professional-competencies
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- PaperJournal of Learning Analytics30 Mar 2026
Advancing 21st-Century Professional Competencies with Learning Analytics in the Age of Generative AI
Abhinava Barthakur, Olga Viberg, René F. Kizilcec, Ryan S. Baker et al.
This special issue presents five studies exploring how learning analytics and generative AI can bridge the gap between institutional commitment to 21st-century professional competencies and actual workforce readiness. The studies benchmark large language models for curricular alignment, design Socratic-style chatbots for scaffolding self-regulated learning, use psychometric modeling to quantify competencies, and address governance of curriculum analytics. The collection advocates for process-sensitive assessments that capture dynamic learner capability over static metrics.
Original abstract
The rise of generative artificial intelligence (GenAI) and accelerated globalization have necessitated a fundamental recalibration of higher education to prioritize domain-agnostic, 21st-century professional competencies. While institutional commitment to these skills is high, their systematic integration into the curriculum and evaluation remains fragmented, highlighting a critical gap between traditional academic success metrics and demonstrated workforce readiness. This special issue presents five complementary studies that investigate how the intersection of learning analytics (LA) and GenAI can bridge the gap between institutional rhetoric and demonstrated professional readiness. The contributions collectively advance a research agenda across four dimensions: 1) benchmarking large language models (LLMs) for curricular-competency alignment using reasoning-based prompting, 2) the iterative design of Socratic-style GenAI chatbots to scaffold self-regulated learning, 3) the application of psychometric modelling and Latent Profile Analysis to quantify 21st-century professional competencies, and 4) institutional governance and adoption of curriculum analytics. Collectively, these studies advocate for an epistemological shift toward process-sensitive assessments that move beyond static, episodic indicators toward dynamic, longitudinal representations of learner capability. We conclude by outlining the sociotechnical infrastructure, including robust governance and interdisciplinary collaboration, required to responsibly transition these AI-driven innovations from research prototypes to sustainable enterprise infrastructure, ensuring that analytics serve the evolving needs of students, educators, and professional bodies.