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This empirical work-in-progress paper examines the impact of two technology-enhanced learning tools, an interactive visualization with multiple representations and explicit scaffolding and a simulation tool with single representation and implicit scaffolding, on students’ conceptual understanding and affective outcomes in semiconductor physics. This domain is known for early conceptual difficulty that often limits student progression into advanced coursework and the semiconductor workforce. Using a crossover design, 20 undergraduate electrical engineering students engaged with both tools and completed pre and post-assessments of conceptual knowledge, perceived understanding, interest, and motivation. A semi-structured focus group was analyzed using thematic analysis to capture students’ learning experiences. The findings reveal that while both tools supported students' cognitive and affective processes, one primarily fostered foundational understanding for novice learners, whereas the other one enabled deeper exploration for more experienced students. Although limited by sample size and short duration, the results suggest that aligning tool design with learners’ developmental stage may support both learning and motivation. Overall, this study demonstrates how instructional design features shape students’ engagement with complex engineering content and underscores the importance of adaptive technology-enhanced learning environments. 

This empirical work-in-progress paper examines the impact of two technology-enhanced learning tools, an interactive visualization with multiple representations and explicit scaffolding and a simulation tool with single representation and implicit scaffolding, on students’ conceptual understanding and affective outcomes in semiconductor physics. This domain is known for early conceptual difficulty that often limits student progression into advanced coursework and the semiconductor workforce. Using a crossover design, 20 undergraduate electrical engineering students engaged with both tools and completed pre and post-assessments of conceptual knowledge, perceived understanding, interest, and motivation. A semi-structured focus group was analyzed using thematic analysis to capture students’ learning experiences. The findings reveal that while both tools supported students' cognitive and affective processes, one primarily fostered foundational understanding for novice learners, whereas the other one enabled deeper exploration for more experienced students. Although limited by sample size and short duration, the results suggest that aligning tool design with learners’ developmental stage may support both learning and motivation. Overall, this study demonstrates how instructional design features shape students’ engagement with complex engineering content and underscores the importance of adaptive technology-enhanced learning environments. 

Quantum well infrared photodetectors (QWIPs) have emerged as a high-performance and versatile platform for IR detection applications owing to their design flexibility, fast response time, and wavelength tunability across a wide spectral range. While research and development in this field have predominantly focused on GaAs-based QWIPs owing to their mature growth technique and well-understood material properties, III-nitride-based QWIPs can offer potential advantages such as a wider bandgap and strong polarization charges. However, the study of GaN-based QWIPs is still in its early stages and requires further exploration to achieve optimal device performance. Compared to n-QWIPs, p-QWIPs allow for normal-incident absorption, significantly reducing device complexity and size by eliminating the light coupler, which is particularly advantageous for hand-held and imaging applications. We perform detailed atomic-scale characterization of the distribution of Mg acceptors in layers and at interfaces of a Mg-doped AlGaN/GaN p-QWIP grown by metal organic chemical vapor deposition. Device design, device structure growth conditions, and electrical characterization of the QWIP are presented. Initial electrical characterizations revealed a small but noticeable increase in photocurrent upon illumination. We suspect that photoresponsivity (∼μA/W) is highly impacted by low or inefficient Mg incorporation in the QWIP, limiting the carrier population in quantum wells. Atom probe tomography was employed to study the Mg concentration and distribution in the Mg-doped AlGaN/GaN p-QWIP and revealed Mg segregation and clustering in QWIP layers. These findings provide critical insights and pathways to enhance photoresponsivity and overall device performance in QW-based devices that require Mg p-doping. 

Modern advances in AI have increased employer interest in tracking workers’ biometric signals — e.g., their brainwaves and facial expressions — to evaluate and make predictions about their performance and productivity. These technologies afford managers information about internal emotional and physiological states that were previously accessible only to individual workers, raising new concerns around worker privacy and autonomy. Yet, the research literature on the impact of AI-powered biometric work monitoring (AI-BWM) technologies on workers remains fragmented across disciplines and industry sectors, limiting our understanding of its impacts on workers at large. In this paper, we sytematically review 129 papers, spanning varied disciplines and industry sectors, that discuss and analyze the impact of AI-powered biometric monitoring technologies in occupational settings. We situate this literature across a process model that spans the development, deployment, and usage phases of these technologies. We further draw on Shelby et al.’s Taxonomy of Socio-technical Harms in AI systems to systematize the harms experienced by workers across the three phases of our process model. We find that the development, deployment, and sustained use of AI-powered biometric work monitoring technologies put workers at risk of a number of the socio-technical harms specified by Shelby et al.: e.g., by forcing workers to exert additional emotional labor to avoid flagging unreliable affect monitoring systems, or through the use of these data to make inferences about productivity. Our research contributes to the field of critical AI studies by highlighting the potential for a cascade of harms to occur when the impact of these technologies on workers is not considered at all phases of our process model. 

How do practitioners who develop consumer AI products scope, motivate, and conduct privacy work? Respecting pri- vacy is a key principle for developing ethical, human-centered AI systems, but we cannot hope to better support practitioners without answers to that question. We interviewed 35 industry AI practitioners to bridge that gap. We found that practitioners viewed privacy as actions taken against pre-defined intrusions that can be exacerbated by the capabilities and requirements of AI, but few were aware of AI-specific privacy intrusions documented in prior literature. We found that their privacy work was rigidly defined and situated, guided by compliance with privacy regulations and policies, and generally demoti- vated beyond meeting minimum requirements. Finally, we found that the methods, tools, and resources they used in their privacy work generally did not help address the unique pri- vacy risks introduced or exacerbated by their use of AI in their products. Collectively, these findings reveal the need and opportunity to create tools, resources, and support structures to improve practitioners’ awareness of AI-specific privacy risks, motivations to do AI privacy work, and ability to ad- dress privacy harms introduced or exacerbated by their use of AI in consumer products.