Competency Based Education in Information Technology: Designing AI-Driven, Outcome-Focused Learning Pathways
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The rapid evolution of information technology demands graduates with verifiable, job-ready competencies rather than mere theoretical knowledge. Competency-Based Education (CBE) offers a learner-centered model where progress is determined by skill mastery rather than seat time. This paper explores integrating CBE into IT education through AI-powered adaptive learning, granular competency mapping, and authentic performance assessments. A systematic literature review across Scopus, Web of Science, IEEE Xplore, ACM Digital Library, and ERIC databases (January 2019 to March 2025) yielded 124 included studies. The study proposes a framework combining Req2XAI for explainable AI with PEARL principles (Personalized, Explainable, Actionable, Reflective, Learner-controlled) to create transparent, personalized CBE pathways. Findings reveal that adaptive learning improves outcomes by 0.35 to 0.65 standard deviations, while explainable AI enhances learning gains with effect sizes from 0.32 (knowledge recall) to 0.58 (complex problem solving). Automated assessment reduces feedback latency from days to seconds and increases student satisfaction by 25 to 30 percent. However, out-of-the-box AI agrees with human evaluators only 67 percent of the time, rising to 89 percent after rubric alignment. Teachers refuse black-box AI for high-stakes decisions, and learners prefer error-specific, actionable explanations. Per-learner AI assessment costs 12 to 18 compared to 45 to 60 for human grading, though upfront development ranges from 50,000 to 200,000. The paper concludes that AI supports CBE through adaptive personalization, real-time formative assessment, explainable competency validation, and blockchain credentialing when five design principles are followed: granular competency specification, transparent AI justification, learner control, participatory co-design, and commitment to assessment validity. Recommendations include developing competency taxonomies aligned with CC2020 and industry certifications, piloting AI assessments in low-stakes contexts, establishing co-design committees, investing in faculty AI literacy, and funding longitudinal research on graduate employment outcomes.
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