Learning desk

MultiAgent EDU StackGather good sources. Teach what matters.
T3Mermaid to ASCII art (mermaid-ascii)T3Kimi K3, and what we can still learn from the pelican benchmarkT3Firefox in WebAssemblyT3Spot birds not golfT3[AINews] Kimi K3 2.8T-A50B: the largest open model ever released; Opus 4.8-class at Sonnet 5 pricingT1From physical surfaces to human-centric heat stress: LST and UTCI heat mapping reveals nonlinear effects of urban morphologyT1DualHNIE: Dual-Channel Hypergraph Learning for Node Importance Estimation in Heterogeneous Knowledge GraphsT1GenTL: A General Transfer Learning Model for Building Thermal DynamicsT1A short review on the maximum clique problem algorithms with classical, AI, and quantum methodsT1Man, Machine, and Masterpiece: Artistic Ownership in the AI EraT1HABIB_TAZ at SemEval-2026 Task 11: Disentangling Formal Logic from Content via Synthetic Training and Multi-Objective OptimizationT1How Well Does AI-Generated Feedback Work? Intrinsic and Extrinsic Evaluation across more than 20,000 EFL Essay DraftsT3Mermaid to ASCII art (mermaid-ascii)T3Kimi K3, and what we can still learn from the pelican benchmarkT3Firefox in WebAssemblyT3Spot birds not golfT3[AINews] Kimi K3 2.8T-A50B: the largest open model ever released; Opus 4.8-class at Sonnet 5 pricingT1From physical surfaces to human-centric heat stress: LST and UTCI heat mapping reveals nonlinear effects of urban morphologyT1DualHNIE: Dual-Channel Hypergraph Learning for Node Importance Estimation in Heterogeneous Knowledge GraphsT1GenTL: A General Transfer Learning Model for Building Thermal DynamicsT1A short review on the maximum clique problem algorithms with classical, AI, and quantum methodsT1Man, Machine, and Masterpiece: Artistic Ownership in the AI EraT1HABIB_TAZ at SemEval-2026 Task 11: Disentangling Formal Logic from Content via Synthetic Training and Multi-Objective OptimizationT1How Well Does AI-Generated Feedback Work? Intrinsic and Extrinsic Evaluation across more than 20,000 EFL Essay Drafts
← Dispatches

The Polemics of Ethical AI Usage in Higher Education: A Case Study Investigating the Axiological Expectations Mismatch

Primary research

#406

T1digested
Topic
Academic Integrity
First seen
2026-07-16 23:33:02
Last seen
2026-07-16 23:33:02

Source raw items (1)

  • Semantic Scholar2026-07-16 23:31:52
    The Polemics of Ethical AI Usage in Higher Education: A Case Study Investigating the Axiological Expectations Mismatch

    The rapid integration of generative artificial intelligence (GenAI) into higher education has created significant tensions around authorship, assessment authenticity, and academic integrity. This paper introduces axiological expectations mismatch as a conceptual framework for understanding a core governance problem: the divergence between institutional value frameworks and the ethical reasoning students apply when using AI in their academic work. Drawing on an interpretive qualitative case study at a single private higher education institution in South Africa, the study analyses twelve institutional documents produced between 2021 and 2025, supplemented by descriptive trend data from 3,854 plagiarism incidents over the same period. Schwartz’s (2012) theory of basic values provides the theoretical lens; reflexive thematic analysis is the analytic method. Three findings emerge: first, a shift from prohibition to conditional permission for AI use, contingent on disclosure and authorship accountability; second, a reframing of academic integrity as a developmental process rather than a purely disciplinary matter; and third, evidence that policy adaptation improved institutional capacity to recognise and classify AI-related misconduct before it reduced its incidence. The paper argues that AI-related integrity disputes are better understood as conflicts between competing values (fairness, accountability, efficiency, and innovation) than as individual moral failings. Implications for policy design, assessment reform, and faculty development are discussed.