An AI Factory for Technology Education: An Agentic Pipeline with Closed Loop Audience Feedback

arXiv:2610.31875v1 Announce Type: new Abstract: Technology tools used for professional upskilling change on a timescale of weeks, and written guidance about them ages at the same pace. Large language models have made it inexpensive to draft a tutorial or a course outline, but most deployed generation systems still produce a piece of content once and never check it against what an audience actually does with it. This paper describes an agentic pipeline, organized as a typed workflow graph, that treats instructional content production as a manufacturing process with explicit checkpoints rather than a single generation call. Four node classes structure the graph: deterministic scripts, model steps bound to a fixed rubric, open-ended agent phases, and human approval gates, arranged along a forward production path and closed by a feedback cycle that rewrites generation rules from post level engagement telemetry. Production of content is split into two layers: a demand layer, built from video and community sources, that shapes topic selection and register, and an authority layer, built from official documentation, that every published claim must trace back to. Over five months of continuous operation, the deployed system published 26 documented guides at roughly 1.5 hours of operator time per week, and the feedback loop identified four measurable engagement effects that became binding rules for later runs, including a hook style that outperformed baseline by a factor of 1.83 in its first analysis window.

The AI factory automates content production, potentially displacing human workers.

The development of an AI-powered content production pipeline for upskilling technology education can improve learning efficiency and relevance through feedback loops, representing a positive societal impact. However, it may also lead to reduced human job roles in content creation and potential issues from automated decision-making. The long-term societal implications remain uncertain.

This is a practical engineering report on automating educational content creation with human approval gates, not a claim about broad societal transformation. It has modest implications for labor efficiency in content production and some positive potential for keeping tech education current, but scale and evidence are limited.

This paper describes an AI-powered system designed to continuously generate and update educational content. The system significantly reduces the time and cost associated with producing technical tutorials, adapting them rapidly based on audience engagement. This represents a substantial positive impact on access to up-to-date technical education.

SOURCE     arXiv cs.CY (Computers and Society) (tier 1)
   ↓
DOCUMENT   a0dd6fcf-26ec-4be6-8296-578273ef3649
           https://arxiv.org/abs/2610.31875
   ↓
EVIDENCE   2 extracted excerpts
   ↓
MODEL RUN  4 runs, methodology 1.0.1
   ↓
SCORE      -5  (Mixed or uncertain)
   ↓
CONFIDENCE 76%