Prompting GPT-5 on Scrum Certification Questions: An Empirical Accuracy Study
Large Language Models (LLMs) are increasingly used in Agile Software Development for documentation, coaching, and training. As practitioners adopt these tools to prepare for certifications such as Professional Scrum Master (PSM), a key question is whether LLMs can reliably reason about Scrum, a framework with normative, well-defined rules described in the Scrum Guide (2020). This paper examines how different prompt techniques affect the factual accuracy of LLM responses to Scrum certification-st
Record details
Published: 29 June 2026
Source: arXiv
Category: Research
Topics: unclassified
Retrieved: 14 July 2026
Related evidence
These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.
Comparing Large Language Models on Scrum Certification-Style Questions: Accuracy, Stability, and Error Patterns
arXiv · 29 June 2026
Can AI help reduce prejudice? Evaluating the effectiveness of AI-powered personalized persuasion on support for transgender rights
OpenAlex · 29 June 2026
Fortress and Gatekeeper: Theorizing Transitive Trust in Third-Party Cybersecurity Risk Governance
arXiv · 25 June 2026
OpenAI single-agent LLM architecture reduces computational overhead relative to multi-agent orchestration in a simulated mars rover decision-support benchmark
Frontiers in Robotics and AI · 6 July 2026
IPO Finance Agent: Benchmark of LLM Financial Analysts Beyond Finance Agent v2, with Automated Rubric Generation, on the SpaceX (SPCX) IPO
arXiv · 22 June 2026
The Impact of Security and Privacy Controls on Users' Emotional Engagement with Generative AI Chatbots
arXiv cs.HC · 7 July 2026
How to cite this record
ethics.ai (29 June 2026), “Prompting GPT-5 on Scrum Certification Questions: An Empirical Accuracy Study,” evidence record 427, https://ethics.ai/record/427 (originally published by arXiv).
Use and limitations
This page is a stable index and citation surface for a source record. ethics.ai did not author the underlying report and has not independently verified every claim. Automatic topics may be imperfect. For consequential use, quote and cite the original publisher.