Will AI Replace Library Science Professors? Teaching the Science of Information in an AI World

The Professors Who Teach People How to Organize Knowledge

In an era when AI can search, categorize, and summarize information at superhuman speed, what happens to the people who teach others how to organize and manage information for a living?

That is the question facing roughly 6,800 library science teachers [Fact] at postsecondary institutions across the United States. These professors teach the next generation of librarians, archivists, and information professionals how to organize knowledge, preserve cultural records, navigate digital systems, and conduct research. It is a small but vital profession that sits at the center of a field AI is transforming from the inside out.

Our analysis shows library science teachers face an overall AI exposure of 57% and an automation risk of 32 out of 100 [Fact]. That is a high exposure level, placing this profession above the average for education roles but below the danger zone occupied by heavily automated fields. The classification is augment, not automate: AI is changing how these professors work, not eliminating the need for them.

The Task-by-Task Reality

The story of AI in library science teaching is really three different stories, depending on which task you examine.

Preparing course materials and reading lists sits at 58% automation [Fact]. This is where AI has the most immediate impact. Library science is a field where the curriculum must constantly evolve to reflect new technologies, new standards, and new approaches to information management. AI tools can now scan current literature, identify trending topics in information science, suggest updated reading lists, and even generate draft syllabi based on learning objectives. A professor who once spent weeks preparing materials for a new course on, say, AI-driven cataloging systems can now have an initial framework in hours.

But here is the nuance that the automation number alone does not capture: library science professors are not just assembling information. They are curating it with expert judgment about what matters, what is rigorous, and what will challenge their students to think critically. The difference between an AI-generated reading list and a professor's curated reading list is the difference between a bibliography and an education.

Grading assignments and evaluating student research is at 52% automation [Fact]. AI-powered grading tools can handle certain types of assessment with reasonable accuracy: multiple-choice tests, standardized rubric application, even preliminary feedback on written assignments. For a field that increasingly involves evaluating students' ability to build metadata schemas, design search algorithms, and manage digital archives, AI can provide first-pass evaluation that saves significant time.

However, evaluating a graduate student's original research on, say, the ethics of AI-driven censorship in digital libraries requires the kind of critical engagement that no automated system can provide. The professor needs to assess not just technical accuracy but originality of thought, quality of argumentation, and contribution to the field.

Conducting seminars and mentoring graduate students sits at just 18% automation [Fact]. This is the irreducible human core of academic work. A seminar is not a lecture. It is a dynamic intellectual exchange where ideas are tested, challenged, and refined in real time. Mentoring involves understanding a student's unique strengths, career aspirations, and intellectual development over months or years. These are fundamentally relational activities that AI cannot perform.

The Exposure Timeline: Steady Climb

Library science teaching shows a consistent upward trajectory:

• 2024: Overall exposure at 52%, observed adoption at 32% [Fact]
• 2025: Exposure at 57%, observed adoption at 38% [Estimate]
• 2026 (projected): Exposure reaches 62%, automation risk at 36% [Estimate]
• 2028 (projected): Exposure at 70%, automation risk 44% [Estimate]

The theoretical exposure reaches 86% by 2028 [Estimate], which might sound alarming. But the gap between theoretical and observed exposure is critical. Just because AI could theoretically assist with 86% of the information-processing aspects of teaching does not mean it will replace 86% of the professor's role. Much of what makes a great teacher, especially in graduate education, is not about information processing at all. It is about inspiration, challenge, and human connection.

The Paradox: AI Makes Library Science More Important

Here is the most interesting twist in this story. The very technologies that increase AI exposure for library science professors also increase the demand for what they teach.

As organizations struggle with information overload, data governance, AI bias in search algorithms, digital preservation challenges, and the ethics of automated classification systems, the expertise that library science programs produce becomes more valuable, not less. Someone has to teach the next generation how to think critically about how knowledge is organized, who controls access, and what happens when AI systems encode biases into cataloging and search.

The BLS projects modest +3% job growth through 2034 [Fact], which reflects the small size of the field rather than declining relevance. The median annual wage of approximately ,540 [Fact] is competitive for postsecondary education. And as more universities expand their information science programs to address the AI-driven demand for data governance expertise, the role of the professor who can bridge traditional library science with emerging AI concepts becomes increasingly valuable.

What Library Science Teachers Should Do Now

Integrate AI into your curriculum, not just your workflow. Your students need to graduate understanding how AI changes information organization, search, and access. Teaching them to use AI tools critically, understand their limitations, and design systems that account for AI bias will make your program more relevant and your graduates more employable.

Use AI to teach better, not just faster. If AI handles the first pass of grading, you can spend more time on the nuanced feedback that actually develops your students' thinking. If AI generates an initial syllabus framework, you can invest that saved time in designing more innovative seminar experiences.

Position yourself at the intersection of tradition and innovation. The professors who will be most valued are those who can connect the enduring principles of information science, classification theory, preservation ethics, equitable access, to the new realities of AI-driven knowledge systems. That bridge-building requires deep expertise in both domains.

Explore the full data for Library Science Teachers on AI Changing Work to see detailed automation metrics and the complete exposure timeline.

Related: AI in Education Roles

• Will AI Replace Teachers? — How AI affects K-12 and postsecondary education
• Will AI Replace Professors? — The broader academic profession
• Will AI Replace Librarians? — What is happening to the profession your students will enter

Explore all occupation analyses on our blog.

Sources

• Anthropic. (2026). The Anthropic Labor Market Impact Report.
• U.S. Bureau of Labor Statistics. Occupational Outlook Handbook — Postsecondary Teachers.
• O*NET OnLine. 25-1082.00 — Library Science Teachers, Postsecondary.

Update History

• 2026-03-30: Initial publication

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This analysis is based on data from the Anthropic Labor Market Report (2026) and U.S. Bureau of Labor Statistics projections. AI-assisted analysis was used in producing this article.