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Background: Contemporary multi-protocol networks necessitate scalability, reliability, energy efficiency, and security due to the increasing number of devices and the diversification of network traffic. Conventional network management methods are inadequate to meet these demands, necessitating sophisticated solutions. Artificial intelligence (AI) has emerged as a significant field, offering advanced methods including predictive maintenance, anomaly detection, and intelligent resource management. Objective: This article aims to critically evaluate the effectiveness, flexibility, and productivity of AI-based applications in addressing major challenges in network management, including performance, scalability, energy consumption, threat detection rates, and cost. Methods: The study employs simulations and modeled datasets to assess AI-oriented solutions across various network environments, such as industrial IoT, smart cities, and telecommunications. The evaluation encompasses factors including Mean Time Between Failure (MTBF), resource utilization, delay minimization, and operating cost reduction. Digital twins, intelligent routing algorithms, and self-attention-based anomaly detection models are utilized, and the overall performance of these integrated technologies is analyzed. Results: The analysis demonstrates that AI-powered systems achieve near-optimal performance across all evaluated indicators. Specifically, the Manufacturing and Automotive Knowledge (MAK) sector observed a 52% increase in MTBF, the Banking, Financial Services, and Insurance (BFSI) sector noted a 32.39% improvement in energy efficiency, and the Defense and Public Enterprise (DPE) sector experienced a 94% increase in advanced threat detection. Conclusion: The findings indicate that AI solutions can effectively address many of the challenges present in current networks, offering cost-efficient and secure methods for implementing new communication networks with vast potential. Nonetheless, further empirical research is necessary to generalize these results and validate their applicability in real-world scenarios.

Progress in International Reading Literacy Study (PIRLS) is an international assessment that measures the reading literacy of fourth-grade students (aged 9-10 years old). PIRLS aims to evaluate and compare the reading abilities of students across different countries. It assesses how well students can understand and interpret written texts, which is fundamental to their overall educational development. In this study, psychometric analyses were run on a portion of the multiple-choice items of PIRLS 2016 taken by 4th graders in the USA. The 3PL item response theory model was utilized to examine the test. Discrimination, difficulty, and guessing parameters were estimated along with the fit values, reliability, item characteristic curves, and item-person map. M2, CFI, TLI, and RMSE statistics showed that the test is reliable and the model, overall, fits the data. Item fit statistics outfit and infit showed that most of the items fit the 3PL model. Findings showed that while all the items have acceptable discrimination values, two items have unacceptable guessing parameters. Examination of the ICCs showed that graphical displays are important, in addition to numerical values, for examining item quality. Item-person map showed that items do not target the whole ability scale.