Using artificial intelligence teaching assistants to guide students in solar energy engineering design

References

• Afzaal, M., Nouri, J., Zia, A., Papapetrou, P., Fors, U., Weegar, R., Wu, Y., & Li, X. (2021). Explainable AI for data-driven feedback and intelligent action recommendations to support students self-regulation. Frontiers in Artificial Intelligence, 4, 723447. https://doi.org/10.3389/frai.2021.723447
   PubMedGoogle Scholar
• Al-Shaery, A. M., Khozium, M. O., Farooqi, N. S., Alshehri, S. S., & Al-Kawa, M. A. M. B. (2022). Problem solving in crowd management using heuristic approach. IEEE Access, 10, 25422–25434. https://doi.org/10.1109/ACCESS.2022.3156008
   Web of Science ®Google Scholar
• An, Y., & Mindrila, D. (2020). Strategies and tools used for learner-centered instruction. International Journal of Technology in Education and Science, 4(2), 133–143. https://doi.org/10.46328/ijtes.v4i2.74
   Google Scholar
• Bethune, K. (2020). Changing trends and rethinking geoscience education in the context of a global crisis. Geoscience Canada, 47(4), 167–169. https://doi.org/10.12789/geocanj.2020.47.164
   Web of Science ®Google Scholar
• Bergey, P. (2020). Generative well pattern design—Principles, implementation, and test on OLYMPUS challenge field development problem. Computational Geosciences, 24(6), 2079–2094. https://doi.org/10.1007/s10596-019-09912-w
   Web of Science ®Google Scholar
• Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
   Google Scholar
• Carberry, A. R., Lee, H.-S., & Ohland, M. W. (2010). Measuring engineering design self-efficacy. Journal of Engineering Education, 99(1), 71–79. https://doi.org/10.1002/j.2168-9830.2010.tb01043.x
   Web of Science ®Google Scholar
• Calvo, R. A., & Ellis, R. A. (2010). Students’ conceptions of tutor and automated feedback in professional writing. Journal of Engineering Education, 99(4), 427–438. https://doi.org/10.1002/j.2168-9830.2010.tb01072.x
   Web of Science ®Google Scholar
• Chen, Y., Jensen, S., Albert, L. J., Gupta, S., & Lee, T. (2023). Artificial intelligence (ai) student assistants in the classroom: Designing chatbots to support student success. Information Systems Frontiers, 25(1), 161–182. https://doi.org/10.1007/s10796-022-10291-4
   Web of Science ®Google Scholar
• Darvishi, A., Khosravi, H., Abdi, S., Sadiq, S., & Gašević, D. (2022 Incorporating training, self-monitoring and AI-assistance to improve peer feedback quality [Paper presentation]. Proceedings of the Ninth ACM Conference on Learning @ Scale (pp. 35–47). https://doi.org/10.1145/3491140.3528265
   Google Scholar
• Goldin, I., Narciss, S., Foltz, P., & Bauer, M. (2017). New directions in formative feedback in interactive learning environments. International Journal of Artificial Intelligence in Education, 27(3), 385–392. https://doi.org/10.1007/s40593-016-0135-7
   Web of Science ®Google Scholar
• Gigerenzer, G. (2008). Why heuristics work. Perspectives on Psychological Science: A Journal of the Association for Psychological Science, 3(1), 20–29. https://doi.org/10.1111/j.1745-6916.2008.00058.x
   PubMedGoogle Scholar
• Haupt, G. (2015). Learning from experts: Fostering extended thinking in the early phases of the design process. International Journal of Technology and Design Education, 25(4), 483–520. https://doi.org/10.1007/s10798-014-9295-7
   Web of Science ®Google Scholar
• Higgins, J. L. (1971). A new look at heuristic teaching. The Mathematics Teacher, 64(6), 487–495. https://doi.org/10.5951/MT.64.6.0487
   Google Scholar
• Hooda, M., Rana, C., Dahiya, O., Rizwan, A., & Hossain, M. S. (2022). Artificial intelligence for assessment and feedback to enhance student success in higher education. Mathematical Problems in Engineering, 2022, e5215722–19. https://doi.org/10.1155/2022/5215722
   Google Scholar
• Hughes, B. B. (1974). Heuristic teaching in mathematics. Educational Studies in Mathematics, 5(1), 291–299. https://doi.org/10.1007/BF01424551
   Google Scholar
• Lucas, J. F. (1974). The teaching of heuristic problem-solving strategies in elementary calculus. Journal for Research in Mathematics Education, 5(1), 36–46. https://doi.org/10.5951/jresematheduc.5.1.0036
   Google Scholar
• Kim, J., Merrill, K., Xu, K., & Sellnow, D. D. (2020). My teacher is a machine: Understanding students’ perceptions of AI teaching assistants in online education. International Journal of Human–Computer Interaction, 36(20), 1902–1911. https://doi.org/10.1080/10447318.2020.1801227
   Web of Science ®Google Scholar
• Kishore, P., & Kisiel, J. (2013). Exploring High School Students’ Perceptions of Solar Energy and Solar Cells. International Journal of Environmental and Science Education, 8(3), 521–534.
