Enabling Automated, Conversational Health Coaching with Human-Centered Artificial Intelligence

Health coaching is a promising approach to support self-management of chronic conditions like type 2 diabetes; however, there aren’t enough coaching practitioners to support those in need. Advances in Artificial Intelligence (AI) and Machine Learning (ML) have the potential to enable innovative, automated health coaching interventions, but important gaps remain in applying AI and ML to coaching interventions. This thesis aims to identify computational approaches and interactive technologies that enable automated health coaching systems. First, I utilized computational approaches that leverage individuals’ self-tracking and health data and used an expert system to translate ML inferences into personalized nutrition goal recommendations. The system, GlucoGoalie, was evaluated in multiple studies including a 4-week deployment study which demonstrated the feasibility of the approach. Second, I compared human-powered and automated/chatbot approaches to health coaching in a 3-week study which found that t2.coach — a scripted, theoretically-grounded chatbot designed through an iterative, user-centered process — cultivated a coach-like experience that had many similarities to the experience of messaging with actual health coaches, and outlined directions for automated, conversational coaching interventions. Third, I examined multiple AI approaches to enable micro-coaching dialogs — brief coaching conversations related to specific meals, to support achievement of nutrition goals — including a knowledge-based system for natural language understanding, and a data-driven, reinforcement learning approach for dialog management. Together, the results of these studies contribute methods and insights that take steps towards more intelligent conversational coaching systems, with resonance to research in informatics, human-computer interaction, and health coaching

Virtual coaches for healthy lifestyle

Since the introduction of the idea of the software interface agent the question recurs whether these agents should be personified and graphically visualized in the interface. In this chapter we look at the use of virtual humans in the interface of healthy lifestyle coaching systems. Based on theory of persuasive communication we analyse the impact that the use of graphical interface agents may have on user experience and on the efficacy of this type of persuasive systems. We argue that research on the impact of a virtual human interface on the efficacy of these systems requires longitudinal field studies in addition to the controlled short-term user evaluations in the field of human computer interaction (HCI). We introduce Kristina, a mobile personal coaching system that monitors its user’s physical activity and that presents feedback messages to the user. We present results of field trials (N = 60, 7 weeks) in which we compare two interface conditions on a smartphone. In one condition feedback messages are presented by a virtual animated human, in the other condition they are displayed on the screen in text. Results of the field trials show that user motivation, use context and the type of device on which the feedback message is received influence the perception of the presentation format of feedback messages and the effect on compliance to the coaching regime

Trust your guts: fostering embodied knowledge and sustainable practices through voice interaction

Despite various attempts to prevent food waste and motivate conscious food handling, household members find it difficult to correctly assess the edibility of food. With the rise of ambient voice assistants, we did a design case study to support households’ in situ decision-making process in collaboration with our voice agent prototype, Fischer Fritz. Therefore, we conducted 15 contextual inquiries to understand food practices at home. Furthermore, we interviewed six fish experts to inform the design of our voice agent on how to guide consumers and teach food literacy. Finally, we created a prototype and discussed with 15 consumers its impact and capability to convey embodied knowledge to the human that is engaged as sensor. Our design research goes beyond current Human-Food Interaction automation approaches by emphasizing the human-food relationship in technology design and demonstrating future complementary human-agent collaboration with the aim to increase humans’ competence to sense, think, and act

Supporting Inclusive Learning Using Chatbots? A Chatbot-Led Interview Study

Supporting student academic success has been one of the major goals for higher education. However, low teacher-to-student ratio makes it difficult for students to receive sufficient and personalized support that they might want to. The advancement of artificial intelligence (AI) and conversational agents, such as chatbots, has provided opportunities for assisting learning for different types of students. This research aims at investigating the opportunities and requirements of chatbots as an intelligent helper to facilitate equity in learning. We developed a chatbot as an experimental platform to investigate the design opportunities of using chatbots to support inclusive learning. Through a chatbot-led user study with 215 undergraduate students, we found chatbots provide the opportunity to support students who are disadvantaged, with diverse life environments, and with varied learning styles. This could be achieved through an accessible, interactive, and confidential way

Principal And Instructional Coach Partnerships For Instructional Leadership: A Case Study Of Interactions And Teacher Perceptions

This qualitative case study examined conversations and interactions between an intermediate school principal and a team of content specific instructional coaches to investigate the presence of shared instructional leadership and how the interactions and responses of the two actors might support teachers’ professional growth and refinement of instructional practices. An initial interview with the campus principal was used to establish her goals for instructional leadership. Over a six-week period, these goals were tracked through observations and coding of weekly meetings between the principal and coaches and then traced through the coaches’ work with teachers. Findings indicated that the principal was attempting to utilize shared leadership to augment her instructional leadership, but that the results were contingent upon the quality of the leadership team’s internal dynamics as well as the strength of focus on the desired goals. Instructional coaches were utilized by both the principal and the teachers as intermediaries of instructional leadership. One coach maintained a strong goal focus, which teachers perceived as very supportive to their growth, resulting in gains of approximately 30 points for struggling students

A Review of Reinforcement Learning for Natural Language Processing, and Applications in Healthcare

Reinforcement learning (RL) has emerged as a powerful approach for tackling complex medical decision-making problems such as treatment planning, personalized medicine, and optimizing the scheduling of surgeries and appointments. It has gained significant attention in the field of Natural Language Processing (NLP) due to its ability to learn optimal strategies for tasks such as dialogue systems, machine translation, and question-answering. This paper presents a review of the RL techniques in NLP, highlighting key advancements, challenges, and applications in healthcare. The review begins by visualizing a roadmap of machine learning and its applications in healthcare. And then it explores the integration of RL with NLP tasks. We examined dialogue systems where RL enables the learning of conversational strategies, RL-based machine translation models, question-answering systems, text summarization, and information extraction. Additionally, ethical considerations and biases in RL-NLP systems are addressed