Generative AI Professional Development Needs for Teacher Educators

Abstract

This study presents findings from a professional development (PD) webinar aimed at sensitizing and gathering teacher educators’ knowledge of Generative Artificial Intelligence (GAI). The primary objective of the webinar was to deepen teacher educators’ understanding and applications of GAI within the context of teacher education in Ghana and to identify areas requiring additional development. Three hundred and seven participants from a diverse group, including teacher educators, administrators, and in-service teachers participated in the PD session. The session was conducted online via Zoom. The video and audio recordings were transcribed and analyzed thematically using MAXQDA version 2022.4. Findings indicate a diverse range of familiarity with GAI among participants. While some expressed knowledge of GAI tools, others were learning about GAI for the first time. Further, the findings showed an increasing curiosity among participants for the inspiring functions of GAI in education, such as automatic scoring, academic writing, assisting teachers with image generation for their classroom practices, etc. The participants demonstrated a willingness to include GAI in their classroom practices and support their students. However, they also identified infrastructural gaps, such as the expense of premium GAI tools, training on GAI promptings, and ethical issues such as transparency, as potential barriers to the successful implementation of GAI in teacher education. Therefore, the study suggests that institutional support should be provided to teacher educators. This support would expand their access to various GAI tools and features. The study further recommends integrating GAI, including explainable GAI and prompt engineering, as a core component of teacher education and continuous professional development programs. Additionally, it emphasizes the importance of strengthening educators' skills in innovative assessment practices.

Keywords

• Generative Artificial Intelligence (GAI)
• Professional Development
• Prompt Engineering
• Teacher Education
• Artificial Intelligence (AI)
• Assessment

References

1. Adeshola, I., & Adepoju, A. P. (2023). The opportunities and challenges of ChatGPT in education. Interactive Learning Environments, 1-14. Doi:10.1080/10494820.2023.2253858
2. Akgun, S., & Greenhow, C. (2021). Artificial intelligence in education: Addressing ethical challenges in K-12 settings. AI and Ethics, 1-10. Doi:10.1007/s43681-021-00096-7
3. Akanzire, N. B., Nyaaba, M. and Nabang, M. (2023). Perceptions and Preparedness: Exploring Teacher Educators’ Views on Integrating Generative AI in Colleges of Education, Ghana). Available at SSRN: https://ssrn.com/abstract=4628153 or Doi:10.2139/ssrn.4628153
4. Alhumaid, K., Naqbi, S., Elsori, D., & Mansoori, M. (2023). The adoption of artificial intelligence applications in education. International Journal of Data and Network Science, 7(1), 457-466.
5. Avidov-Ungar, O. (2023). The professional learning expectations of teachers in different professional development periods. Professional Development in Education, 49(1), 123-134.
6. Baidoo-Anu, D., & Ansah, L. O. (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.
7. Baeesa, S. (2020). Perception of Neurosurgery Residents and Attendings on Online Webinars During COVID19 Pandemic and Implications on Future Education. World Neurosurgery, 146, e811 – e816. Doi:10.1016/j.wneu.2020.11.015
8. Betül B. (2014). “An investigation of using video vs. audio for teaching vocabulary.” Procedia-Social and Behavioral Sciences 143: 450-457. Doi:10.1016/j.sbspro.2014.07.516

