While consciousness has been historically a heavily debated topic, awareness had less success in raising the interest of scholars. However, more and more researchers are getting interested in answering questions concerning what awareness is and how it can be artificially generated. The landscape is rapidly evolving, with multiple voices and interpretations of the concept being conceived and techniques being developed. The goal of this paper is to summarize and discuss the ones among these voices connected with projects funded by the EIC Pathfinder Challenge “Awareness Inside” callwithin Horizon Europe, designed specifically for fostering research on natural and synthetic awareness. In this perspective, we dedicate special attention to challenges and promises of applying synthetic awareness in robotics, as the development of mature techniques in this new field is expected to have a special impact on generating more capable and trustworthy embodied systems.

This study explores the concept of awareness in robots. Human awareness, rooted in psychological and cognitive sciences, encompasses conscious perception of self and the environment. In contrast, robotic awareness is engineered, focusing on programmed perception, autonomy, and interaction with humans and the environment. This research highlights the converging functionalities of cognitive capabilities in robots.

The objective of this study is to adapt and evaluate the Turkish version of the Chatbot Usability Scale (BUS-11) through a confirmatory factor analysis method. The BUS-11 scale has been established in various languages except for Turkish; thus, its validation and dissemination could serve as a means to improve chatbot interaction satisfaction among the Turkish-speaking population and hence foster growth in Turkey’s conversational agent market. To achieve this aim, seven customer-oriented chatbots were rated on pre-designed tasks by participants. Data collection involved using the Turkish-adapted BUS11 (TBUS-11) to assess individuals’ experiences after interacting with Turkish-speaking chatbots, along with the Turkish version of the UMUX-LITE scale. Results show that TBUS-11 has been demonstrated to be highly reliable with a strong convergent validity with the UMUX-LITE already validated in Turkish. Moreover, the analysis demonstrated that the dataset supported the five-factor structure of the original version of the scale, thus confirming the psychometric properties of the TBUS. The study successfully adapted the BUS-11 into Turkish, providing a reliable and valid tool for assessing chatbot usability in the Turkish-speaking market. This can potentially enhance user satisfaction and promote the growth of conversational agents in Turkiye.

Active or engaged learning is often seen as a way to improve students’ performance concerning STEM topics. When following such a form of self-directed learning, students often need to receive feedback on their progress. Giving real-time feedback on an individual basis is usually beyond the teacher’s capacity; in digital learning environments, this opens the door for exploring automated feedback. In the current study, a posttest only design was used to investigate the effect of providing students with different forms of automated feedback while they were creating a concept map about photosynthesis in an online inquiry learning environment. Participants were high school students (N = 138), divided over two experimental groups. In one group, feedback was given by a humanoid robot and in the other group via an avatar. The effects of the different feedback forms were compared for the two groups in terms of the frequency with which students consulted the feedback, concept map quality, and students’ attitudes. Results showed that the robot group consulted feedback more often than the avatar group. Moreover, the robot group had higher scores on a scale measuring enjoyment than the avatar group. Both of these differences were statistically significant. However, the average quality of the concept maps created by both groups was similar.

This study examines the impact of educational robotics applications in Information Technologies and Software courses on the motivation, attitude towards robotics, and academic achievement of 6th-grade secondary school students. The research employed pre-experimental method encompassing 112 students. Students got training in robotics using the Arduino education kit over the course of eight weeks consisted of programming and electronic concepts. Findings indicate that while students' motivation levels were moderate both before and after the implementation, there was a relative decrease in course motivation scores after the implementation. Students exhibited positive attitudes towards robotics and achieved a good level of success. Additionally, a significant effect of motivation on attitudes towards robotics was observed. Gender was found to have no effects on motivation, attitude, or achievement. It was determined that students held positive attitudes towards robotics and developed favorable views of their robotics skills. The implications are discussed in terms of theoretical insights, practices and directions for further research.