Towards the Integration of Generative AI into the Cooperative Extension Service: Insights for Guiding Professional Development
DOI:
https://doi.org/10.5032/jae.v67i2.3302Keywords:
Gen AI, Cooperative Extension Service, Extension professionals, utilization, professional development, TOEAbstract
Generative artificial intelligence (Gen AI) can enhance the efficiency, productivity and accessibility of Cooperative Extension Service’ (CES) work, yet empirically grounded guidance for its ethical and effective utilization remains limited. Guided by the Technology-Organization-Environment (TOE) framework, this study employed a three-round electronic Delphi technique involving six CES professionals experienced in Gen AI applications, to build consensus on CES-appropriate Gen AI tools, compatible Extension tasks, training and resource needs, barriers to utilization, and best practices. Within the technological context, Copilot, ChatGPT, and Gemini (Advanced) were identified as the most suitable Gen AI tools for Extension work. These tools were perceived as most effective in supporting audio-to-text and text-to-audio conversion, administrative writing tasks, and idea generation for research, Extension programming, and answering clientele questions. The organizational context revealed key barriers: limited ability to blend professional expertise with AI-generated content, low Gen AI/digital literacy, and concerns about overreliance on AI undermining critical thinking. Correspondingly, priority training and resource needs emphasized ethical AI use, evaluating the reliability of AI-generated content, distinguishing research-based information from AI outputs, prompt engineering, and institutional AI policy awareness. In the environmental context, the absence of clear AI policies and unclear accountability for AI-generated content emerged as critical constraints. Seven best practices, emphasizing ethical use, transparency, critical thinking, fact-checking, and effective prompting, were identified to guide responsible Gen AI utilization. The findings offer Extension leaders and educators evidence-based insights for designing Gen AI professional development and drafting AI policy guidelines that support responsible utilization, and advance organizational AI utilization literature.
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Al Hadwer, A., Tavana, M., Gillis, D., & Rezania, D. (2021a). A systematic review of organizational factors impacting Cloud-based technology adoption using Technology-Organization-Environment Framework. Internet of Things, 15, 100407. https://doi.org/10.1016/j.iot.2021.100407
Al Hadwer, A., Tavana, M., Gillis, D., & Rezania, D. (2021b). A Systematic review of organizational factors impacting Cloud-based technology adoption using Technology-Organization-Environment framework. Internet of Things, 15, 100407. https://doi.org/10.1016/j.iot.2021.100407
AlAli, R., Wardat, Y., Al-Saud, K., & Alhayek, K. A. (2024). Generative AI in Education: Best practices for successful implementation. International Journal of Religion, 5(9), 1016–1025. https://doi.org/10.61707/pkwb8402
Albishri, N., Rai, J. S., Attri, R., Yaqub, M. Z., & Walsh, S. T. (2025). Breaking barriers: Investigating generative AI adoption and organizational use. Journal of Enterprise Information Management, 1–22. https://doi.org/10.1108/JEIM-01-2025-0010
Alhur, A. A. (2025). Redefining healthcare with Artificial Intelligence (AI): The contributions of ChatGPT, Gemini, and Co-pilot. Cureus, 4(16). https://doi.org/10.7759/cureus.57795
