{
  "abstract": "Generative AI tools have become part of how students study physics, yet unstructured use may reduce students' opportunities to work through problems independently. The Physics AI-Replication (P-A.I.R.) framework structures AI use as a four-step cycle: students identify a challenging problem, obtain an AI-generated explanation and solution, request similar practice problems, and solve them independently before reviewing the AI's solutions. We evaluated P-A.I.R. in 11 algebra- and calculus-based introductory physics sections at four U.S. institutions using a common end-of-semester survey. Most students reported improved conceptual understanding (90.9\\%) and found the replicated practice problems helpful (97.7\\%), and these two ratings were strongly associated ($r_s = 0.58$). Perceptions of both components, as well as post-engagement confidence ($μ= 7.18$ on a 1-10 scale), were consistent across institutions. Post-engagement confidence was associated with perceived improvement in conceptual understanding ($r_s = 0.37$) and the helpfulness of replicated practice ($r_s = 0.35$), but not with how often students used AI ($r_s = 0.02$) or with self-reported GPA ($r_s = 0.13$). Thematic analysis of open-ended responses showed that students valued AI primarily for conceptual clarification and structured problem solving (63.0\\%), while their main concerns were AI accuracy (34.2\\%) and the need for explanations that guide rather than simply provide answers (29.7\\%). These findings suggest that how students engage with AI matters more than how often they use it, and that frameworks sequencing AI assistance with independent problem solving offer a practical, transferable approach for physics instruction.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2611.04148",
  "author_total": 5,
  "authors": [
    {
      "name": "Bilas Paul"
    },
    {
      "name": "Shantanu Chakraborty"
    },
    {
      "name": "Shruti Shrestha"
    },
    {
      "name": "Binod Nainabasti"
    },
    {
      "name": "Bisaj Paul"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/30wb93yvta0de.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2611.04148",
  "fulltext_url": null,
  "id": "30wb93yvta0de",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2611.04148",
  "language": null,
  "licence": "CC-BY-NC-SA-4.0",
  "licence_label": "CC BY-NC-SA 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by-nc-sa/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "physics.ed-ph"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2611.04148",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-03",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/30wb93yvta0de.ris",
  "sources": [
    {
      "first_seen": "2026-11-04T15:21:25Z",
      "native_id": "2611.04148",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2611.04148"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.ed-ph",
      "subfield": "general"
    }
  ],
  "title": "Active Learning with Generative AI in Introductory Physics: A Multi-Institutional Study of the P-A.I.R. Framework",
  "title_text": "Active Learning with Generative AI in Introductory Physics: A Multi-Institutional Study of the P-A.I.R. Framework",
  "type": "preprint",
  "updated_at": "2026-11-06T18:21:01Z",
  "url": "https://research.draconitic.com/paper/30wb93yvta0de",
  "venue": "arXiv",
  "versions": []
}