Mid Sweden University

Generative AI (GenAI) tools are increasingly used by students in higher education, including in technically demanding engineering courses. However, fluent AI-generated responses may still contain incorrect or incomplete information, creating a risk that students overestimate their reliability. This exploratory study investigates the relationship between students' perceived usefulness of GenAI and an instructor-benchmarked reference evaluation of model outputs in two digital systems design courses. The study involved voluntary survey responses from 32 students in an undergraduate course at MIUN and 20 students in a graduate-level course at UNISA. Student perception data were combined with teacher-side benchmarking of selected GenAI models on tasks categorized by cognitive depth. Findings indicate that prior GenAI familiarity was associated with interaction frequency and average perceived usefulness, whereas self-assessed subject knowledge showed limited association. A perception-performance gap emerged, with students often rating GenAI outputs as useful even when the instructor-side evaluation identified limitations in correctness or required substantial human scaffolding. The proposed framework should be interpreted as an exploratory guideline for studying and guiding GenAI use in engineering education, rather than as a definitive benchmark of model performance.

Engineering education constantly evolves to meet engineering fields' demands and keep up with the latest technological advances. Generative AI is considered one of the most significant technological developments in recent decades, impacting numerous fields. It has excellent potential as a versatile and valuable tool for information systems education inside and outside universities. ChatGPT significantly enhances personalized learning in education, providing quick access to information. However, this comes with the duality where, from one side, it can serve as a means for students’ access to tailored knowledge at any time. Conversely, it can hinder students’ education, leading them to loss of interest, lack of critical thinking, or even acts of plagiarism. Focusing on four categories of cognitive depth in the form of several assignments on the subject of Digital Systems Design, we present a pre-study on the perceived usefulness of ChatGPT from the student’s perspective. We also provide ideas for the future research.

In recent years, there has been a significant increase in multi-sensor data across various fields, spanning from environmental monitoring and industrial automation to smart agriculture, surveillance systems, healthcare analytics, robotics, remote sensing, smart cities, and beyond. The fundamental drive behind leveraging multimodal data is the amalgamation of complementary information extracted from various sensors, facilitating more comprehensive insights and informed decision-making compared to reliance on a single modality.

The analysis of multi-sensor data presents substantial challenges due to its vastness and the presence of structured, semi-structured, and unstructured data, spanning different modalities with distinct sources, types, and distributions. Data fusion, the integration of information from diverse modalities, becomes crucial in addressing inference problems arising from multi-sensor data. Both analytics-based and learning-based data fusion approaches are widely used, with learning-based approaches, leveraging machine learning and deep learning methods, showing notable effectiveness.

However, the question of "where" and "how" to fuse different modalities remains an open challenge. To explore this, the study focused on three applications as case studies to employ data fusion approaches for estimating and predicting physical quantities. These applications include analysing the correlation between the change in the geometrical dimension of a free-falling molten glass gob and its viscosity using Pearson correlation coefficient as analytical method, predicting fuel consumption of city buses through machine learning methods, and classifying and measuring hazardous gases i.e. hydrogen sulfide (H₂S) and methyl mercaptan (CH₃SH) using deep learning methods.

Results from these case studies indicate that the choice between traditional, machine learning, or deep learning-based data fusion depends on the specific application, as well as the size and quality of the data. Despite this, advancements in computing power and deep learning technology havemade data more accessible and have enhanced its complementarity. Therefore, a comprehensive review to compare a range of deep learning-based data fusion strategies is conducted. The review provides an examination of various feature extraction methods, as well as an outline and identification of the research fields that stand to derive the greatest benefits from these evolving approaches.

The rapid progress in sensor technology and computational capabilities has significantly improved real-time data collection, enabling precise monitoring of various phenomena and industrial processes. However, the volume and complexity of heterogeneous data present substantial processing challenges. Traditional data-processing techniques, such as data aggregation, filtering, and statistical analysis, are increasingly supplemented by data fusion methods. These methods can be broadly categorised into traditional analytics-based approaches, like the Kalman Filter and Particle Filter, and learning-based approaches, utilising machine learning and deep learning techniques such as Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN). These techniques combine data from multiple sources to provide a comprehensive and accurate representation of information, which is critical in number of fields. Despite this, a comprehensive review of learning-based, particularly deep learning-based, data fusion strategies is lacking. This paper presents a thorough review of deep learning-based data fusion methodologies across various fields, examining their evolution over the past five years. It highlights applications in remote sensing, healthcare, industrial fault diagnosis, intelligent transportation, and other domains. The paper categories fusion strategies into early-level, intermediate-level, late-level, and hybrid fusion, emphasising their synergies, challenges, and suitability. It outlines significant advancements, the comparative advantages of deep learning-based methods over traditional approaches, and emerging trends and future directions. To ensure a comprehensive analysis, the review is structured using the ProKnow-C methodology, a rigorous selection process that focuses on relevant literature from recent years.

