± SCIENCE Today’s artificial intelligence still faces two major challenges. One is that, in most industries, data exist…+ SCIENCE Deep learning takes on protein folding In 1972, Anfinsen won a Nobel prize for demonstrating a connection bet…± SCIENCE Significance Deep neural networks are currently the most successful machine-learning technique for solving a…+ SCIENCE Deep learning allows computational models that are composed of multiple processing layers to learn representa…+ HEALTH For the task of analyzing survival data to derive risk factors associated with mortality, physicians, researc…+ SCIENCE Artificial intelligence (AI) coupled with promising machine learning (ML) techniques well known from computer…+ SCIENCE Deep learning is revolutionizing many areas of science and technology, especially image, text, and speech rec…+ SCIENCE Current deep-learning models are mostly built upon neural networks, i.e. multiple layers of parameterized dif…
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TRV-2026-0189Certified recordPeer-reviewed

From AI adoption to AI governance: Developing a Buddhist interpretive framework for higher education

Artificial intelligence is increasingly being adopted in higher education to support teaching, learning, administration, quality assurance, and institutional planning. However, much of the current discussion remains focused on adoption, efficiency, and technological capability, with less attention to the interpretive and governance conditions required for responsible institutional use. This article addresses that gap by developing a Buddhist interpretive framework for AI governance in higher education. Drawing o…

Policy · P Space — documented harm · certified 2026-07-13 · v1 · article view · machine-readable

Current reading — problem

Current discussion of AI in higher education focuses on adoption and efficiency with insufficient attention to interpretive and governance conditions needed for responsible institutional use

What this doesn’t fix

Findings are based on a small qualitative sample of 16 interview informants and 9 focus group participants with document analysis, limiting generalizability beyond studied university contexts

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Truvace Impact Record TRV-2026-0189, v1: “From AI adoption to AI governance: Developing a Buddhist interpretive framework for higher education.” Truvace, 2026-07-13. /record/TRV-2026-0189 (accessed at citation time). sha256 51fd1e1ccfd5e166…

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