The Saturation of Ease: Why Technical Competence Fails to Drive Artificial Intelligence Adoption in Agricultural Literacy
DOI:
https://doi.org/10.5032/jae.v67i3.3332Keywords:
artificial intelligence, epistemic friction, human-computer interaction, large language models (LLMs), pedagogical stewardship, secondary education, technology acceptance model, Diffusion of innovationAbstract
Generative Artificial Intelligence (AI) introduces a challenge to agricultural literacy by potentially separating the product of knowledge from the process of synthesis. Participants were a purposive sample of Alabama school-based agricultural education teachers (N = 75). The study examined whether behavioral intention to adopt AI for literacy instruction was associated more strongly with operational competency or perceived pedagogical validity. A regression analysis indicated the model explained 36.2% of the variance in behavioral intention (R² = .362, adjusted R² = .323). The findings revealed a distinction between participants’ high general comfort with technology integration (M = 4.30, SD = .76) and their comparatively lower experiential validation of AI outputs (M = 3.34, SD = .67). Pedagogical validity was the only statistically significant predictor in the final model (B = .403, SE = .108, β = .434, p < .001). Operational competency was not statistically significant after accounting for general technology integration, pedagogical validity, and experiential validation (B = .117, SE = .115, β = .141, p = .311). These findings suggest that participants’ behavioral intention to adopt AI for literacy instruction was associated more strongly with perceived pedagogical validity than with operational competency.
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