Higher education leaders are moving artificial intelligence from isolated pilots to responsible, mission-driven scale. Their lessons: prioritize data, infrastructure, talent, equity, and measurable outcomes.
Proposed changes to federal grant regulations could affect how colleges and universities are awarded and manage grant funding. The proposals from the Office of Management and Budget, the National Science Foundation, and the U.S. Department of Education would introduce changes to grant compliance, award management, allowable costs, and continuation funding.
After shoulder surgery limited the use of his dominant arm, a faculty member used generative artificial intelligence (GenAI) to support teaching, research, communication, and software development. His experience shows how GenAI can reduce barriers to meaningful work while introducing new demands for verification and judgment.
Many higher education institutions treat artificial intelligence (AI) in education as a single category. A four-layer framework helps institutional leaders distinguish AI adoption from AI readiness and identify gaps in AI policies and investments, including limited support for profession-specific AI competency.
As colleges and universities accelerate adoption of artificial intelligence (AI), leaders need a practical framework for evaluating AI workloads and determining how those workloads should be governed, funded, and supported.
The disruptions from artificial intelligence reach across campus, raising the need for professional development for faculty and staff to ensure responsible use of the technology.
Arkansas State University is adopting Scaffold DataX in phases, starting with Banner and Canvas data for student success and financial reporting. In the second phase, the university is putting Canvas data into production for backup and continuity.
Most campus chatbots stopped delivering because they lacked student context, institutional data, and the ability to take action. Three capabilities separate tools students love from legacy software they do not.
Artificial intelligence (AI) may be everywhere, but widespread adoption does not automatically create value. Resistance to AI is not a barrier to innovation in higher education. It is a necessary force for asking hard questions, protecting core values, and ensuring that technology adoption remains purposeful and mission-driven.
The potential sources of disruption to teaching and learning are expanding, as are the consequences, increasing the importance of being able to maintain high-quality academic activity during interruptions.
