{"ID":6621307,"CreatedAt":"2026-07-15T01:01:48.440468303Z","UpdatedAt":"2026-07-15T03:28:55.185153975Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.12221","arxiv_id":"2607.12221","title":"From Chaos to Clarity: A Framework for Program-Level AI Learning Outcomes","abstract":"Industry is leaning into generative artificial intelligence (GenAI), and higher education is under pressure to prepare graduates for a GenAI-augmented workforce. Yet, there is still no clear structure for defining AI readiness across disciplines, programs, courses, and assignments. Current approaches often rely on broad institutional policies or individual course-level decisions, which can also create mixed messages for students, fragmented expectations across programs, and limited visibility for university leaders. In this paper, we argue that higher education needs a more coherent way to connect institutional priorities to curriculum-level action. We propose Program-Level AI Learning Outcomes (PLAI-LOs) as a framework for defining what students graduating from a program should know and be able to do with, without, and about GenAI in a given discipline. The PLAI-LOs framework complements existing program-level learning outcomes and supports alignment across institutional priorities, program-level AI learning outcomes, course-level learning outcomes, and assignment-level objectives. We illustrate the framework with examples from computing and music and show how PLAI-LOs can be implemented through artifact-level GenAI policies, helping programs decide where GenAI should be taught and used, and when students should be expected to work without GenAI. We offer PLAI-LOs as a concrete, measurable, and adaptable path for moving higher education from scattered GenAI rules toward a strategy with clear, learning-centered alignment.","short_abstract":"Industry is leaning into generative artificial intelligence (GenAI), and higher education is under pressure to prepare graduates for a GenAI-augmented workforce. Yet, there is still no clear structure for defining AI readiness across disciplines, programs, courses, and assignments. Current approaches often rely on broa...","url_abs":"https://arxiv.org/abs/2607.12221","url_pdf":"https://arxiv.org/pdf/2607.12221v1","authors":"[\"Grace Barkhuff\",\"Ian Pruitt\",\"William Gregory Johnson\",\"Rodrigo Borela\",\"Ben Rydal Shapiro\",\"Anu G. Bourgeois\"]","published":"2026-07-13T23:45:40Z","proceeding":"cs.HC","tasks":"[\"cs.HC\"]","methods":"[]","has_code":false}