigm. Since social factors influence learning from this paradigm, understanding which social behaviors a teachable agent should embody is an important first step for designing such an agent. Here, we focus on the impact of causal attributions made by a teachable agent. To obtain data on student perceptions of agent attributions, we conducted a study involving students interacting with a social robot that made attributions to ability and effort, and to the student, itself, or both. We analyzed data from semi-structured interviews to understand how different attributions influence student perceptions, and discuss design opportunities for manipulating these attributions to improve student motivation.

To date, relatively little work has explored how students learn about a particular class of processes, namely emergent ones. The research that has investigated these processes has primarily employed a case-study methodology. Here, we report on a controlled experiment comparing how students learn about the emergent topic of diffusion from self-explaining vs. from reading. In contrast to a prior study that found self-explanation was not associated with learning about emergence, students learned significantly more in the self-explaining condition. To shed light on how different types of self-explanations are related to learning, we analyze the content of students' explanations and their association to learning outcomes; we also present qualitative analysis of students' misconceptions and how these relate to existing theories of emergent attributes.