Awa, H. O., Ojiabo, O., & Orokor, L. (2017). Integrated technology-organization-environment (T-O-E) taxonomies for technology adoption. Journal of Enterprise Information Management, 30, 00–00. https://doi.org/10.1108/JEIM-03-2016-0079
Bahir, D., Zur, O., Attal, L., Nujeidat, Z., Knaanie, A., Pikkel, J., Mimouni, M., & Plopsky, G. (2025). Gemini AI vs. ChatGPT: A comprehensive examination alongside ophthalmology residents in medical knowledge. Graefe’s Archive for Clinical and Experimental Ophthalmology = Albrecht Von Graefes Archiv Fur Klinische Und Experimentelle Ophthalmologie, 263(2), 527–536. https://doi.org/10.1007/s00417-024-06625-4
Baker, J. (2012). The Technology–Organization–Environment framework. In Y. K. Dwivedi, M. R. Wade, & S. L. Schneberger (Eds.), Information Systems Theory: Explaining and Predicting Our Digital Society, Vol. 1 (pp. 231–245). Springer. https://doi.org/10.1007/978-1-4419-6108-2_12
Beiderbeck, D., Frevel, N., von der Gracht, H. A., Schmidt, S. L., & Schweitzer, V. M. (2021). Preparing, conducting, and analyzing Delphi surveys: Cross-disciplinary practices, new directions, and advancements. MethodsX, 8, 101401. https://doi.org/10.1016/j.mex.2021.101401
Brynjolfsson, E., Li, D., & Raymond, L. (2025). Generative AI at work. The Quarterly Journal of Economics, 140(2), 889–942. https://doi.org/10.1093/qje/qjae044
Chatzoglou, P., & Chatzoudes, D. (2016). Factors affecting e-business adoption in SMEs: An empirical research. Journal of Enterprise Information Management, 29(3), 327–358. https://doi.org/10.1108/JEIM-03-2014-0033
Chin, S., & Kimberg, D. (2024). Text-to-audio conversion using LLMs to preserve character identification. Defensive Publications Series. https://www.tdcommons.org/dpubs_series/7267
Chiu, T. K. F. (2023). The impact of Generative AI (GenAI) on practices, policies and research direction in education: A case of ChatGPT and Midjourney. Interactive Learning Environments, 0(0), 1–17. https://doi.org/10.1080/10494820.2023.2253861
Cho, H.-Y., Yang, H.-C., & Hwang, B.-J. (2023). The effect of ChatGPT factors & innovativeness on switching intention: Using Theory of Reasoned Action (TRA). Journal of Distribution Science, 21(8), 83–96. https://doi.org/10.15722/jds.21.08.202308.83
Collins, P. D., Hage, J., & Hull, F. M. (1988). Organizational and technological predictors of change in automaticity. Academy of Management Journal, 31(3), 512–543. https://doi.org/10.2307/256458
Deanna, S. (2025). Extension’s Guiding Principles for Artificial Intelligence. https://kb.wisc.edu/extension/151383
DeCamp, M., & Tilburt, J. C. (2019). Why we cannot trust artificial intelligence in medicine. The Lancet Digital Health, 1(8). https://doi.org/10.1016/S2589-7500(19)30197-9
Delbecq, A. L., Van de Ven, A. H., & Gustafson, D. H. (1975). Group techniques for program planning: A guide to nominal group and Delphi processes. Glenview IL, Scott Foresman and Company. https://library.wur.nl/WebQuery/titel/347478
Diaz, J., Chaudhary, A. K., Jayaratne, K. S. U., & Assan, E. (2020). Expanding evaluator competency research: Exploring competencies for program evaluation using the context of non-formal education. Evaluation and Program Planning, 79, 101790. https://doi.org/10.1016/j.evalprogplan.2020.101790
Diaz, J. M., Jayaratne, K. S. U., & Kumar Chaudhary, A. (2020). Evaluation competencies and challenges faced by early career extension professionals: Developing a competency model through consensus building. The Journal of Agricultural Education and Extension, 26(2), 183–201. https://doi.org/10.1080/1389224X.2019.1671204
Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., Albanna, H., Albashrawi, M. A., Al-Busaidi, A. S., Balakrishnan, J., Barlette, Y., Basu, S., Bose, I., Brooks, L., Buhalis, D., … Wright, R. (2023). Opinion Paper: “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642
