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Artificial Intelligence Driven Physics Assignments using Context Prompts — arXiv:2609.22578

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.22578
License (as archived): CC0-1.0
Archived: 2026-10-09T23:45:12.951Z
SHA-256 of the archived text: 09faa1c0069159499649744d4ef86c9c023c161eea5835883179dbe5eea2db6b
Stable URL of this page: https://metraipolis.com/documento/dc_576190817152

How to cite

Artificial Intelligence Driven Physics Assignments using Context Prompts — arXiv:2609.22578 — arXiv (en), https://arxiv.org/abs/2609.22578 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_576190817152 (Metraipolis archive dc_576190817152, sha256 09faa1c0069159499649744d4ef86c9c023c161eea5835883179dbe5eea2db6b)

BibTeX

@misc{dc576190817152,
  title = {Artificial Intelligence Driven Physics Assignments using Context Prompts — arXiv:2609.22578},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.22578},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_576190817152; sha256 09faa1c0069159499649744d4ef86c9c023c161eea5835883179dbe5eea2db6b},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.22578. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Generative artificial intelligence (Gen AI) is increasingly shaping how students access information and engage with physics content, yet many instructional approaches still assume a pre-AI classroom. In this paper, we describe a practical framework for integrating Gen AI into physics instruction through the use of context prompts: instructor-designed prompts that define the role, task, and boundaries of the AI interaction. We focus on AI-supported exploration assignments implemented in an asynchronous introductory physics course enrolling approximately 70 students. In these activities, students used AI as a guided tutor to explore selected topics, ask follow-up questions, request explanations and analogies, and connect physics concepts to their personal interests and everyday experiences. We argue that this structure can help move students beyond answer-seeking and toward more dialogic and personally meaningful engagement with course content. We also discuss reflection as a natural extension of the same framework, while distinguishing it from the implemented exploration activities described here. Finally, we highlight practical considerations that emerged in use, including uneven access to AI platforms, grading workload, the need to teach students how to engage AI conversationally, and the growing importance of AI literacy in physics education.

Tema degli esploratori: ricerca e scienza aperta (retro-catalogo arXiv: annunci del 2026-09-22 in physics.ed-ph).
Categorie arXiv: Physics Education (physics.ed-ph).
Pagina del lavoro: https://arxiv.org/abs/2609.22578 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-09T22:49:36.874Z dalla pagina https://arxiv.org/catchup/physics.ed-ph/2026-09-22?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).