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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. 

Abstract Kepler-51 is a 500 Myr G dwarf hosting three “super-puffs” and one low-mass nontransiting planet. Kepler-51d, the coolest (T_eq∼ 350 K) transiting planet in this system is also one of the lowest-density super-puffs known to date (ρ_p= 0.038 ± 0.009 g cm⁻³). With a planetary mass ofM_p= 5.6 ± 1.2M_⊕and a radius ofR_p= 9.32 ± 0.18R_⊕, the observed properties of this planet are not readily explained by most planet formation theories. Hypotheses explaining Kepler-51d’s low density range from a substantial H/He envelope comprising >30% its mass, a high-altitude haze layer, to a tilted ring system. To test these hypotheses, we present the NIRSpec-PRISM 0.6–5.3μm transmission spectrum of Kepler-51d observed by the James Webb Space Telescope. We find a spectrum best fit by a sloped line covering the entire wavelength range. Based on forward modeling and atmosphere retrievals, Kepler-51d likely possesses a low-metallicity atmosphere with high-altitude hazes of submicron particle sizes spanning pressures of 1–100μbar. However, the spectrum could also be explained by a tilted ring with an estimated lifetime on the order of ∼0.1 Myr. We also investigate the stellar activity of this young Sun-like star, extracting a spot temperature significantly hotter than sunspots and spot covering fractions on the order of 0.1%–10% depending on assumed spot parameters. 

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/ 

Fostering public AI literacy has been a growing area of interest at CHI for several years, and a substantial community is forming around issues such as teaching children how to build and program AI systems, designing learning experiences to broaden public understanding of AI, developing explainable AI systems, understanding how novices make sense of AI, and exploring the relationship between public policy, ethics, and AI literacy. Previous workshops related to AI literacy have been held at other conferences (e.g., SIGCSE, AAAI) that have been mostly focused on bringing together researchers and educators interested in AI education in K-12 classroom environments, an important subfield of this area. Our workshop seeks to cast a wider net that encompasses both HCI research related to introducing AI in K-12 education and also HCI research that is concerned with issues of AI literacy more broadly, including adult education, interactions with AI in the workplace, understanding how users make sense of and learn about AI systems, research on developing explainable AI (XAI) for non-expert users, and public policy issues related to AI literacy.