Year: 2024
Type: article
Abstract: Purpose: Large Language Models (LLMs) are employed in a growing range of domains.They automatise processes and improve work efficiency.However, their application in website quality testing leaves a research gap.The paper aims to assess the capabilities and limitations of using selected LLM-based AI tools for SEO auditing as a critical component of a university's marketing strategy in a case study of a university website.Design/methodology/approach: The paper reports a case study.The audited website belongs to a university where a new content management system (CMS) is deployed.At this stage, the audit minimises such adverse issues as deindexing and visibility loss.The SEO audit employed the following AI tools: ChatGPT, Microsoft Copilot, Gemini Google DeepMind, and Perplexity AI.Selected AI responses are quoted verbatim, while others have been synthesised.Some of them were subjected to semantic analysis.Findings: LLM-based AI tools do not conduct an SEO audit in real time.LLMs can easily generate articles, abstracts, summaries, and other texts that may be used as input for meta tags or headings, such as H1.Therefore, LLMs can be useful for optimisation rather than quality assessment.Originality/value: It is one of the first studies on the capabilities and limitations of Large Language Models regarding SEO audits presented with a case study of a university website.
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4408695155