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In this paper, we present a case study of designing AI-human partnerships in a realworld context of science classrooms. We designed a classroom environment where AI technologies, teachers and peers worked synergistically to support students’ writing in science. In addition to an NLP algorithm to automatically assess students’ essays, we also designed (i) feedback that was easier for students to understand; (ii) participatory structures in the classroom focusing on reflection, peer review and discussion, and (iii) scaffolding by teachers to help students understand the feedback. Our results showed that students improved their written explanations, after receiving feedback and engaging in reflection activities. Our case study illustrates that Augmented Intelligence (USDoE, 2023), in which the strengths of AI complement the strengths of teachers and peers, while also overcoming the limitations of each, can provide multiple forms of support to foster learning and teaching. 

The present study examined teachers’ conceptualization of the role of AI in addressing inequity. Grounded in speculative design and education, we examined eight secondary public teachers’ thinking about AI in teaching and learning that may go beyond present horizons. Data were collected from individual interviews. Findings suggest that not only equity consciousness but also present engagement in a context of inequities were crucial to future dreaming of AI that does not harm but improve equity. 

Eighth grade students received automated feedback from PyrEval - an NLP tool - about their science essays. We examined essay quality change when revised. Regardless of prior physics knowledge, essay quality improved. Grounded in literature on AI explainability and trust in automated feedback, we also examined which PyrEval explanation predicted essay quality change. Essay quality improvement was predicted by high- and medium-accuracy feedback. 

Helping students learn how to write is essential. However, students have few opportunities to develop this skill, since giving timely feedback is difficult for teachers. AI applications can provide quick feedback on students’ writing. But, ensuring accurate assessment can be challenging, since students’ writing quality can vary. We examined the impact of students’ writing quality on the error rate of our natural language processing (NLP) system when assessing scientific content in initial and revised design essays. We also explored whether aspects of writing quality were linked to the number of NLP errors. Despite finding that students’ revised essays were significantly different from their initial essays in a few ways, our NLP systems’ accuracy was similar. Further, our multiple regression analyses showed, overall, that students’ writing quality did not impact our NLP systems’ accuracy. This is promising in terms of ensuring students with different writing skills get similarly accurate feedback. 

Nature experiences, especially the visual aspects of nature, have been widely used to facilitate relaxation. Fueled by digital technology, simulated visual nature experiences have gained popularity in creating healing environments that induce relaxation. However, while easily applicable, not all nature-imitating visuals lead to relaxation. How to effectively design relaxing visual nature experiences remains largely unexplored. This paper investigates how different nature qualities facilitate relaxing visual experiences and the roles of two personal characteristics (mood and nature-relatedness) play. Through an online survey and interviews, we assessed 16 nature video clips, representing eight distinctive nature qualities, and compared perceived experiences while considering the influence of personal characteristics. The results indicate four types of visual qualities (engaging, instinctive, ambient, and derivative) underlying nature-induced relaxation, and show that nature relatedness influences the degree to which nature video clips elicit relaxation. We discuss design implications for creating personalized digital nature. 

To use robots within early childhood education requires the preparation of early childhood teachers to use and teach block-based programming. We used a hierarchical linear model approach to address our research question: How can study cohort, cognitive challenge types, and motivational challenge types be used to predict lesson plan quality? Positive motivational challenge predictors were task value of programming, task value of teaching, mastery goals of programming, belonging in teaching, and autonomy in robotics. Negative motivational challenge predictors were mastery goals of teaching, belonging in robotics, self-efficacy in teaching, autonomy in programming, and autonomy in teaching. Positive cognitive challenge predictors were technical issues, problem solving - higher-order skills, and lesson design - other issues. 

In order to explore the consequences of spin–orbit coupling on spin–phonon interactions in a set of chemically similar mixed metal oxides, we measured the infrared vibrational properties of Co4B2O9 (B = Nb, Ta) as a function of temperature and compared our findings with lattice dynamics calculations and several different models of spin–phonon coupling. Frequency vs temperature trends for the Co2+ shearing mode near 150 cm−1 reveal significant shifts across the magnetic ordering temperature that are especially large in relative terms. Bringing these results together and accounting for noncollinearity, we obtain spin–phonon coupling constants of −3.4 and −4.3 cm−1 for Co4Nb2O9 and the Ta analog, respectively. Analysis reveals that these coupling constants are derived from interlayer (rather than intralayer) exchange interactions and that the interlayer interactions contain competing antiferromagnetic and ferromagnetic contributions. At the same time, beyond-Heisenberg terms are minimized due to fortuitous symmetry considerations, different from most other 4d- and 5d-containing oxides. Comparison with other contemporary oxides shows that spin–phonon coupling in this family of materials is among the strongest ever reported, suggesting an origin for magnetoelectric coupling. 

Abstract Recent rapid thinning of West Antarctic ice shelves are believed to be caused by intrusions of warm deep water that induce basal melting and seaward meltwater export. This study uses data from three bottom-mounted mooring arrays to show seasonal variability and local forcing for the currents moving into and out of the Dotson ice shelf cavity. A southward flow of warm, salty water had maximum current velocities along the eastern channel slope, while northward outflows of freshened ice shelf meltwater spread at intermediate depth above the western slope. The inflow correlated with the local ocean surface stress curl. At the western slope, meltwater outflows followed the warm influx along the eastern slope with a ~2–3 month delay. Ocean circulation near Dotson Ice Shelf, affected by sea ice distribution and wind, appears to significantly control the inflow of warm water and subsequent ice shelf melting on seasonal time-scales.