9. Bewersdorff, A., Zhai, X., Roberts, J., & Nerdel, C. (2023). Myths, mis-and preconceptions of artificial intelligence: A review of the literature. Computers and Education: Artificial Intelligence, 100143. Doi:10.1016/j.caeai.2023.100143
10. Brouwer, W., van Baal, P., van Exel, J., & Versteegh, M. (2019). When is it too expensive? Cost-effectiveness thresholds and health care decision-making. The European Journal of Health Economics, 20, 175-180.
11. Cerovski, J. (2016). The process of accepting technology innovation for rural teachers (Doctoral dissertation, Capella University).
12. Carvalho-Silva, D., García, L., Morgan, S., Brooksbank, C., & Dunham, I. (2018). Ten simple rules for delivering live distance training in bioinformatics across the globe using webinars. PloS Computational Biology, 14. Doi:10.1371/journal.pcbi.1006419.
13. Chen, L., Chen, P., & Lin, Z. (2020). Artificial Intelligence in Education: A Review. IEEE Access, 8, 75264-75278. Doi:10.1109/ACCESS.2020.2988510.
14. Chiu, T. (2021). A Holistic Approach to the Design of Artificial Intelligence (AI) Education for K-12 Schools. TechTrends, 65, 796 – 807. Doi:10.1007/s11528-021-00637-1.
15. Clarke, V., & Braun, V. (2017). Thematic analysis. The Journal of Positive Psychology, 12(3), 297-298. Doi:10.1080/17439760.2016.1262613
16. Dhirasasna, N., & Sahin, O. (2021). A system dynamics model for renewable energy technology adoption of the hotel sector. Renewable Energy, 163, 1994-2007. Doi:10.1016/j.renene.2020.10.088.
17. Emo, W. (2015). Teachers’ motivations for initiating innovations. Journal of Educational Change, 16, 171-195. Doi:10.1007/S10833-015-9243-7.
18. Floridi, L. (2023). The Ethics of Artificial Intelligence: principles, challenges, and opportunities.
19. Gbemu, L. A., Sarfo, F. K., Adentwi, K. I., & Aklassu-Ganan, E. K. K. (2020). Teacher Educators’ Self-Efficacy Beliefs and Actual Use of ICTs in Teaching in the Kumasi Metropolis. Turkish Online Journal of Educational Technology-TOJET, 19(2), 13-23.
20. Gill, S. S., Xu, M., Patros, P., Wu, H., Kaur, R., Kaur, K., … & Buyya, R. (2024). Transformative effects of ChatGPT on modern education: Emerging Era of AI Chatbots. Internet of Things and Cyber-Physical Systems, 4, 19-23. Doi:10.1016/j.iotcps.2023.06.002
21. Haenlein, M., & Kaplan, A. (2019). A brief history of artificial intelligence: On the past, present, and future of artificial intelligence. California management review, 61(4), 5-14. Doi:10.1177/0008125619864925
22. Heath, C., Hindmarsh, J., & Luff, P. (2010). Video in qualitative research. Sage Publications.
23. Herdiska, A., & Zhai, X. (in press). Artificial Intelligence-Based Scientific Inquiry. In X. Zhai & J. Krajcik (Eds.), Uses of Artificial Intelligence in STEM Education (pp. xxx-xxx). Oxford University Press.
24. Hristov, Kalin, Artificial Intelligence and the Copyright Survey (April 1, 2020). JSPG, Vol. 16, Issue 1, April 2020, Available at SSRN: https://ssrn.com/abstract=3490458 or Doi:10.2139/ssrn.3490458
25. Holzinger, A. (2019). Introduction to machine learning & knowledge extraction (make). Machine learning and knowledge extraction, 1(1), 1-20. Doi:10.3390/make1010001
26. Huber, M. (2020). Video-based content analysis. Analyzing group interactions: A guidebook for qualitative, quantitative and mixed methods, 37-48.
27. Kaplan-Rakowski, R., Grotewold, K., Hartwick, P., & Papin, K. (2023). Generative AI and Teachers’ Perspectives on Its Implementation in Education. Journal of Interactive Learning Research, 34(2), 313-338.
28. Kenny, D. (2007). Student plagiarism and professional practice. Nurse education today, 27 1, 14-8. Doi:10.1016/J.NEDT.2006.02.004.
29. Kim, J., Merrill, K., Xu, K., & Sellnow, D. (2020). My Teacher Is a Machine: Understanding Students’ Perceptions of AI Teaching Assistants in Online Education. International Journal of Human–Computer Interaction, 36, 1902 – 1911. Doi:10.1080/10447318.2020.1801227.
30. Koh, J. H. L., Chai, C. S., & Tsai, C. C. (2010). Examining the technological pedagogical content knowledge of Singapore pre‐service teachers with a large‐scale survey. Journal of Computer Assisted Learning, 26(6), 563-573. Doi:10.1111/j.1365-2729.2010.00372.x
31. Lawrence, J. E., & Tar, U. A. (2018). Factors that influence teachers’ adoption and integration of ICT in teaching/learning process. Educational Media International, 55(1), 79-105. Doi:10.1080/09523987.2018.1439712
32. Lim, W. M., Gunasekara, A., Pallant, J. L., Pallant, J. I., & Pechenkina, E. (2023). Generative AI and the future of education: Ragnarök or reformation? A paradoxical perspective from management educators. The International Journal of Management Education, 21(2), 100790.
33. Liu, M., Ren, Y., Nyagoga, L. M., Stonier, F., Wu, Z., & Yu, L. (2023). Future of education in the era of generative artificial intelligence: Consensus among Chinese scholars on applications of ChatGPT in schools. Future in Educational Research.
34. Magsamen-Conrad, K., & Dillon, J. M. (2020). Mobile technology adoption across the lifespan: A mixed methods investigation to clarify adoption stages, and the influence of diffusion attributes. Computers in Human Behavior, 112, 106456. Doi:10.1016/j.chb.2020.106456
