This synthesis reviews AI in university-library services, from chat assistance, recommendations and research support to algorithmic bias, privacy, copyright, infrastructure and changing librarian roles.
Key findings
- The synthesis identifies opportunities in reference, recommendation and research support, alongside risks from bias, personal data, copyright and professional change. Strategic planning, workforce development and governance are presented as prerequisites for scale.
Why this matters globally
The issues align with libraries globally as they protect trust, equitable access and user rights in the AI era. The framework could inform procurement, auditing and service evaluation.
Thai researcher contribution
Surachat Putthima of Chiang Mai Rajabhat University synthesised the issues for university-library contexts, translating AI debates into institutional management questions relevant to Thailand.
Limitations to consider
Search databases, dates, terms, inclusion criteria and quality appraisal are not reported, so this is not a systematic review and may have selection bias. Most proposals are conceptual, without experimental service, cost, fairness or user-experience metrics.