Advancements in large language models (LLMs) have transformed human-computer interaction, enhancing engagement with conversational agents like chatbots, virtual agents, and social robots. This study explored how different conversational interfaces (computer, Furhat, NAO, and Pepper) impact perception and acceptance using the Human-Robot Interaction Evaluation Scale (HRIES) and the Technology Acceptance Model (TAM). One hundred participants rated interfaces through a video-based survey, revealing significant differences: computers scored highest in Perceived Usefulness (PU) and Ease of Use (PEU), while social robots, particularly Pepper and NAO, excelled in Sociability. A Structural Equation Model (SEM) indicated that HRIES dimensions of Sociability and Agency positively influenced Perceived Enjoyment (PENJ) and PEU, whereas Disturbance negatively affected both. These findings highlight the nuanced interplay between interface design and user acceptance, suggesting that while factors like Sociability and Agency are pivotal for enhancing enjoyment and ease of use, they alone are insufficient to account for overall acceptance.

This lighting talk discusses the potential for using coding as a new means of self-expression. Coding has been identified as a key 21st-century skill, and as such, it is expected to be vastly acquired already in the early ages of formal education. Coding is a language that adheres to a system of symbols and rules, which, when combined, convey meaning. This postulation has led a few scholars to sustain that coding is a language functioning like any other language. Thus, coding might enable its coders to produce expressive artifacts, similarly to any other natural language, potentially offering a new form of creative writing in the 21st century. A celebrated use of creative writing is in the field of emotional therapy. Writing has been proven an efficient tool in both clinical and non-clinical populations. Nevertheless, despite its effectiveness, some populations are deprived of the great benefits creative writing may enable. Among these are children who do not know how to read and write, as well as adults who struggle with expressive writing. Therefore, we suggest a way for expanding the boundaries of coding to the field of self-expressive writing. We believe that apart from promoting its acquisition as an essential tool in the 21st century, knowing coding at an early age may also endorse general well-being, offering a new, somewhat more reachable, means of self-expression.

The shift for online learning, accelerated during the COVID-19 pandemic, has highlighted the importance of self-regulated learning skills and the challenges many students face while practicing online learning. Could social robots help by serving as motivating learning companions in online learning settings? In this study, we explore the perceived potential of motivating learning companion robots designed based on Self-Determination Theory. One hundred and eighty-five participants watched one of five videos displaying a simulated student-robot interaction and rated their perceptions of the intrinsic motivation of the student in the videos and the type of support provided by the social robot. Overall, the motivating learning companion affected the perceived sense of relatedness and perceived level of anxiety of the student in the video, as well as the social robot's perceived support. Differences between the conditions are discussed.

The COVID-19 pandemic has triggered us to explore a new teacher preparation paradigm aligned with Bloom's one-to-one and mastery learning vision. In 1984, Benjamin Bloom reported a phenomenon he named "The 2 sigma problem". In a series of studies, Bloom found that combining one-to-one tutoring and mastery learning techniques increased the average student performance by two standard deviations compared to traditional instructional methods. Bloom referred to this phenomenon as a problem given the infeasibility of implementing such instructional strategy on a large scale and invited educators to search for feasible alternative solutions. While pedagogical and technological innovations, such as Intelligent Tutoring Systems and Massive Open Online Courses have emerged, traditional instructional methods (e.g., a teacher lecturing) and structures (e.g., class size), still dominate the public educational system. The proposed paradigm combines the development of teaching for mastery and teaching one-on-one competencies. For addressing mastery learning, the pre-service teachers created asynchronous short videos followed by tasks for high school CS pupils. For addressing one-to-one tutoring, each student was paired with a high school pupil and tutored him\her for one hour per week for eight weeks synchronously via Zoom. The proposed teacher preparation model suggests a scalable and sustainable learning environment that enables a smooth transition from face-to-face to online learning while addressing the 2 sigma problem.

Machine learning is a fast-growing field with various applications in artificial intelligence and data science. Recently, a new machine learning program have been integrated into the Israeli high school computer science curriculum and thus we added a new machine learning module to the Methods of Teaching Computer Science (MTCS) course, which is part of the teachers' preparation program. This machine learning module provides us a unique opportunity to teach both pedagogy and content with the same subject matter. After teaching the basics of machine learning, we asked the students to find similarities between human learning theories and machine learning algorithms. Students identified several interesting parallels: (a) Supervised learning is similar to behavioral learning as the machine learns to connect training examples (stimuli) with labels (behavior). Also, the learning is based on minimizing error (punishment) function, (b) Reinforcement learning is similar to behavioral learning as learning is based on feedback from the environment, (c) Constructivism can be identified in the iterative convergence of the algorithm; the inner model improves each iteration based on the current knowledge, and (d) Social learning is reflected in clustering as each cluster affects the learning of the other clusters. In our talk, we present the idea that computational mental models may be used to reinforce pedagogical mental models and vice versa.

