-- Understanding how scaffolding strategies influence human understanding in human-robot interaction is important for developing effective assistive systems. This empirical study investigates linguistic scaffolding strategies based on negation as an important means that de-biases the user from potential errors but increases processing costs and hesitations as a means to ameliorate processing costs. In an adaptive strategy, the user state with respect to the current state of understanding and processing capacity was estimated via a scoring scheme based on task performance, prior scaffolding strategy, and current eye gaze behavior . In the study, the adaptive strategy of providing negations and hesitations was compared with a nonadaptive strategy of providing only affirmations. The adaptive scaffolding strategy was generated using the computational model SHIFT . Our findings indicate that using adaptive scaffolding strategies with SHIFT tends to (1) increased processing costs, as reflected in longer reaction times, but (2) improved task understanding, evidenced by a lower error rate of almost 23%. We assessed the efficiency of SHIFT's selected scaffolding strategies across different cognitive states, finding that in three out of five states, the error rate was lower compared to the baseline condition. We discuss how these results align with the assumptions of the SHIFT model and highlight areas for refinement. Moreover, we demonstrate how scaffolding strategies, such as negation and hesitation, contribute to more effective human-robot explanatory dialogues. In the growing field of social robotics, robots are increasingly being designed to assist people in their everyday lives.

In this work, we present a domain-independent approach for adaptive scaffolding in robotic explanation generation to guide tasks in human-robot interaction. We present a method for incorporating interdisciplinary research results into a computational model as a pre-configured scoring system implemented in a framework called SHIFT. This involves outlining a procedure for integrating concepts from disciplines outside traditional computer science into a robotics computational framework. Our approach allows us to model the human cognitive state into six observable states within the human partner model. To study the pre-configuration of the system, we implement a reinforcement learning approach on top of our model. This approach allows adaptation to individuals who deviate from the configuration of the scoring system. Therefore, in our proof-of-concept evaluation, the model's adaptability on four different user types shows that the models' adaptation performs better, i.e., recouped faster after exploration and has a higher accumulated reward with our pre-configured scoring system than without it. We discuss further strategies of speeding up the learning phase to enable a realistic adaptation behavior to real users. The system is accessible through docker and supports querying via ROS.

Explainability has become an important topic in computer science and artificial intelligence, leading to a subfield called Explainable Artificial Intelligence (XAI). The goal of providing or seeking explanations is to achieve (better) 'understanding' on the part of the explainee. However, what it means to 'understand' is still not clearly defined, and the concept itself is rarely the subject of scientific investigation. This conceptual article aims to present a model of forms of understanding in the context of XAI and beyond. From an interdisciplinary perspective bringing together computer science, linguistics, sociology, and psychology, a definition of understanding and its forms, assessment, and dynamics during the process of giving everyday explanations are explored. Two types of understanding are considered as possible outcomes of explanations, namely enabledness, 'knowing how' to do or decide something, and comprehension, 'knowing that' -- both in different degrees (from shallow to deep). Explanations regularly start with shallow understanding in a specific domain and can lead to deep comprehension and enabledness of the explanandum, which we see as a prerequisite for human users to gain agency. In this process, the increase of comprehension and enabledness are highly interdependent. Against the background of this systematization, special challenges of understanding in XAI are discussed.