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Who Is Doing the Thinking? Characterising Human Agency in First-Year Students' AI Interactions — arXiv:2611.04783

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.04783
License (as archived): CC0-1.0
Archived: 2026-11-04T12:39:01.407Z
SHA-256 of the archived text: 3515c02f40f606a4b8910234afae92aa07ee866fb53aba689fca31581eac8875
Stable URL of this page: https://metraipolis.com/documento/dc_db077985a78e

How to cite

Who Is Doing the Thinking? Characterising Human Agency in First-Year Students' AI Interactions — arXiv:2611.04783 — arXiv (en), https://arxiv.org/abs/2611.04783 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_db077985a78e (Metraipolis archive dc_db077985a78e, sha256 3515c02f40f606a4b8910234afae92aa07ee866fb53aba689fca31581eac8875)

BibTeX

@misc{dcdb077985a78e,
  title = {Who Is Doing the Thinking? Characterising Human Agency in First-Year Students' AI Interactions — arXiv:2611.04783},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.04783},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_db077985a78e; sha256 3515c02f40f606a4b8910234afae92aa07ee866fb53aba689fca31581eac8875},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.04783. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

When students use generative AI to produce their work, the learning process becomes invisible. Established frameworks such as Bloom's taxonomy then classify the cognitive level of what the AI produced, not what the student contributed. We propose the Human Agency Depth Taxonomy (HADT), a preliminary six-tier taxonomy that classifies the engineering contribution a student makes in a prompt, independent of the AI's output. HADT combines three dimensions: contextual bandwidth, engineering abstraction and student agency. We applied HADT to 962 prompts from 105 students' structured AI Journals in an AI-permissive, first-year software engineering course of 145 students, and examined how agency changed over ten journalled weeks. Most prompts (84.2%) carried some student contribution beyond delegation. Agency tracked the development lifecycle. Architecture-discovery prompts dominated the requirements phase (48% of turns) and fell to 5-12% from week 9, while prompts analysing existing systems rose during build and verification (odds ratio 2.88, with turns clustered by student). Two-thirds of the variance in tier lay within students, so agency depended more on the phase of work than on the student. A turn-level rise in delegation in deadline weeks did not hold once turns were clustered by student. Explicitly specifying constraints on the AI's output was rare throughout, which we interpret as a knowledge bottleneck for novices rather than a gap in instruction. We discuss implications for course and assessment design, and for using HADT as a formative lens on how students engage with AI.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.HC, nuovi lavori del giorno).
Categorie arXiv: Software Engineering (cs.SE) ; Human-Computer Interaction (cs.HC).
Pagina del lavoro: https://arxiv.org/abs/2611.04783 — 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 2026-11-04T12:29:32.093Z dalla pagina https://arxiv.org/list/cs.HC/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).