摘要
Journal of Engineering EducationVolume 112, Issue 3 p. 578-582 GUEST EDITORIAL Envisioning the future of learning and teaching engineering in the artificial intelligence era: Opportunities and challenges Muhsin Menekse, Corresponding Author Muhsin Menekse [email protected] orcid.org/0000-0002-5547-5455 School of Engineering Education, Purdue University, West Lafayette, Indiana, USA Department of Curriculum and Instruction, Purdue University, West Lafayette, Indiana, USA Correspondence Muhsin Menekse, School of Engineering Education, Purdue University, West Lafayette, IN, USA. Email: [email protected]Search for more papers by this author Muhsin Menekse, Corresponding Author Muhsin Menekse [email protected] orcid.org/0000-0002-5547-5455 School of Engineering Education, Purdue University, West Lafayette, Indiana, USA Department of Curriculum and Instruction, Purdue University, West Lafayette, Indiana, USA Correspondence Muhsin Menekse, School of Engineering Education, Purdue University, West Lafayette, IN, USA. Email: [email protected]Search for more papers by this author First published: 20 June 2023 https://doi.org/10.1002/jee.20539Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat REFERENCES Akgun, S., & Greenhow, C. (2022). Artificial intelligence in education: Addressing ethical challenges in K–12 settings. AI Ethics, 2, 431–440. https://doi.org/10.1007/s43681-021-00096-7 Alevin, V., McLaughlin, E. A., Glenn, R. A., & Koedinger, K. R. (2016). Instruction based on adaptive learning technologies. In R. E. Mayer & P. A. Alexander (Eds.), Handbook of research on learning and instruction (pp. 522–560). Routledge. ISBN: 113883176X. Balakrishnan, B., & Long, C. Y. (2020). An effective self-directed personalized learning environment for engineering students during the COVID-19 pandemic. Advances in Engineering Education, 8(4), 1–8. Bearman, M., Boud, D., & Ajjawi, R. (2020). New directions for assessment in a digital world. In M. Bearman, P. Dawson, R. Ajjawi, J. Tai, & D. Boud (Eds.), Re-imagining university assessment in a digital world (pp. 7–18). The enabling power of assessment (Vol. 7). Springer. https://doi.org/10.1007/978-3-030-41956-1_2 Chase, C. C., Chin, D. B., Oppezzo, M. A., & Schwartz, D. L. (2009). Teachable agents and the Protégé effect: Increasing the effort towards learning. Journal of Science Education and Technology, 18(4), 334–352. https://doi.org/10.1007/s10956-009-9180-4 Chi, M. T. H., Siler, S. A., Jeong, H., Yamauchi, T., & Hausmann, T. (2001). Learning from human tutoring. Cognitive Science, 25(4), 471–533. https://doi.org/10.1016/S0364-0213(01)00044-1 Chou, C. Y., & Chan, T. W. (2016). Reciprocal tutoring: Design with cognitive load sharing. International Journal of Artificial Intelligence in Education, 26, 512–535. Dawani, S. (2023). Integrating artificial intelligence into creativity education: Developing a creative problem-solving course for higher education. Creative Studies Graduate Student Master's Projects. https://digitalcommons.buffalostate.edu/creativeprojects/363 Duran, D., & Topping, K. J. (2017). Learning by teaching: Evidence-based strategies to enhance learning in the classroom. Routledge. Estrada, M., Burnett, M., Campbell, A. G., Campbell, P. B., Denetclaw, W. F., Gutiérrez, C. G., Hurtado, S., John, G. H., Matsui, J., McGee, R., Okpodu, C. M., Robinson, T. J., Summers, M. F., Werner–Washburn, M., & Zavala, M. (2016). Improving underrepresented minority student persistence in STEM. CBE—Life Sciences Education, 15(3), es5. https://doi.org/10.1187/cbe.16-01-0038 Fan, X., Luo, W., Menekse, M., Litman, D., & Wang, J. (2015). Course MIRROR: enhancing large classroom instructor-student interactions via mobile interfaces and natural language processing. Proceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA '15) (pp. 1473–1478). Association for Computing Machinery. https://doi.org/10.1145/2702613.2732853 Huang, K. (2023). Alarmed by AI chatbots, universities start revamping how they teach. New York Times. https://www.nytimes.com/2023/01/16/technology/chatgpt-artificial-intelligence-universities.html Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Kasneci, E., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., … Kasneci, G. (2023). Chat GPT for good? on opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274 Koretsky, M. D., Amatore, D., Barnes, C., & Kimura, S. (2008). Enhancement of student learning in experimental design using a virtual laboratory. IEEE Transactions on Education, 51(1), 76–85. Leelawong, K., & Biswas, G. (2008). Designing learning by teaching agents: The Betty's brain system. International Journal of Artificial Intelligence in Education, 18(3), 181–208. Luo, W., Fan, X., Menekse, M., Wang, J., & Litman, D. (2015). Enhancing instructor-student and student-student interactions with mobile interfaces and summarization. Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations (pp. 16–20, May 31 – June 5, 2015). https://doi.org/10.3115/v1/N15-3004 Menekse, M. (2020). The reflection-informed learning and instruction to improve students' academic success in undergraduate classrooms. The Journal of Experimental Education, 88(2), 183–199. https://doi.org/10.1080/00220973.2019.1620159 Menekse, M., Anwar, S., & Akdemir, Z. G. (2022). How do different reflection prompts affect engineering students' academic performance and engagement? The Journal of Experimental Education, 90(2), 261–279. Menekse, M., Anwar, S., & Purzer, S. (2018). Self-Efficacy and mobile learning technologies: A case study of course MIRROR. In C. Hodges (Ed.), Self-efficacy in instructional technology contexts. Springer. https://doi.org/10.1007/978-3-319-99858-9_4 Menekse, M., Stump, G., Krause, S., & Chi, M. T. H. (2013). Differentiated overt learning activities for effective instruction in engineering classrooms. Journal of Engineering Education, 102(3), 346–374. https://doi.org/10.1002/jee.20021 Miller, R. L., Streveler, R. A., Yang, D., & Santiago Román, A. I. (2011). Identifying and repairing student misconceptions in thermal and transport science: Concept inventories and schema training studies. Chemical Engineering Education, 45(3), 203–210. Mollick, E. R., & Mollick, L. (2023). Using AI to implement effective teaching strategies in classrooms: five strategies, including prompts (March 17, 2023). Available at SSRN: https://ssrn.com/abstract=4391243 or https://doi.org/10.2139/ssrn.4391243 Nie, J., Yuan, Y., Chao, X., Li, Y., & Lv, L. (2023). In smart classroom: Investigating the relationship between human–computer interaction, cognitive load and academic emotion. International Journal of Human–Computer Interaction. https://doi.org/10.1080/10447318.2023.2190257 Nikolic, S., Daniel, S., Haque, R., Belkina, M., Hassan, G. M., Grundy, S., Lyden, S., Neal, P., & Sandison, C. (2023). ChatGPT versus engineering education assessment: A multidisciplinary and multi-institutional benchmarking and analysis of this generative artificial intelligence tool to investigate assessment integrity. European Journal of Engineering Education. https://doi.org/10.1080/03043797.2023.2213169 OpenAI. (2023). GPT-4 technical report. https://arxiv.org/pdf/2303.08774.pdf Reeves, S. M., & Crippen, K. J. (2021). Virtual laboratories in undergraduate science and engineering courses: A systematic review, 2009–2019. Journal of Science Education and Technology, 30, 16–30. Roscoe, R. D., & Chi, M. T. (2007). Understanding tutor learning: Knowledge-building and knowledge-telling in peer tutors' explanations and questions. Review of Educational Research, 77(4), 534–574. Streveler, R., & Menekse, M. (2017). Taking a closer look at active learning. Journal of Engineering Education, 106(2), 186–190. https://doi.org/10.1002/jee.20160 Sun, L., Wei, M., Sun, Y., Suh, Y. J., Shen, L., & Yang, S. (2023). Smiling women pitching down: Auditing representational and presentational gender biases in image generative AI. arXiv preprint arXiv:2305.10566. Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257–285. U.S. Department of Education. (2023). Artificial intelligence and future of teaching and learning: Insights and recommendations. Office of Educational Technology. https://tech.ed.gov/ai-future-of-teaching-and-learning/ Van Merrienboer, J. J., & Sweller, J. (2005). Cognitive load theory and complex learning: Recent developments and future directions. Educational Psychology Review, 17, 147–177. Walker, E., Rummel, N., & Koedinger, K. R. (2015). Adaptive intelligent support to improve peer tutoring in algebra. International Journal of Artificial Intelligence in Education, 24, 33–61. https://doi.org/10.1007/s40593-013-0001-9 Volume112, Issue3July 2023Pages 578-582 ReferencesRelatedInformation