Research on the optimization of the training system of university faculty development centers in the context of GenAI: a comparative analysis based on Chinese and Kazakhstani universities

Abstract

Introduction: This mixed-methods field study examined generative artificial intelligence (GenAI) pedagogical readiness among university faculty in five participating universities in China and Kazakhstan, with particular attention to cross-sample differences and the effects of faculty development training. Methods: Baseline survey data were collected from 568 faculty members, including 340 from China and 228 from Kazakhstan. Readiness was assessed across seven dimensions: basic GenAI understanding, prompt design, AI-supported instructional design, AI-supported assessment, critical judgement, educational ethics, and disciplinary transfer. A quasi-experimental subsample of 160 faculty members compared structured GenAI prompt-task training with conventional GenAI-focused faculty development. Prompt logs were additionally coded to identify post-training faculty development needs. Results: = 0.041 after prior GenAI use, recent AI-related training, multilingual resource access, institutional support, perceived permission, policy clarity, risk sensitivity, and disciplinary background were included. Post-test comparisons showed greater readiness in the structured prompt-task training group than in the conventional group, particularly for prompt design and critical judgement. Prompt-design gains remained above baseline at delayed testing. Prompt evidence linked these gains to objective specification, constraint setting, assessment criteria, faculty verification, and risk-control prompts. Discussion: The findings indicate that GenAI pedagogical readiness is shaped by faculty course tasks, institutional support, resource access, and training design. Structured prompt-task training may therefore provide a useful approach to strengthening course-level faculty readiness for pedagogical uses of GenAI.

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