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1. How outreach camps incorporate design affects female high school students’ interest in engineering and perceptions of engineering design (Evaluation).

   Tyler, K.; Johnson-Glauch, N.; Dean, L. M.; Krogstad, J. A. (June 2019, ASEE annual conference exposition proceedings)

   Outreach summer camps, particularly those focused on increasing the number of women in engineering, are commonplace. Some camps take the approach of a broad survey of engineering as a whole, while others focus on one specific discipline. Within the discipline-specific camps, there is a high degree of variability in curriculum and structure. This is apparent when considering if and how engineering design is built into the camp structure. While many studies have investigated the impact of outreach camps on engineering self-confidence among participants, few studies have sought to understand how the camp curriculum as a whole can influence these outcomes. To begin to understand the connection between outreach camp curriculum and engineering self-confidence among participants, we studied outreach camps targeted to high school women that varied in the incorporation of design into their structure. We chose to study three camps: (1) a design-focused camp, (2) a design-incorporated camp (run by the authors), and a (3) design-absent camp. All three camps were at the same university but based in different engineering disciplines. Results from pre-post survey Wilcoxon Signed Rank tests showed that design-focused and design-incorporated camps were able to improve students’ perspective of what engineering is (p <.01 and p = .02), while the design-absent camp had no change. The design-incorporated camp increased the participants’ desire to be an engineer (p = .02) while the design-absent camp decreased the participants’ desire to be an engineer (p = .02) and the design-focused camp had no effect. The design-absent camp also decreased the participants’ overall interest in engineering (p = .02). Additionally, both the design-incorporated and design-focused camps increased the participants’ confidence in conducting engineering design (p <.01 and p <.01), but only the design-incorporated camp had consistent improvements throughout the entire design cycle. Motivated by these results, we intend in future studies to more systematically probe the potential of different outreach curricula and structures to positively influence engineering perceptions. 

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2. Co‐designing teacher support technology for problem‐based learning in middle school science

   https://doi.org/10.1111/bjet.13363  

   Hutchins, Nicole M.; Biswas, Gautam (July 2023, British Journal of Educational Technology)

   Abstract This paper provides an experience report on a co‐design approach with teachers to co‐create learning analytics‐based technology to support problem‐based learning in middle school science classrooms. We have mapped out a workflow for such applications and developed design narratives to investigate the implementation, modifications and temporal roles of the participants in the design process. Our results provide precedent knowledge on co‐designing with experienced and novice teachers and co‐constructing actionable insight that can help teachers engage more effectively with their students' learning and problem‐solving processes during classroom PBL implementations. Practitioner notesWhat is already known about this topicSuccess of educational technology depends in large part on the technology's alignment with teachers' goals for their students, teaching strategies and classroom context.Teacher and researcher co‐design of educational technology and supporting curricula has proven to be an effective way for integrating teacher insight and supporting their implementation needs.Co‐designing learning analytics and support technologies with teachers is difficult due to differences in design and development goals, workplace norms, and AI‐literacy and learning analytics background of teachers.What this paper addsWe provide a co‐design workflow for middle school teachers that centres on co‐designing and developing actionable insights to support problem‐based learning (PBL) by systematic development of responsive teaching practices using AI‐generated learning analytics.We adapt established human‐computer interaction (HCI) methods to tackle the complex task of classroom PBL implementation, working with experienced and novice teachers to create a learning analytics dashboard for a PBL curriculum.We demonstrate researcher and teacher roles and needs in ensuring co‐design collaboration and the co‐construction of actionable insight to support middle school PBL.Implications for practice and/or policyLearning analytics researchers will be able to use the workflow as a tool to support their PBL co‐design processes.Learning analytics researchers will be able to apply adapted HCI methods for effective co‐design processes.Co‐design teams will be able to pre‐emptively prepare for the difficulties and needs of teachers when integrating middle school teacher feedback during the co‐design process in support of PBL technologies. 

