High education institutions face significant challenges in hiring part-time faculty, a process that is often tedious and ineffective. Part-time professors struggle to find employment that aligns with their skills, experience, and preferences. This issue arises from the lack of national/international standards for profiling professors and courses, leading to ambiguity in matching faculty with appropriate courses. The importance of resolving this issue lies in its direct impact on the quality and efficiency of educational institutions. This research seeks to address the primary gap in the literature, which is the absence of an inter-institutional mechanism for the standardized profiling and matching of educators to courses in higher education. While there are governmental efforts to catalogue fields of knowledge, they rarely translate into practical, regulated applications within the educational sector. The contribution of this paper relies on developing a transdisciplinary profiling standard utilizing national and international methodologies, coupled with artificial intelligence, to create a profiling and coding algorithm. This approach is validated through quantitative analysis using actual data from a university system, including pre and post-implementation variations in key performance indicators. By standardizing and enhancing the match between educators’ skills and competencies versus course requirements, reducing costs, and increasing opportunities for educators, this research fosters a more efficient and socially responsive educational environment aligned with all stakeholders (faculty, students, accreditation bodies, and university managers, among others). It encapsulates the essence of engineering for societal advancement, improving the quality of education and creating a more dynamic and equitable academic landscape.