Generative Artificial Intelligence (AI) has emerged as a disruptive technology with transformative potential for academic libraries. The *Library Trends* two-part series (Vol. 73, Issues 3 & 4, 2025) provides a foundational exploration of AI’s impact on libraries from multiple perspectives, including ethics, pedagogy, labour, and decolonial approaches.

Ethical Challenges and Bias in Generative AI

Generative AI systems pose significant ethical challenges that academic libraries must navigate carefully. One key concern is algorithmic bias, where AI models trained on historical data amplify existing societal inequities, leading to unfair or inaccurate information retrieval outcomes. A 2025 scoping review by Igbinovia highlights how AI biases affect Information Retrieval Systems (IRS) and calls upon LIS professionals to engage in ethical data curation, algorithmic auditing, and policy advocacy to mitigate harm [1].

Beyond bias, reliable and trustworthy output remains a challenge. Generative AI is prone to “hallucinations,” producing factually incorrect or fabricated information, which can impair academic integrity [2]. Georgetown University’s guidance emphasises that AI-generated text must be critically evaluated and transparently attributed to avoid plagiarism and misinformation [3].

Ethical AI practice mandates human accountability, transparency, data privacy, and fairness [2][4]. Stahl et al. (2022) link these principles to European regulations, emphasising protection of fundamental rights in AI governance [5]. Researchers advocate integrating moral values into AI systems through frameworks such as utilitarianism, deontology, virtue ethics, and care ethics to promote equitable AI designs [6]. Virtue ethics, in particular, offers nuanced guidance focusing on moral character in decision-making, echoing the calls in *Library Trends* for character-based ethical frameworks around AI use [7][5].

AI Literacy: Skills and Pedagogy in Academic Libraries

Effective AI literacy emerges as a critical response to ethical and practical challenges. Leo S. Lo’s framework for AI literacy in academic libraries underscores the need for broad technical knowledge, ethical awareness, critical thinking, and practical skills to empower users and librarians alike [8]. The widespread recognition of AI’s impact has driven many academic libraries to develop literacy programs; Clarivate and ACRL Choice launched a free eight-week micro-course on AI literacy essentials addressing this urgent need [9].

Studies consistently reveal gaps in preparedness among LIS professionals to teach AI literacy, with softer ethical competencies sometimes outperforming harder technical skills [10]. Pedagogical research stresses incorporating critical information literacy, enabling users to evaluate biases and misinformation in AI-generated content [7][11]. Workshop case studies demonstrate successful models for teaching responsible AI use grounded in theoretical frameworks such as post-phenomenology and critical pedagogy [12].

Impacts on Library Labour and Professional Practice

Generative AI is reshaping library workflows and professional roles, presenting both opportunities and disruptions. Research shows growing adoption of AI tools to improve productivity in cataloguing, classification, reference, and research services [13]. However, concerns persist about job displacement, skill obsolescence, and ethical use of automations [7][14].

Luo’s survey highlights varied librarian experiences using AI in daily tasks, emphasising the need for ongoing training and support [14]. The impact of labour extends to how libraries organise instruction and reference service labour—areas analysed in *Library Trends* through the lens of material conditions of instruction and professional identity shifts [7]. Scholars call for thoughtful policy development to balance AI efficiency gains with humane labour practices that preserve professional autonomy [15].

Addressing Algorithmic Bias in Information Retrieval

Algorithmic bias is widely acknowledged as a serious risk in library AI applications. Workshops like the BIAS 2025 at SIGIR concentrate on developing strategies for fairer search and recommendation systems [16]. These initiatives complement academic calls for algorithmic audits and inclusion of diverse datasets to improve AI fairness and transparency [1]. LIS professionals’ role is pivotal in advocating for ethical AI in information retrieval, ensuring algorithms do not perpetuate discriminatory outcomes. Training in algorithmic literacy allows librarians to audit AI tools critically and promote equitable access to information [1]

Decolonial and Equity-Oriented AI Perspectives

Decolonial approaches to AI demand centring Indigenous knowledge systems and challenging Western epistemologies embedded in AI designs. Works like those by Cox and Jimenez in *Library Trends* highlight the necessity of decolonising digital libraries through ethical AI frameworks [7]. Such perspectives align with broader global calls to recognise AI’s sociocultural impacts and counteract systemic biases [7].

These approaches intersect with data privacy and user equity concerns, emphasising transparency, inclusiveness, and community engagement as core principles for responsible AI governance in libraries [17].