Ehrhardt, M. (2025, February 13). The Human Touch: Why AI Will Never Fully Replace Human Creativity. Medium. https://medium.com/@milanehr/the-human-touch-why-ai-will-never-fully-replace-human-creativity-0159fd508834
Einarsson, H., Lund, S. H., & Jónsdóttir, A. H. (2024). Application of ChatGPT for automated problem reframing across academic domains. Computers and Education: Artificial Intelligence, 6, 100194. https://doi.org/10.1016/j.caeai.2023.100194
Evaluators are interpreters. What about ChatGPT? | Eval Forward. (n.d.). Retrieved April 1, 2024, from https://www.evalforward.org/blog/evaluators-chatgpt
Fu, X., Wang, R., & Li, C. (2023). Can ChatGPT evaluate plans? Journal of the American Planning Association, 0(0), 1–12. https://doi.org/10.1080/01944363.2023.2271893
Garbuio, M., & Lin, N. (2021). Innovative idea generation in problem finding: Abductive reasoning, cognitive impediments, and the promise of artificial intelligence. Journal of Product Innovation Management, 38(6), 701–725. https://doi.org/10.1111/jpim.12602
Gargon, E., Crew, R., Burnside, G., & Williamson, P. R. (2019). Higher number of items associated with significantly lower response rates in COS Delphi surveys. Journal of Clinical Epidemiology, 108, 110–120. https://doi.org/10.1016/j.jclinepi.2018.12.010
Hashimy, L., Jain, G., & Grifell-Tatjé, E. (2023). Determinants of blockchain adoption as decentralized business model by Spanish firms–an innovation theory perspective. Industrial Management & Data Systems, 123(1), 204–228. https://doi.org/10.1108/IMDS-01-2022-0030
Hasson, F., Keeney, S., & McKenna, H. (2025). Revisiting the Delphi technique - research thinking and practice: A discussion paper. International Journal of Nursing Studies, 168, 105119. https://doi.org/10.1016/j.ijnurstu.2025.105119
Hernández, N. C. (2024). Adoption and adaptation of generative artificial intelligence in organizations: Actions for efficient and responsible use in interaction with collaborators. International Journal of Current Science Research and Review, 7(03), 1940–1947.
Hill, P., & Narine, L. (2023). Ensuring responsible and transparent use of generative AI in Extension. The Journal of Extension, 61(2). https://doi.org/10.34068/joe.61.02.13
Hill, P., Narine, L., & Miller, A. (2024). Prompt engineering principles for Generative AI use in Extension. The Journal of Extension, 62(3). https://open.clemson.edu/joe/vol62/iss3/20
Hoang, T. D. L., Nguyen, H. K., & Nguyen, H. T. (2021). Towards an economic recovery after the COVID-19 pandemic: Empirical study on electronic commerce adoption of small and medium enterprises in Vietnam. Management & Marketing, 16(1), 47–68. https://doi.org/10.2478/mmcks-2021-0004
Jadhav, D., Agrawal, S., Jagdale, S., Salunkhe, P., & Salunkhe, R. (2024). AI-Driven Text-to-Multimedia Content Generation: Enhancing Modern Content Creation. 2024 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), 1610–1615. https://doi.org/10.1109/I-SMAC61858.2024.10714771
Janokar, S., Ratnaparkhi, S., Rathi, M., & Rathod, A. (2023). Text-to-Speech and Speech-to-Text Converter—Voice Assistant. In V. Suma, P. Lorenz, & Z. Baig (Eds.), Inventive Systems and Control (pp. 653–664). https://doi.org/10.1007/978-981-99-1624-5_48
Jeblick, K., Schachtner, B., Dexl, J., Mittermeier, A., Stüber, A. T., Topalis, J., Weber, T., Wesp, P., Sabel, B. O., Ricke, J., & Ingrisch, M. (2024). ChatGPT makes medicine easy to swallow: An exploratory case study on simplified radiology reports. European Radiology, 34(5), 2817–2825. https://doi.org/10.1007/s00330-023-10213-1
