Beyond Prompt-to-App: Accountable Translation in Teacher-Facing Agentic Authoring
On the map: LLM agents and tool use → Agent skill learning and evolution
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Agent skill learning and evolution, one of 16 directions in LLM agents and tool use. Builds on 12 of its 37 references that are in the map. First posted on arXiv 4 Sep 2026.
Builds on
12 of 37 references.
References · 12
- 2020Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performancecited by 399
- 2021AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Promptscited by 268
- 2022Grounded Copilot: How Programmers Interact with Code-Generating Modelscited by 218
- 2020Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthycited by 183
- 2025Interactive Debugging and Steering of Multi-Agent AI Systemscited by 68
- 2019Ask Not What AI Can Do, But What AI Should Do: Towards a Framework of Task Delegabilitycited by 36
- 2025Generative and Malleable User Interfaces with Generative and Evolving Task-Driven Data Modelcited by 26
- 2025Beyond Code Generation: LLM-supported Exploration of the Program Design Spacecited by 13
- 2025When Should Users Check? Modeling Confirmation Frequency in Multi-Step Agentic AI Taskscited by 6
- 2025Design Considerations for Human Oversight of AI: Insights from Co-Design Workshops and Work Design Theorycited by 2
- 2026From Tools to Teacher-Built Teammates: No-Code Pedagogical Plugin Authoring with LearnAdapt Agentic Studio and PedOS 1.1 Luminacited by 2
- 2026Bridging Gulfs in UI Generation through Semantic Guidancecited by 1
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- 2026KUPAS MASTER: Distilling the Tacit Expertise of Master Practitioners into Agent-Ready Experience Corpora
- 2026HANDBOOK.md: A Benchmark for Long-Context Agentic Instruction Following
- 2025Agent READMEs: An Empirical Study of Context Files for Agentic Coding
- 2026From Agent Behaviour to Agent-Friendly Documentation: An Empirical Study of How Coding Agents Discover, Read, and Write Technical Documentation
- 2026"Nobody Did This": Contribution, Originality, and Accountability in Agent-Mediated Collaboration
- 2026DeepTrans Studio: Turning Expert Interventions into Shared Team Knowledge in Agentic Translation Workflows
- 2026From Anatomy to Smells: An Empirical Study of SKILL.md in Agent Skills
- 2025From Prompt to Product: A Human-Centered Benchmark of Agentic App Generation Systems