Embodied conversational agents (ECAs) that can interact with users in a human-like manner have demonstrated promising potential in various endeavors. With the ongoing advancement in extended reality (XR) and artificial intelligence (AI), ECAs are becoming increasingly sophisticated. Although previous reviews predominantly focused on ECAs for non-XR applications, there is a growing number of research exploring the capabilities of ECAs utilizing XR technologies. Our work identified the gap between the existing reviews and the current trends in XR ECAs. We began with 1717 related papers from 01/2014 to 06/2024. We narrowed them down to 23 papers through the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework with an iterative screening procedure and criteria defined by our research team. The resulting papers were analyzed and discussed with respect to the features of the ECA application, design and implementation, and use cases. This systematic review aimed to provide trending insights that would benefit the research community in the future development of XR ECAs.

Immersive virtual reality (IVR) is widely recognized as an innovative technology that enhances learning experiences. IVR affordances could serve as a learning medium, and instructors and designers should consider using available tools to teach practical STEM (science, technology, engineering, and mathematics) subjects, incorporating the latest practices and recommendations. Due to innovations in virtual reality hardware and affordability in recent years, there is a higher chance that IVR will be adopted in educational settings. Thus, designers and developers must stay informed about current IVR trends and the aspects addressed and recommended for instructing STEM concepts. To target this issue, we systematically reviewed the design and development of customized IVR experiences and their multiple effects on STEM learning settings. In this systematic review, we identified several advantages and disadvantages reported in 30 papers based on user studies in higher education scenarios. We proposed a conceptual framework to categorize the design and features of the IVR tools discussed in the papers, based on their levels of embodiment, immersion, and the type of learning they facilitate. IVR can provide an intuitive and practical immersive learning experience, reporting multiple results on motivation, engagement, usability, and learning performance. However, we should consider the features that could directly affect the user experience and learning outcomes. We recommend that designers and developers explore developing customized IVR experiences tailored for STEM learning, especially for invisible and complex subjects where 3D visualization could benefit students.

The wireless sensor networks (WSN) are immersed in multiple areas of knowledge and are the alternative that allows analyzing, solving, and preventing problems under different facets. The RPL protocol (IPv6 Routing Protocol for Low power a Lossy Networks) arises for the management of this type of network restricted by their physical capabilities through different rules and operations that simplify the communications and constructions of the WSN. Although RPL is an efficient and standardized protocol, it does not consider high traffic handling and presents severe problems regarding load balancing, which leads to service disruption. In this article, WRF-RPL is proposed as a protocol for considering load balancing over RPL to distribute communications and messages into a network topology to avoid one preferred parent's congestions. The proposed protocol aims to improve the network's lifetime and the packet delivery through source nodes to the sink in an energy-efficient manner. Different simulated scenarios were conducted over the Cooja simulator. The results show that WRF-RPL protocols outperform the standard RPL protocol over Network Lifetime, PDR, control message overhead, and energy consumption compared with other existing protocols.

Evaluation and monitoring of wireless sensor networks (WSN) and the parameters defining their operations and design, such as energy consumption, latency, and stability, is a complex task due to interaction with real devices. For greater control of these variables, the use of simulators arises as an alternative. Cooja is a WSN simulator/emulator which handles the devices’ controllers and multiple communication protocol implementations, such as RPL (RPL is one of the most used protocol in IoT). However, Cooja does not consider either the implementation of an energy model (it has infinite energy consumption) nor the visual behavior of the topology construction, although these aspects are crucial for effective network analysis and decision taking. This paper presents the design and the implementation of ViTool-BC, a software built on top of Cooja, which allows the creation of different energy estimation models and also to visualize in real time the behavior of WSN topology construction. In addition, ViTool-BC offers a heat map of energy consumption traces. Therefore, this tool helps researchers to monitor in real time the topology construction, node disconnection, and battery depletion, aspects to be considered in the analysis of the available routing protocols in Cooja.

Synthesizing game levels that evoke players’ curiosity, driving them to explore different level parts, is time-consuming and tedious. Typically, game level designers manually perform this synthesis using trial and error. In this paper, we propose a method with which to replace this manual, time-consuming process. We benefited from recent work that had proposed game level design patterns to evoke curiosity, and we propose an approach to automatically synthesizing game levels in order to encourage players to pursue designer-specified exploration goals. We started by creating a dataset of level assets, based on the four design patterns that evoke curiosity-driven exploration in games (reaching extreme points, resolving visual obstructions, out-of-place objects, and understanding spatial connections). We annotated the assets in our dataset with spatial exploration measurements (the time players took to explore an asset over their total time spent in the game level). We then formulated game level design as an optimization problem, encoding both spatial exploration (mean spatial exploration, spatial exploration variance, and spatial exploration distribution) and game level design (occupied area, adjacent penalty, and height distribution) decisions. Then, we solved this problem by implementing a reversible-jump Markov chain Monte Carlo method. We demonstrate our method’s ability to synthesize game level variations with different spatial exploration and level design decisions. Finally, a user study showed that our approach can automatically synthesize game levels, encouraging a certain amount of spatial exploration by players.