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Transferring programming skills learned in the classroom to diverse real-world scenarios is both essential and challenging in computing education. This experience report describes an approach to facilitate learning transfer by fostering adaptive expertise. Students were engaged in co-creating contextualized worked-out examples, including step-by-step solutions. Through three homework assignments in a Spring 2023 database programming course, we observed substantial improvements, where students generated detailed and accurate solutions and enriched their problem-solving contexts from simple phrases to detailed stories, drawn from 17 real-life scenarios. Our results also suggest that the peer assessment process cultivated a supportive learning environment and fostered adaptive expertise. We discuss the lessons learned and draw pedagogical implications for integrating student-generated contextualized materials in other programming courses. 

Abstract Localized atomistic disorder in halide‐based solid electrolytes (SEs) can be leveraged to boost Li⁺mobility. In this study, Li⁺transport in structurally modified Li₃HoCl₆, via Br⁻introduction and Li⁺deficiency, is explored. The optimized Li_3‐3yHo_1+yCl_6‐xBr_xachieves an ionic conductivity of 3.8 mS cm⁻¹at 25 °C, the highest reported for holmium halide materials.^6,7Li nuclear magnetic resonance and relaxometry investigations unveil enhanced ion dynamics with bromination, attaining a Li⁺motional rate neighboring 116 MHz. X‐ray diffraction analyses reveal mixed‐anion‐induced phase transitions with disproportionate octahedral expansions and distortions, creating Ho‐free planes with favorable energetics for Li⁺migration. Bond valence site energy analysis highlights preferred Li⁺transport pathways, particularly in structural planes devoid of Ho³⁺blocking effects. Molecular dynamics simulations corroborate enhanced Li⁺diffusion with Br⁻introduction into Li₃HoCl₆. Li‐Ho electrostatic repulsions in the (001) plane presumably drive Li⁺diffusion into the Ho‐free (002) layer, enabling rapid intraplanar Li⁺motion and exchange between the 2d and 4h sites. Li_3‐3yHo_1+yCl_6‐xBr_xalso demonstrates good battery cycling stability. These findings offer valuable insights into the intricate correlations between structure and ion transport and will help guide the design of high‐performance fast ion conductors for all‐solid‐state batteries. 

Maraging steels are known for their exceptional strength but suffer from limited work hardening and ductility. Here, we report an intermittent printing approach to tailor the microstructure and mechanical properties of maraging 250 steel via engineering of the thermal history during plasma arc additive manufacturing (PAAM). Through introducing a dwell time between adjacent layers, the maraging 250 steel is cooled below the martensite start temperature, triggering a thermally driven, in-situ martensitic transformation during the printing process. Re-heating or thermal cycling during subsequent layer deposition impedes complete martensitic transformation, enabling coexistence of martensite and retained austenite phases with elemental segregation. The enrichment of Ni in the austenite phase promotes stabilization of the retained austenite upon cooling down to room temperature. The retained austenite is yet metastable during deformation, leading to stress-induced martensitic transformation under loading. Specifically, a 3 min interlayer dwell time produces a maraging 250 steel with approximately 8% retained austenite, resulting in improved work hardening via martensitic transformation induced plasticity (TRIP) during deformation. Meanwhile, the higher cooling rate induced by the dwell time results in substantially refined grain structures with an increased dislocation density, leading to a simultaneously improved yield strength. Notably, the yield strength increases from 836 MPa (0 min dwell) to 990 MPa (3 min dwell), and the uniform elongation increases from 3.2% (0 min dwell) to 6.5% (3 min dwell). This intermittent deposition strategy demonstrates the potential to tune the microstructure and mechanical properties of maraging steels through engineering the thermal history during additive manufacturing. 

Motivations:Recent research has emerged on generally how to improve AI products’ Human-AI Interaction (HAI) User Experience (UX), but relatively little is known about HAI-UX inclusivity. For example, what kinds of users are supported, and who are left out? What product changes would make it more inclusive? Objectives:To help fill this gap, we present an approach to measuring what kinds of diverse users an AI product leaves out and how to act upon that knowledge. To bring actionability to the results, the approach focuses on users’ problem-solving diversity. Thus, our specific objectives were: (1) to show how the measure can reveal which participants with diverse problem-solving styles were left behind in a set of AI products; and (2) to relate participants’ problem-solving diversity to their demographic diversity, specifically gender and age. Methods:We performed 18 experiments, discarding two that failed manipulation checks. Each experiment was a 2x2 factorial experiment with online participants, comparing two AI products: one deliberately violating one of 18 HAI guideline and the other applying the same guideline. For our first objective, we used our measure to analyze how much each AI product gained/lost HAI-UX inclusivity compared to its counterpart, where inclusivity meant supportiveness to participants with particular problem-solving styles. For our second objective, we analyzed how participants’ problem-solving styles aligned with their gender identities and ages. Results & Implications:Participants’ diverse problem-solving styles revealed six types of inclusivity results: (1) the AI products that followed an HAI guideline were almost always more inclusive across diversity of problem-solving styles than the products that did not follow that guideline—but “who” got most of the inclusivity varied widely by guideline and by problem-solving style; (2) when an AI product had risk implications, four variables’ values varied in tandem: participants’ feelings of control, their (lack of) suspicion, their trust in the product, and their certainty while using the product; (3) the more control an AI product offered users, the more inclusive it was; (4) whether an AI product was learning from “my” data or other people’s affected how inclusive that product was; (5) participants’ problem-solving styles skewed differently by gender and age group; and (6) almost all of the results suggested actions that HAI practitioners could take to improve their products’ inclusivity further. Together, these results suggest that a key to improving the demographic inclusivity of an AI product (e.g., across a wide range of genders, ages, etc.) can often be obtained by improving the product’s support of diverse problem-solving styles.