Listen to the Story: Banksy, Tyler the Creator, and the Growing Nihilistic Mindset

Art, as an expression of feelings, worldviews, and personal beliefs, is a reflection of our environment and how we interact with it. In this way, urban art such as rap music and graffiti can serve as a lens through which we are able to examine the state of the urban environment. Building on community literature that addresses the presence of nihilism in rap music, this work will establish that nihilism is a prevalent theme in the work of two artists: Tyler the Creator\u27s rap music and BANKSY\u27s graffiti art. By examining the growing subculture and appeal of urban art in relation to these two artists, this paper will argue that BANKSY and Tyler the Creator belong to a new wave of urban art, one that appeals to and originates from people of all races and classes. The current work will then examine these artists\u27 motives in including nihilism within their art in order to argue that the disillusionment and nihilism once found primarily within inner cities is now spreading to new frontiers. Using this analysis, the current work will raise questions as to the possible causes and consequences of this spreading nihilistic mindset

Mixed-focus Difficulty-Triggered Collaborative Writing: Interoperable Architecture, Implementation, and Evaluation

Many students face difficulty when writing documents due to various reasons such as language barriers, content misunderstanding, or lack of formal writing education. They are often too shy or busy to visit a writing center or speak with a professor during office hours. Technology also falls short in this arena. Asynchronous collaboration systems require students to self-report when they are struggling and many students tend to under-report difficulty. Synchronous collaboration systems eliminate the need for self-reporting, but require teachers to constantly monitor their students. By combining synchronous and asynchronous collaboration paradigms, this project is able to create a mixed-focus collaborative writing system in which students and teachers engage in collaboration only when triggered by an automatically or manually generated event that indicates the student is facing difficulty. This mixed-focus system was created by combining two existing architectures: 1) the EclipseHelper difficulty architecture for inferring programming difficulty, and 2) the Google Docs collaborative writing environment. The new, combined architecture allows teachers to intervene and offer remote assistance to their students when they are automatically notified that a student is facing difficulty. A user study was conducted to evaluate this new architecture. Students used the system to complete a two-page paper given in a class they were taking, and data were recorded during the writing and help-giving process. Using both qualitative and quantitative analysis, the data were evaluated. Overall, students found the help-giving model easy to use and appreciated the feedback they received. However, difficulty was predicted infrequently, likely as a result of inherent differences between writing and programming. Future work will involve further analysis of the data in order to improve the difficulty prediction algorithm.Bachelor of Scienc

Fostering public understanding of AI through education and design

Presented online via Bluejeans Events on September 9, 2021 at 12:30 p.m.Duri Long is a Research Scientist at Georgia Tech working in the Expressive Machinery Lab with Dr. Brian Magerko. She is a human-centered AI researcher interested in issues surrounding AI literacy and human-AI interaction. Her research looks to how humans interact and learn as a way of informing the design of public AI literacy interventions as well as the development of AI that can interact naturally and improvise creatively with people in complex social environments.Runtime: 50:08 minutesAs artificial intelligence is integrated into our schools, homes, and workplaces, it becomes increasingly important to foster public understanding of AI. My research has explored two important facets of this issue—developing AI education initiatives and expanding existing AI systems to improve their ability to interact naturally with humans and foster understanding through interaction. Several design themes guide my work, including using embodied interaction, collaboration, and creative exploration to reduce intimidation and foster curiosity and learning. I will discuss my research investigating how to define AI literacy, and I will present several activities I have designed for informal learning spaces that can foster family learning about AI. I will also discuss how my research on developing co-creative AI systems can inform the development of more understandable AI systems. My talk will conclude with reflections on how education and system design can be leveraged to create more equitable, understandable AI in our everyday lives

Designing Co-Creative, Embodied AI Literacy Interventions for Informal Learning Spaces

Artificial intelligence (AI) is increasingly prevalent in everyday life, but there are still many misconceptions about what exactly AI is, what it is capable of, and how it works. This suggests a need for learning experiences that offer audiences the opportunity to gain a high-level or “casual” understanding of AI. Informal learning spaces like museums are particularly well-suited for such public science communication efforts, but there is little research investigating how to design AI learning experiences for these spaces. In this dissertation, I take a research-through-design approach to explore how to design AI literacy learning experiences for informal spaces. I focus on incorporating collaborative, creative, and embodied interactions in my designs, as these features have been shown to facilitate open-ended, social learning experiences that work well in informal spaces and can foster interest in computing-related topics. I use reflective design practice, co-design, and iterative prototyping/testing as methodological tools in my research. This dissertation consists of three main contributions: 1) a definition of AI literacy and a related set of competencies; 2) a set of three replicable museum exhibits for communicating AI literacy learning outcomes; and 3) design principles for creating AI literacy interventions in informal learning spaces. This work contributes to research on AI/CS education, human-centered AI, and museum exhibit design by providing transferable design principles/competencies for designing informal learning experiences around AI and a model for how to operationalize these principles/competencies in practice. This work also demonstrates the potential of collaboration, creativity, and embodiment as design considerations for AI literacy learning experiences.Ph.D

Model AI Assignments 2021

The Model AI Assignments session seeks to gather and disseminate the best assignment designs of the Artificial Intelligence (AI) Education community. Recognizing that assignments form the core of student learning experience, we here present abstracts of six AI assignments from the 2021 session that are easily adoptable, playfully engaging, and flexible for a variety of instructor needs. Assignment specifications and supporting resources may be found at http://modelai.gettysburg.edu