Large Language Models (LLMs) have shown remarkable capabilities in knowledge-intensive tasks, while they remain vulnerable when encountering misinformation. Existing studies have explored the role of LLMs in combating misinformation, but there is still a lack of fine-grained analysis on the specific aspects and extent to which LLMs are influenced by misinformation. To bridge this gap, we present MisBench, the current largest and most comprehensive benchmark for evaluating LLMs' behavior and knowledge preference toward misinformation. MisBench consists of 10,346,712 pieces of misinformation, which uniquely considers both knowledge-based conflicts and stylistic variations in misinformation. Empirical results reveal that while LLMs demonstrate comparable abilities in discerning misinformation, they still remain susceptible to knowledge conflicts and stylistic variations. Based on these findings, we further propose a novel approach called Reconstruct to Discriminate (RtD) to strengthen LLMs' ability to detect misinformation. Our study provides valuable insights into LLMs' interactions with misinformation, and we believe MisBench can serve as an effective benchmark for evaluating LLM-based detectors and enhancing their reliability in real-world applications. Codes and data are available at https://github.com/GKNL/MisBench.

Design educators are finding ways to support students in skillfully using Generative Artificial Intelligence (GenAI) tools in their practices while encouraging the critical scrutiny of ethical and social issues around these technologies. However, the problem of environmental sustainability remains largely unaddressed. There is a lack of both resources to grasp the environmental costs of genAI in education and a lack of shared practices around the issue. This work contributes filling this gap by counting the energy costs of using genAI in design education and critically reflecting on the impact of these costs. We leverage the image data collected during a genAI workshop for designers held in 2023 with 49 students, to calculate the energy costs of these types of activities. The results reveal that a genAI workshop for designers can easily double the energy costs associated with students' use of computers, countering the efforts of educational institutions to minimize their energy expenditure. We critically reflect on this finding to distill a set of five alternative stances, with related actions, that can support a conscious use of genAI in design education, while respecting individual positions. The work contributes to the field of design pedagogy, and education more broadly, by bringing together ways for educators to reflect on their practices and informing the future development of educational programs around genAI.

We investigate how to use AI-based analytics to support design education. The analytics at hand measure multiscale design, that is, students' use of space and scale to visually and conceptually organize their design work. With the goal of making the analytics intelligible to instructors, we developed a research artifact integrating a design analytics dashboard with design instances, and the design environment that students use to create them. We theorize about how Suchman's notion of mutual intelligibility requires contextualized investigation of AI in order to develop findings about how analytics work for people. We studied the research artifact in 5 situated course contexts, in 3 departments. A total of 236 students used the multiscale design environment. The 9 instructors who taught those students experienced the analytics via the new research artifact. We derive findings from a qualitative analysis of interviews with instructors regarding their experiences. Instructors reflected on how the analytics and their presentation in the dashboard have the potential to affect design education. We develop research implications addressing: (1) how indexing design analytics in the dashboard to actual design work instances helps design instructors reflect on what they mean and, more broadly, is a technique for how AI-based design analytics can support instructors' assessment and feedback experiences in situated course contexts; and (2) how multiscale design analytics, in particular, have the potential to support design education. By indexing, we mean linking which provides context, here connecting the numbers of the analytics with visually annotated design work instances.

This paper introduces CADgpt, an innovative plugin integrating Natural Language Processing (NLP) with Rhino3D for enhancing 3D modelling in computer-aided design (CAD) environments. Leveraging OpenAI's GPT-4, CADgpt simplifies the CAD interface, enabling users, particularly beginners, to perform complex 3D modelling tasks through intuitive natural language commands. This approach significantly reduces the learning curve associated with traditional CAD software, fostering a more inclusive and engaging educational environment. The paper discusses CADgpt's technical architecture, including its integration within Rhino3D and the adaptation of GPT-4 capabilities for CAD tasks. It presents case studies demonstrating CADgpt's efficacy in various design scenarios, highlighting its potential to democratise design education by making sophisticated design tools accessible to a broader range of students. The discussion further explores CADgpt's implications for pedagogy and curriculum development, emphasising its role in enhancing creative exploration and conceptual thinking in design education. Keywords: Natural Language Processing, Computer-Aided Design, 3D Modelling, Design Automation, Design Education, Architectural Education

I just came back from CES (thanks to Here for flying me over to see their work, I'll write about that soon too) and wanted to write down some thoughts I've been having over the past few months which crystallised during this trip. I'll be giving a talk tomorrow in Oslo at an event organised by Telenor which will touch some of what I'll write about here too. I've been thinking about product design. Technically I studied industrial design, graduated from a B.A.(Sc) Sp. in 2004. We were never introduced to programming, computer science principles, electronics design or prototyping and the internet was an image search tool for presentations.