Jeong, D., Aggarwal, S., Robinson, J., Kumar, N., Spearot, A., & Park, D. S. (2023). Exhaustive or exhausting? Evidence on respondent fatigue in long surveys. Journal of Development Economics, 161, 102992. https://doi.org/10.1016/j.jdeveco.2022.102992
Kadaruddin, K. (2023). Empowering education through Generative AI: Innovative instructional strategies for tomorrow’s learners. International Journal of Business, Law, and Education, 4(2). https://doi.org/10.56442/ijble.v4i2.215
Kakara, A., & Masambuka-Kanchewa, F. (2025). Revisiting the role of agricultural Extension as a technology transfer model: Insights from students’ reflections. Journal of Agricultural Education, 66(3), 5–5. https://doi.org/10.5032/jae.v66i3.2939
Kavadella, A., Dias da Silva, M. A., Kaklamanos, E. G., Stamatopoulos, V., & Giannakopoulos, K. (2024). Evaluation of ChatGPT’s real-life implementation in undergraduate dental education: Mixed methods study. JMIR Medical Education, 10. https://doi.org/10.2196/51344
Kearney, A., Williamson, P., Young, B., Bagley, H., Gamble, C., Denegri, S., Muir, D., Simon, N. A., Thomas, S., Elliot, J. T., Bulbeck, H., Crocker, J. C., Planner, C., Vale, C., Clarke, M., Sprosen, T., & Woolfall, K. (2017). Priorities for methodological research on patient and public involvement in clinical trials: A modified Delphi process. Health Expectations, 20(6), 1401–1410. https://doi.org/10.1111/hex.12583
Kpodo, J., Kordjamshidi, P., & Nejadhashemi, A. P. (2024). AgXQA: A benchmark for advanced Agricultural Extension question answering. Computers and Electronics in Agriculture, 225, 109349. https://doi.org/10.1016/j.compag.2024.109349
Kuşcu, O., Pamuk, A. E., Sütay Süslü, N., & Hosal, S. (2023). Is ChatGPT accurate and reliable in answering questions regarding head and neck cancer? Frontiers in Oncology, 13. https://doi.org/10.3389/fonc.2023.1256459
Lawani, A., de Koff, J., Muriuki, J., & Ihuhwa, A.-L. (2025). Determinants of knowledge and usage of generative artificial intelligence in agricultural Extension: Evidence from Tennessee Extension personnel. Agribusiness, n/a(n/a). https://doi.org/10.1002/agr.70025
Lee, I., & Perret, B. (2022). Preparing high school teachers to integrate AI methods into STEM classrooms. Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 12783–12791. https://doi.org/10.1609/aaai.v36i11.21557
Ma, X., & Huo, Y. (2023). Are users willing to embrace ChatGPT? Exploring the factors on the acceptance of chatbots from the perspective of AIDUA framework. Technology in Society, 75, 102362. https://doi.org/10.1016/j.techsoc.2023.102362
Mahakittikun, T., Suntrayuth, S., & Bhatiasevi, V. (2020). The impact of technological-organizational-environmental (TOE) factors on firm performance: Merchant’s perspective of mobile payment from Thailand’s retail and service firms. Journal of Asia Business Studies, 15(2), 359–383. https://doi.org/10.1108/JABS-01-2020-0012
Malik, S., Chadhar, M., Vatanasakdakul, S., & Chetty, M. (2021). Factors affecting the organizational adoption of blockchain technology: Extending the Technology–Organization–Environment (TOE) framework in the Australian context. Sustainability, 13(16), 9404. https://doi.org/10.3390/su13169404
Martin, K. (2019). Designing ethical algorithms. MIS Quarterly Executive, 18(2). https://aisel.aisnet.org/misqe/vol18/iss2/5
Martin, N., Fischer, D. A., Kerpedzhiev, G. D., Goel, K., Leemans, S. J. J., Röglinger, M., van der Aalst, W. M. P., Dumas, M., La Rosa, M., & Wynn, M. T. (2021). Opportunities and challenges for process mining in organizations: Results of a Delphi study. Business & Information Systems Engineering, 63(5), 511–527. https://doi.org/10.1007/s12599-021-00720-0
