arXiv:2608. 03584v1 Announce Type: new Abstract: Artificial intelligence (AI) is rapidly transforming high-skilled domains, requiring higher education institutions (HEI) to balance the teaching of foundational principles with the integration of emerging tools to ensure workforce readiness.
arXiv:2607. 08748v1 Announce Type: new Abstract: In this study, we present a large-scale descriptive analysis of the use of an AI-based learning assistant (Syntea) in higher education.
arXiv:2607. 12296v1 Announce Type: cross Abstract: With the increased use of generative AI (GenAI) applications such as ChatGPT, higher education institutions (HEIs) have released a range of guidelines and policies to direct adoption within their institutions.
arXiv:2609.14638v1 Announce Type: cross Abstract: This paper is an encore submission of our 2026 journal article "Expertise and Information Seeking in the Age of Generative AI: New Procedures, New Pr...
arXiv:2606. 00040v1 Announce Type: cross Abstract: As Generative AI (GenAI) becomes integral to education, fostering GenAI literacy is critical.
arXiv:2608. 16016v1 Announce Type: cross Abstract: Generative Artificial Intelligence (GenAI) can produce high-quality essays, code, and design artefacts, challenging the validity of conventional assessments that rely on single-point submissions and product-only grading.
The study examines how practitioners in AI-driven systems define, assess, and manage data quality, revealing six key themes. It highlights shifts in traceability, the use of models as quality assessors, and the emergence of new data objects such as agent context and synthetic data. The research proposes a lifecycle assurance framework to provide evidence that data supports specific AI claims throughout model behavior, judgments, and agent actions.
arXiv:2606. 00038v1 Announce Type: cross Abstract: Artificial intelligence (AI) literacy is increasingly recognized as a foundational competency for all university graduates.
The paper investigates how large language models (LLMs) can perform multi-coder qualitative coding by independently coding, debating, and reconciling disagreements. It quantifies the effectiveness of this approach across diverse datasets, identifying key factors—such as codebook length, data similarity, and agent disagreement—that influence coding accuracy. The study finds that intense, unresolved debates improve accuracy but that LLMs still lack adaptive responsiveness to context, leading to design recommendations for automated coding systems.
arXiv:2607. 28650v1 Announce Type: cross Abstract: While industry discourse often emphasizes immediate productivity gains and frames GenAI primarily as a tool for automation, the integration of GenAI into system administration may involve deeper shifts in professional practice that are not yet fully understood.
arXiv:2608. 12104v1 Announce Type: cross Abstract: The increasing deployment of autonomous, agentic AI systems challenges traditional accountability mechanisms.
Transparency has become a central principle in AI governance, yet there is little agreement about what transparency means, for whom, and in what contexts. Drawing on interpretive flexibility within th...