Although many agree that critical thinking and problem-solving (CT/PS) are vital skills for 21st-century learners, our classrooms often prioritize memorization and procedural learning, as opposed to higher-order thinking. Given the critical role tutors play in comparing through deliberative instead of expressive student writing, this present study explores how artificial intelligence (AI) can be used to generate and exploit CT/PS via creating ITS, Socratic AI tutor, and AWE software. Underpinned by constructivist, socio-constructivist and met cognitive frames, the research employed a mix-method design with 400 students from Grades 7-10 in secondary schools. In the experimental group, a 12-week Artificial Intelligence (Challenging, Prompting, and Scaffolding) intervention was implemented that was compared against traditional instruction in the control. In terms of quantitative findings, the improvements in CT and PS after exposure to AI-CPS among students were statistically significant. ANCOVA outcomes confirmed the variation in favor of the experimental group over the control group by greater than 10 points usually and with a large significant impact (Cohen`s d=1.04). Finally, regression analysis identified Socratic prompting as the greatest predictor of CT gains, with ITS and then AWE feedback following. The correlational data also evidenced a substantial positive relationship (r = 0.62) between AI tool usage to students' achievement. These results were supplemented with qualitative findings to provide student and teacher voices. The thematic analysis resulted in six broad themes: guided reasoning, meta-cognitive growth, resolute problem solving, robust argumentation, teacher orchestration and equality of access. By comparison, students noted the importance of persistence and thoughtful routines they adopted yet their words suggested a stricter or more linear daily structure whereas teachers focused on their shifting role as the conductor over AI-driven learning. They also uncovered problems of equity, such as issues with device access and limited connectivity, inspiring innovative teacher-led solutions like offline practice packs. The study as a whole suggests that AI has the greatest impact on CT/PS when implemented within strategic pedagogy, facilitated by teacher expertise, and monitored with equity safeguards. Conclusion: The AI-CPS framework can serve as a testable model of responsible AI integration and offers practical, policy, and research implications for promoting higher-order learning across varied education settings.

Radio Frequency Identification (RFID) is a key component in automatic systems that address challenges in environment monitoring. However, tag collision continues to be an essential challenge in such applications due to high-density RFID deployments. This paper addresses the issue of RFID tag collision in large-scale intensive tags, particularly in industrial membrane contamination monitoring systems, and improves the system performance by minimizing collision rates through an innovative collision-avoiding algorithm. This research improved the Predictive Framed Slotted ALOHA–Collision Tracking Tree (PRFSCT) algorithm by cooperating probabilistic and deterministic methods through dynamic frame length adjustment and multi-branch tree processes. After simulation and validation in MATLAB R2023a, we performed a hardware test with the RFM3200 and UHFReader18 passive tags. The method’s efficiency is evaluated through collision slot reduction, delay minimization, and enhanced throughput. PRFSCT significantly reduces collision slots when the number of tags to identify is the same for PRFSCT, Framed Slotted ALOHA (FSA), and Collision Tracking Tree (CTT); the PRFSCT method needs the fewest time slots. When identifying more than 200 tags, PRFSCT has 225 collision slots for 500 tags compared to FSA and CTT, which have approximately 715 and 883 for 500 tags, respectively. It demonstrates exceptional stability and adaptability under increased density needs while improving tag reading at distances.