A Data-Driven Needs Analysis of the E-SIS Competency System for Integrating AI into POLYCC-TVET Talent Management

Implementing Artificial Intelligence (AI) in talent management systems has become an urgent force behind enhancing the workforce capacity, especially in the Jabatan Pendidikan Politeknik dan Kolej Komuniti - Technical and Vocational Education and Training (POLYCC-TVET) ecosystems that are being digitalized. This paper provides a quantitative needs analysis of the e-SIS competency system in order to assess the system’s being ready, needing, and able to integrate AI into assisting with talent analytics among POLYCC-TVET educators. The study used a quantitative survey design to analyse seven (7) important constructs, which include system efficiency, data and infrastructure preparedness, competency analytics capability, support of leadership development, acceptance of AI/ML integration, individual literacy and technical skills, and perceived effect of AI-enabled talent management (used 144 lecturers and training officers in polytechnics and community colleges). Empirical strength was ensured with the use of descriptive analytics, competency gap analysis and reliability testing (Cronbach’s Alpha=0.898-0.973). The results show that the general level of agreement is 85.3%, which means that institutions are highly prepared to start adopting AI. The highest were the system efficiency (95.3%), AI acceptance (98.2%), and the perceived AI impact (95.2%) as it reflects trust in the system’s possibility to improve the precision of competency evaluation and information-driven decision-making. Nevertheless, gaps were found in standardised competency taxonomies, API interoperability, and personal technical literacy and particularly scripting and model interpretation (62.2%). These points indicate the necessity of well-organized AI capacity-building programmes, better data governance and system interoperability to use AI-enabled competency analytics to their full capacity. This research gives a substantial evidence-based foundation to the creation of an AI-based POLYCC-TVET talent management model and offers policymakers, system developers and institutional leaders information on how to modernise talent governance in national TVET systems.

Artificial Intelligence; POLYCC-TVET; Talent Management; Competency Analytics; Competency Systems.

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