35. Meskó, B. (2023). Prompt Engineering as an Important Emerging Skill for Medical Professionals: Tutorial. Journal of Medical Internet Research, 25, e50638. Doi:10.2196/50638
36. Mogavi, R. H., Deng, C., Kim, J. J., Zhou, P., Kwon, Y. D., Metwally, A. H. S., ... & Hui, P. (2023). Exploring user perspectives on chatgpt: Applications, perceptions, and implications for ai-integrated education. arXiv preprint arXiv:2305.13114. Doi:10.48550/arXiv.2305.13114
37. Natia, J., & Al-hassan, S. (2015). Promoting teaching and learning in Ghanaian Basic Schools through ICT. International Journal of Education and Development using ICT, 11(2).
38. Ng, D. T. K., Leung, J. K. L., Su, J., Ng, R. C. W., & Chu, S. K. W. (2023). Teachers’ AI digital competencies and twenty-first century skills in the post-pandemic world. Educational technology research and development, 71(1), 137-161. Doi:10.1007/s11423-023-10203-6
39. Opfer, V., & Pedder, D. (2011). The lost promise of teacher professional development in England. European Journal of Teacher Education, 34, 24 – 3. Doi:10.1080/02619768.2010.534131.
40. Poola, I. (2023). Overcoming ChatGPTs inaccuracies with Pre-Trained AI Prompt Engineering Sequencing Process. . International Journal of Technology and Emerging Sciences (IJTES), 3 (3), 16-19.
41. Qadir, J. (2023, May). Engineering education in the era of ChatGPT: Promise and pitfalls of generative AI for education. In 2023 IEEE Global Engineering Education Conference (EDUCON) (pp. 1-9). IEEE. Doi:10.1109/EDUCON54358.2023.10125121.
42. Ravhuhali, F., Kutame, A. P., & Mutshaeni, H. N. (2015). Teachers’ perceptions of the impact of continuing professional development on promoting quality teaching and learning. International Journal of Educational Sciences, 10(1), 1-7. Doi:10.1080/09751122.2015.11890332
43. Rowland, D. R. (2023). Two frameworks to guide discussions around levels of acceptable use of generative AI in student academic research and writing. Journal of Academic Language and Learning, 17(1), T31-T69.
44. Rudolph, J., Tan, S., & Tan, S. (2023). ChatGPT: Bullshit spewer or the end of traditional assessments in higher education?. Journal of Applied Learning and Teaching, 6(1).
45. Samek, W., & Müller, K. R. (2019). Towards explainable artificial intelligence. Explainable AI: interpreting, explaining and visualizing deep learning, 5-22.
46. Sancar, R., Atal, D., & Deryakulu, D. (2021). A new framework for teachers’ professional development. Teaching and Teacher Education, 101, 103305. Doi:10.1016/j.tate.2021.103305
47. Stenberg, P. (2017). The purchase of Internet subscriptions in Native American households. Telecommunications Policy, 42, 51-60. Doi:10.1016/J.TELPOL.2017.08.003.
48. Simhadri, N., & Swamy, T. N. V. R. (2023). Awareness among teaching on AI and ML applications based on fuzzy in education sector at USA. Soft Computing, 1-9. Doi:10.1007/s00500-023-08329-z
49. Topor, D., & Budson, A. (2020). Twelve tips to present an effective webinar. Medical Teacher, 42, 1216 – 1220. Doi:10.1080/0142159x.2020.1775185.
50. Tounsi, A., Elkefi, S., & Bhar, S. L. (2023). Exploring the Reactions of Early Users of ChatGPT to the Tool using Twitter Data: Sentiment and Topic Analyses. In 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET) (pp. 1-6). IEEE.
51. Thorp, H. H. (2023). ChatGPT is fun, but not an author. Science, 379(6630), 313-313. Doi:10.1126/science.adg7879
52. Wang, S. K., Hsu, H. Y., Reeves, T. C., & Coster, D. C. (2014). Professional development to enhance teachers’ practices in using information and communication technologies (ICTs) as cognitive tools: Lessons learned from a design-based research study. Computers & Education, 79, 101-115. Doi:10.1016/j.chb.2004.02.005
53. Whalen, J., & Mouza, C. (2023). ChatGPT: Challenges, Opportunities, and Implications for Teacher Education. Contemporary Issues in Technology and Teacher Education, 23(1), 1-23.
54. Wong, S., Lim, S., & Quinlan, K. (2016). Integrity in and Beyond Contemporary Higher Education: What Does it Mean to University Students? Frontiers in Psychology, 7. Doi:10.3389/fpsyg.2016.01094.
55. Zhang, H. (2021). Exploring Automated Essay Scoring Models for Multiple Corpora and Topical Component Extraction from Student Essays (Doctoral dissertation, University of Pittsburgh).
56. Zhai, X., & Krajcik, J. (2022). Pseudo AI Bias. In arXiv preprint. Doi:10.48550/arXiv.2210.08141
57. Zhai, X., Shi, L., & Nehm, R. H. (2021). A meta-analysis of machine learning-based science assessments: Factors impacting machine-human score agreements. Journal of Science Education and Technology, 30, 361-379. Doi:10.1007/s10956-020-09875-z
58. Zhai, X. (2023). Chatgpt for next generation science learning. XRDS: Crossroads, The ACM Magazine for Students, 29(3), 42-46.
59. Zhai, X. (2022). ChatGPT user experience: Implications for education. Available at SSRN 4312418.
60. Zerfass, A., Hagelstein, J., & Tench, R. (2020). Artificial intelligence in communication management: a cross-national study on adoption and knowledge, impact, challenges and risks. Journal of Communication Management, 24(4), 377-389.