{
  "id": 16952,
  "url": "https://arxiv.org/abs/2608.04148v1",
  "title": "AgentForge: An Immersive Role-Playing Platform for Learning Agentic Software Engineering",
  "summary": "Agentic AI is increasingly used to coordinate planning, implementation, review, and testing in software development, yet it often offers limited transparency into its decisions and interactions. Many such systems also assume that users can effectively guide the AI's decisions and validate its outputs. This assumption poses a particular challenge for novices, who must simultaneously learn how agentic AI works, how to collaborate with it effectively, and how to evaluate its outputs critically. To",
  "authors": "Zihan Fang, Yueke Zhang, Yu Huang",
  "category": "research",
  "topics": "agents-autonomy,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-04T18:58:18.000Z",
  "fetched_at": "2026-08-06T05:10:11.148Z",
  "source_slug": "x-arxiv-cs-hc",
  "source_name": "arXiv cs.HC",
  "source_homepage": "https://arxiv.org/list/cs.HC/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/16952",
  "original_url": "https://arxiv.org/abs/2608.04148v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}