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Generative AI chatbots enable personalized problem-solving, but effective learning requires students to self-regulate both how they seek help and how they use AI-generated responses. Considering engagement modes across these two actions reveals nuanced reliance patterns: for example, a student may actively engage in help-seeking by clearly specifying areas of need, yet engage passively in response-use by copying AI outputs, or vice versa. However, existing research lacks systematic tools for jointly capturing engagement across help-seeking and response-use, limiting the analysis of such reliance behaviors. We introduce RelianceScope, an analytical framework that characterizes students' reliance on chatbots during problem-solving. RelianceScope (1) operationalizes reliance into nine patterns based on combinations of engagement modes in help-seeking and response-use, and (2) situates these patterns within a knowledge-context lens that accounts for students' prior knowledge and the instructional significance of knowledge components. Rather than prescribing optimal AI use, the framework enables fine-grained analysis of reliance in open-ended student-AI interactions. As an illustrative application, we applied RelianceScope to analyze chat and code-edit logs from 79 college students in a web programming course. Results show that active help-seeking is associated with active response-use, whereas reliance patterns remain similar across knowledge mastery levels. Students often struggled to articulate their knowledge gaps and to adapt AI responses. Using our annotated dataset as a benchmark, we further demonstrate that large language models can reliably detect reliance during help-seeking and response-use. We conclude by discussing the implications of RelianceScope and the design guidelines for AI-supported educational systems. 

Abstract Although metal-polymer heterogeneous structures possess exceptional mechanical, thermal, and electrical properties, their fabrication remains challenging due to the reactive nature of the materials and the risk of property alteration during manufacturing. This study investigates the printing quality of metal-polymer structures fabricated using electrically assisted heterogeneous material printing (EF-HMP), focusing on the relationship between the polymer and metal layers and their electrical properties. The developed printing solution enables the transport of metal ions for metal printing onto a polymer matrix under a controlled electrical field. The study emphasizes the critical role of polymer microstructures in influencing metal electrodeposition, including printing time and morphology. Three microstructure geometries—rectangular, trapezoidal, and semicircular—were designed based on manufacturability and surface-area-to-volume ratio and evaluated for their impact on metal-polymer fabrication via EF-HMP process. Scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDS), and electrical conductivity tests revealed that the semicircular microstructure provided the best printing performance, forming a robust metal structure in a short time and achieving the lowest resistance of 12 kΩ. This research highlights the potential of EF-HMP for metal-polymer fabrication, offering new insights into the influence of interfacial polymer microstructures on metal printing at room temperature. These findings pave the way for optimizing the design and functionality of metal-polymer components in metamaterials, thermal management, and flexible electronics applications. 

Laser based additive manufacturing (AM) methods, that incorporate a high-density laser to sinter, melt, or solidify the desired material, have developed into an ideal technology for the design and fabrication of robust and highly customizable functional devices which aim to address key challenges in the aerospace, biomedical, and defense sectors. Recent advancements in powder bed fusion (PBF) approaches, such as selective laser sintering (SLS) and melting (SLM) have significantly improved the range of printable materials, minimum feature size, and microstructure evolution, endowing precise control over the physical properties of the final printed part. Furthermore, studies on novel photoresist materials and laser scanning strategies used during multiphoton lithography (MPL) approaches indicated that nanoscale spatial resolution could be achieved, allowing for the design of intricate biomedical implants or smooth optical devices. This chapter focuses on an extensive review of current research being conducted on laser-based AM technologies highlighting the current compatible materials and applications of SLS, SLM, and MLP printed functional devices. Future perspectives and notable challenges of the laser-based AM technologies are discussed in detail with the purpose of identifying critical research areas for each methodology.