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PurposeChallenges in teaching the engineering design process (EDP) at the high-school level, such as promoting good documentation practices, are well-documented. While developments in educational artificial intelligence (AI) systems have the potential to assist in addressing these challenges, the open-ended nature of the EDP leads to challenges that often lack the specificity required for actionable AI development. In addition, conventional educational AI systems (e.g. intelligent tutoring systems) primarily target procedural domain tasks with well-defined outcomes and problem-solving strategies, while the EDP involves open-ended problems and multiple correct solutions, making AI intervention timing and appropriateness complex. Design/methodology/approachAuthors conducted a six-week-long Research through Co-Design (RtCD) process (i.e. a co-design process rooted in Research through Design) with two experienced high-school engineering teachers to co-construct actionable insight in the form of AI intervention points (AI-IPs) in engineering education where an AI system can effectively intervene to support them while highlighting their pedagogical practices. FindingsThis paper leveraged the design of task models to iteratively refine our prior understanding of teachers’ experiences with teaching the EDP into three AI-IPs related to documentation, ephemeral interactions between teachers and students and disruptive failures that can serve as a focus for intelligent educational system designs. Originality/valueThis paper discusses the implications of these AI-IPs for designing educational AI systems to support engineering education as well as the importance of leveraging RtCD methodologies to engage teachers in developing intelligent educational systems that align with their needs and afford them control over computational interventions in their classrooms. 

Data visualization literacy is essential for K-12 students, yet existing practices emphasize interpreting pre-made visualizations rather than creating them. To address this, we developed the DPV (Domain, Purpose, Visual) framework, which guides middle school students through the visualization design process. The framework simplifies design into three stages: understanding the problem domain, specifying the communication purpose, and translating data into effective visuals. Implemented in a twoweek summer camp as a usage scenario, the DPV framework enabled students to create visualizations addressing community issues. Evaluation of student artifacts, focus group interviews, and surveys demonstrated its effectiveness in enhancing students' design skills and understanding of visualization concepts. This work highlights the DPV framework's potential to foster data visualization literacy for K-12 education and broaden participation in the data visualization community. 

The “Accessible Oceans” pilot project aims to inclusively design auditory displays that support perception and understanding of ocean data in informal learning environments (ILEs). The project’s multi-disciplinary team includes expertise from all related fields — ocean scientists, dataset experts, a sound designer with specialization in data sonification, and a learning sciences researcher. In addition, the PI is blind and provides a crucial perspective in our research. We describe the sound design of informative sonifications and respective auditory displays based on iterative design with user input at each stage, including from blind and low-vision (BLV) students, their teachers, and subject-matter experts. We discuss the importance of framing data sonifications through an auditory presentation of contextual information. We also report on our latest auditory display evaluation using Auditory Interface UX Scale (BUZZ) surveys at three ILE test sites. These responses further affirm our auditory display design developments. We include access to the auditory displays media and lessons learned over the course of this multi-year NSF-funded Advancing Informal Stem Learning (AISL) grant https://accessibleoceans.whoi.edu/ 

Abstract We describe the discovery and characterization of TOI-7149 b, a 0.705 ± 0.075M_J, 1.18 ± 0.045R_Jgas giant on a ∼2.65 days period orbit transiting an M4V star with a mass of 0.344 ± 0.030M_⊙and an effective temperature of 3363 ± 59 K. The planet was first discovered using NASA’s TESS mission, which we confirmed using a combination of ground-based photometry, radial velocities, and speckle imaging. The planet has one of the deepest transits of all known main-sequence planet hosts at ∼12% (R_p/R_⋆∼ 0.33). Pushing the bounds of previous discoveries of giant exoplanets around M-dwarf stars (GEMS), TOI-7149 is one of the lowest mass M-dwarfs to host a transiting giant planet. We compare the sample of transiting GEMS to stars within 200 pc with a Gaia color–magnitude diagram and find that the GEMS hosts are likely to be high metallicity stars. We also analyze the sample of transiting giant planets using the nonparametricMRExoframework to compare the bulk density of warm Jupiters across stellar masses. We confirm our previous result that transiting Jupiters around early M-dwarfs have similar masses and densities to warm Jupiters around FGK stars, and extend this to mid M-dwarfs, thereby suggesting a potential commonality in their formation mechanisms. 

