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This paper explores the addition of AI into ChangeMaker K-12, a set of experiences and materials designed to support teacher candidates (TC) in learning to teach engineering design. The ChangeMaker K-12 model and materials consist of four progressive stages: design awareness, design for function, design with empathy, and design for change. This new research explores the introduction of AI assistance into these four stages. 

Learning modalities implemented for reopening during COVID-19 impacted effectiveness of science and mathematics teachers in high-need local educational agencies (HN-LEAs). The distribution of learning modalities was very similar between Title I and SRSA/RLIS eligible HN-LEAs, with approximately half of each reopening in a hybrid fashion. From 2019 to 2022, students who initially returned to learning in-person had higher graduation rates and performance on science and mathematics tests than those who returned to remote or hybrid learning environments. However, these differences were already present in the 2018 –2019 pre-pandemic baseline, suggesting reopening choices reflected yet-to-be-determined disparities between districts. 

This paper explores the addition of AI into ChangeMaker K-12, a set of experiences and materials designed to support teacher candidates (TC) in learning to teach engineering design. The ChangeMaker K-12 model and materials consist of four progressive stages: design awareness, design for function, design with empathy, and design for change. This new research explores the introduction of AI assistance into these four stages. 

This paper explores the addition of AI into ChangeMaker K-12, a set of experiences and materials designed to support teacher candidates (TC) in learning to teach engineering design. The ChangeMaker K-12 model and materials consist of four progressive stages: design awareness, design for function, design with empathy, and design for change. This new research explores the introduction of AI assistance into these four stages. 

This paper is focused on utilizing a common language and is grounded in current research conducted through a Noyce track IV grant. The research project utilized large national and state data sets to select districts and determine impacts of modes of returning in Fall 2020 after the COVID-19 pandemic closure. During the process, the research team encountered unexpected barriers, including a lack of clear and operationalized terminology for defining high-need districts. Thus, this paper focuses on building a community of practice where the lack of clarity in the definition of high-need local educational agency (LEA) is addressed. 

Context. Blazars, comprising BL Lacertae objects (BL Lacs) and flat-spectrum radio quasars, are the most luminous extragalactic sources that dominate the γ-ray sky. They account for approximately 56% of the sources listed in the recentFermi-LAT (Large Area Telescope) catalogue (4FGL-DR4). The optical and UV spectra of BL Lacs are nearly featureless, making it difficult to precisely determine their redshifts. Consequently, nearly half of the γ-ray BL Lacs lack reliable redshift measurements. This poses a significant challenge, since redshift is crucial for studying the cosmic evolution of the blazar population and for understanding their intrinsic emission mechanisms. Additionally, it is vital for γ-ray propagation studies, such as indirect evidence of extragalactic background light (EBL), placing constraints on the intergalactic magnetic field (IGMF), and searches for Lorentz invariance violation (LIV) and axion-like particles (ALPs). Aims. This paper is the fourth in a series dedicated to determining the redshift of a sample of blazars identified as key targets for future observations with the Cherenkov Telescope Array Observatory (CTAO). The precise determination of the redshifts of these objects plays a crucial role in planning future CTAO observations. Methods. We carried out Monte Carlo simulations to identify potential γ-ray blazars with hard spectra detected by theFermi-LAT telescope that currently lack redshift measurements. These simulations selected the blazars that are anticipated to be detectable by the CTAO within 30 hours or less of exposure assuming an average flux state. In this fourth paper, we report the results of detailed spectroscopic observations of 29 blazars using the ESO/VLT, Keck II, and SALT telescopes. Our analysis involved a thorough search for spectral lines in the spectra of each blazar, and when features of the host galaxy were identified, we modelled its properties. Moreover, we compared the magnitudes of the targets during the observations to their long-term light curves. Results. In the sample studied, 9 of 29 sources were observed with a high signal-to-noise ratio (S/N > 100), while the remaining 20 were observed with a moderate or low S/N. We successfully determined firm redshifts for 12 blazars, ranging from 0.1636 to 1.1427, and identified two lower limit redshifts atz> 1.0196 andz> 1.4454. The remaining 15 BL Lac objects exhibited featureless spectra under the observed S/N. 

We report the first observations of a long-duration very-high-energy (VHE; E>100 GeV) flare from BL Lacertae (VER J2202+422), taken with the Very Energetic Radiation Imaging Telescope Array System (VERITAS). On October 15, 2022, the Fermi-Large Area Telescope (LAT) detected elevated GeV activity originating from this blazar. This triggered a multiwavelength campaign, which includes observations from VERITAS, Swift, NuSTAR, and select optical and radio observatories. VERITAS observed the source for a total of ~9.8 hours between September 1, 2022 and December 1, 2022. An analysis of these data yields a ~28 sigma detection of the source. While previously observed VHE flares from BL Lacertae have lasted on time-scales of minutes to days, VERITAS continued to detect flaring activity from the source for over a month (~40 days) after the original flaring activity was detected with Fermi-LAT. Broadband spectral modeling shows that a synchrotron self-Compton (SSC) model with an external inverse-Compton (EC) component is preferred over a one-zone SSC model.