Abstract
K-12 digital learning is increasingly shaped by emerging technologies layered onto existing digital infrastructures. In practice, the technologies that dominate attention, especially generative and assistive AI, arrive bundled with new assessment tensions, data flows, acquisition constraints, and inequities in access and support. This article proposes a practitioner-oriented framework for understanding emerging technologies as sociotechnical-immersive systems rather than standalone tools. The framework connects three lenses: (1) a macro sociotechnical circle that foregrounds policy, markets, equity, and governance; (2) a meso environment-design circle that analyzes how learning experiences are configured through system, narrative, and agency; and (3) a micro educational-approaches circle that focuses on the instructional activities educators enact within those environments, using the Immersive Learning Brain (ILB) as a map of practice and strategies. We developed this framework through practitioner sensemaking grounded in practitioner focus group data and aligned it with recent research syntheses on emerging technologies. We illustrate the framework through one worked example and two comparative mini-cases. We conclude with an agenda for researchers and practitioners focused on assessment, equitable infrastructure and support, data stewardship, and environment-design descriptions that move beyond technocentric labels.

Abstract
One of the main challenges in modern industrial environments is managing the large amount of physical documentation obtained during the production process. Companies increasingly seek to adopt paperless alternatives to promote production efficiency and reduce their industrial environmental impact. On the shop floor, each production line relies on standardised forms to verify parameters and conditions before and after production begins; however, the large volume of paper documentation generated from these records led to the need to develop a digital platform capable of streamlining and digitising forms, enhancing process sustainability and efficiency. The proposed interoperable web application provides various features that allow users to create, customise, submit and approve forms digitally. It also integrates automated notifications and alerts for specific situations, enabling more effective responses to the production process’s momentary needs. By unifying all processes related to forms management within a digital infrastructure, this solution aligns with the current industrial paradigm, reducing reliance on paper, optimising workflow efficiency, and incorporating innovative and industrial advancements. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Abstract
This paper presents a game-simulated environment that mimics real-world conditions, with a focus on autonomous vehicle navigation. Despite significant advances in the field of games and simulations, there are still a number of challenges to overcome, in particular, the ability to accurately transfer what has been learned in virtual environments to the real world. This project recreates an agent (a motorcycle), modeled with complex physics, navigating autonomously on a detailed map based on the urban geography of Vila Real, Portugal, recreated from real data, implemented in the Unity game engine. In this paper, we provide a detailed overview of the environment and agent creation processes, highlighting the integration of realistic road networks, obstacles, and interaction mechanics that enhance the fidelity of the simulation. The experimental phase demonstrates the motorcycles ability to navigate efficiently, adapting to road layouts, avoiding obstacles, and adjusting to dynamic conditions. The insights from this study can be applied and transferred to real-world application scenarios, particularly in optimizing route planning and driving behaviour for electric motorcycles.

Abstract
Nim is a mathematical combinatorial game in which two players take turns removing, or nimming, objects from distinct heaps or piles Although its rules are simple, which makes it extremely easy to play, it requires a solid strategic reasoning in order to win against experienced players. This study presents an optimised strategic approach to the game of Nim, which represents the guaranteed winning strategy for this game for the first player to take action. The proposed approach is a fundamental combinatorial game rooted in Boolean algebra and the XOR operation. Unlike traditional strategies that solely rely on XOR calculations to determine winning and losing positions, this research identifies and analyses anomalous strategic behaviours that challenge conventional Nim theory, revealing previously unexplored patterns in specific game configurations. To validate these findings, a Python-based application has been developed, implementing the proposed strategy to ensure consistent victory. The algorithm systematically applies XOR calculations, executes optimal moves, and dynamically adapts to anomalies, demonstrating how these irregularities can be leveraged for strategic advantage. This computational validation reinforces the theoretical framework and provides new insights into the limitations and extensions of classical Nim strategies. Beyond its implications for Nim, this research highlights the broader potential of AI-driven decision-making in combinatorial games. By demonstrating how algorithmic intelligence can analyse game states, predict outcomes, and refine strategies, this study contributes to advancements in artificial intelligence, optimisation algorithms, and complex strategic decision-making models.

Abstract
Growing urbanisation, the development of smart cities, and environmental concerns have driven the implementation of advanced technologies and the modernisation of transport systems. Electric motorcycles have emerged as an effective solution for mobility, but they also present specific challenges, particularly related to the mode of riding, which is more complex than that of other vehicles and requires greater attention, skill, and preparation. Therefore, the interaction between the rider and the support system must be carefully designed, with particular emphasis on the interface and the adaptation of route information. This interface should be intuitive, accessible, and capable of presenting relevant information in a clear and objective manner, minimising distractions while riding. In addition, it must be adaptable to user preferences, allowing for customisation such as colour themes, levels of detail in the information displayed, or specific notifications regarding adverse weather and road conditions. Adapting route information provides a more efficient, safe, and satisfactory user experience. It enables riders to access personalised information, continuously updated in real time and tailored to the situation and their specific needs, including traffic conditions, road surface state, and weather conditions. This optimisation leads to better time management, energy consumption, and overall ride quality, enhancing urgent and non-urgent services. Moreover, integrating clearly and objectively features such as voice commands and compatibility with mobile or wearable devices (e.g., smartwatches) can facilitate real-time interaction without compromising safety. The interface should also offer advanced functionalities in line with technological developments and user needs, adapting to each rider’s specific requirements. This not only improves the individual experience but also promotes efficiency and sustainability, contributing to the advancement of smart cities and innovative mobility solutions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Abstract
This study highlights the critical role of Large Language Model (LLM) in simplifying technical content and integrating visual data for accessible communication. It compares GPT-4 and Llama-3.2-90b-Vision-Preview, focusing on readability, semantic similarity, and multimodal interpretation using robust metrics like Flesch Reading Ease, Gunning Fog Index, and CLIP Score. GPT-4 retains key information and achieves high semantic and textual integration scores, making it more suitable for complex technical scenarios. Furthermore, LLaMA prioritizes readability and simplicity, outperforming in generating accessible captions. Both models show optimal performance with a temperature setting of 0.5, balancing simplicity and meaning preservation. The research underscores LLM potential to democratize technical knowledge across disciplines but notes precision and multimodal integration limitations. Future directions include fine-tuning for domain-specific applications and expanding input modalities to enhance accessibility and efficiency in real-world technical tasks.