{
  "id": 3202,
  "url": "https://arxiv.org/abs/2606.03814v1",
  "title": "Leveraging BART to Assess CS1 C++ Programming Assignments using Rubric-based Criteria",
  "summary": "This paper investigates rubric-aware, multitask fine-tuning of transformer models for automated grading of introductory C++ programming assignments, with the goal of producing grade predictions that better reflect instructor grading behavior than general-purpose LLMs. Using multi-semester CS1 data, student submissions are paired with numeric scores, letter-grade buckets, and assignment rubrics, then preprocessed into unified sequences for transformer input. A BART encoder-decoder with LoRA adapt",
  "authors": "Kelsey Rainey, Jesse Roberts",
  "category": "research",
  "topics": "children-education,finance-investment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-06-02T15:57:14.000Z",
  "fetched_at": "2026-07-14T16:30:05.529Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/3202",
  "original_url": "https://arxiv.org/abs/2606.03814v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}