This study aims to develop and test a multi-criteria decision model for assessing the preparedness of academic libraries for AI adoption in higher education. Using input from 21 head librarians in Poland, we identified 25 key indicators of AI readiness. Probabilistic weights at both domain and indicator levels were estimated using a hierarchical Bayesian Best–Worst Method, with partial-order priorities expressed through credal rankings. A fuzzy VIKOR synthesis produced a compromise readiness ranking for five institutional archetypes. Domain priorities emphasize AI’s impact on staff roles (0.219) and its use as a tool (0.209), followed by current AI use (0.194), assistant functions (0.189), and reversed AI-as-threat (0.189). Sensitivity analysis reveals a tipping point: when the weight for current use exceeds approximately 0.30, practice-and-skill indicators surpass adoption concerns. Fuzzy VIKOR analysis ranked polytechnic libraries as the most prepared for AI adoption in higher education institutions, followed by research libraries.
AlamAFSubaveerapandiyanAMvulaD, et al. (2024) AI literacy and Zambian librarians: A study of perceptions and applications. Open Information Science8(1): 20220166.
2.
BegumDElahiMH (2025) Readiness to adopt artificial intelligence (AI) in libraries: A study from the lens of information professionals of Bangladesh. Digital Library Perspectives41(4): 666–682.
3.
BellmanREZadehLA (1970) Decision-making in a fuzzy environment. Management Science17(4): 141.
4.
BiswasS (2017) I-V fuzzy shortest path in a multigraph. Oriental Journal of Computer Science and Technology10(2): 364–370.
5.
BoatengF (2025) The transformative potential of generative AI in academic library access services: Opportunities and challenges. Information Services & Use45(1-2): 140–147.
6.
BøyumIKhosrowjerdiM (2025) ‘It’s such a blackbox function’: Norwegian academic librarians’ ambivalent perceptions towards AI use in academic libraries and academia. Information Research: An International Electronic Journal30: 91–104.
CharlesL (2017) Embracing challenges in times of change: A survey of the readiness of academic librarians in New Jersey for transition to the ACRL framework. Communications in Information Literacy11(1): 221–245.
9.
ChaseM (2024) Academic libraries can develop AI chatbots for virtual reference services with minimal technical knowledge and limited resources. Evidence Based Library and Information Practice19(2): 136–138.
10.
ChenJKChenIS (2008) VIKOR method for selecting universities for future development based on Innovation. Journal of Global Business Issues2(1): 53–59.
11.
ChildreyC (2025) Scaling innovation: How academic library leaders can develop and sustain next generation makerspaces for student success and community engagement. Journal of Library Administration65(2): 235–243.
12.
CorazzaMGioveS (2009) Fuzzy interval net present value. In: Frontiers in Artificial Intelligence and Applications, vol. 193. IOS Press, pp. 214–222. https://doi.org/10.3233/978-1-58603-984-4-214
13.
CoxA (2023) How artificial intelligence might change academic library work: Applying the competencies literature and the theory of the professions. Journal of the Association for Information Science and Technology74(3): 367–380.
14.
CoxA (2024) Developing a library strategic response to artificial intelligence. eLucidate16(4): 1–10.
15.
CoxAMPinfieldSRutterS (2019) The intelligent library: Thought leaders’ views on the likely impact of artificial intelligence on academic libraries. Library Hi Tech37(3): 418–435.
16.
CsárdiGNepuszTMüllerK, et al. (2025) igraph for R: R interface of the igraph library for graph theory and network analysis. Zenodo. Available at: https://doi.org/10.5281/zenodo.7682609 (accessed 20 August 2025).
17.
DejaMRakDBellB (2021) Digital transformation readiness: Perspectives on academia and library outcomes in information literacy. Journal of Academic Librarianship47(5): 102403.
18.
EkelPYAMenezesM.NetoFHS (2007) Decision making in a fuzzy environment and its application to multicriteria power engineering problems. In: Proceedings of the international conference on hybrid systems and applications, Lafayette, LA, USA, May 2006, Part I, Vol. 1, No. 4, pp. 527–536.
19.
ElaiessDR (2023) Digital transformation readiness in libraries: Insights and implications. International Journal of Academic Information Systems Research7(5): 4–6.
20.
FilsonCKAtuaseD (2024) Artificial intelligence and academic integrity: The role of academic librarians. Information Development41(3): 781–793.
21.
GabryJSimpsonDVehtariA, et al. (2019) Visualization in Bayesian workflow. Journal of the Royal Statistical Society Series A: Statistics in Society182(2): 389–402.
HarisantyDAnnaNEVPutriTE, et al. (2024) Leaders, practitioners and scientists’ awareness of artificial intelligence in libraries: A pilot study. Library Hi Tech42(3): 809–825.
24.
Hashemi PetrudiSHGhomiHMazaheriasadM (2022) An integrated fuzzy Delphi and best worst method (BWM) for performance measurement in higher education. Decision Analytics Journal4: 100121.
25.
Hashemi PetrudiSHTavanaMAbdiM (2020) A comprehensive framework for analyzing challenges in humanitarian supply chain management: A case study of the Iranian red crescent society. International Journal of Disaster Risk Reduction42: 101340.
26.
KalbandeDYuvarajMVermaMK, et al. (2024) Exploring the integration of artificial intelligence in academic libraries: A study on librarians’ perspectives in India. Open Information Science8(1): 20240006.
27.
KannanGHaqAN (2007) Analysis of interactions of criteria and sub-criteria for the selection of supplier in the built-in-order supply chain environment. International Journal of Production Research45(17): 3831–3852.