We are experiencing a prolific growth of Artificial Intelligence (AI) that is enabling its ubiquitous diffusion. As part of it, generative AI models have gained particular attention due to their promising capabilities in solving complex tasks previously associated solely to human cognitive capabilities. In this article we focus on a specific AI tool, ChatGPT, which has been developed with the vision of behaving as an educational tool tailored to everyone's learning needs. This case study analyses the capabilities of such a tool in solving a predefined set of tasks in the subject area of Digital Systems Design, with the scope of designing robust assignments for students that cannot be solved and plagiarised with this tool. The results observed across different categories of cognitive depth show that ChatGPT has extensive conceptual knowledge in the area. However, this tool has important limitations when it comes to optimisation tasks, device specific configurations and overlaying of concepts, putting an emphasis on the importance of using such aspects in the design of robust tasks.

Significant risks to public health and the environment are posed by the release of hazardous gases from industries such as pulp and paper. In this study, the aim was to develop a multi-sensor system with a minimal number of sensors to detect and identify hazardous gases. Training and test data for two gases, hydrogen sulfide and methyl mercaptan, which are known to contribute significantly to odors, were generated in a controlled laboratory environment. The performance of two deep learning models, a 1d-CNN and a stacked LSTM, for data fusion with different sensor configurations was evaluated. The performance of these models was compared with a baseline machine learning model. It was observed that the baseline model was outperformed by the deep learning models and achieved good accuracy with a four-sensor configuration. The potential of a cost-effective multi-sensor system and deep learning models in detecting and identifying hazardous gases is demonstrated by this study, which can be used to collect data from multiple locations and help guide the development of in-situ measurement systems for real-time detection and identification of hazardous gases at industrial sites. The proposed system has important implications for reducing pollution and protecting public health.

Recorded lectures and online tutorials have become common in higher education, offering students flexibility in their learning. New teaching methods like flipped classrooms aim to prioritise active, student-centred learning. However, these recorded materials often lack interactivity. Incorporating interactive annotations in videos and texts can enhance engagement and learning but presents challenges in terms of tool availability, integration with Learning Management System, and impact measurement. Here an action research approach is used to evaluate three tools (FeedbackFruits, H5P, Kaltura Quiz) for creating asynchronous interactive modules in online engineering education, with future research focused on assessing their impact on student learning and engagement.

The dataset includes time-series data collected by four different sensors, which measure two target gases, Hydrogen Sulfide and Methyl Mercaptan, in the presence of air. To obtain measurements, each gas was individually exposed to the multi-sensor setup while in the presence of air. The dataset is particularly useful for gas classification tasks, as deep learning and data fusion techniques can be applied to identify the target gases.

To produce flawless glass containers, continuous monitoring of the glass gob is required. It is essential to ensure production of molten glass gobs with the right shape, temperature, viscosity and weight. At present, manual monitoring is common practice in the glass container industry, which heavily depends on previous experience, operator knowledge and trial and error. This results in inconsistent measurements and consequently loss of production. In this article, a multi-camera based setup is used as a non-invasive real-time monitoring system. We have shown that under certain conditions, such as keeping the glass composition constant, it is possible to do in-line measurement of viscosity using sensor fusion to correlate the rate of geometrical change in the gob and its temperature. The correlation models presented in this article show that there is a strong correlation, i.e., 0.65, between our measurements and the projected viscosity.

The sophistication and high accuracy of Deep Neural Networks have gotten significant attention in recent years, with a wide range of applications making use of their capabilities. However, the deployment of such networks still faces limitations due to the high volume of data to be processed and the high computational requirements. In this article we focus on the effects that data volume reduction, due to image compression and scaling down the image resolution, will have on the detection accuracy for the design case of a powered wheelchair guidance system. Throughout our analysis we show that the reduction in image resolution to a factor of 16× in image area alongside with JPEG compression provides a detection accuracy of over 0.93 in mAP, while the additional error in the position estimation of the caregiver is less than 0.5 cm. By reducing the data volume we inherently reduce the communication energy consumption, which is reduced by more than one order of magnitude. These results prove that we can overcome the complexity of high data volume for the deployment of DNNs in resource constrained IoT applications by interlacing the effects of image compression and resolution reduction, maintaining the accuracy and reducing the node energy consumption.