BackShowcasing Research on Technology in Teacher Education: Preservice Teachers’ Views on GenAI
,
HBGCC - 217A
Trips and Tours
Blended ContentSession description
Outline
The presenters will welcome attendees and ask some ice-breaker questions about where they are from, what institutions they represent, and where they find research (5 minutes). The presentation will then move to an exploration of the journal, its history and scope, and a showcase of some of the latest and most popular research articles published in the journal (15 minutes). The journal editors will then announce the recipient of the Best Research Paper Award and invite the authors to speak (25 minutes). A Q&A will follow (10 minutes). Attendees will be able to take sample copies of the journal from the session.
Supporting research
https://www.tandfonline.com/journals/ujdl20
Framework
Technology Acceptance Model
Methods
The researchers designed a Google Form survey to explore elementary preservice teachers’(PSTs’) perceptions of using Generative AI (GenAI) as part of an authentic literacy methods course activity. Following the activity, responses to a qualitative survey were analyzed to learn about PSTs’ experience of using GenAI in developing questions for a read-aloud.
Results
Findings indicated that many PSTs perceived GenAI as a useful teaching tool. In addition, they shared their concerns that GenAI may limit creativity and teacher agency. We also found a positive correlation between the use of GenAI in the activity and PSTs’ intentions to use GenAI in the future.
Importance
The study adds to the current literature about TAM with GenAI and underscores the value of GenAI in promoting critical reasoning among pre-service teachers.
References
Anderson, L. W., & D. R. Krathwohl (Eds.). (2001). A taxonomy for learning, teaching, and assessing: A revision of bloom’s taxonomy of educational objectives. Addison Wesley Longman, Inc.
Baidoo-Anu, D., & Owusu Ansah, L. (2023). Education in the era of generative artificial intelligence (AI): Understanding the potential benefits of ChatGPT in promoting teaching and learning. Journal of AI, 7(1), 52–62. https://doi.org/10.61969/jai.1337500
Behizadeh, N., Johnson, L. L., & Garcia, M. (2023). Invited response: Promise and Perils of GenAI in English Education: Reflections from the National Technology Leadership Summit. English Education, 56(1), 8–19. https://doi.org/10.58680/ee20235618
Bezanson, J., Karpinski, S., Shah, V., & Edelman, A. (2012). Julia: A fast dynamic language for technical computing. https://arxiv.org/abs/1209.5145.
Bloom, B. S. (1956). Taxonomy of educational objectives – The classification of educational goals – Handbook 1: Cognitive domain. Longman.
Braun, V., Clarke, V., Boulton, E., Davey, L., & McEvoy, C. (2021). The online survey as a qualitative research tool. International Journal of Social Research Methodology, 24(6), 641–654. https://doi.org/10.1080/13645579.2020.1805550
Buabeng-Andoh, C., & Baah, C. (2020). Pre-service teachers’ intention to use learning management system: An integration of UTAUT and TAM. Interactive Technology and Smart Education, 17(4), 455–474. https://doi.org/10.1108/ITSE-02-2020-0028
Chai, C. S., Lin, P. Y., Jong, M. S. Y., Dai, Y., Chiu, T. K., & Qin, J. (2021). Perceptions of and behavioral intentions towards learning artificial intelligence in primary school students. Educational Technology & Society, 24(3), 89–101.
Channa, F. R., Sarhandi, P. S. A., Bugti, F., & Pathan, H. (2021). Harnessing artificial intelligence in education for preparing learners for the 21st century. Elementary Education Online, 20(5), 3186–3186.
Presenters
Session specifications
Topic:
Audience:
Attendee devices:
Attendee device specification:
Laptop: Chromebook, Mac, PC
Tablet: Android, iOS, Windows
Subject area:
ISTE Standards:
Learner
- Stay current with research that supports improved student learning outcomes, including findings from the learning sciences.