This lightning talk describes the authors' initiative to deliver effective and enjoyable online learning of programming-basics to middle-school and beyond students with no programming background. With the COVID-19 long-term implications that have driven school systems to online learning, students face new challenges as they struggle to maintain their focus during long online sessions, with some experiencing physical impacts (e.g., 'zoom fatigue'). Microlearning, a set of small (5-8 min of length) learning units targeting small learning objectives and usually delivered online, appears as a promising direction for educators to face these challenges. However, because of its fragmented nature, and difficulty to provide immediate 'value-based' feedback to the learner, it is hard to apply microlearning in complex domains such as Computer-Science and programming. We are looking to develop a task-driven online microlearning environment for learning programming-basics. The environment will include motivating microtasks as well as formative feedback per microlearning unit. In a pilot study, we included short recorded lectures and project-driven activities. For the learning Integrated Development Environment (IDE) we used the application Tinkercad, which enables building programmable electronic devices. This learning IDE enabled the students to exercise 'micro-projects' (involving software and simulated hardware) in which their code had to 'operate' electronic device. We believe this concept can inspire a pedagogical framework that utilizes Microlearning for teaching programming and we will welcome the audience's input.

Artificial curiosity, based on developmental psychology concepts wherein an agent attempts to maximize its learning progress, has gained much attention in recent years. Similarly, social robots are slowly integrating into our daily lives, in schools, factories, and in our homes. In this contribution, we integrate recent advances in artificial curiosity and social robots into a single expressive cognitive architecture. It is composed of artificial curiosity and social expressivity modules and their unique link, i.e., the robot verbally and non-verbally communicates its internally estimated learning progress, or learnability, to its human companion. We implemented this architecture in an interaction where a fully autonomous robot took turns with a child trying to select and solve tangram puzzles on a tablet. During the curious robot’s turn, it selected its estimated most learnable tangram to play, communicated its selection to the child, and then attempted at solving it. We validated the implemented architecture and showed that the robot learned, estimated its learnability, and improved when its selection was based on its learnability estimation. Moreover, we ran a comparison study between curious and non-curious robots, and showed that the robot’s curiosity-based behavior influenced the child’s selections. Based on the artificial curiosity module of the robot, we have formulated an equation that estimates each child’s moment-by-moment curiosity based on their selections. This analysis revealed an overall significant decrease in estimated curiosity during the interaction. However, this drop in estimated curiosity was significantly larger with the non-curious robot, compared to the curious one. These results suggest that the new architecture is a promising new approach to integrate state-of-the-art curiosity-based algorithms to the growing field of social robots.

Can we find real value for educational social robots in the very near future? We argue that the answer is yes. Specifically, in a classroom we observed, we identified a common gap: the instructor divided the class into small groups to work on a learning activity and could not address all their questions simultaneously. The purpose of this study was to examine whether social robots can assist in this scenario. In particular, we were interested to find whether a physical robot serves this purpose better than other technologies such as tablets. Benefits and drawbacks of the robot facilitator are discussed.

Mindset has been shown to have a large impact on people's academic, social, and work achievements. A growth mindset, i.e., the belief that success comes from effort and perseverance, is a better indicator of higher achievements as compared to a fixed mindset, i.e., the belief that things are set and cannot be changed. Interventions aimed at promoting a growth mindset in children range from teaching about the brain's ability to learn and change, to playing computer games that grant brain points for effort rather than success. This work explores a novel paradigm to foster a growth mindset in young children where they play a puzzle solving game with a peer-like social robot. The social robot is fully autonomous and programmed with behaviors suggestive of it having either a growth mindset or a neutral mindset as it plays puzzle games with the child. We measure the mindset of children before and after interacting with the peer-like robot, in addition to measuring their problem solving behavior when faced with a challenging puzzle. We found that children who played with a growth-mindset robot 1) self-reported having a stronger growth mindset and 2) tried harder during a challenging task, as compared to children who played with the neutral-mindset robot. These results suggest that interacting with peer-like social robot with a growth mindset can promote the same mindset in children.