This study developed and validated an instrument to measure students’ readiness to learn about artificial intelligence (AI). The designed survey questionnaire was administrated in a school district in Beijing after an AI course was developed and implemented. The collected data and analytical results provided insights regarding the self-reported perceptions of primary students’ AI readiness and enabled the identification of factors that may influence this parameter. The results indicated that AI literacy was not predictive of AI readiness. The influences of AI literacy were mediated by the students’ confidence and perception of AI relevance. The students’ AI readiness was not influenced by a reduction in their anxiety regarding AI and an enhancement in their AI literacy. Male students reported a higher confidence, relevance, and readiness for AI than female students did. The sentiments reflected by the open-ended responses of the students indicated that the students were generally excited to learn about AI and viewed AI as a powerful and useful technology. The student sentiments confirmed the quantitative findings. The validated survey can help teachers better understand and monitor students’ learning, as well as reflect on the design of the AI curriculum and the associated teaching effectiveness.

Existing robotic water jet control methods are limited by fixed spray configurations and low adaptability to complex or dynamic environments. These constraints hinder precise targeting in three-dimensional spaces. To overcome this, we propose a reinforcement learning-based water jet control framework that achieves accurate targeting without pose or angle restrictions. Specifically, we introduce Goal-Priority Hindsight Experience Replay (GPHER), a replay strategy that integrates the principles of Hindsight Experience Replay (HER), Prioritized Experience Replay (PER), and curriculum learning. GPHER dynamically adjusts sampling priorities based on goal-space distance, guiding training from simple to complex goals. Combined with Truncated Quantile Critics (TQCs), this approach accelerates convergence and enhances success rates. Both simulation and real-world experiments validate the robustness and adaptability of the proposed method, demonstrating its effectiveness for real-time robotic fluid control.