Abstract:
The changing requirements of the Sri Lankan IT industry continue to affect Sri Lankan graduates’
employment prospects due to an insufficient alignment between the skills of IT undergraduates
and the industry’s needs. This paper aims to fill this gap by explicitly outlining the
skill gap among IT undergraduates and industry demands, and by introducing a new AI-based
framework integrating real-time actual labour market data to facilitate adaptive and personalized
career guidance. It seeks to answer the following three research questions: (1) What
current career guidance frameworks are most critical to address? (2) What emerging technological
capabilities enable personalisation and real-time relevancy? (3) What elements ensure
the framework aligns with critical and evolving IT position requirements? A systematic literature
review and initial data-driven analysis were carried out using IEEE Xplore, ResearchGate
and Elsevier to strengthen the methodological strength and justify the design choices of the
proposed framework. Thematic analysis was applied to fifteen highly relevant studies grouped
as existing frameworks, technological solutions and present concerns. Findings indicated that
while AI-based systems improved personalisation, they lacked real-time labour market integration,
dynamic responsiveness or empirical grounding, highlighting the originality and salience
of the proposed framework’s data-based, real-time approach. Traditional systems do not rigorously
analyse competencies to create tailored, precise target-configurable recommendations.
The need for predictive analytics for this specific system, contextualised industry collaboration
and the evaluation of system-wide competencies, as well as industry-wide competencies, was
particularly noted as an area for improvement. AI-powered career guidance frameworks are
necessary, and it is proposed here that they utilise Natural Language Processing (NLP) for CV
analysis, machine learning algorithms such as Random Forest and SVM for algorithmic role assignment,
as well as continuous monitoring of the labor market. Such frameworks would aim to
provide and constantly refine tailored suggestions through responsive industry adaptation. The
next phase will include prototype creation, pilot testing, and verification through user surveys
to assess employability impact and productivity increase, addressing future work suggested by
reviewers. This work seeks to provide an adaptable and context-sensitive model to improve
decision-making in career choice, thereby enabling IT graduates to better manage the intricate
and rapidly evolving pathways of their professional lives.
