Educational remote laboratories allow educators and students to perform experiments with real hardware remotely, addressing geographical and financial barriers in engineering education. However, their potential is often limited by the cost and complexity of specialized physical equipment. The REDTAIL project bridges this gap by integrating high-fidelity simulations with real hardware, creating a more immersive and scalable remote lab experience. This paper presents the REDTAIL unified platform and its accompanying software development kit (SDK), which empowers educators to develop custom simulations, utilize a library of default and community-built assets, and deploy them seamlessly within their courses. Development of default simulations shows the tool chain’s capability to create effective simulations with varying levels of complexity and cross-domain communication between hardware and simulations. Furthermore, we report on successful undergraduate classroom deployments, validating the platform's suitability for higher education. Ongoing development and outreach aim to expand the simulation repository and refine the SDK, advancing the platform's role in promoting a more accessible and scalable future for engineering education.

Abstract. Accurate and timely identification of malignant morphology in histopathology slides remains a major challenge and is still largely performed by expert pathologists scanning whole-slide images for nuclear atypia, crowding, and architectural distortion. Conventional deep learning systems can detect cancer-associated patterns but are typically trained in a static “train-once/predict-once” fashion, behave as black boxes, and offer limited support when confidence is low or domain shift occurs. To address these gaps, we present Look, Mark, Explain, a minimal agentic AI framework that combines classical computer vision with large language model (LLM) reasoning to support cancer slide review while keeping the pathologist in the loop. High-resolution H&E images are processed with a contrast-enhanced, marker-controlled watershed pipeline to segment nuclei and extract 27 interpretable features per cell capturing size, shape, chromatin texture, nucleoli, and spatial context. For user-selected nuclei, a multimodal LLM receives a cell close-up, a contextual tissue view, and the feature vector, and returns a cancer-likeness assessment, confidence, and a pathology-style explanation that explicitly links its judgment to the provided measurements and visible cues, without issuing a formal diagnosis. We demonstrate the system on a breast cancer sample with 971 segmented nuclei, showing that population statistics, size-stratified comparisons, clustering metrics, and LLM narratives align with classical cytologic criteria and highlight enlarged, hyperchromatic, irregular cells in crowded, disorganized regions. The prototype uses standard digitized slides without stain normalization and is embedded in an interactive interface for slide exploration and on-demand AI consultation. While not intended for clinical use, it illustrates how a simple, reasoning-first, pathologist-in-the-loop agent can “look, measure, and explain” in a way that complements existing workflows and provides a testbed for future human–AI studies in digital pathology.

In Electrical and Computer Engineering (ECE) curricula, students typically interact with real hardware to gain hands-on experience in dedicated in-person laboratory sections. Remote laboratories are an alternative by allowing students to interact with the real hardware remotely. However, they are often constrained by the cost and space of physical equipment. This paper presents the development of a novel educational remote laboratory for Programmable Logic Controllers (PLCs) that will integrate real hardware with interactive 3D digital twins. Part of Project REDTAIL, this work details the design and implementation of a unified architecture that synchronizes an Arduino Opta PLC with a simulation of a water bottling plant. Our methodology involved instructor interviews to define requirements, followed by the parallel development of the hardware interface, the backend framework, and the 3D simulation. The primary outcome is a functional prototype that validates the technical feasibility of this hybrid approach. By augmenting a single, physical PLC with diverse virtual scenarios, the system directly addresses the scalability limitations of traditional PLC remote laboratories.

In Electrical and Computer Engineering (ECE) curricula, the hands-on laboratory experience is often hindered by issues like limited physical resources and scheduling conflicts; to address these challenges, REDTAIL implements a multi-module approach, which is centralized in the REDTAIL Simulation Repository (RSR). This paper focuses on the RSR, a scalable, open-source centralized hub designed to provide instructors with a dedicated access and deployment point offering lesson plans, exercises, and various other tools. It features a robust web interface that delivers a clear educational experience, connecting the back-end of simulations, and through integration with \LabsLand, seamlessly incorporates access to remote laboratories. Future work will focus on cultivating an active community of educators within the RSR, enabling them to share and customize educational materials, as well as integrating an AI assistant to provide real-time support and facilitate automated lesson customization and scaffolding. The RSR is thus a valuable step toward solving major issues limiting ECE curricula, empowering instructors, and promoting more efficient learning outcomes.

This paper presents the development and pedagogical integration of a Financial and Accounting Education Application (TECBOOKS) designed to bridge the theory-practice gap for industrial engineering students. Students in this field are required to master complex, interconnected concepts across finance, accounting, and production to evaluate real-world projects effectively. Traditional classroom methods often fail to provide the hands-on, interactive experience necessary to internalize the complexities of project evaluation. This critical skill determines the financial viability of investments.

The TECBOOKS application is a modern, modular, single-page application built on React, specifically engineered to function as a powerful, visual, and accessible analytical tool. Its core innovation lies in its capacity for seamless integration with a separate production line/ERP simulator. This connectivity allows the application to automatically retrieve simulated, personalized production data generated by the student’s strategic decisions in the ERP environment.

Once connected, the TECBOOKS dashboard processes this raw data to generate crucial analytical outputs in real-time. These outputs include dynamic financial statements (Income Statement, Balance Sheet), Key Performance Indicators (KPIs), and sophisticated project evaluation metrics such as Net Present Value (NPV) and Internal Rate of Return (IRR), utilizing various statistical forecasting methods. This integrated approach ensures that the calculation of robust metrics, such as NPV, is not merely a theoretical exercise but is grounded in the realistic operational complexities of a simulated production environment.

The modular design also ensures that individual components—like the statistical forecasting or financial statement generators—can be used independently, making the app scalable and applicable to students in diverse academic fields. Although currently in the early stages of institutional adoption, this solution aims to significantly boost student engagement, financial literacy, and overall comprehension of real-world project complexities through a gamified and data-driven learning ecosystem. Future work will focus on empirical evaluation of student outcomes to quantify its effectiveness.