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1. Promoting Design Thinking, Energy Science Learning, and Economic Decision-Making Through AI-Supported Simulation-Based Design

   Romero, Julian; Magana, Alejandra; Newell, Brittany; Pessler, Devon (June 2026, ASEE)

   Engineering technology education often struggles to help students connect abstract energy concepts to authentic design contexts. This study examines the integration of an AI-supported scaffold within a first-year energy-efficiency design challenge. Students used a simulation tool to optimize a solar-powered house affected by tree shading, balancing technical, economic, and environmental constraints. A large language model served as a conversational design counterpart under two conditions: a scaffolded model that prompted evidence-based argumentation and a non-technical model that engaged in open-ended dialogue. Pre- and post-test results indicated significant gains in conceptual knowledge and self-reported decision-making confidence. Student reflections suggested that AI-supported dialogue encouraged clearer justification of design choices and deeper consideration of trade-offs. Rather than isolating specific causal mechanisms, this study characterizes the overall impact of embedding AI-driven scaffolding within simulation-based learning. Findings suggest that structured AI interaction can support reflective reasoning and evidence-based design thinking in early engineering technology education. 

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2. From Simulation Prototyping to AI-Facilitated Argumentation: A Design-Based Approach to Fostering Design Thinking in Engineering Education

   Romero, Julian; Newell, Brittany; Magana, Alejandra; Pessler, Devon (June 2026, American Society of Engineering Education (ASEE))

   Engineering technology education faces the ongoing challenge of equipping students with skills to master complex concepts through authentic design experiences. A key opportunity lies in integrating artificial intelligence with simulation tools to foster design thinking and economic decision making and science based knowledge. This paper explores the implementation of large language models alongside simulation software to enhance learning by design in project based settings. Despite interest in emerging technologies, insufficient attention has been given to the synergy between these models and simulation based design. This study addresses the gap by grounding the approach in complex learning principles within a first year energy efficiency course. The intervention unfolded over two phases including an initial lecture based session introducing energy principles followed by assessments of conceptual understanding and self efficacy and metacognitive reasoning. This foundational knowledge prepared students for the subsequent design phase. In the second phase, students used Aladdin simulation software to model a house with solar panels. Guided by design based learning, students clarified specifications such as budget constraints and net energy goals and environmental responsibilities. They then used the simulation tool to generate alternatives and prototype solutions and test performance. To augment this process, a conversational agent facilitated reflection and communication. Two variants were deployed including a scaffolded version providing structured prompts for scientific rationale and a non scaffolded version enabling open ended dialogue. Data from assessments and reflections demonstrated significant gains in conceptual grasp and decision making confidence and the ability to articulate multifaceted design rationales. The integration of simulation for prototyping and artificial intelligence for reflective argumentation proved effective in deepening systems thinking and uncovering sustainability trade offs. This work illustrates the feasibility of combining these tools in design based learning and offers a scalable model for engineering technology education. By embedding reflection into simulation workflows, the approach enhances technical proficiency and promotes critical engagement with economic and social and environmental contexts. 

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3. From Simulation Prototyping to AI-Facilitated Argumentation: A Design-Based Approach to Fostering Design thinking in Engineering Education

   Romero, Julian; Newell, Brittany; Magana, Alejandra; Pessler, Devon (July 2026, American Society of Engineering Education)

