Job Performance of Agricultural Professionals in AI-Enabled Agricultural Extension Services: A Descriptive Assessment of Five Performance Components

Abstract

Artificial intelligence (AI) and digital technologies are increasingly influencing agricultural extension by improving access to information, facilitating communication, and supporting professional decision-making. The present study evaluated the job performance of 243 agricultural professionals across five functional dimensions: knowledge building and communication, help seeking, knowledge updating and facilitation, need assessment, and technology development. Based on the reported mean scores, knowledge updating and facilitation emerged as the highest-performing domain (14.01), followed by knowledge building and communication (13.04) and help seeking (12.49). Comparatively lower mean scores were recorded for need assessment (11.60) and technology development (10.76). Overall, more than half of the professionals (55.14%) exhibited a medium level of job performance, while 27.16% demonstrated a high level and 17.70% a low level of performance. The findings suggest that agricultural professionals are relatively stronger in updating their knowledge and facilitating access to information and technologies, whereas greater attention is required for systematic need assessment and active involvement in technology development. Strengthening practical training, field-level exposure, participatory need assessment, and collaboration among extension professionals, researchers, farmers, and technology providers could further enhance job performance in AI-enabled agricultural extension.