Information Literacy (IL), defined by the Association of College and Research Libraries (ACRL) as “the set of integrated abilities encompassing the reflective discovery of information, the understanding of how information is produced and valued, and the use of information ethically”. This concept is a fundamental pillar in education. In today’s era of artificial intelligence, these essential skills must confront new challenges and opportunities. Learners are now required to critically evaluate machine-generated outputs that, despite their authoritative appearance, may harbor inaccuracies, biases, or fabricated sources. Developing strong information literacy skills is essential for navigating this complex ecosystem effectively.

Why IL Matters More in the AI Era

Generative AI offers powerful capabilities but also presents significant risks. AI models can generate convincing but false information, reinforce societal biases found in their training data, obscure the original sources of information, and create content at scale without transparency. Users who lack strong information literacy skills may accept AI outputs at face value, which can lead to the spread of misinformation, compromised academic integrity, flawed decision-making in professional settings, and ethical dilemmas related to authorship and originality.

Key reasons IL remains vital include:

• Evaluating Authority and Credibility: AI-generated content lacks clear authorship or verifiable sources. Traditional IL skills empower users to cross-check claims, assess the reliability of underlying data, and understand potential algorithmic biases in AI systems. For instance, users must learn to question whether an AI summary accurately represents peer-reviewed literature or simply synthesizes popular web content.
• Recognizing Bias and Ethical Issues: AI systems can reflect biases present in their massive training datasets. Critical information literacy encourages learners to examine these systems not as neutral tools but as sociotechnical artifacts shaped by human choices, power structures, and commercial interests. This includes scrutinizing issues of data privacy, labor exploitation in data labeling, and environmental costs of large language models.
• Navigating Information Creation: The ACRL Framework views information creation as a dynamic, iterative process. Generative AI accelerates this dramatically but raises questions about originality, intellectual property, attribution, and the changing value of human creativity. Understanding these changes is essential for both consumers and creators of information.
• Combating Misinformation and Synthetic Media: In an environment filled with deepfakes, AI-generated images, and synthetic voices, media and information literacy (MIL) becomes essential for verifying content authenticity and understanding the broader societal implications of AI technologies.

Recent studies indicate that while AI systems can effectively support IL instruction, by scaffolding research processes, generating discussion prompts, or helping break down complex topics, critical evaluation skills must still be taught and practiced. AI should serve as a complement, not a substitute, for human judgment.

A Proposed Way Forward: Integrating AI Literacy with IL

Rather than viewing AI as a threat to information literacy, scholars and practitioners advocate for a thoughtful integration of AI Literacy, the ability to understand, use, evaluate, and critique AI technologies, within existing IL frameworks. AI literacy does not replace traditional IL but extends and enriches it, creating a more comprehensive approach suitable for the current technological landscape.

Practical Implications for Educators and Librarians:

• Teach students how to create effective prompts while always ensuring they verify AI outputs against credible, human-authored sources.
• Apply the ACRL Framework lenses, such as “Authority is Constructed and Contextual” and “Information Creation as a Process,” to analyze AI-generated content critically.
• Develop hybrid assignments that combine AI assistance with reflective components, such as comparing AI-generated summaries to peer-reviewed articles or discussing the ethical implications of using AI in research.
• Explicitly address ethical issues, including the proper citation of AI use, data privacy concerns, intellectual property rights, and the responsible use of these tools in academic and professional settings.
• Utilize libraries as central hubs for AI literacy workshops, providing opportunities for hands-on experimentation alongside critical discussions.

Academic libraries and educational institutions are well-positioned to lead AI integration. Many are already developing AI competencies based on established information literacy principles, positioning librarians as key supporters in helping users succeed in a rapidly changing tech environment.

Conclusion

Information literacy is essential in the AI era. it is evolving and becoming even more important. As generative AI transforms the way we access information, robust information literacy is crucial for leveraging AI’s advantages while minimizing its risks. Educators, librarians, researchers, and policymakers must collaborate to prioritize teaching updated information literacy that incorporates AI technologies, tools and skills. We must ensure that individuals are not just passive consumers of AI-generated content but become active, ethical, and empowered participants in an increasingly automated information world.

Acknowledgement

The author acknowledges the use of generative AI tools (Perplexity, Consensus, ChatGPT) for assisting with the identification of relevant scholarly references and for providing language support during the drafting process.