Artificial intelligence (AI) knowledge generation between acceptance and rejection as a tool to enhance project based learning and professors’ performance in private higher education sector in Egypt

Abstract

This study aims to test the effectiveness of AI (Artificial Intelligence), which took a new turn after ChatGPT as a tool for the social sustainability of academics in the Egyptian private higher education sector. Digitalization reflects the intensity of artificial intelligence usage in enhancing the performance of professors and its reflection on their quality of life. Moreover, the degree of facilitation and progress can provide educators with the best educational experience they can provide to students. This study relies on two theories and their backgrounds. The first is the theory of project-based learning as a tool for enhancing the quality of education using AI. The second is Martec’s Law, which is a derivation of the law of accelerating returns. Two main assumptions are addressed in this study, the first is: Using artificial intelligence as a tool that can facilitate, enhance, and provide a variety of ways for professors to engage their students online and in class. The second is based on measuring the degree of effectiveness and performance advancement seen by professors in their social sustainability. Enhanced experience of the students will be measured by their rates of attendance and engagement. The amount of impact on project-based learning is going to be measured by the degree of reliance of professors on digital learning methods and their reliance on using artificial intelligence in constructing them. Data will be provided by professors through a constructed survey. The professor’s social sustainability will be measured by quality time saved and related career advancement. Data collection depends on testing faculty members at 4 private universities in the greater Cairo area. A cross-sectional survey was conducted on a single shot in time. Results showed that we accepted the hypotheses and that there is a strong relevance between the variables.

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