Future Directions and Recommendations

• The converging research points to several actionable recommendations for academic libraries integrating generative AI:
• Develop comprehensive AI literacy programs_ that include ethics, critical thinking, and technical training for librarians and patrons [8][9].
• Engage in ongoing algorithmic auditing and bias mitigation efforts, leveraging multi-disciplinary partnerships to ensure fair and transparent systems [1][16].
• Adopt ethical frameworks, including virtue ethics, to guide AI policy, design, and usage decisions, emphasising accountability and human flourishing [7][5][6].
• Support library labour through upskilling and redefining roles to optimise human-AI collaboration rather than simple automation-driven displacement [7][14].
• Incorporate decolonial methodologies in AI development and deployment to elevate marginalised perspectives and knowledge systems [7].
• Maintain vigilant attention to data privacy and user consent within AI systems, upholding trust and ethical standards [2].

Selected References

• 1. Igbinovia, M. O. (2025). Artificial intelligence algorithm bias in information retrieval: Implications for LIS professionals. Journal of Information Science, 51(4). https://doi.org/10.1080/07317131.2025.2512282
• 2. Dilmegani, C., & Ermut, S. (2025). Generative AI Ethics: Concerns and How to Manage Them? AI Multiple. https://research.aimultiple.com/generative-ai-ethics/
• 3. Lo, L. S. (2025). AI Literacy: A Guide for Academic Libraries. College & Research Libraries News, 86(3). https://digitalrepository.unm.edu/ulls_fsp/210/
• 4. Georgetown University Libraries. (2023). Ethics & AI. https://guides.library.georgetown.edu/ai/ethics
• 5. Gmiterek, G. (2025). Generative artificial intelligence in the activities of librarians. Journal of Academic Librarianship. https://www.sciencedirect.com/science/article/abs/pii/S0099133325000394
• 6. Mwantimwa, K. (2025). Application of generative artificial intelligence in library operations. Library Hi Tech News. https://www.tandfonline.com/doi/full/10.1080/07317131.2025.2467574
• 7. Stahl, B. C., et al. (2022). AI ethics and governance in Europe. Ethics and Information Technology, 24(1). https://link.springer.com/article/10.1007/s10676-021-09598-z
• 8. Education and Library Trends on AI, 2025. Library Trends Vol. 73(3) & (4). https://ischool.illinois.edu/news-events/news/2025/09/library-trends-completes-two-part-series-ai-and-libraries
• 9. BIAS Workshop @ SIGIR 2025. (2025). International Workshop on Algorithmic Bias. https://biasinrecsys.github.io/sigir2025/

Sources:

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• [2] Generative AI Ethics: Concerns and How to Manage Them? https://research.aimultiple.com/generative-ai-ethics/
• [3] Ethics & AI – Artificial Intelligence (Generative) Resources https://guides.library.georgetown.edu/ai/ethics
• [4] AI Ethical Guidelines. https://library.educause.edu/resources/2025/6/ai-ethical-guidelines
• [5] Philosophy and Ethics in the Age of Artificial Intelligence https://jisem-journal.com/index.php/journal/article/download/9232/4266/15377
• [6] Integrating Moral Values in AI: Addressing Ethical … https://journals.mmupress.com/index.php/jiwe/article/view/1255
• [7] Library Trends completes two-part series on AI and libraries https://ischool.illinois.edu/news-events/news/2025/09/library-trends-completes-two-part-series-ai-and-libraries
• [8] AI Literacy: A Guide for Academic Libraries by Leo S. Lo https://digitalrepository.unm.edu/ulls_fsp/210/
• [9] Bridging the AI skills gap: Literacy program academic … https://about.proquest.com/en/blog/2025/bridging-the-ai-skills-gap-a-new-literacy-program-for-academic-libraries/
• [10] AILIS 1.0: A new framework to measure AI literacy in library … AILIS 1.0: A new framework to measure AI literacy in library and information science (LIS) https://www.sciencedirect.com/science/article/abs/pii/S0099133325001144
• [11] Information Literacy for Generative AI https://edtechbooks.org/ai_in_education/information_literacy_for_generative_ai?tab=images
• [12] Fostering AI Literacy in Undergraduates: A ChatGPT Workshop Case Study https://digitalcommons.lmu.edu/cgi/viewcontent.cgi?article=1178&context=librarian_pubs
• [13] Application of generative artificial intelligence in library operations and service delivery: A scoping review. https://www.tandfonline.com/doi/full/10.1080/07317131.2025.2467574
• [14] Library Trends examines generative AI in libraries http://ischool.illinois.edu/news-events/news/2025/06/library-trends-examines-generative-ai-libraries
• [15] Leo Lo – libraries #generativeai #openaccess #innovation https://www.linkedin.com/posts/leoslo_libraries-generativeai-openaccess-activity-7269345269811408896-jWcM
• [16] International Workshop on Algorithmic Bias in Search and Recommendation (BIAS 2025) https://dl.acm.org/doi/10.1145/3726302.3730357
• [17] Exploring the integration of artificial intelligence in libraries https://ijlsit.org/archive/volume/9/issue/1/article/3116
• [18] Generative artificial intelligence in the activities of academic libraries of public universities in Poland. https://www.sciencedirect.com/science/article/abs/pii/S0099133325000394
• [19] Practical Considerations for Adopting Generative AI Tools in Academic Libraries https://www.tandfonline.com/doi/full/10.1080/01930826.2025.2506151?src=exp-la
• [20] The transformative potential of Generative AI in academic library access services: Opportunities and challenges. https://journals.sagepub.com/doi/10.1177/18758789251332800
• [21] How National Libraries Are Embracing AI for Digital Transformation. https://librarin.eu/how-national-libraries-are-embracing-ai-for-digital-transformation/
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• [24] Investigating the “Feeling Rules” of Generative AI and Imagining Alternative Futures.  https://www.inthelibrarywiththeleadpipe.org/2025/ai-feeling-rules/

