Design Foundations for AI Assisted Decision Making: A Self Determination Theory Approach

Progress of technology and processing power has enabled the advent of sophisticated technology including Artificial Intelligence (AI) agents. AI agents have penetrated society in many forms including conversation agents or chatbots. As these chatbots have a social component to them, is it critical to evaluate the social aspects of their design and its impact on user outcomes. This study employs Social Determination Theory to examine the effect of the three motivational needs on user interaction outcome variables of a decision-making chatbot. Specifically, this study looks at the influence of relatedness, competency, and autonomy on user satisfaction, engagement, decision efficiency, and decision accuracy. A carefully designed experiment revealed that all three needs are important for user satisfaction and engagement while competency and autonomy is associated with decision accuracy. These findings highlight the importance of considering psychological constructs during AI design. Our findings also offer useful implications for AI designers and organizations that plan on using AI assisted chatbots to improve decision-making efforts

Game elements from literature review of gamification in healthcare context

Gamification is a conceptual framework to apply game elements and techniques to improve the interesting process in non-game context. Gamification offers the motivation approach to motivate the player to handle the challenge tasks with game mechanics, game dynamics, and components. Nowadays, To discover the set of game elements and techniques from evaluating the existing related research is more opportunity for success in the exciting process. The core objective of this paper is to review the literature by using descriptive statistics of game elements with the review methodology and evaluate the model with multi-label classification with a dataset from this literature examined. The reviewed literature was first coded author-centrally. After each paper was scrutinized for the analysis, the perspective was pivoted, and further analyses were conducted concept-centrally. A systematic review has been conducted that proves the wide variety of game elements, being retrieved a total of fifteen terms of game elements from twenty-two selected papers that were screened from a total of eighty-two documents. Only a few terms are used: points, feedback, levels, leaderboards, challenges, badges,  avatars, competition, and cooperation. However, only some can be considered actual elements mechanics and that have not a similar abstraction level. Additionally, the authors examined the relationship between game elements and STD: Competence, Autonomy, and Relatedness with a Data mining technique, Multi-label classification to discovery knowledge of game elements. The results indicated that rFerns algorithm provides the lowest Hamming Loss with 4.17%. Furthermore, It shows that Multi-label Rain Forest (rfsrc) in Algorithm adaptation method and Rain Forest (RF) in Problem transformation method provide the same Hamming Loss with 29.17%. Moreover, rFerns algorithm provides the highest accuracy with 87.5% for Competence, and 100% for Autonomy and Relatedness. Furthermore, It shows that Multi-label Rain Forest (rfsrc) in Algorithm adaptation method and Rain Forest (RF) in Problem transformation method provide the same Accuracy with 87.5% for Competence, and 62.5% for Autonomy and Relatedness. The results from this study will be used to design a gamified system in a healthcare context to promote physical activity

Qualitative Research in HRI: A Review and Taxonomy

The field of human–robot interaction (HRI) is young and highly inter-disciplinary, and the approaches, standards and methods proper to it are still in the process of negotiation. This paper reviews the use of qualitative methods and approaches in the HRI literature in order to contribute to the development of a foundation of approaches and methodologies for these new research areas. In total, 73 papers that use qualitative methods were systematically reviewed. The review reveals that there is widespread use of qualitative methods in HRI, but very different approaches to reporting on it, and high variance in the rigour with which the approaches are applied. We also identify the key qualitative methods used. A major contribution of this paper is a taxonomy categorizing qualitative research in HRI in two dimensions: by ’study type’ and based on the specific qualitative method use