   Google Scholar
• Lutz, S., & Huitt, W. (2004). Connecting cognitive development and constructivism: Implications from theory for instruction and assessment. Constructivism in the Human Sciences, 9(1), 67–90.
   Google Scholar
• Massachusetts Department of Education (2006). Massachusetts science and technology/engineering curriculum framework. (Original work published 2001)
   Google Scholar
• Miller, E., Manz, E., Russ, R., Stroupe, D., & Berland, L. (2018). Addressing the epistemic elephant in the room: Epistemic agency and the next generation science standards. Journal of Research in Science Teaching, 55(7), 1053–1075. https://doi.org/10.1002/tea.21459
   Web of Science ®Google Scholar
• Mirchi, N., Bissonnette, V., Yilmaz, R., Ledwos, N., Winkler-Schwartz, A., & Maestro, R. F. D. (2020). The virtual operative assistant: An explainable artificial intelligence tool for simulation-based training in surgery and medicine. PloS One, 15(2), e0229596. https://doi.org/10.1371/journal.pone.0229596
   PubMed Web of Science ®Google Scholar
• National Research Council (2012). A framework for K-12 science education: Practices, crosscutting concepts, and core ideas. Committee on a conceptual framework for new K-12 science education standards. Board on science education, division of behavioral and social sciences and education. The National Academies Press.
   Google Scholar
• NGSS Lead States (2013). Next Generation Science Standards: For states, by states. The National Academies Press.
   Google Scholar
• Porter, B., & Grippa, F. (2020). A Platform for AI-enabled real-time feedback to promote digital collaboration. Sustainability, 12(24)Article, 10243. https://doi.org/10.3390/su122410243
   Web of Science ®Google Scholar
• Porter, S. G., Greene, K., & Esposito, M. C. K. (2021). Access and inclusion of students with disabilities in virtual learning environments: Implications for post-pandemic teaching. International Journal of Multicultural Education, 23(3)Article, 43–61. https://doi.org/10.18251/ijme.v23i3.3011
   Web of Science ®Google Scholar
• Purzer, S., Schimpf, C., Quintana-Cifuentes, J., Sereiviene, E., Lingam, I., & Jiang, R. (2022). Refine by design. The Science Teacher, 89(6), 34–39. https://doi.org/10.1080/00368555.2022.12293711
   Google Scholar
• Radford, J., Bosanquet, P., Webster, R., Blatchford, P., & Rubie-Davies, C. (2014). Fostering learner independence through heuristic scaffolding: A valuable role for teaching assistants. International Journal of Educational Research, 63, 116–126. https://doi.org/10.1016/j.ijer.2013.02.010
   Web of Science ®Google Scholar
• Riggs, E. M. (2020). Access and success in geoscience education in the age of COVID-19. ED027–05.
   Google Scholar
• Saldaña, J. (2021). The coding manual for qualitative researchers (4E [Fourth edition]). SAGE.
   Google Scholar
• Schwartz, D. L., Tsang, J. M., & Blair, K. P. (2016). The ABCs of how we learn: 26 scientifically proven approaches, how they work, and when to use them. WW Norton & Company.