Mensah, E. A., & Osman, N. (2024). Integrating Generative AI tools into Extension Program Planning and Evaluation | Mississippi State University Extension Service. Mississippi State University Extension. https://extension.msstate.edu/publications/integrating-generative-ai-tools-extension-program-planning-and-evaluation
Moore, J. (2023, October 9). AI guidance and best practices—Marketing | NC State Extension. Marketing. https://marketing.ces.ncsu.edu/ai-guidance/
Nasa, P., Jain, R., & Juneja, D. (2021). Delphi methodology in healthcare research: How to decide its appropriateness. World Journal of Methodology, 11(4), 116–129. https://doi.org/10.5662/wjm.v11.i4.116
Nguyen, T. H., Le, X. C., & Vu, T. H. L. (2022). An Extended Technology-Organization-Environment (TOE) Framework for online retailing utilization in digital transformation: Empirical evidence from Vietnam. Journal of Open Innovation: Technology, Market, and Complexity, 8(4), 200. https://doi.org/10.3390/joitmc8040200
Okoli, C., & Pawlowski, S. D. (2004). The Delphi method as a research tool: An example, design considerations and applications. Information & Management, 42(1), 15–29. https://doi.org/10.1016/j.im.2003.11.002
Pehrson, S., Sorensen, T. J., Wood, M. J., Burrows, M. S., & Furse, J. (2025). Examining AI perceptions and utilization among agricultural educators in Utah. Journal of Agricultural Education, 66(3), 2–2. https://doi.org/10.5032/jae.v66i3.2910
Perez Perez, J. E. (2024). The application of Gen-AI and creativity in the context of public education in frontier environments. Journal of Enabling Technologies, 18(4), 223–231. https://doi.org/10.1108/JET-05-2024-0030
Plätke, O., & Geibel, R. C. (2024). The Use of Artificial Intelligence for Idea Generation in the Innovation Process. In R. C. Geibel & S. Machavariani (Eds.), Digital Management to Shape the Future (pp. 181–198). https://doi.org/10.1007/978-3-031-66517-2_14
Prasad Agrawal, K. (2024). Towards adoption of Generative AI in organizational settings. Journal of Computer Information Systems, 64(5), 636–651. https://doi.org/10.1080/08874417.2023.2240744
Prasad, K. D. V., & De, T. (2024). Generative AI as a catalyst for HRM practices: Mediating effects of trust. Humanities and Social Sciences Communications, 11(1), 1362. https://doi.org/10.1057/s41599-024-03842-4
Prestegaard-Wilson, J., & Vitale, J. (2024). Generative artificial intelligence in extension: A new era of support for livestock producers. Animal Frontiers, 14(6), 57–59. https://doi.org/10.1093/af/vfae024
Ray, P. P. (2023). ChatGPT: A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope. Internet of Things and Cyber-Physical Systems, 3, 121–154. https://doi.org/10.1016/j.iotcps.2023.04.003
Renieris, E. M., Kiron, D., & Mills, S. (2024, September 24). Artificial Intelligence Disclosures Are Key to Customer Trust. MIT Sloan Management Review. https://sloanreview.mit.edu/article/artificial-intelligence-disclosures-are-key-to-customer-trust/
Sabarre, N. R., Beckmann, B., Bhaskara, S., & Doll, K. (2023). Using AI to disrupt business as usual in small evaluation firms. New Directions for Evaluation, 2023(178–179), 59–71. https://doi.org/10.1002/ev.20562
Sætra, H. S. (2023). Generative AI: Here to stay, but for good? Technology in Society, 75, 102372. https://doi.org/10.1016/j.techsoc.2023.102372
Salamzadeh, A., Ashkani, M., & Asgharifar, M. H. (2025). The impact of AI adoption on organizational performance: Ethical and technological factors in Iran’s startup ecosystem. Strategy & Leadership, 1–30. https://doi.org/10.1108/SL-04-2025-0091
