Artificial intelligence in education: Perspectives of secondary teachers

James R. Lindner; Christopher A. Clemons; Jason D. McKibben

doi:10.37433/aad.v7i2.637

Authors

James R. Lindner Auburn University, Alabama, USA https://orcid.org/0000-0002-1448-3846
Christopher A. Clemons Auburn University, Alabama, USA https://orcid.org/0000-0001-9879-0888
Jason D. McKibben Auburn University, Alabama, USA https://orcid.org/0000-0003-2080-202X

DOI:

https://doi.org/10.37433/aad.v7i2.637

Keywords:

agricultural technology, educator attitudes, professional development, technology adoption, technology integration challenges

Abstract

This descriptive and correlational study investigated the adoption of generative artificial intelligence (AI) by agricultural educators in Alabama, focusing on their perceptions, attitudes, and experiences (N = 80). Grounded by Rogers’ diffusion of innovation theory and Davis’s technology acceptance model, a mixed-mode survey design was used to assess educators’ awareness, perceived benefits, competencies, and barriers to AI adoption. Findings revealed a significant experiential divide across all measured themes, where early-career educators (with ≤5 years of experience) reported significantly higher awareness, perceived benefits, competence, and optimism about overcoming barriers than experienced educators (with ≥ 15 years of experience). The primary barrier to adoption was a shared and uniform high level of concern regarding the pedagogical and ethical implications of AI. This contrast suggests that the central challenge is a pedagogical adoption gap between educators’ operational skills and their deeper apprehension of reconciling AI with their professional identity. This study confirms prior research on technology adoption while identifying a novel ethical barrier associated with the use of generative AI in agricultural education. The findings support the recommendation of differentiated approaches to enhance the confidence of experienced educators and reinforce the ethical best practices for early-career educators.

Downloads

Download data is not yet available.

References

Afzal, A., Khan, S., Daud, S., Ahmad, Z., & Butt, A. (2023). Addressing the digital divide: Access and use of technology in education. Journal of Social Sciences Review, 3(2), 883–895. https://doi.org/10.54183/jssr.v3i2.326 DOI: https://doi.org/10.54183/jssr.v3i2.326

Bartlett, J. E., II, Kotrlik, J. W., & Higgins, C. C. (2001). Organizational research: Determining appropriate sample size in survey research. Information Technology, Learning, and Performance, 19(1). 43. https://www.opalco.com/wp-content/uploads/2014/10/Reading-Sample-Size1.pdf

Card, S. K., Moran, T. P., & Newell, A. (1983). The Psychology of Human-Computer Interaction. Lawrence Erlbaum Associates.

Croom, D. B., Yopp, A. M., Edgar, D., Roberts, R., Jagger, C., Clemons, C., McKibbon, J., McCubbins, O. P., & Wagner, J. (2023). Technical professional development needs of agricultural education teachers in the southeastern United States by career pathway. Journal of Southern Agricultural Education Research, 73, 1–15. http://jsaer.org/2023/04/10/technical-professional-development-needs-of-agricultural-education-teachers-in-the-southeastern-united-states-by-career-pathway/

Davis, F. D. (1985). A technology acceptance model for empirically testing new end-user information systems: Theory and results [Doctoral dissertation, Massachusetts Institute of Technology].

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008 DOI: https://doi.org/10.2307/249008

Dillman, D. A., Smyth, J. D., & Christian, L. M. (2014). Internet, phone, mail, and mixed mode surveys: The tailored design method (4th ed.). John Wiley & Sons. DOI: https://doi.org/10.1002/9781394260645

Emerson, C., Myers, B. E., Bunch, J. C., & Dossett, J. (2024). Determining collaboration between University of Florida Institute of Food and Agricultural Sciences Extension agents and secondary school-based agricultural educators. Advancements in Agricultural Development, 5(4), 1–13. https://doi.org/10.37433/aad.v5i4.469 DOI: https://doi.org/10.37433/aad.v5i4.469

Field, A. (2024). Discovering statistics using IBM SPSS statistics, North American Edition (6th ed.). Sage.

