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1. NSF-IUSE: Multiple/Single Representations and Scaffolding Design in Technology-enhanced Semiconductor Learning: A Pilot Crossover Study

   Kim, H; Dyehouse, M; Das, S; Douglas, K_A; Geddis, D; Hill, S_D; Jiang, B; Nare, O; Parvin, N; Vasileska, D; et al (June 2026, American Society of Engineering Education (ASEE))

   This empirical work-in-progress paper examines the impact of two technology-enhanced learning tools, an interactive visualization with multiple representations and explicit scaffolding and a simulation tool with single representation and implicit scaffolding, on students’ conceptual understanding and affective outcomes in semiconductor physics. This domain is known for early conceptual difficulty that often limits student progression into advanced coursework and the semiconductor workforce. Using a crossover design, 20 undergraduate electrical engineering students engaged with both tools and completed pre and post-assessments of conceptual knowledge, perceived understanding, interest, and motivation. A semi-structured focus group was analyzed using thematic analysis to capture students’ learning experiences. The findings reveal that while both tools supported students' cognitive and affective processes, one primarily fostered foundational understanding for novice learners, whereas the other one enabled deeper exploration for more experienced students. Although limited by sample size and short duration, the results suggest that aligning tool design with learners’ developmental stage may support both learning and motivation. Overall, this study demonstrates how instructional design features shape students’ engagement with complex engineering content and underscores the importance of adaptive technology-enhanced learning environments. 

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

   Romero, Julian; Magana, Alejandra; Newell, Brittany; Pessler, Devon (July 2026, American Society of Engineering Education (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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4. Unpacking High School Students’ Motivational Influences in Project-Based Learning

   https://doi.org/10.1109/TE.2023.3299173  

   Shekhar, Prateek; Dominguez, Heydi; Abichandani, Pramod; Iaboni, Craig (February 2024, IEEE Transactions on Education)

   Purpose: The presented study was conducted to unpack high school students’ motivational influences in engineering/computer science project-based learning (PjBL), using the attention, relevance, confidence, and satisfaction (ARCS) model of motivation as a conceptual framework. Methods: A qualitative research approach was used with student focus groups as the data source. A total of six focus groups with 32 student participants was conducted. The students were enrolled in high schools located in four different states in the U.S. The qualitative analysis of transcripts was performed using first and second cycle coding methods. Findings: The findings show that student motivation is nuanced in regard with attention, relevance, confidence, and satisfaction. The findings identify research-based strategies for fostering student motivation, such as implementing learner-focused scaffolding in PjBL environments, improving the relevance of the classroom content with the real-world context that students have experiences in or are knowledgeable about, and focusing on stimulating intrinsic motivation in addition to extrinsic rewards. Conclusion: The findings provide support for the comprehensibility and utility of the ARCS model of motivation in high school engineering/CS education, and more importantly empirically unpacks what the model factors mean from students’ perspective. Practitioners may use these findings to inform the design, development, and implementation of PjBL in high school settings. 

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5. HydroLearn: Improving Students’ Conceptual Understanding and Technical Skills in a Civil Engineering Senior Design Course

   Gallagher, M. A. (July 2021, 2021 ASEE Virtual Annual Conference)

   null (Ed.)

   Engineering graduates need a deep understanding of key concepts in addition to technical skills to be successful in the workforce. However, traditional methods of instruction (e.g., lecture) do not foster deep conceptual understanding and make it challenging for students to learn the technical skills, (e.g., professional modeling software), that they need to know. This study builds on prior work to assess engineering students’ conceptual and procedural knowledge. The results provide an insight into how the use of authentic online learning modules influence engineering students’ conceptual knowledge and procedural skills. We designed online active learning modules to support and deepen undergraduate students’ understanding of key concepts in hydrology and water resources engineering (e.g., watershed delineation, rainfall-runoff processes, design storms), as well as their technical skills (e.g., obtaining and interpreting relevant information for a watershed, proficiency using HEC-HMS and HEC-RAS modeling tools). These modules integrated instructional content, real data, and modeling resources to support students’ solving of complex, authentic problems. The purpose of our study was to examine changes in students’ self-reported understanding of concepts and skills after completing these modules. The participants in this study were 32 undergraduate students at a southern U.S. university in a civil engineering senior design course who were assigned four of these active learning modules over the course of one semester to be completed outside of class time. Participants completed the Student Assessment of Learning Gains (SALG) survey immediately before starting the first module (time 1) and after completing the last module (time 2). The SALG is a modifiable survey meant to be specific to the learning tasks that are the focus of instruction. We created versions of the SALG for each module, which asked students to self-report their understanding of concepts and ability to implement skills that are the focus of each module. We calculated learning gains by examining differences in students’ self-reported understanding of concepts and skills from time 1 to time 2. Responses were analyzed using eight paired samples t-tests (two for each module used, concepts and skills). The analyses suggested that students reported gains in both conceptual knowledge and procedural skills. The data also indicated that the students’ self-reported gain in skills was greater than their gain in concepts. This study provides support for enhancing student learning in undergraduate hydrology and water resources engineering courses by connecting conceptual knowledge and procedural skills to complex, real-world problems. 

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