28.
KavakAYılmazE (2024) Information professionals’ metaphorical perceptions of artificial intelligence concept. Journal of Library Administration64(6): 695–718.
29.
KhanRGuptaNSinhababuA, et al. (2024) Impact of conversational and generative AI systems on libraries: A use case large language model (LLM). Science & Technology Libraries43(4): 319–333.
30.
Kisilowska-SzurmińskaM (2025) Artificial intelligence in academic libraries – A tool, a collaborator, an adversary? A Delphi study of university librarians in Poland. Journal of Academic Librarianship51(5): 103114.
31.
KuoYFChenPC (2008) Constructing performance appraisal indicators for mobility of the service industries using fuzzy Delphi method. Expert Systems with Applications35(4): 1930–1939.
32.
LiD (2024) Adoption of artificial intelligence in public and private libraries of China: Determinants, challenges, and perceived benefits. Profesional de la Información33(4): ene.0416.
33.
LiLWangXRezaeiJ (2020) A Bayesian best-worst method-based multicriteria competence analysis of crowdsourcing delivery personnel. Complexity2020: 1–17.
34.
MallikarjunaC (2024) Integrating artificial intelligence in academic libraries: An analysis. DESIDOC Journal of Library & Information Technology44(2): 124–129.
35.
MannheimerS (2024) Responsible AI practice in libraries and archives: A review of the literature. Information Technology and Libraries43(3): 17245.
36.
MatthewK (2024) tidybayes: Tidy data and geoms for Bayesian models. Zenodo. Available at: https://doi.org/10.5281/zenodo.1308151 (accessed 20 August 2025).
37.
McClellandDC (1973) Testing for competence rather than for ‘intelligence. American Psychologist28(1): 1–14.
38.
MithaSBOmarsaibM (2025) Emerging technologies and higher education libraries: A bibliometric analysis of the global literature. Library Hi Tech43(2-3): 1248–1270.
39.
MolaudziAINgulubeP (2025) Use of artificial intelligence innovations in public academic libraries. IFLA Journal51: 660–670.
40.
MunimZHBalasubramaniyanSKouhizadehM, et al. (2022) Assessing blockchain technology adoption in the Norwegian oil and gas industry using Bayesian best worst method. Journal of Industrial Information Integration28: 100346.
41.
MurrayTJPipinoLLvan GigchJP (1985) A pilot study of fuzzy set modification of Delphi. Human Systems Management5(1): 76–80.
42.
NasseriSH (2008) A new method for solving fuzzy linear programming by solving linear programming. Applied Mathematical Sciences2(50): 2473–2480.
43.
OgwoUIbegbulemFNwachukwuV (2023) Applications and perceived impact of artificial intelligence in academic libraries in Nigeria. Library Philosophy and Practice2023(1): 7907.
44.
OpricovicS (2011) Fuzzy VIKOR with an application to water resources planning. Expert Systems with Applications38(10): 12983–12990.
45.
OpricovicSTzengG-H (2004) Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. European Journal of Operational Research156(2): 445–455.
46.
OyedokunTT (2025) Navigating the dynamics of present-day academic libraries: An in-depth analysis of strategies, challenges, and emerging trends. IFLA Journal51(2): 470–489.
47.
RevittELuykS (2019) The role of library councils in Canadian higher education: An exploratory study. Canadian Journal of Higher Education49(1): 140–158.
48.
SaatyTL (1977) A scaling method for priorities in hierarchical structures. Journal of Mathematical Psychology15(3): 234–281.
49.
SanerHSYucesanMGulM (2022) A Bayesian BWM and VIKOR-based model for assessing hospital preparedness in the face of disasters. Natural Hazards111(2): 1603–1635.
50.
ShahzadKKhanSAIqbalA, et al. (2026) Identifying university librarians’ readiness to adopt artificial intelligence (AI) for innovative learning experiences and smart library services: An empirical investigation. Global Knowledge, Memory and Communication75: 646–668. Epub ahead of print 3 June 2024. https://doi.org/10.1108/GKMC-12-2023-0496
51.
ShalTGhamrawiNNaccacheH (2024) Leadership styles and AI acceptance in academic libraries in higher education. Journal of Academic Librarianship50(2): 102849.
52.
TalibFAsjadMAttriR, et al. (2019) Ranking model of total quality management enablers in healthcare establishments using the best-worst method. TQM Journal31(5): 790–814.
53.
VehtariAGelmanAGabryJ (2017) Erratum to: Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Statistics and Computing27(5): 1433–1432.
54.
VranaR (2026) A study of Croatian public libraries: Challenges and opportunities in embracing artificial intelligence. Public Library Quarterly45: 144–166.
55.
WangYYeoGTNgAKY (2014) Choosing optimal bunkering ports for liner shipping companies: A hybrid fuzzy-Delphi–TOPSIS approach. Transport Policy35: 358–365.
56.
WickhamHAverickMBryanJ, et al. (2019) Welcome to the tidyverse. Journal of Open Source Software4(43): 1686.
57.
WinklerBKiszlP (2022) Views of academic library directors on artificial intelligence: A representative survey in Hungary. New Review of Academic Librarianship28(3): 256–278.
58.
ZadehLA (1965) Fuzzy sets. Information and Control8(3): 338–353.
59.
ZhaiY (2025) Research on library management paradigm in the AIGC era: Theoretical construction and practical exploration. Journal of Academic Librarianship51(4): 103085.
60.
ZondiNPEpizitoneANkomoN, et al. (2024) A review of artificial intelligence implementation in academic library services. South African Journal of Libraries and Information Science90(2): 1–8.
