Abstract
With the rise of makerspaces and fabrication technologies in schools, there has been an increase in experiential learning opportunities in K-12 education. The Educational CAD Model Library is the result of the collaborative effort between the Make to Learn Laboratory, the University of Virginia Department of Computer Science, the University of Virginia Library, and the NTLS Coalition to support development of tools to facilitate effective use of educational makerspaces. In contrast to other CAD model repositories, the CAD Library addresses the lack of K-12 open-hardware models by peer-reviewing educational objects prior to publication and aligning them with K-12 instructional objectives and standards. The CAD Library also aims to provide a teacher-friendly user interface with the key objectives of implementing search and filtering functionalities, integrating object metadata standards, creating a robust object submission form, and designing an accessible and effective platform for educators to discover educational objects for hands-on learning in the classroom. We prioritize establishing an effective search and discovery system, an essential feature of the CAD Library that presents important metadata, ensuring that teachers can locate educational objects and lesson plans that correspond to their instructional objectives. The CAD Library front-end is now a fully functional website with search, email, and submission functionality. Users are able to view descriptions for educational objects and directly download the fabrication guide and instructional packages if they are available. This paper aims to highlight the technical aspects and design considerations necessary for creating an accessible and effective platform for educators to discover objects to incorporate hands-on learning in the classroom.
Generative artificial intelligence (AI) tools, such as ChatGPT, are becoming increasingly popular and powerful with numerous innovative applications in modern society. However, the integration of ChatGPT into the education system presents both opportunities and challenges, making it difficult for academic institutions to regulate generative AI usage while implementing new pedagogical methods. The integration of ChatGPT into the education system leads to concerns about academic integrity and learning outcomes, especially because cheating with ChatGPT can take different forms. This leads to my research question: “How does ChatGPT impact academic integrity and learning outcomes in education from an ethical perspective?” To answer my question, I conducted a literature review of academic research into ChatGPT, ethics, and education. I also surveyed current students and recent graduates from the University of Virginia on their perspectives and experiences using ChatGPT for educational purposes. I performed an ethical analysis of these results through deontological and consequentialist frameworks. Overall, there are conflicting opinions when considering academic integrity and learning outcomes from these perspectives. Deontologists would maintain academic integrity, even if the learning outcome or grades are not good. Meanwhile, consequentialists only focus on the consequences of achieving those better learning outcomes and grades. Despite these two opposing viewpoints, it appears that most people follow a more deontological ethical perspective, so policies should be developed with preserving academic integrity in mind. However, learning outcomes are also important in the educational experience, so we must find a balance between the two to effectively integrate ChatGPT into academic institutions.
My technical project and STS research project both examine the impacts of technological advancements on the education system and how students can potentially benefit from them. The increase in makerspaces and developments in 3D-printing technology opens up opportunities for more interactive learning with hands-on physical objects while increasing sustainability and accessibility of educational materials. On the other hand, generative AI tools like ChatGPT showcase the growing popularity of machine learning and software platforms which can also provide interactive learning experiences and transform traditional pedagogical methods. Furthermore, both of these technologies foster growth across STEM disciplines, potentially paving the way for engineering careers for the younger generation. The CAD Library itself also relates to integrating AI tools for education as it includes published open-source hardware that developers can extend upon to improve the learning experience for students. For example, the Make to Learn Laboratory is currently working on incorporating machine learning and AI features into the specimen identification process to help K-12 students learn about biology. A key objective is to design the system such that students do not become overreliant on the AI features, which is consistent with what I found in the literature review about ChatGPT in education. Therefore, it is important to design other technologies for education outside of AI with considerations about how AI is changing the educational landscape.
