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While K-12 schools are increasingly adopting Artificial Intelligence (AI)-powered learning environments to provide dynamic support and enhance student engagement, the integration of advanced AI applications in specific domains remains uneven. Generative design, an innovative approach in design and engineering that leverages AI and computational power to explore and generate multiple design solutions, has gained traction in the engineering design field. However, integrating generative design into the teaching and learning of engineering design remains a relatively unexplored area. This study introduces an instructional model guiding the integration of generative design into students’ engineering design processes. We validated this model through an AI-powered engineering design curriculum, which involved a total of 109 students from two suburban high schools in the Midwestern United States. Results revealed significant increases in students’ perceived engineering design knowledge, actual engineering design knowledge, and confidence in iterative design. Additionally, students acknowledged the benefits of generative design following their engagement with the curriculum. Nonetheless, concerns emerged regarding the ease of use and students’ comfort levels in utilizing generative design. This study presents implications for STEM research and practice, particularly regarding integrating generative design into K-12 classroom settings for engineering design processes. Furthermore, it offers insights for researchers aiming to optimize AI-powered learning environments. 

Abstract Engineering design has been widely implemented in K-12 curricula to cultivate future workforce. In this study, seventh-grade students (N = 38) participated in theSolarizing Your Schoolcurriculum, an action-oriented program where they engaged in engineering design processes to tackle a real-world problem related to renewable energy adoption. The study sought to explore how students balanced constraints and criteria in engineering design. Over a five-day period, seventh-grade students developed plans for adopting solar energy on their school campus and simulated the plan on a technology-enhanced epistemic tool, Aladdin (https://intofuture.org/aladdin.html). Data was collected through design artifacts, log data from design processes, and surveys about their learning experience. Three distinct patterns of balancing design criteria and constraints emerged, including designing for practice, for performance, and for irrelevant goals. The group who designed for practice gave priority to criteria and constraints recorded a higher level of design performance. The study underscores the benefits of integrating action-oriented learning opportunities via engineering design processes in science education. 

Engineering design that requires mathematical analysis, scientific understanding, and technology is critical for preparing students for solving engineering problems. In simulated design environments, students are expected to learn about science and engineering through their design. However, there is a lack of understanding concerning linking science concepts with design problems to design artifacts. This study investigated how 99 high school students applied science concepts to solarize their school using a computer-aided engineering design software, aiming to explore the interaction between students’ science concepts and engineering design behaviors. Students were assigned to three groups based on their design performance: the achieving group, proficient group, and emerging group. By mining log activities, we explored the interactions among students’ application of science concepts, engineering design behaviors, design iterations, and their design performance. We found that the achieving group has a statistically higher number of design iterations than the other two performance groups. We also identified distinctive transition patterns in students’ applying science concepts and exercising design behaviors among three groups. The implications of this study are then discussed. 

ABSTRACT The integration of chatbots and generative artificial intelligence (AI) tools into engineering education is rapidly changing the way students learn, interact, and solve complex problems. These technologies offer new opportunities for personalized learning, real‐time feedback, and enhanced student engagement. However, a comprehensive understanding of their implementation, pedagogical value, and limitations in engineering education remains limited. This structured literature review examines how chatbots and generative AI tools are integrated into engineering education and evaluates their educational impact and associated challenges. The review focuses on their effects on student learning outcomes, engagement, and skills development, as well as the challenges associated with their implementation. Following PRISMA guidelines, literature was identified through Scopus, Google Scholar, Taylor & Francis Online, and ScienceDirect. After applying predefined inclusion criteria, 16 studies were included in the final review and were analyzed thematically. The findings show that chatbots and generative AI tools can improve learning outcomes in engineering education by promoting student engagement, supporting conceptual understanding, encouraging self‐directed learning, and contributing to the development of problem‐solving and creative thinking skills. However, the review also identifies challenges related to ethical concerns, accuracy limitations, and the risk of student overreliance, which can impact the development of deeper learning and collaboration. 

Self-regulation is crucial for student success in scientific inquiry and engineering design. However, it remains unclear how students dynamically engage in self-regulated learning (SRL) processes to achieve high performance. In this study, we investigated the temporal nature of self-regulation during engineering design by leveraging computer trace data from 101 high school students who designed an energy-plus house in a simulated learning environment. Using sequential mining, we found that high-performing students were more engaged in the Observation, Analysis, and Evaluation phases of SRL than low-performing students. Additionally, high-performing students demonstrated consecutive sequential patterns between Observation and Analysis, Reformation and Evaluation, and Analysis and Evaluation behaviors. These findings provide insights into students’ SRL processes and the design of scaffoldings. 

Engineering projects, such as designing a solar farm that converts solar radiation shined on the Earth into electricity, engage students in addressing real-world challenges by learning and applying geoscience knowledge. To improve their designs, students benefit from frequent and informative feedback as they iterate. However, teacher attention may be limited or inadequate, both during COVID-19 and beyond. We present Aladdin, a web-based computer-aided design (CAD) platform for engineering design with a built-in artificial intelligence teaching assistant (AITA). We also present two curriculum units (Solar Energy Science and Solar Farm Design), where students explore the Sun-Earth relationship and optimize the energy output and yearly profit of a solar farm with the help of the AITA. We tested the software and curriculum units with over 100 students in two Midwestern high schools. Pre- and post-survey data showed improvements in understanding of science concepts and self-efficacy in engineering design. Pre-post analysis of design performance gains reveals that AI helped lower achievers more than higher achievers. Interviews revealed students’ values and preferences when receiving feedback. Our findings suggest that AITAs may be helpful as an additional feedback mechanism for geoscience and engineering education. Future efforts should focus on improving the usability of the software and providing multiple types of feedback to promote inclusive and equitable use of AI in education. 

First-year engineering students are often introduced to the engineering design process through project-based learning situated in a concrete design context. Design contexts like mechanical engineering are commonly used, but students and teachers may need more options. In this article, we show how sustainable building design can serve as an alternative for students of diverse backgrounds and with various interests. The proposed Net Zero Energy Challenge is an engineering design project in which students practice the full engineering design cycle to create a virtual house that generates renewable energy on-site, with the goal to achieve net zero energy consumption. Such a design challenge is made possible by Aladdin, an integrated tool that supports building design, simulation, and analysis within a single package. A pilot study of the Net Zero Energy Challenge at a university in Mid-Atlantic United States suggests that around half of the students were able to achieve the design goal.