Salman, H., Aliif, M., Ibrahim, R., & Mahmood, J. (2026). Prioritizing benefits, costs, and contextual and individual factors in researchers’ adoption of generative artificial intelligence: A multi-criteria decision-making analysis. PeerJ Computer Science, 12. https://doi.org/10.7717/peerj-cs.3417
Salman, I. M., Ameer, O. Z., Khanfar, M. A., & Hsieh, Y.-H. (2025). Artificial intelligence in healthcare education: Evaluating the accuracy of ChatGPT, Copilot, and Google Gemini in cardiovascular pharmacology. Frontiers in Medicine, 12, 1495378. https://doi.org/10.3389/fmed.2025.1495378
Sankar, B., & Sen, D. (2025). A novel idea generation tool using a structured conversational AI (CAI) system. AI EDAM, 39. https://doi.org/10.1017/S089006042500006X
Schilke, O., & Reimann, M. (2025). The transparency dilemma: How AI disclosure erodes trust. Organizational Behavior and Human Decision Processes, 188, 104405. https://doi.org/10.1016/j.obhdp.2025.104405
Schmidt, R. C. (1997). Managing Delphi surveys using nonparametric statistical techniques. Decision Sciences, 28(3), 763–774. https://doi.org/10.1111/j.1540-5915.1997.tb01330.x
Schwedux, T. (2024, August 16). What is ChatGPT and why is it so popular? Medium. https://medium.com/@t.schwedux/what-is-chatgpt-and-why-is-it-so-popular-f1e053225811
Shang, Z. (2023). Use of Delphi in health sciences research: A narrative review. Medicine, 102(7). https://doi.org/10.1097/MD.0000000000032829
Shoja, M. M., Van de Ridder, J. M. M., & Rajput, V. (2023). The emerging role of Generative Artificial Intelligence in medical education, research, and practice. Cureus, 15(6). https://doi.org/10.7759/cureus.40883
Srivastava, A. (2025, April 3). Copilot VS ChatGPT VS Gemini—Here we goooo…. Medium. https://arpitsrivstva.medium.com/copilot-vs-chatgpt-vs-gemini-here-we-goooo-28ea2bea73ef
Sun, J., Ma, H., Zeng, Y., Han, D., & Jin, Y. (2022). Promoting the AI teaching competency of K-12 computer science teachers: A TPACK-based professional development approach. Education and Information Technologies, 28(2), 1509–1533. https://doi.org/10.1007/s10639-022-11256-5
Tajudeen, F. P., Jaafar, N. I., & Ainin, S. (2018). Understanding the impact of social media usage among organizations. Information & Management, 55(3), 308–321. https://doi.org/10.1016/j.im.2017.08.004
Teferi, B., Omar, M., Jeyakumar, T., Charow, R., Gillan, C., Jardine, J., Mattson, J., Dhalla, A., Kocak, S. A., Salhia, M., Davies, B., Clare, M., Younus, S., & Wiljer, D. (2024). Accelerating the appropriate adoption of Artificial Intelligence in health care: Prioritizing IDEA to champion a collaborative educational approach in a stressed system. Education Sciences, 14(1). https://doi.org/10.3390/educsci14010039
Tornatzky, L., & Fleischer, M. (1990). The process of technology innovation, Lexington, MA. Lexington books.
Wang, W., Wang, Y., Chen, L., Ma, R., & Zhang, M. (2024). Justice at the Forefront: Cultivating felt accountability towards Artificial Intelligence among healthcare professionals. Social Science & Medicine, 347, 116717. https://doi.org/10.1016/j.socscimed.2024.116717
Warner, L. A., Israel, G. D., & Diaz, J. M. (2019). Identifying and meeting the needs of Extension’s target audiences. EDIS, 2019(3). https://doi.org/10.32473/edis-wc336-2019
Yusuf, A., Pervin, N., & Román-González, M. (2024). Generative AI and the future of higher education: A
threat to academic integrity or reformation? Evidence from multicultural perspectives.
International Journal of Educational Technology in Higher Education, 21(1), 21.
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