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This study evaluates the effectiveness of a gamified interactive educational tool (G-IET) in addressing reversal errors in algebra, a persistent misinterpretation of relational language in comparative word problems. The G-IET was designed and developed within a didactic framework specifically aimed at developing algebraic learning, providing a concrete visual learning approach and immediate feedback with the goal of enhancing students’ understanding of equality and symbolic representation in algebraic contexts. A longitudinal design was employed with students who initially committed reversal errors. Participants engaged with the G-IET across eight sessions. Quantitative data were collected via pre- and post-tests involving algebraic translation tasks under both timed and untimed conditions. Qualitative data were obtained through open-ended responses and emotional engagement surveys grounded in flow theory. Results indicated substantial improvements in students’ algebraic performance (large effect): an 87% reduction in reversal errors was achieved, accompanied by substantial decreases in operator errors (87.5%) and a complete elimination of hybrid errors (100%). Qualitative analyses revealed enhanced comprehension of problem statements, improved equation formulation, and greater self-regulation in learning strategies. Students highlighted the visual design, progressive structure, and motivational aspects of the tool as beneficial for learning. Emotional engagement remained consistently high throughout the learning experience, contributing to sustained attention and motivation. In conclusion, the G-IET demonstrated strong pedagogical effectiveness by significantly reducing reversal errors and supporting the development of conceptual understanding in algebra. These findings emphasize the importance of integrating interactive, visually supported environments in mathematics instruction to target and remediate specific misconceptions while maintaining student engagement.

When we humans observe objects, we take views and perspectives. These views can present different features depending on the location observed, which are oriented or symmetric. This paper presents a Qualitative Object Descriptor (QOD) to model objects as embedded in a 3D cube. The QOD extracts a spatial descriptor of an object which allows human understanding. This paper also presents a visual similarity measure for QOD (SimVis) which has been used to analyze the similarity between scenes that contain pairs of objects as those in the Cube Comparison Test (CCT) which has been extensively used in the literature to measure spatial skills in people.

Grounding spatial deixis is essential for establishing shared spatial understanding in HRI. This paper presents the Spatial Deixis Model (SDM), a perceptual framework allowing a robot to infer the English spatial deixis here and there from pointing gestures and using a dynamic, embodied peri-personal space. We performed an empirical evaluation of the SDM with 12 participants in 5 scenarios with different contexts (e.g., varying distances and/or heights with respect to human and robot). Results show that the localization accuracy for the pointed-at objects across 174 trials is 92% and the overall agreement across all trials is 63.7%, demonstrating that SDM generally captures the dynamic notion of spatial deixis.

How an origin and a destination align with the street network may, anecdotally, be used as a heuristic to infer the length and complexity of routes from the origin to the destination. However, no method of measuring alignment with a street network exists, and furthermore, it is unclear whether and under what circumstances it is useful as a heuristic. In this paper, we propose a novel method for measuring alignment using the Earth Mover's Distance (EMD) between orientation distributions. We evaluated this metric using a dataset of 77,293 origin and destination pairs from 100 cities with different street networks, in order to test the hypothesis that an increasing degree of misalignment is predictive of a longer and more complex route from origin to destination. Our evaluation shows that our method for measuring alignment becomes more useful as a heuristic the more grid-like the street network surrounding the origin and destination is. To conclude, the results obtained indicate that alignment is an important factor for route properties, especially in grid-like street networks, a subclass of routes within an environment where the configuration of the street network makes predicting the length and complexity easier.

The routes displayed on maps by navigation support systems are intended to help users to orient themselves towards reaching the destination and to infer information related to their navigation. Inferring how complex a route is, including how well you think you can remember it and the likelihood of getting lost, may influence expectations on how it is navigated. However, it is not well understood when and where a route displayed on a map is perceived as complex and why someone perceives it this way. Current methods for assessing complexity tend to focus either on (i) the complexity of the route or on (ii) the complexity of the environment as a static and global property. By taking inspiration from navigational map reading and how routes and street networks are perceived on a map, this paper investigates how environmental complexity influences route complexity and length.We developed a new approach to gauge the alignment between the orientation of a route’s origin and destination with respect to the orientation of the streets within the network, and we investigated how this measure relates to route complexity and length.

