Did generative AI replace university library borrowing?
Not measurably at one Chinese university. A nine-year, 3.2-million-transaction record found no significant change in print borrowing after January 2023, while a 368-student survey showed only a weak substitution signal.
The short version
- What happened
- The study combined 368 questionnaires with 3.2 million print-library transactions from January 2017 to December 2025 and digital-platform statistics through the first half of 2026.
- Who it affects
- Students may need different tools for deep reading, verified retrieval and rapid synthesis; a single 'AI versus library' policy would miss those task differences.
- What remains unproven
- One university in China
Keep in mind
One university in China
Cross-sectional survey of 368 students
Survey responses could not be linked to individual borrowing records
Several constructs used single questionnaire items
The interrupted series cannot isolate every post-2023 institutional change
No learning, comprehension or academic-integrity outcome
Source analysis · 1 linked source. Primary analysed source date: 26 October 2026.
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The long record did not show an AI break
Generative AI had not measurably accelerated the decline in print borrowing at the China University of Mining and Technology by the end of 2025. Researchers analysed 107 months of circulation data—about 3.2 million borrowing, return and renewal records—from January 2017 to December 2025. February 2020 was unavailable, and the model separately represented the pandemic shock, seasonality and January 2023 as the prespecified GenAI breakpoint.
Print loans were already falling by about 536 books a month before the pandemic. The estimated January 2023 level change was 1,167 additional books and the later slope change was 160 fewer books a month, but neither result was statistically significant. Alternative breakpoints in March 2023, September 2023 and January 2025 gave the same null pattern. A null result does not prove no student changed behaviour; it means the aggregate record did not detect a new institution-wide break.[1]
Survey answers and platform use pointed to coexistence
A separate 2026 questionnaire of 368 enrolled students distinguished auxiliary use—searching, summarising and comparing—from substitutive use, such as generating complete assignment passages. Substitutive use had a weak negative association with intention to keep using print resources (standardised coefficient −0.100; effect size f² 0.104). Auxiliary use was positively associated with continued digital-resource use, although those two survey constructs were not statistically distinct enough to support a clean mechanism.
Digital-platform totals also resisted a simple replacement story. Traditional operations rose from 280 million in 2021 to 317 million in 2025. Embedded AI features recorded 326,000 operations in 2025 and 766,000 in the first half of 2026, but represented only 0.10% and 0.53% of platform activity. That is early uptake layered onto existing services, not evidence that AI displaced them.[1]
The distinction that matters
Three evidence streams, three different questions
The study triangulates behaviour and perception without claiming they are the same dataset.
Print logs
3.2m transactions over 107 months
No significant post-January 2023 break
Student survey
368 cross-sectional responses
Weak print-substitution association
Digital platform
Operations from 2021 to mid-2026
AI grew from a very small base alongside traditional use
Libraries should test tasks, not assume extinction
For librarians and teachers, the result supports designing around different tasks: print for sustained reading, licensed databases for sourced retrieval and AI for navigation or synthesis with verification. It does not justify cutting collections, nor does it establish that AI improves learning. The study measured intentions, transactions and platform operations—not comprehension, grades, source quality or academic integrity.
The survey and logs could not be linked person by person, several survey concepts used single questions, and all data came from one research-intensive Chinese university. Funding was reported from the Chinese Academy of Agricultural Sciences' Agricultural Information Institute. Replication across institutions, linked privacy-preserving usage records and measured learning outcomes would show whether coexistence persists as embedded AI becomes a larger share of academic-resource use.[1]
Watch the background, then check the evidence
Illustrative photo · not a video frame
UNESCO · 8 min 30 sec · Background explainer
Teacher to Teacher: Is AI reshaping education for the better?
YouTube loads only when you choose; playback starts when you press play.
A conversation about how teachers can guide AI use while preserving pupils’ judgement. Watch for the difference between helping a learner think and supplying an answer.
Evidence check: Teachers’ perspectives provide context. They do not establish that a particular AI tool improves learning outcomes.
Published 2025-01-23. Source checked 14 October 2026. Check captions on the publisher’s video page.
Read the education evidencePhoto: Vitaly Gariev / Unsplash · Licence
Continue the story
Read next
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Read the next reportExplore the full source and evidence record
What this means for people
- Students may need different tools for deep reading, verified retrieval and rapid synthesis; a single 'AI versus library' policy would miss those task differences.
Global context
Collection access, platform licensing, student habits and AI availability differ across universities and countries. The design is informative, but its numerical results should not be transported without local records.
What the evidence does not yet show
- One university in China
- Cross-sectional survey of 368 students
- Survey responses could not be linked to individual borrowing records
- Several constructs used single questionnaire items
- The interrupted series cannot isolate every post-2023 institutional change
- No learning, comprehension or academic-integrity outcome
What to watch next
- Multi-university replications with the same interruption model
- Privacy-preserving linkage of student choices to actual resource use
- Learning and source-quality outcomes by task
- Whether embedded AI use remains complementary as its share grows
Living evidence record
Impact record IAI-1WIWJDO
Evidence stage
Studied
Confidence
Supported
Reporting basis
Source analysis
Independent or research support
Present
Record status
Monitoring
Last checked
26 October 2026
Source trail
1 direct source across 1 source type.
People impact
Documented in this record.
Uncertainty
Limits and next checks are explicit.
Stages describe the evidence available—not whether a technology is good or bad. See the public method.
Single-source reporting disclosure
This record analyses one direct source. It can establish what Frontiers in Psychology published or reported, but it is not independent corroboration of every performance claim or predicted outcome. The confidence label will change only when broader evidence is added.
Evidence trail
Sources used for this report
Links checked 26 October 2026
Research question, topics and regions
Whether generative AI substitutes for or complements university students' use of print and digital academic resources
Topics: Universities · Libraries · Student research · Generative AI
Regions: China · Asia
This report is labelled source analysis. We summarise and analyse source material in our own words; company statements remain attributed claims until independently supported. Translated summaries preserve the meaning of the original source and link back to it. Read our editorial standards.
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