From Tool Literacy to Workflow Fluency: Designing AI-agent Exemplary Cases for the Professional Development of Early-career University Faculty

Ping Zhang*, Juliet Zhu
School of Computer Science, Baoji University of Arts and Sciences, Baoji 721016, China
*Corresponding email: cszhangping@bjwlxy.edu.cn
https://doi.org/10.71052/grb2025/BXJN3424

Most institutional training for early-career university teachers still treats artificial intelligence (AI) as a set of stand-alone tools to be demonstrated in one-off seminars. Teachers leave such sessions able to name a chatbot but unable to fold it into the messy, multi-step tasks that fill a working week. We report on the design, delivery, and evaluation of a different approach: a layered, scenario-based training pathway organised around reusable AI-agent exemplary cases. The cases were built on an accessible low-code stack (Coze for workflow orchestration, retrieval-augmented generation flow for retrieval-augmented question answering over local corpora, a fine-tuned Llama 3 model for domain question answering, and robotic process automation for routine office work), and were routed to participants according to a diagnostic competency profile. Using a quasi-experimental design at a regional comprehensive university in western China, we compared an experimental group that received the case-based pathway (n=45) with a comparison group that received a conventional lecture-style AI-literacy workshop (n=41) over one semester. Self-rated competency across six dimensions, task-completion time on four authentic tasks, and a technology-acceptance survey were collected, complemented by twelve semi-structured interviews. The experimental group improved substantially on every competency dimension, with the largest gains in agent and workflow construction; between-group differences on the post-test were significant after controlling for the pre-test (ANCOVA, p<0.001), and the time needed for four representative tasks fell by 55-78%. Acceptance ratings were high, and interviews pointed to a shift from isolated tool use toward thinking in workflows. We argue that exemplary cases, paired with diagnostic routing, offer a transferable template for institutions seeking to move faculty AI training beyond tool demonstrations.

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Share and Cite
Zhang, P., Zhu, J. (2026) From Tool Literacy to Workflow Fluency: Designing AI-agent Exemplary Cases for the Professional Development of Early-career University Faculty. Global Education Bulletin, 3(3), 1-9. https://doi.org/10.71052/grb2025/BXJN3424

Published

16/06/2026