The evaluation of many STEM (Science, Technology, Engineering and Mathematics) interventions focuses on planned outcomes. However, the impact of unintended consequences from interventions remains largely unexplored in the literature. This case study presents the design and evaluation of a school-based workshop about an engineering-adjacent career: environmental planning. The authors used a community of inquiry action research approach focusing on knowledge and aspirational outcomes from outreach, particularly around broadening children's knowledge and understanding of careers. The developed evaluation tools allowed identification of intended and unintended consequences from the intervention. Participants were children aged 10-11 years (n = 83) from two schools in North East England. Results showed an increase in knowledge of environmental planning. The qualitative data indicate unanticipated consequences due to children's temporal misunderstanding of timescales involved in coastal changes. Findings are discussed in the context of 'lessons learned' and highlight the importance for engineering and technology outreach interventions of incorporating a well-designed evaluation strategy and feedback mechanism to identify and mitigate unintended and unanticipated consequences.
Since the advent of COVID-19, educational institutions worldwide have increasingly adopted online learning where challenges in maintaining student engagement have been observed. Consequently, innovative solutions for monitoring and improving engagement in virtual environments have been explored. This paper presents the design and implementation of an affordable, efficient device equipped with a camera that infers student engagement in real time using Deep Learning techniques. The system utilizes a convolutional neural network to analyze visual data and classify student behavior into three categories; focusing on paper, focusing on the screen, and being distracted. To ensure compatibility with resource-constrained edge platforms, the trained model was quantized using TensorFlow Lite (TFLite), enabling efficient deployment across a variety of devices, ranging from low-power microcontrollers like the Sony Spresence and OpenMV cam to more powerful GPU and TPU-based edge platforms such as NVIDIA Jetson Orin and Google Coral. We also established a baseline on the Raspberry Pi 5, optimizing for performance and resource constraints. A key focus of the work presented here was safeguarding student privacy. All data processing occurs locally on the device, ensuring that no sensitive information is transmitted externally. The quantized model enables real-time performance without compromising privacy, as all engagement data is securely analyzed on the edge device; only the student's engagement state is transmitted across potentially insecure networks, while the sensitive video feed remains confined to the student's device. A comprehensive comparison between the quantized, non-quantized, and quantization-aware models was conducted to assess the impact of quantization on accuracy and inference speed. In addition, trade-offs between power consumption and performance across different devices were evaluated. These findings demonstrate a balanced approach to achieving computational efficiency while ensuring real-time student engagement monitoring. By leveraging these cost-effective technologies, our approach offers an accessible and scalable realtime student engagement monitoring tool and addresses privacy challenges posed by remote education, even beyond the pandemic.
Artificial Intelligence (AI) is transforming education by offering innovative tools that enhance teaching, learning, and administrative processes. However, its integration introduces significant ethical challenges that demand critical attention. This systematic literature review (SLR) explores key ethical concerns associated with AI-driven educational technologies, including data privacy, algorithmic bias, student autonomy, and inclusivity. It systematically analyzing existing literature to provide actionable guidelines for promoting ethical AI use, emphasizing transparency, fairness, and accountability. The review also examines the impact of AI on the dynamics of instructor-student relationships, highlighting both opportunities for personalized learning and risks of reduced human interaction. By addressing these challenges and proposing strategies for responsible AI implementation, this study aims to guide educational institutions in navigating the complexities of AI adoption while fostering equitable and meaningful learning experiences.
The standards system of Geometrical Product Specifications (GPS) is fundamental to the creation of technical drawings and/or 3D CAD models. It provides a non-verbal symbolic language to geometrically describe and verify components. In addition to the benefits of having a global means of communication between companies, it also brings challenges. Teaching GPS is particularly difficult because the GPS system can be considered complex and there are few teaching approaches [1]. This paper presents an interdisciplinary teaching concept that combines selected GPS content from different areas of mechanical engineering such as design, manufacturing and quality control. This is done on the basis of the GPS competences that teachers and students considered necessary in a previous study at universities in Germany [2]. The developed teaching examples take into account the advancing digitalization in teaching as well as the use of haptic models. To evaluate the results, the content will be used and assessed in a course of the mechanical engineering bachelor's degree with focus on GPS. The aim of this study is also to find out whether the cross-module concept can promote students' interdisciplinary understanding of GPS. Or does the importance of the correlation between functionality, manufacturing and metrology in design work remain intangible? What needs to be optimized for futureoriented teaching in mechanical engineering?
The metaverse is a concept that has recently emerged as a key topic of discussion across various industries, including education. Although there is no universally accepted definition of the metaverse, it is generally understood as a collective technology-supported virtual space that facilitates interactive communication, collaborative learning, and innovative content creation. This paper seeks to investigate multiple perspectives on the definition of the metaverse, placing special emphasis on its applications within the educational sector, and employing keyword analysis from relevant academic literature to highlight current trends and key insights. The need for this study stems from the potential of the metaverse to transform educational experiences by offering personalized, immersive, and engaging learning opportunities. Nonetheless, successful implementation of the metaverse in education requires overcoming a range of technical, security, and social challenges. Educational institutions must invest in the necessary infrastructure, training, and resources to effectively integrate these technologies into their curricula. This paper contributes to the body of research with a coherent overview of how the metaverse can be defined and applied in educational processes, and by analyzing key terms and trends, aims to highlight the essential technologies and approaches that shape this emerging field. The research findings indicate that the metaverse in education is increasingly linked to the development and application of cutting-edge technologies that foster richer interactive experiences. Key technologies such as virtual reality (VR) and augmented reality (AR), as well as artificial intelligence (AI), Internet-of-Things (IoT), blockchain technologies and others, are revolutionizing traditional educational paradigms by providing immersive environments where students can engage with content in new and innovative ways. Critical issues like ethical concerns, data privacy, AI bias, and the psychological effects of immersive environments, are pivotal to the ongoing discussion and essential for the integration of the metaverse into education in a sustainable, scalable and holistic manner.