Fuerst, W. L., & Cheney, P. H. (1982). Concepts, theory, and techniques: Factors affecting the perceived utilization of computer‐based decision support systems in the oil industry. Decision Sciences, 13(4), 554–569. https://doi.org/10.1111/j.1540-5915.1982.tb01182.x DOI: https://doi.org/10.1111/j.1540-5915.1982.tb01182.x

Google. (2025, October 15). Gemini API release notes. Google for AI Developers. https://ai.google.dev/gemini-api/docs/changelog

Grace, K., Stewart, H., Sandkühler, J. F., Thomas, S., Weinstein-Raun, B., & Brauner, J. (2024). Thousands of AI authors on the future of AI. arXiv. https://doi.org/10.48550/arXiv.2401.02843 DOI: https://doi.org/10.1613/jair.1.19087

Hancock, G., Hancock, C., & McKibben, J. (2024). The teaching techniques of Alabama's traditionally and alternatively certified school-based agricultural education instructors. Journal of Agricultural Education, 65(3), 18–34. https://doi.org/10.5032/jae.v65i3.166 DOI: https://doi.org/10.5032/jae.v65i3.166

Harder, A. M. (2009). Characteristics and barriers impacting the diffusion of e-extension among Texas Cooperative Extension County Extension agents [Doctoral dissertation, Texas A&M University]. https://hdl.handle.net/1969.1/ETD-TAMU-1408

Hill, J., & Reimer, T. (2024). Technology as a tool to address educational inequities: Practices implemented during the COVID-19 pandemic that have been sustained. Education and Information Technologies, 29(9), 10879–10898. https://doi.org/10.1007/s10639-023-12236-z DOI: https://doi.org/10.1007/s10639-023-12236-z

Hoffman, R., & Beato, G. (2025). Superagency: What could possibly go right with our AI future? Authors Equity.

Kelly, S., Kaye, S. A., & Oviedo-Trespalacios, O. (2023). What factors contribute to the acceptance of artificial intelligence? A systematic review. Telematics and Information, (77), 101925. https://doi.org/10.1016/j.tele.2022.101925 DOI: https://doi.org/10.1016/j.tele.2022.101925

King, R. N., & Rollins, T. (1995). Factors influencing the adopters and nonadopters. Journal of Agricultural Education, 36(4), 39–48. https://doi.org/10.5032/jae.1995.04039 DOI: https://doi.org/10.5032/jae.1995.04039

Kline, P. (1999). The handbook of psychological testing (2nd ed.). Routledge.

Kotrlik, J. W., & Redmann, D. H. (2009). A trend study: Technology adoption in the teaching-learning process by secondary agriscience teachers-2002 and 2007. Journal of Agricultural Education, 50(2), 62–74. https://doi.org/10.5032/jae.2009.02062 DOI: https://doi.org/10.5032/jae.2009.02062

Lai, P. P. (2013, May). Efficient and effective information finding on small screen devices. In Proceedings of the 10th International Cross-Disciplinary Conference on Web Accessibility (pp. 1–10). https://doi.org/10.1145/2461121.2461127 DOI: https://doi.org/10.1145/2461121.2461127

Liehner, G. L., Biermann, H., Hick, A., Brauner, P., & Ziefle, M. (2023). Perceptions, attitudes and trust towards artificial intelligence—An assessment of the public opinion. Artificial Intelligence and Social Computing, 72(72), 32–41. https://doi.org/10.54941/ahfe1003271 DOI: https://doi.org/10.54941/ahfe1003271

Lindner, J. R. (2002). Handling of nonresponse error in the Journal of International Agricultural and Extension Education. Journal of International Agricultural and Extension Education, 9(3), 55–60. https://www.researchgate.net/profile/Ismet-Boz-3/publication/271247374_Does_Early_Adoption_Affect_Farmers'_Use_of_the_Extension_Service/links/5b44ae1da6fdcc661914822c/Does-Early-Adoption-Affect-Farmers-Use-of-the-Extension-Service.pdf#page=57 DOI: https://doi.org/10.5191/jiaee.2002.09307

Lindner, J. R., & Lindner, N. (2024). Interpreting Likert-type, summated, unidimensional, and attitudinal scales: I neither agree nor disagree, Likert or not. Advancements in Agricultural Development, 5(2), 152–163. https://doi.org/10.37433/aad.v5i2.351 DOI: https://doi.org/10.37433/aad.v5i2.351

Malik, A. (2024). The future of AI: Predicting technological advancements and their societal impact. Frontiers in Artificial Intelligence Research, 1(3), 467–493. https://sprcopen.org/index.php/FAIR/article/view/49/47

McKibben, J. D., Clemons, C. A., & Blythe, J. M. (2025). Survey response rates by requestor’s characteristics. Advancements in Agricultural Development, 6(1), 43–54. https://doi.org/10.37433/aad.v6i1.525 DOI: https://doi.org/10.37433/aad.v6i1.525

McKibben, J. D., Holler, K., Clemons, C., & Lindner, J. (2023). Locus of control and pedagogy in skill-based agricultural mechanics. NACTA Journal, 67, 257–261. https://doi.org/10.56103/nactaj.v67i1.88 DOI: https://doi.org/10.56103/nactaj.v67i1.88