When I imagine redesigning the Library and Information Science curriculum for the age of AI, I see it semester by semester, like walking through the library stacks, each level taking me closer to new knowledge, but always with a familiar fragrance of books and values.

Semester 1 – The Roots
Here I would begin with Foundations of Library Science, Information Sources & Services, and alongside them introduce Introduction to AI and Data Literacy. Students should learn what algorithms are, how language models work, and why data matters. Just remember, this is not to turn them into computer scientists, but into informed professionals who can converse with both technology and community.

Semester 2 – The Tools
This stage could focus on Knowledge Organization, Cataloguing and Metadata, but reframed to show how AI assists in subject indexing, semantic search, and linked data. Alongside, a course on Digital Libraries and Discovery Systems will let them experiment with AI-powered platforms. By the way, assignments could include building small datasets and watching how AI classifies them — both the brilliance and the flaws.

Semester 3 – The Questions
Here ethics must enter the room strongly. A full course on AI, Ethics, and Information Policy is essential: patron privacy, copyright, algorithmic bias, transparency. At the same time, practical subjects like Digital Curation and Preservation should demonstrate how AI restores manuscripts, enhances images, or predicts file degradation. No wonder, students will begin to see AI as both a tool and a responsibility.

Semester 4 – The Bridge
I see this as a turning point: courses on Human–AI Interaction in Libraries, Information Literacy Instruction in the AI Era, and Data Visualization for Librarians. Students would learn to teach communities about AI tools, to verify machine answers, and to advocate for responsible use. A lab-based course could even simulate AI chatbots for reference desks, showing how humans must stay in the loop.

Semester 5 – The Expansion
By now, students are ready for deeper exploration. They could take electives like AI in Scholarly Communication (covering plagiarism detection, trend forecasting, citation networks) or AI for Community Engagement (local language NLP, accessibility, inclusive design). At the same time, collaboration with computer science or digital humanities departments could be formalized as joint workshops.

Semester 6 – The Future
The final stage should be open-ended: a Capstone Project in AI and Libraries, where each student selects a challenge — say, AI in cataloguing, or a chatbot for local history archives — and builds a small prototype or research study. Supplement this with an Internship or Residency in a library, tech lab, or cultural institution. Just imagine the confidence this gives: they graduate not as passive observers of AI but as active participants in shaping it.

And beyond…
I must not forget lifelong learning. The curriculum should be porous, allowing micro-credentials, short courses, and professional updates, because AI won’t stop evolving. In fact, it will keep testing us — and so our readiness must be continuous.

Looking back at this imagined curriculum, I feel it keeps the spirit of librarianship alive — service, access, ethics — while opening the doors to AI-driven realities. It is like adding a new wing to the old library: modern, glowing, full of machines perhaps, but still part of the same house of knowledge where the librarian remains a human guide.