Scene Graph Generation (SGG) encodes visual relationships between objects in images as graph structures. Thanks to the advances of Vision-Language Models (VLMs), the task of Open-Vocabulary SGG has been recently proposed where models are evaluated on their functionality to learn a wide and diverse range of relations. Current benchmarks in SGG, however, possess a very limited vocabulary, making the evaluation of open-source models inefficient. In this paper, we propose a new reference-free metric to fairly evaluate the open-vocabulary capabilities of VLMs for relation prediction. Another limitation of Open-Vocabulary SGG is the reliance on weakly supervised data of poor quality for pre-training. We also propose a new solution for quickly generating high-quality synthetic data through region-specific prompt tuning of VLMs. Experimental results show that pre-training with this new data split can benefit the generalization capabilities of Open-Voc SGG models11Code and data available at https://github.com/Maelic/OpenVocSGG.

Everyday life unfolds in both space and time, with our spatial experiences playing a central role in our interactions with the world. To grasp human cognition, it s essential to understand how we perceive spatial relationships and tackle spatio-temporal challenges. Over the past few decades, research in spatial cognition has made significant strides, particularly in developing computational methods for knowledge representation and reasoning. This special issue explores various approaches to formalizing, implementing, and automating solutions for spatial problems. In this introduction, we provide a current literature review to contextualize the three contributions featured in this issue.

This paper introduces UJI-Butler, an innovative multi-robot framework that blends symbolic and non-symbolic artificial intelligence methods. Unlike previous systems, UJI-Butler integrates large language models (LLMs) with a knowledge base akin to RAG-based systems, while imposing logical reasoning on LLM-generated results. It facilitates multi-modal interaction with human users through speech, sign language, and physical interaction, fostering a human-in-the-loop learning paradigm. By acquiring new knowledge through verbal communication and mastering manipulation skills via human-lead-through programming, UJI-Butler enhances transparency and trust by incorporating human feedback during operations. Experimental results demonstrate that UJI-Butler’s combination of symbolic and non-symbolic AI offers intuitive interaction and accelerates the learning process with experience. It adeptly stores and utilizes knowledge gained from verbal communication, recognizing hand gestures for requests. Additionally, UJI-Butler successfully performs user-taught physical skills and generalizes them to varying object sizes and locations. The explicit nature of acquired knowledge enables seamless transferability to other platforms and modification by human users. The code of the whole project is available on Github, in addition, video demonstrations of the UJI-Butler system are available online in a Youtube Playlist.

Today, anyone feeling lost in a city or unsure about how to navigate can use navigation services to look up routes to where they want to go. Current research investigating these services has primarily focused on how to find an appropriate route and how to best support navigation along it, and not how routes and the maps they are presented on are perceived. What makes one route look more difficult to navigate than another? And how does experience with using navigation services and maps in daily life influence how difficult a route is perceived to be? We explored these questions in a survey study where participants rated the perceived difficulty of pedestrian routes in ten different cities. The results show that routes in more complex urban environments were perceived as more complex than routes in easier environments. At least partly, perceived difficulty seems to follow earlier conceptualizations of route complexity, but open questions remain regarding the interplay of environmental structure, route properties, and the map representation.

Introduction: Paper folding and punched hole tests are used to measure spatial abilities in humans. These abilities are relevant since they are associated with success in STEM (Science, Technology, Engineering, and Mathematics). This study addresses the challenge of teaching spatial reasoning skills using an educational videogame, the Paper Folding Reasoning Game.

Methods: The Paper Folding Reasoning Game is an interactive game which presents activities intended to help users train and understand how to fold a paper to get a specific shape (Part I) and the consequence of punching a hole on a folded paper (Part II). This educational videogame can automatically generate paper-folding-and-punched-hole questions with varying degrees of difficulty depending on the number of folds and holes made, thus producing additional levels for training due to its embedded reasoning mechanisms (Part III).

Results: This manuscript presents the results of analyzing the gameplay data gathered by the Paper Folding Reasoning Game in its three parts. For Parts I and II, the data provided by 225 anonymous unique players are analyzed. For Part III (Mastermode), the data obtained from 894 gameplays by 311 anonymous unique players are analyzed. In our analysis, we found out a significant difference in performance regarding the players who trained (i.e., played Parts I and II) before playing the Mastermode (Part III) vs. the group of players who did not train. We also found a significant difference in players' performance who used the visual help (i.e., re-watch the animated sequence of paper folds) vs. the group of players who did not use it, confirming the effectiveness of the Paper Folding Reasoning Game to train paper-folding-and-punched-hole reasoning skills. Statistically significant gender differences in performance were also found.