Abstract We present the discovery of TOI-6303b and TOI-6330b, two massive transiting super-Jupiters orbiting a M0 and a M2 dwarf star, respectively, as part of the Searching for Giant Exoplanets around M-dwarf Stars (GEMS) survey. These were detected by NASA’s Transiting Exoplanet Survey Satellite and then confirmed via ground-based photometry and radial velocity observations with the Habitable-zone Planet Finder. TOI-6303b has a mass of 7.84 ± 0.31M_J, a radius of 1.03 ± 0.06R_J, and an orbital period of 9.485 days. TOI-6330b has a mass of 10.00 ± 0.31M_J, a radius of 0.97 ± 0.03R_J, and an orbital period of 6.850 days. We put these planets in the context of super-Jupiters around M dwarfs discovered from radial-velocity surveys, as well as recent discoveries from astrometry. These planets have masses that can be attributed to two dominant planet formation mechanisms—gravitational instability and core accretion. Their masses necessitate massive protoplanetary disks that should either be gravitationally unstable, i.e., forming through gravitational instability, or be among the most massive protoplanetary disks known to date to form objects through core accretion. We also discuss their possible migration mechanisms via their eccentricity distribution. 

ABSTRACT We report the first instance of an M dwarf/brown dwarf obliquity measurement for the TOI-2119 system using the Rossiter–McLaughlin effect. TOI-2119 b is a transiting brown dwarf orbiting a young, active early M dwarf ($$T_{\rm {eff}}$$ = 3553 K). It has a mass of 64.4 M$$_{\rm {J}}$$ and radius of 1.08 R$$_{\rm {J}}$$, with an eccentric orbit (e = 0.3) at a period of 7.2 d. For this analysis, we utilize NEID spectroscopic transit observations and ground-based simultaneous transit photometry from the Astrophysical Research Consortium and the Las Campanas Remote Observatory. We fit all available data of TOI-2119 b to refine the brown dwarf parameters and update the ephemeris. The classical Rossiter–McLaughlin technique yields a projected star–planet obliquity of $$\lambda =-0.8\pm 1.1^\circ$$ and a three-dimensional obliquity of $$\psi =15.7\pm 5.5^\circ$$. Additionally, we spatially resolve the stellar surface of TOI-2119 utilizing the Reloaded Rossiter–McLaughlin technique to determine the projected star–planet obliquity as $$\lambda =1.26 \pm 1.3^{\circ }$$. Both of these results agree within $$2\sigma$$ and confirm the system is aligned, where TOI-2119 b joins an emerging group of aligned brown dwarf obliquities. We also probe stellar surface activity on the surface of TOI-2119 in the form of centre-to-limb variations as well as the potential for differential rotation. Overall, we find tentative evidence for centre-to-limb variations on the star but do not detect evidence of differential rotation. 

Abstract Brown dwarfs bridge the gap between stars and planets, providing valuable insight into both planetary and stellar-formation mechanisms. Yet the census of transiting brown-dwarf companions, in particular around M-dwarf stars, remains incomplete. We report the discovery of two transiting brown dwarfs around low-mass hosts using a combination of space- and ground-based photometry along with near-infrared radial velocities. We characterize TOI-5389Ab ( $68.0_{-2.2}^{+2.2}{M}_{\mathrm{J}}$) and TOI-5610b ( $40.4_{-1.0}^{+1.0}{M}_{\mathrm{J}}$), two moderately massive brown dwarfs orbiting early M-dwarf hosts (T_eff = 3569 ± 59 K and 3618 ± 59 K, respectively). For TOI-5389Ab, the best fitting parameters are periodP = 10.40046 ± 0.00002 days, radius $R_{\mathrm{BD}}=0.824_{-0.031}^{+0.033}$R_J, and low eccentricity $e=0.0962_{-0.0046}^{+0.0027}$. In particular, this constitutes one of the most extreme substellar-stellar companion-to-host mass ratios ofq= 0.150. For TOI-5610b, the best-fitting parameters are periodP = 7.95346 ± 0.00002 days, radius $R_{\mathrm{BD}}=0.887_{-0.031}^{+0.031}$R_J, and moderate eccentricity $e=0.354_{-0.012}^{+0.011}$. Both targets are expected to have shallow, but potentially observable, occultations: ≲500 ppm in the JohnsonKband. A statistical analysis of M-dwarf/BD systems reveals for the first time that those at short orbital periods (P < 13 days) exhibit a dearth of 13M_J < M_BD < 40M_Jcompanions (q < 0.1) compared to those at slightly wider separations. 

Fostering young learners’ literacy surrounding AI technologies is becoming increasingly important as AI is becoming integrated in many aspects of our lives and is having far-reaching impacts on society. We have developed Knowledge Net and Creature Features, two activity boxes for family groups to engage with in their homes that communicate AI literacy competencies such as understanding knowledge representations, the steps of machine learning, and AI ethics. Our current work is exploring how to transform these activity boxes into museum exhibits for middle-school age learners, focusing on three key considerations: centering learner interests, generating personally meaningful outputs, and incorporating embodiment and collaboration on a larger scale. Our demonstration will feature the existing Knowledge Net and Creature Features activity boxes alongside early-stage prototypes adapting these activities into larger-scale museum exhibits. This paper contributes an exploration into how to design AI literacy learning interventions for varied informal learning contexts.