---
title: "How Generative AI Reshapes Academic Library Resource Discovery: An Empirical Study Based on Three-Year Web Analytics"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2607.20328v1"
permalink: "https://feed.astralor.com/items/itm_926881f07d78d27d3082c041"
published: "2026-07-22T16:14:38.000Z"
score: 79
channel: "ai"
tags: ["AI Retrieval","ChatGPT","Gemini","Perplexity","Academic Library","Open Access"]
---

# How Generative AI Reshapes Academic Library Resource Discovery: An Empirical Study Based on Three-Year Web Analytics

## AI commentary

> This paper offers a rare empirical window into how AI platforms are actually reshaping the flow of academic information. For library directors and digital resource managers, the data signals an urgent need to refine metadata strategies and Open Access policies in the age of AI. For AI developers, the observed user behavior patterns provide concrete inputs for improving retrieval ranking and citation features. A must-read for anyone concerned with scholarly communication.

## At a glance

This study empirically analyzes the impact of generative AI as an emerging discovery pathway for academic library resources using web analytics data from August 2023 to October 2025. Results show a significant increase in AI-mediated traffic, especially after integrating linked citation features. ChatGPT, Perplexity, and Gemini are the primary platforms driving traffic, with a substantial portion of users accessing the institutional repository for electronic theses and dissertations. The findings indicate that AI retrieval mechanisms effectively surface resources with structured metadata and stable permalinks that are Open Access, offering strategic insights for libraries in the evolving AI landscape.

## Key points

- AI-mediated traffic to academic library resources increased significantly from 2023 to 2025, especially after the deployment of linked citation features.
- ChatGPT, Perplexity, and Gemini are the three primary AI platforms driving traffic to institutional repositories.
- AI retrieval excels at surfacing resources with structured metadata and stable permalinks, particularly Open Access electronic theses and dissertations.
- The study provides an evidence-based foundation for libraries to develop strategic responses to the evolving AI ecosystem.

## Full text

This study empirically analyzed generative AI as an emerging discovery pathway to academic library resources. Utilizing web analytics from August 2023 to October 2025, the research identifies a significant increase in AI-mediated traffic, particularly following the integration of linked citation features. Referral analysis identified ChatGPT, Perplexity, and Gemini as the primary platforms driving this traffic. A substantial portion of users reached the institutional repository, primarily accessing electronic theses and dissertations. This pattern suggests that AI retrieval mechanisms effectively surface resources with structured metadata and stable permalinks that are Open Access and freely available. The results illustrate how AI ecosystems currently expose library resources and underscore the need for continued analysis and a strategic response to the evolving AI landscape.

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