Artificial Intelligence and Building Information Modeling Software Tools Research Paper

My discipline is Networking and I have chosen two articles related to the modern technology. The first article is written by Benzaid and Taleb (2020) published in IEEE Network, and it examines how artificial intelligence influence 5G network. The second article is by Zhang et al. (2021) and it analyzes the interoperability of BIM software tools and addresses the problems in the process of data exchange. It was published in Computer Applications in Engineering Education journal. In addition to researchers, the audience of the first article is data security agents as it provides some insights on how the use of artificial intelligence to 5G can be venerable to external attacks. The second article’s audience is educators in engineering education as it discusses about the method of teaching engineering. This paper examines articles of Benzaid and Taleb (2020) and Zhang et al. (2021).

To begin with, there is a need to understand the content of each article. Both articles are scientific and made by the use of quantitative and qualitative research methods. Benzaid and Taleb (2020) warn the audience that to fully reap the benefits of 5G, it is critical to develop robust and long-lasting security mechanisms that can deal with the ever-changing threat landscape. They suggest that traditional security measures are not enough. This is because of given the growing number of vulnerabilities, the sophistication of cyber threats, the high volume of traffic, and the diverse technologies (e.g., SDN, NFV) and services that will shape next-generation wireless networks. According to Benzaid and Taleb (2020), a new security measure that needs to be considers is the adoption of Artificial Intelligence (AI). They describe AI as a method that would enable intelligent, adaptive, and autonomous security management, allowing for prompt and cost-effective detection and mitigation of security threats. Their description provides an impression that AI is a promising direction. Moreover, Benzaid and Taleb (2020) illustrate AI’s positive aides, such as its ability to identify hidden patterns in a vast set of time-varying multi-dimensional data that allow faster and more accurate decision-making.

With regards to the second article of Zhang et al. (2021), it reports a case study of the development of the new capstone project for engineering major students. It is based on team-based learning (TBL) combined with the 360-degree evaluation feedback method to increase students’ BIM competency. Data is collected and analyzed using a mix of qualitative and quantitative methodologies in order to assess students’ learning outcomes and BIM competency. The findings show that TBL, when combined with 360-degree feedback in the capstone project, can significantly improve graduates’ BIM expertise. This research examines the interoperability of BIM software platforms, data sharing issues, and recommendations for improving the course and BIM team collaboration. Compared to the first article, the second one is based on a study and has significant evidence to support their claim. The article of Benzaid and Taleb (2020) appeals to the audience by considering all potential security scenarios and analyzing specific aspects of AI and 5G network.

The study of Zhang et al. (2021) discovered how students’ professional capabilities may be increased through a capstone project, and educators can use the BIM course to build engineers that closely match industry needs. The paper makes a case for using the capstone project to help engineering students improve and cultivate their BIM proficiency in MEP systems. This research established a new paradigm for using TBL and 360-degree feedback in engineering education. In terms of the support for each article’s claims, Zhang et al. (2021) is more reliable as it has a specific case.

In terms of the format of both articles, they are both heavily theoretical, meaning that there are numerous citations and the use of previous literature. Benzaid and Taleb (2020) provide images of how AI and 5G work, interpreting their explanation. It was done to make easier understanding for the audience. For example, they illustrate mapping of the adversarial ML attacks to the ML5G high-level architecture. Meanwhile, Zhang et al. (2021) provide descriptive statistics and t test results as a table while making a comprehensive analysis of the data. As such, it can be assumed that both articles rely on logos rather than pathos as they are written scientifically, meaning that they are based on facts. In both articles, there was also a logical progression of ideas and claims that were supported by a great variety of numerical facts and evidence.

The importance of AI in encouraging security in 5G and beyond networks was highlighted in Benzaid and Taleb (2020) article. Meanwh