AI & ML
Beyond Prompt-to-App: Accountable Translation in Teacher-Facing Agentic Authoring
arXiv cs.HC0 views
arXiv:2609.04679v2 Announce Type: replace Abstract: Natural-language app builders let domain experts create software, but their pipelines transform professional intent across compilation, generation, checking, and approval. We report a bounded trace study of a teacher-facing agentic authoring system. Evidence comprises six eligible build attempts across three accounts; a separate corpus of 37 workshop units from 23 display names contextualizes commitments without person-level linkage. Compiled specifications added governance requirements, while downstream representations sometimes normalized case-specific learning relations. Two drafts met a stored package/security threshold despite analyzer reservations and unresolved correspondence to their briefs; four attempts in one account produced no usable payload, and repair messages did not translate internal terms into domain-legible revisions. We develop accountable translation as an analytic framework for making consequential changes attributable, inspectable, scoped in validation, and contestable. It extends HCI accounts of traceability and end-user debugging by locating professional authority and repair rights across heterogeneous technical and organizational handoffs.
Source:https://arxiv.org/abs/2609.04679PreviousFrom Misconceptions to Evidence: What Science Teachers Make Visible When Co-Designing Agentic Learning AppsNextWhen Can You Trust Your Synthetic Users? Diagnostics and Corrections for LLM Consumer Panels
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