   Google Scholar
• Shi, H., & Aryadoust, V. (2024). A systematic review of AI-based automated written feedback research. ReCALL, 36(2), 187–209. https://doi.org/10.1017/S0958344023000265
   Google Scholar
• Shu, D., Cunningham, J., Stump, G., Miller, S. W., Yukish, M. A., Simpson, T. W., & Tucker, C. S. (2019). 3D Design using generative adversarial networks and physics-based validation. Journal of Mechanical Design, 142(7) https://doi.org/10.1115/1.4045419
   Web of Science ®Google Scholar
• Sung, S., Jiang, R., Huang, X., & Xie, C. (2022). Panels v. Trees: Broadening the pathways of engineering education through integration with social studies. Science Scope, 45(6), 24–33. https://doi.org/10.1080/08872376.2022.12291485
   Google Scholar
• The United States Government (2021). Fact sheet: President Biden signs executive order catalyzing america’s clean energy economy through Federal Sustainability. The White House, December 13. Retrieved August 5, 2022, from https://www.whitehouse.gov/briefing-room/statements-releases/2021/12/08/fact-sheet-president-biden-signs-executive-order-catalyzing-americas-clean-energy-economy-through-federal-sustainability/
   Google Scholar
• Thornton, C. P., Ruble, K., & Jacobson, L. A. (2022). Education for children with chronic illness: Moving forward in online and virtual learning. JAMA Pediatrics, 176(4), 341–342. https://doi.org/10.1001/jamapediatrics.2021.5643
   PubMed Web of Science ®Google Scholar
• U.S. Department of Energy (2021). Solar futures study. Retrieved August 5, 2022, from https://www.energy.gov/eere/solar/solar-futures-study
   Google Scholar
• U.S. Energy Information Administration (2022). Electric power monthly - U.S. Energy Information Administration (EIA). Electric Power Monthly. Retrieved August 5, 2022, from https://www.eia.gov/electricity/monthly/epm_table_grapher.php?t=table_es1a
   Google Scholar
• van der Hoeven Kraft, K. J. (2017). Developing student interest: An overview of the research and implications for geoscience education research and teaching practice. Journal of Geoscience Education, 65(4), 594–603. https://doi.org/10.5408/16-215.1
   Google Scholar
• Vahdatikhaki, F., Salimzadeh, N., & Hammad, A. (2022). Optimization of PV modules layout on high-rise building skins using a BIM-based generative design approach. Energy and Buildings, 258, 111787. https://doi.org/10.1016/j.enbuild.2021.111787
   Web of Science ®Google Scholar
• White, R., & Gunstone, R. (2014). Probing understanding. Routledge.
   Google Scholar
• Wilson, H. J., & Daugherty, P. R. (2018). Collaborative intelligence: Humans and AI are joining forces. Harvard Business Review, 96(4), 114–123.
   Web of Science ®Google Scholar
• Wood, D., & O'Malley, C. (1996). Collaborative learning between peers: An overview. Educational Psychology in Practice, 11(4), 4–9. https://doi.org/10.1080/0266736960110402
   Google Scholar
• Xie, C., Schimpf, C., Chao, J., Nourian, S., & Massicotte, J. (2018). Learning and teaching engineering design through modeling and simulation on a CAD platform. Computer Applications in Engineering Education, 26(4), 824–840. https://doi.org/10.1002/cae.21920
   Web of Science ®Google Scholar
• Xie, C., Ding, X., Jiang, R., Santos, B. S., Magana, A. J., & Zara, J. (2023). Using computer graphics to make science visible in engineering education. IEEE Computer Graphics and Applications, 43(5), 99–106. https://doi.org/10.1109/MCG.2023.3298386
   PubMed Web of Science ®Google Scholar
• Xing, W., Li, C., Chen, G., Huang, X., Chao, J., Massicotte, J., & Xie, C. (2021). Automatic assessment of students’ engineering design performance using a bayesian network model. Journal of Educational Computing Research, 59(2), 230–256. https://doi.org/10.1177/0735633120960422
   Web of Science ®Google Scholar
• Yeager, D. S., & Walton, G. M. (2011). Social-psychological interventions in education: They’re not magic. Review of Educational Research, 81(2), 267–301. https://doi.org/10.3102/0034654311405999
   Web of Science ®Google Scholar
• Yoo, S., Lee, S., Kim, S., Hwang, K. H., Park, J. H., & Kang, N. (2021). Integrating deep learning into CAD/CAE system: Generative design and evaluation of 3D conceptual wheel. Structural and Multidisciplinary Optimization, 64(4), 2725–2747. https://doi.org/10.1007/s00158-021-02953-9
   Web of Science ®Google Scholar