Schimpf, C. T., & Huang, X., & Xie, C., & Sha, Z., & Massicotte, J. E. (2019, June), Developing Instructional Design Agents to Support Novice and K-12 Design Education  Paper presented at 2019 ASEE Annual Conference & Exposition , Tampa, Florida. 10.18260/1-2--32640

\bibitem{asee_peer_32640}
  Schimpf, C. T., \& Huang, X., \& Xie, C., \& Sha, Z., \& Massicotte, J. E. (2019, June), \emph{Developing Instructional Design Agents to Support Novice and K-12 Design Education}
  Paper presented at 2019 ASEE Annual Conference \& Exposition , Tampa, Florida.
  10.18260/1-2--32640

Corey T. Schimpf, Xudong Huang, Charles Xie, Zhenghui Sha, and Joyce E. Massicotte.  "Developing Instructional Design Agents to Support Novice and K-12 Design Education".  2019 ASEE Annual Conference & Exposition , Tampa, Florida, 2019, June.  ASEE Conferences, 2019.  https://strategy.asee.org/32640 Internet.  23 May, 2024

\bibitem{asee_peer_32640}
  Corey T. Schimpf, Xudong Huang, Charles Xie, Zhenghui Sha, and Joyce E. Massicotte.
  "Developing Instructional Design Agents to Support Novice and K-12 Design Education".
  \emph{2019 ASEE Annual Conference \& Exposition , Tampa, Florida, 2019, June}.
  ASEE Conferences, 2019.
  https://strategy.asee.org/32640 Internet.  23 May, 2024

@INPROCEEDINGS{asee_peer_32640
author = "Corey T. Schimpf, Xudong Huang, Charles Xie, Zhenghui Sha, and Joyce E. Massicotte"
title = "Developing Instructional Design Agents to Support Novice and K-12 Design Education"
booktitle = "2019 ASEE Annual Conference \& Exposition "
year = "2019"
month = "June"
address = "Tampa, Florida"
publisher = "ASEE Conferences"
note = {https://strategy.asee.org/32640}
number = {10.18260/1-2--32640}
}

TY  - CPAPER
AB  - There is a growing interest from teachers in incorporating more engineering design into K-12 classrooms. This has been bolstered by initiatives such as the Next Generation Science Standards that push for greater integration of engineering and other STEM topics into K-12. However, there are several challenges to implementing design projects in these settings. Many K-12 teachers have limited or no experience in teaching engineering or design. Even those who have some experience teaching design, trying to oversee an entire classroom of students navigating open, complex design problems can prove to be a daunting task. This is exacerbated further for teachers with no design teaching experience. As a result, many may avoid trying design projects due to time commitments and a steep learning curves.

To encourage broader adoption of design projects in K-12 classrooms we propose virtual design agents, whose behavior is structured by one or more artificial intelligence (AI) algorithms and who are embedded in design platforms such as computer-aided-design (CAD), to assist teachers.  These design agents can engage in non-trivial design practices and strategies to help teachers in supporting and managing students’ design process as well as facilitate and scaffold students’ learning experience. By embodying design practices these agents encode some basic design intelligence such as being able to generate new designs or taking an existing design and searching for an improved design.  When employing these design agents in the classroom, there are three pedagogical decisions to consider: the role/mode of the agent, the role of the student and the interaction between them both. This tripartite model allows for the flexible creation of design scenarios to introduce and immerse students in design projects. Here mode refers to how many design agents are active in the project. For example, one design scenario could involve multiple agents who represent junior members of a design team with the student acting as the lead designer. The student may take the lead with creating an initial design and seek new designs from their design agent “team” to ideate and diverge in exploring design solutions. 

In this work, we ground the design agent and the tripartite model in our specific curriculum and platform where students design a solar farm in the CAD platform Energy3D. Design agents in Energy3D rely on several genetic algorithms, a common set of AI algorithms which involve creating several populations of design artifacts and keeping those that perform better than others, which enables them to create new designs and improve students’ designs. We discuss several possible design scenarios that can emerge from different model arrangements for this challenge and then report the results of a pilot study where students acted as mentees to an expert design agent who helped them learn about the performance of solar panel racks under several conditions. 

Design agents hold promise for augmenting teachers’ ability to run design projects and supporting novice students in engaging in such projects. In this way, they may represent a powerful approach for promoting greater adoption of design projects in K-12 settings. 

AU  - Corey T. Schimpf
AU  - Xudong Huang
AU  - Charles Xie
AU  - Zhenghui Sha
AU  - Joyce E. Massicotte
CY  - Tampa, Florida
DA  - 2019/06/15
PB  - ASEE Conferences
TI  - Developing Instructional Design Agents to Support Novice and K-12 Design Education
UR  - https://strategy.asee.org/32640
DO  - 10.18260/1-2--32640
ER  -