Volume 13(1) - Advancing 21st-Century Professional Competencies with Learning Analytics in the Age of Generative AI

Oct 16, 2026

Uploaded by Aurelia

The rise of generative artificial intelligence (GenAI) and accelerated globalization requires higher education to recalibrate toward domain-agnostic, 21st-century professional competencies. Despite strong institutional intent, curriculum integration and assessment practices remain fragmented, creating a gap between academic success metrics and workforce readiness evidence. This special issue brings five studies on how learning analytics (LA) and GenAI can connect institutional claims to demonstrated readiness. Together, they benchmark LLMs for curricular-alignment, design Socratic-style GenAI scaffolds, quantify competencies via psychometrics and Latent Profile Analysis, and address curriculum analytics governance and adoption. The issue calls for process-sensitive assessment and concludes with sociotechnical infrastructure needs for responsible, sustainable enterprise implementation.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages6

Reading Time15 min

AccessFree to download

Abstract

Introduction and Motivation

Notes for Practice

Volume 13(1), 1–6 . [https://doi.org/10.18608/jla.2026.9743](https://doi.org/10.18608/jla.2026.9743) Advancing 21st-Century Professional Competencies with Learning Analytics in the Age of Generative AI Abhinava Barthakur 1 , Olga Viberg2 , René F. Kizilcec3 , Ryan S. Baker4 and Shane Dawson5 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 processsensitive 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. | Notes for Practice
• Conventional metrics of academic success often obscure the nuanced, non-linear development of 21st century professional competencies.
• The synergy GenAI and LA offers transformative ways to capture student skills development, but their application must be grounded in human-centred design, ethical governance, and robust data-privacy protections.
• Transitioning LA from isolated research prototypes to enterprise-wide infrastructure requires robust governance aligned with professional accreditation standards and a sustained focus on building sociotechnical capacity among educators, administrators, and learners. | | --- | | Keywords: Curriculum analytics, generative artificial intelligence, learning analytics, 21st-century professional competencies, workforce readiness
Submitted: 28/02/2026—Accepted: 22/03/2025—Published: 30/03/2026 | [Corresponding author](Corresponding author1Email: abhinava.barthakur@adelaide.edu.au Address: School of Education)[1](Corresponding author1Email: abhinava.barthakur@adelaide.edu.au Address: School of Education)[Email:](Corresponding author1Email: abhinava.barthakur@adelaide.edu.au Address: School of Education)[ ](Corresponding author1Email: abhinava.barthakur@adelaide.edu.au Address: School of Education)[abhinava.barthakur@adelaide.edu.au](Corresponding author1Email: abhinava.barthakur@adelaide.edu.au Address: School of Education)[ Address: School of Education](Corresponding author1Email: abhinava.barthakur@adelaide.edu.au Address: School of Education), College of Education, Behavioural and Social Sciences, Adelaide University, 61—68 North Terrace, Adelaide SA5005, Australia. ORCID iD: [https://orcid.org/0000-0002-2437-6892](https://orcid.org/0000-0002-2437-6892) 2Email: [oviberg@kth.se](oviberg@kth.se) Address: Division of Media Technology and Interaction Design, Department of Human-Centered Technology, School of Electrical Engineering and C

FAQs

What does the issue recommend for responsible and sustainable implementation?

It advocates a shift toward process-sensitive assessments and emphasizes sociotechnical infrastructure, including robust governance and interdisciplinary collaboration, to transition AI innovations from prototypes to enterprise-grade systems.

Oct 16, 2026

Uploaded by Aurelia