   Engineering technology education faces the ongoing challenge of equipping students with skills to master complex concepts through authentic design experiences. A key opportunity lies in integrating artificial intelligence with simulation tools to foster design thinking and economic decision making and science based knowledge. This paper explores the implementation of large language models alongside simulation software to enhance learning by design in project based settings. Despite interest in emerging technologies, insufficient attention has been given to the synergy between these models and simulation based design. This study addresses the gap by grounding the approach in complex learning principles within a first year energy efficiency course. The intervention unfolded over two phases including an initial lecture based session introducing energy principles followed by assessments of conceptual understanding and self efficacy and metacognitive reasoning. This foundational knowledge prepared students for the subsequent design phase. In the second phase, students used Aladdin simulation software to model a house with solar panels. Guided by design based learning, students clarified specifications such as budget constraints and net energy goals and environmental responsibilities. They then used the simulation tool to generate alternatives and prototype solutions and test performance. To augment this process, a conversational agent facilitated reflection and communication. Two variants were deployed including a scaffolded version providing structured prompts for scientific rationale and a non scaffolded version enabling open ended dialogue. Data from assessments and reflections demonstrated significant gains in conceptual grasp and decision making confidence and the ability to articulate multifaceted design rationales. The integration of simulation for prototyping and artificial intelligence for reflective argumentation proved effective in deepening systems thinking and uncovering sustainability trade offs. This work illustrates the feasibility of combining these tools in design based learning and offers a scalable model for engineering technology education. By embedding reflection into simulation workflows, the approach enhances technical proficiency and promotes critical engagement with economic and social and environmental contexts. 

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4. Understanding Student Conceptualizations of the Market Context in Engineering Design

   https://doi.org/10.18260/1-2--35426  

   Hoffenson, Steven; Pitterson, Nicole; Driscoll, Jessica (June 2020, American Society of Engineering Education 2020)

   Research has shown that engineering students are graduating without all of the skills that they need to succeed in professional engineering practice. While undergraduate engineering programs emphasize technical design and analysis, they generally do not adequately teach or discuss marketability, and evidence suggests that engineering students are graduating without a sufficient grasp of the bigger picture of design. This is reinforced by the available tools for use in engineering education, which are highly focused on ensuring technical feasibility, and a corresponding lack of tools for engineers to explore other design needs. At the same time, research in engineering design has resulted in new market-driven design techniques, which provide guidance for design practitioners regarding how to develop products that are both technically sound and marketable. However, this concept of market-driven design has not yet been widely integrated into engineering curricula. By exploring how current students conceptualize design, this study seeks to contribute to a more balanced perspective on design in undergraduate engineers that accounts for both technical feasibility and market needs. In this paper, we examine third-year Engineering Management students’ mental models of design prior to and after a project-based design course that emphasizes market-driven design concepts and tools. The fundamental research questions are: (1) To what extent do undergraduate engineering students' initial conceptions of design account for the market context, such as competition and consumer considerations? (2) In what ways do these design conceptions change after introducing market-driven design techniques and tools in a design course? Using concept mapping exercises (pre- and post-course), open-ended reflection assignments, surveys, and an assessment of project performance, we reveal how students conceive of and learn about the market context as an integral part of the design process. This contributes to insights regarding how students conceptually balance the technical and non-technical elements of design, as well as evidence regarding the value of a constructivism-based educational approach to advancing student understanding of market-driven design. The results provide a foundational understanding and recommendations regarding holistic design education for engineers in order to reduce the school to work transition gap. 

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5. Engaging High School Students in Robotics and Artificial Intelligence Through Engineering Design Robotics Education

   https://doi.org/10.3390/educsci16060987  

   Novak, Elena; Ahmadi, Sima; Smith, Shannon; Matar, Sophia Naser; Borgerding, Lisa (June 2026, Education Sciences)

   Engineering design education is an effective instructional approach for enhancing students’ motivation, interest, and creativity while introducing them to the engineering design process (EDP). However, there is limited knowledge on how to integrate the EDP into robotics education, particularly AI-robotics, and how students experience AI-enabled robotics project-based learning grounded in an EDP. This pre-/posttest embedded mixed-methods study adds to the scarce body of literature on interdisciplinary education in engineering design, robotics, and AI. This project developed, implemented, and evaluated a project-based engineering design AI-robotics curriculum that introduced novice Computer Science (CS) high school students to robotics, machine learning, and AI. Students’ collaborative robotics projects were grounded in an EDP to introduce the students to engineering practices and promote engagement and interest through design-based, hands-on learning. An analysis of quantitative and qualitative data revealed an improvement in students’ CS attitudes, collaboration, and social interactions after participating in the curriculum. Recommendations for designing AI-robotics projects grounded in an EDP are discussed. 

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