{
  "id": 10538,
  "url": "https://arxiv.org/abs/2607.13094",
  "title": "Analyzing Curricular Pattern Complexity Using AI to Improve On-Time Graduation Rates",
  "summary": "arXiv:2607.13094v1 Announce Type: new Abstract: The rise of Artificial Intelligence (AI) enables automatic analysis of large amounts of data. Previously time-consuming and labor-intensive tasks can be completed much more efficiently with the use of AI. This work uses AI techniques to analyze and revise curricular patterns in an undergraduate degree for Software Engineering. Curricula often have long sequences where failure to pass a class within the sequence may jeopardize completion of the degr",
  "authors": "Lynn Vonderhaar, Juan Couder, Siri Siqveland, Omar Ochoa, James Pembridge",
  "category": "research",
  "topics": "jobs-economy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-16T04:00:00.000Z",
  "fetched_at": "2026-07-16T05:10:56.605Z",
  "source_slug": "arxiv-cscy",
  "source_name": "arXiv cs.CY",
  "source_homepage": "https://arxiv.org/list/cs.CY/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/10538",
  "original_url": "https://arxiv.org/abs/2607.13094",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}