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This original research article focuses on the investigation of the use of generative artificial intelligence (GAI) use among students in communication-intensive STEM courses and how this engagement shapes their scientific communication practices, competencies, confidence, and science identity. Using a mixed-methods approach, patterns were identified in how students perceived their current science identity and use of incorporating artificial intelligence (AI) into writing, oral, and technical tasks. Thematic analysis reveals that students use AI for a range of STEM communication endeavors such as structuring lab reports, brainstorming presentation ideas, and verifying code. While many minoritized students explicitly describe AI as a confidence-boosting, timesaving, and competence-enhancing tool, others—particularly those from privileged backgrounds—downplay its influence, despite evidence of its significant role in their science identity. These results suggest the reframing of science identity as being shaped by technological usage and social contingency. This research illuminates both the potential and pitfalls of AI-use in shaping the next generation of scientists. 

Anomaly/Outlier detection (AD/OD) is often used in controversial applications to detect unusual behavior which is then further investigated or policed. This means an explanation of why something was predicted as an anomaly is desirable not only for individuals but also for the general population and policy-makers. However, existing explainable AI (XAI) methods are not well suited for Explainable Anomaly detection (XAD). In particular, most XAI methods provide instance-level explanations, whereas a model/global-level explanation is desirable for a complete understanding of the definition of normality or abnormality used by an AD algorithm. Further, existing XAI methods try to explain an algorithm’s behavior by finding an explanation of why an instance belongs to a category. However, by definition, anomalies/outliers are chosen because they are different from the normal instances. We propose a new style of model agnostic explanation, called contrastive explanation, that is designed specifically for AD algorithms which use semantic tags to create explanations. It addresses the novel challenge of providing a model-agnostic and global-level explanation by finding contrasts between the outlier group of instances and the normal group. We propose three formulations: (i) Contrastive Explanation, (ii) Strongly Contrastive Explanation, and (iii) Multiple Strong Contrastive Explanations. The last formulation is specifically for the case where a given dataset is believed to have many types of anomalies. For the first two formulations, we show the underlying problem is in the computational class P by presenting linear and polynomial time exact algorithms. We show that the last formulation is computationally intractable, and we use an integer linear program for that version to generate experimental results. We demonstrate our work on several data sets such as the CelebA image data set, the HateXplain language data set, and the COMPAS dataset on fairness. These data sets are chosen as their ground truth explanations are clear or well-known. 

NoElastocaloric cooling is a promising solid-state alternative to vapor-compression refrigeration. In conventional systems, such as natural rubber, deformation induces entropy change accompanied by temperature release. Unloading the material restores the entropic state and is accompanied by cooling. Inverse elastocaloric effects have been detailed in shape memory alloys, where deformation induces loss of order and cooling. Here, we report on a distinctive inverse elastocaloric effect in liquid crystalline elastomers (LCEs) containing supramolecular hydrogen bonds. Upon deformation, the supramolecular LCE exhibits initial organization but then disorganizes as the intramesogenic hydrogen bonds are broken. Due to the liquid crystalline nature of the dimeric supramolecular bonds, the mechanochemical bond breakage manifests in a disruption in order. By disrupting the extent of liquid crystallinity in the system, we hypothesize that the network disorganizes to the deformation (e.g., entropy increases) and produces an inverse elastocaloric output.t Available