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Ceramics practice is an embodied activity where creators use manual tools in unique ways to shape physical material. Clay 3D printing uses the same material as manual ceramics craft, enabling new opportunities for form and texture by precisely controlling the 3D printing toolpath. However, current clay 3D printing design workflows require developing forms through digital software rather than tool-based making. We present ARTools, an augmented reality (AR) system for designing clay 3D printed vessels. We developed ARTools in collaboration with a professional ceramicist to create AR toolpath editing operations that reference manual use of ceramic tools. Through the design and fabrication of 3D-printed clay artifacts, we demonstrate how AR ceramic tools enable precise and controllable modifications of the toolpath, from the overall form down to individual toolpath points. We demonstrate how extending physical tool metaphors with digital representations and numerical precision enables craft-like interaction with CAM-based design techniques. 

Researchers can build craft-aligned digital fabrication technologies by designing interfaces inspired by craft tools. This often demands real-time physical interactions not supported by today’s automation-focused CNC control systems. We theorize we can lower engineering challenges for craft-aligned CNC prototyping by allowing designers to modify existing CNCs to support both automated and real-time control. We contribute a new creative motion control system, StepDance, which consists of two elements: 1) modular controllers that replace the GCode controller of a CNC and can be chained together to develop new interfaces, and 2) a modular programming library that supports declarative mappings between live user input, pre-programmed operations, and machine motion. We developed StepDance with practitioners at a craft school, where we used the system to modify commercial plotters and 3D printers. We analyze the resulting artifacts, interactions, and ideas to discuss how StepDance can broaden the practice of CNC design via physical metaphor. 

Desktop digital fabrication presumes a form-factor optimized for workbenches, limiting how it fits into existing spaces and workflows. We identify the shelf as an important alternative locale for convenient placement. We further envision a class of “rackable” digital fabrication technologies that support personal factories by improving organization and functionality for collections of machines. However, new positioning mechanisms and machine architectures are needed to achieve form-factors optimized for shelving. We introduce the Cantilevered DeltaXY mechanism as a first step towards shelf-mounted digital fabrication, enabling narrow, deep form factors with high lateral spatial efficiencies (LSE). We develop a set of first-order analyses and guidelines to aid the implementation of DeltaXY machines. We use DeltaXY to create Fab Unit, a “bookshelf 3D printer” with an LSE significantly higher than similar commercial desktop machines. Together, DeltaXY and Fab Unit illustrate the design space of rackable digital fabrication for future HCI fabrication research. 

Effective feedback is essential for refining instructional practices in mathematics education, and researchers often turn to advanced natural language processing (NLP) models to analyze classroom dialogues from multiple perspectives. However, utterance-level discourse analysis encounters two primary challenges: (1) multi-functionality, where a single utterance may serve multiple purposes that a single tag cannot capture, and (2) the exclusion of many utterances from domain-specific discourse move classifications, leading to their omission in feedback. To address these challenges, we proposed a multi-perspective discourse analysis that integrates domain-specific talk moves with dialogue act (using the flattened multi-functional SWBD-MASL schema with 43 tags) and discourse relation (applying Segmented Discourse Representation Theory with 16 relations). Our top-down analysis framework enables a comprehensive understanding of utterances that contain talk moves, as well as utterances that do not contain talk moves. This is applied to two mathematics education datasets: TalkMoves (teaching) and SAGA22 (tutoring). Through distributional unigram analysis, sequential talk move analysis, and multi-view deep dive, we discovered meaningful discourse patterns, and revealed the vital role of utterances without talk moves, demonstrating that these utterances, far from being mere fillers, serve crucial functions in guiding, acknowledging, and structuring classroom discourse. These insights underscore the importance of incorporating discourse relations and dialogue acts into AI-assisted education systems to enhance feedback and create more responsive learning environments. Our framework may prove helpful for providing human educator feedback, but also aiding in the development of AI agents that can effectively emulate the roles of both educators and students. 

Biases in artificial intelligence (AI) training datasets have led to models that perpetuate harmful racial stereotypes about Black communities while neglecting accurate and affirming representations of Black culture. We build on calls to create transformative technology that transcends harm mitigation–a space we categorize as liberatory technology. Within this space, we identify liberatory collections–community-led repositories that amplify Black voices–as a form of data collection that empowers and benefits communities historically harmed by traditional AI and archival practices. We survey fourteen liberatory collections, uncovering critical cultural perspectives and innovative approaches to obtaining, preserving, sharing, and valuing human information. Our findings emphasize the need for structural changes in AI training datasets and models, moving beyond representational adjustments toward strategies for creating consent-driven training models, funding community-based initiatives, and embedding Black communities’ multifaceted cultures and histories within AI systems. This research underscores liberatory collections’ potential as a framework for reimagining ethical AI development.