TIM-8102

Alabed, A., Javornik, A., & Gregory-Smith, D. (2022). AI anthropomorphism and its effect on users’ self-congruence and self–AI integration: A theoretical framework and research agenda. Technological forecasting & social change, 182. https://doi.org/10.1016/j.techfore.2022.121786
This journal article examines how users of anthropomorphized artificially intelligent (AI) agents, which possess capabilities to mimic humanlike behavior, relate psychologically to such agents in terms of their self-concept.

Lin, B., Bouneffouf, D., & Cecchi, G. (2022). Predicting human decision making in psychological tasks with recurrent neural networks. PloS One, 17 (5), e0267907. https://doi.org/10.1371/journal.pone.0267907
In this journal article, the authors propose to use a recurrent neural network architecture based on long short-term memory networks (LSTM) to predict the time series of the actions taken by human subjects engaged in gaming activity, the first application of such methods in this research domain. Implications for decision-making are discussed.

Channa, F. R., Sarhandi, P. S. A., Bugti, F., & Brohi, I. A. (2021). Supporting self-regulated learning by affect detection and responding in ai-driven learning systems. Ilkogretim Online, 20 (5), 3205–3211. https://www.bibliomed.org/mnsfulltext/218/218-1616322507.pdf?1671642253
This journal article explores how emotions are used in self-regulated learning processes and influence cognitive functioning. Artificial intelligent learning systems and the ability to detect the affective states of the learner as the agent are detailed and implications for future research are discussed.

Torabzadeh, S. A., Tavakkoli-Moghaddam, R., Samieinasab, M., & Hamid, M. (2022). An intelligent algorithm to evaluate and improve the performance of a home healthcare center considering trust indicators. Computers in Biology and Medicine, 146. https://doi.org/10.1016/j.compbiomed.2022.105656
In this journal article, an intelligent algorithm is proposed to assess the performance of an HHC center considering trust indicators. Efficiency scores for each decision-making unit were computed using an artificial neural network and statistical methods. Implications for future research are discussed.

Robots in Care and Everyday Life. (2023). Springer nature. https://doi.org/10.1007/978-3-031-11447-2
This open access book presents detailed findings about the ethical, legal, and social acceptance of robots in the German and European contexts. The focus is on trust in robotic assistance, human willingness to use this assistance, and the expected personal well-being in human-robot interaction. This book raises the core issue of how governments can gain the needed social, ethical, and user acceptance of AI and robots in everyday life. This highly topical book is of interest to researchers, professionals and policy makers working on various aspects of human-robot interaction.