Murphrey, T. P., & Dooley, K. E. (2000). Perceived strengths, weaknesses, opportunities, and threats impacting the diffusion of distance education technologies in a college of agriculture and life science. Journal of Agricultural Education, 41(4), 39–50. https://doi.org/10.5032/jae.2000.04039 DOI: https://doi.org/10.5032/jae.2000.04039

Murphrey, T. P., Miller, K. A., & Roberts, T. G. (2009). Agricultural science and technology teachers' perceptions of iPod and MP3 technology integration into curricular and cocurricular Activities. Journal of Agricultural Education, 50(4), 110–119. https://doi.org/10.5032/jae.2009.04110 DOI: https://doi.org/10.5032/jae.2009.04110

Nambisan, S., & Wang, Y. M. (2000). Web technology adoption and knowledge barriers. Journal of Organizational Computing and Electronic Commerce, 10(2), 129–147. https://doi.org/10.1207/S15327744JOCE1002_4 DOI: https://doi.org/10.1207/S15327744JOCE1002_4

Pilisuk, M., Parks, S. H., & Hawkes, G. (1987). Public perception of technological risk. The Social Science Journal, 24(4), 403–413. https://doi.org/10.1016/0362-3319(87)90056-5 DOI: https://doi.org/10.1016/0362-3319(87)90056-5

Rogers, E. M. (1962). Diffusion of innovations. Free Press.

Ross, S. M., Morrison, G. R., & Lowther, D. L. (2010). Educational technology research past and present: Balancing rigor and relevance to impact school learning. Contemporary Educational Technology, 1(1), 17–35. https://dergipark.org.tr/en/pub/cet/issue/25719/271396 DOI: https://doi.org/10.30935/cedtech/5959

Selwyn, N. (2021). Education and technology: Key issues and debates. Bloomsbury Publishing. DOI: https://doi.org/10.5040/9781350145573

Sevnarayan, K., & Potter, M. A. (2024). Generative artificial intelligence in distance education: Transformations, challenges, and impact on academic integrity and student voice. Journal of Applied Learning and Teaching, 7(1), 104–114. https://doi.org/10.37074/jalt.2024.7.1.41 DOI: https://doi.org/10.37074/jalt.2024.7.1.41

Smith, H. E., Stair, K. S., Blackburn, J. J., & Easley, M. (2018). Is there an app for that? Describing smartphone availability and educational technology adoption level of Louisiana school-based agricultural educators. Journal of Agricultural Education, 59(1), 238–254. https://doi.org/10.5032/jae.2018.01238 DOI: https://doi.org/10.5032/jae.2018.01238

Tan, M., & Teo, T. S. H. (1998). Factors influencing the adoption of the Internet. International Journal of Electronic Commerce, 2(3), 5–18. https://doi.org/10.1080/10864415.1998.11518312 DOI: https://doi.org/10.1080/10864415.1998.11518312

Vaske, J. J., Beaman, J., & Sponarski, C. (2017). Rethinking internal consistency in Cronbach's alpha. Leisure Sciences, 39(2), 163–173. https://doi.org/10.1080/01490400.2015.1127189 DOI: https://doi.org/10.1080/01490400.2015.1127189

Verčič, A. T., Verčič, D., Čož, S., & Špoljarić, A. (2024). A systematic review of digital internal communication. Public Relations Review, 50(1), 102400. https://doi.org/10.1016/j.pubrev.2023.102400 DOI: https://doi.org/10.1016/j.pubrev.2023.102400

Williams, M. D., Rana, N. P., & Dwivedi, Y. K. (2015). The unified theory of acceptance and use of technology (UTAUT): A literature review. Journal of Enterprise Information Management, 28(3), 443–488. https://doi.org/10.1108/JEIM-09-2014-0088 DOI: https://doi.org/10.1108/JEIM-09-2014-0088

Williams, M. R., Warner, W. J., Flowers, J. L., & Croom, D. B. (2014). Teaching with technology: North Carolina agriculture teachers' knowledge acquisition, attitudes, and identified barriers. Journal of Agricultural Education, 55(5), 1–15. https://doi.org/10.5032/jae.2014.05001 DOI: https://doi.org/10.5032/jae.2014.05001

Willis, J., Thompson, A., & Sadera, W. (1999). Research on technology and teacher education: Current status and future directions. Educational Technology Research and Development, 47(4), 29–45. https://doi.org/10.1007/BF02299596 DOI: https://doi.org/10.1007/BF02299596

Zhu, Y., Huang, Z., Dou, Z., & Wen, J. R. (2025). One token can help! Learning scalable and pluggable virtual tokens for retrieval-augmented large language models. In Proceedings of the AAAI Conference on Artificial Intelligence, 39(24), 26166–26174. https://doi.org/10.1609/aaai.v39i24.34813 DOI: https://doi.org/10.1609/aaai.v39i24.34813