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3. Value‐sensitive design of chatbots in environmental education: Supporting identity, connectedness, well‐being and sustainability

   https://doi.org/10.1111/bjet.13568  

   Nguyen, Ha; Nguyen, Victoria; Ludovise, Sara; Santagata, Rossella (January 2025, British Journal of Educational Technology)

   While offering the potential to support learning interactions, emerging AI applications like Large Language Models (LLMs) come with ethical concerns. Grounding technology design in human values can address AI ethics and ensure adoption. To this end, we apply Value‐Sensitive Design—involving empirical, conceptual and technical investigations—to centre human values in the development and evaluation of LLM‐based chatbots within a high school environmental science curriculum. Representing multiple perspectives and expertise, the chatbots help students refine their causal models of climate change's impact on local marine ecosystems, communities and individuals. We first perform an empirical investigation leveraging participatory design to explore the values that motivate students and educators to engage with the chatbots. Then, we conceptualize the values that emerge from the empirical investigation by grounding them in research in ethical AI design, human values, human‐AI interactions and environmental education. Findings illuminate considerations for the chatbots to support students' identity development, well‐being, human–chatbot relationships and environmental sustainability. We further map the values onto design principles and illustrate how these principles can guide the development and evaluation of the chatbots. Our research demonstrates how to conduct contextual, value‐sensitive inquiries of emergent AI technologies in educational settings. Practitioner notesWhat is already known about this topicGenerative artificial intelligence (GenAI) technologies like Large Language Models (LLMs) can not only support learning, but also raise ethical concerns such as transparency, trust and accountability.Value‐sensitive design (VSD) presents a systematic approach to centring human values in technology design.What this paper addsWe apply VSD to design LLM‐based chatbots in environmental education and identify values central to supporting students' learning.We map the values emerging from the VSD investigations to several stages of GenAI technology development: conceptualization, development and evaluation.Implications for practice and/or policyIdentity development, well‐being, human–AI relationships and environmental sustainability are key values for designing LLM‐based chatbots in environmental education.Using educational stakeholders' values to generate design principles and evaluation metrics for learning technologies can promote technology adoption and engagement. 

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4. To design, or not to design? Comparison of beetle ultraconserved element probe set utility based on phylogenetic distance, breadth, and method of probe design

   https://doi.org/10.5281/zenodo.7926017  

   Grey, Gustafson; Rachel, Glynn; Andrew, Short; Sergei, Tarasov; Nicole, Gunter (January 2023, Zenodo)

   This repository contains Materials and designed UCE probe sets for the manuscript entitled "To design or not to design? Comparison of beetle ultraconserved element probe set utility based on phylogenetic distance, breadth, and method of probe design". 

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5. An Empirical Study Measuring the Effects of Generative AI on Early-Stage Engineering Design and Neurocognition

   https://doi.org/10.1115/DETC2025-169255  

   Aruon, Avinash; Shealy, Tripp; Gero, John (August 2025, American Society of Mechanical Engineers)

   Abstract Engineering design is a continuous and iterative process, where early-stage decisions significantly impact subsequent design outcomes. This study investigates the influence of AI-assistance during early stages of design on subsequent design stages and measures the change in both design outcomes and cognitive processing in the brain. Sixty undergraduate engineering students participated in a two-stage design task. Students were first asked to identify design constraints related to the sustainable redevelopment of a site on campus either using human imagination or utilizing generative AI to assist them. Students, in both groups, without the aid of generative AI, then developed conceptual design ideas for redevelopment. The results indicate that the AI-assisted group identified significantly more design constraints (p < 0.05) and subsequently without the aid of AI developed a greater number of design concepts related to environmental sustainability. Brain imaging analysis revealed that AI assistance reduced the neuro-cognitive effort during constraints identification and had a residual effect in reducing neuro-cognitive effort during the concept design phase, particularly in the right frontopolar prefrontal cortex – a region associated with complex, abstract thinking. These findings suggest that AI-assisted design can enhance design efficiency by optimizing reducing cognitive effort and improving early-stage design outcomes. Future research should explore human-AI collaboration strategies to maximize its benefits in engineering design workflows. 

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