Perspectives on the Framework

Chereeka Garner

Information as Currency

The Social and Cultural Value of Knowledge in the Algorithmic Age

Chereeka Garner is the digital learning and engagement librarian at the University of Central Florida, email: Chereeka.Garner@ucf.edu.

© 2026 Chereeka Garner

In recent years, generative artificial intelligence (AI) content has been integrated into everyday life. Generative AI tools produce meal plans and shape travel itineraries, often without clear source acknowledgment or attribution. Rapid expansion of generative AI warrants renewed attention and focus on the ACRL Framework for Information Literacy in Higher Education’s Information Has Value frame. Novice learners have difficulty understanding the value of information when free information is abundant.¹ Generative AI further complicates this and issues of authorship, equity, and trust. In the algorithmic age, information operates as economic, social, and cultural currency with its value being shaped equally by technologies, human intentions, and novice learners’ assumptions about AI authority. Unlike value, which suggests a fixed worth, currency emphasizes circulation and exchange gaining worth through movement only within systems that authorize use, gatekeep access, and assign legitimacy. Framing information as currency provides an opportunity for examining generative AI’s impact on these systems in relation to attribution, consent, cultural representation, and institutional authority, all of which must be addressed more directly within the Information Has Value frame.²

Reframing Information Has Value for the Algorithmic Era

The ACRL Framework includes Information Has Value as one of its six core frames, encouraging learners to recognize that information is shaped by core principles: intellectual property, ethics and ethical usage, proper attribution, and the labor of knowledge creation.³ These principles were developed in an ecosystem where creators can be identified, sources traced, and the genesis of ideas is straightforward. Generative AI disrupts this. Large language models (LLMs) use uncredited datasets obscuring origins and authorship. Content is attributed to human knowledge, uncredited data, and secret algorithmic processes with the role of each collaborator unknown. Shifts spurred by AI dictate a reevaluation of how information gains value and who has the authority to define value.

Willful and Forced Engagement with AI Tools

Students’ reasons for using generative AI include AI’s production speed, AI being integrated into software already being used, and student perceptions that everyone else is using it.⁴ Essentially, AI has become available and normalized within academic environments. A recent review of university policies on AI on fifty campuses in the United States found that, often, policy development is left up to individual professors. Allowing individuals to create policy is great for autonomy but unintentionally causes confusion for students with differing guidelines across classes, uncertainty for faculty seeking guidance, and difficulties for librarians working with both groups.⁵

Often, academic institutions promote AI readiness as a strategic priority, declaring adoption as essential for competitiveness and innovation. As a result, students may feel compelled to rely on tools they neither fully trust nor understand. For some, this pressure creates an uneven power dynamic with participation in AI-integrated systems becoming a requirement, not a choice. In these environments, meaningful consent becomes difficult to achieve, and users have limited control over how their data, writing, and behaviors are captured and analyzed. These pressures raise important questions about user autonomy within AI-rich learning environments. Coerced AI engagement forces students to transact in currency systems with rules of value and exchange that are not fully understood.

Cultural Erasure and Algorithmic Bias in AI Training Data

The rise of generative AI has intensified decades-old concerns about whose knowledge is preserved and amplified in information ecosystems. LLMs contain data scraped from the open Internet, reflecting biases and harm embedded in this landscape. The data and how they are used vary widely among tools, with stark differences between free and paid tools.⁶ Knowledge systems from underrepresented cultures and nondigital materials are excluded, presented without context, or misrepresented. Westernized English language content is the majority of what is freely available and becomes disproportionately represented. These imbalances result in tangible harmful consequences. When information functions as cultural currency, its circulation, control, and value are shaped by whose knowledge is within AI training systems.

AI-generated outputs are presented with an illusion of objectivity, but alternative viewpoints may not be included. Control over cultural exchange resides with AI developers and technology companies who determine what data is included, excluded, or weighted during training. These decisions are influenced by market incentives, data availability, and technical convenience rather than by ethical commitments to representation. Communities whose knowledge is absorbed into AI systems rarely maintain agency over how their information is represented, reused, or monetized. Conversely, some AI tools present dynamic viewpoints without being asked. This dynamic moves beyond unintentionally distorting cultures and risks perpetuating harm, when students rely on AI to learn about other communities. Communities underrepresented or misrepresented mimic broader systemic inequities.

These inequalities are intensified when algorithms assign legitimacy and visibility at scale, transforming cultural value into social and economic capital. Ultimately, librarians can teach how AI tools are created and which voices are amplified or diminished. Through the incorporation of critical information literacy, critiques of power systems in relation to information, and the Information has Value frame, students can be encouraged to think before they prompt.⁷

Information as Social Currency in the Algorithmic Age

When information functions as social currency, its value stems from visibility and credibility as assigned and reinforced by algorithms and design choices. In AI-mediated ecosystems, algorithms operate as arbiters of value, shaping what circulates widely, what is legitimized, and whose labor is uncredited. AI accelerates circulation by producing polished and seemingly authoritative content on a large scale, enabling ideas to move quickly before being critically examined. For students who simply want to get an answer and move on, the speed and perceived confidence of AI responses often outweigh concerns about accuracy. As outputs are shared, paraphrased, and recombined, information gains social value through circulation, with AI-generated information being seen and echoed, rather than circulating ideas with traceable contributions or original insight.⁸ Recognizing information as adaptable currency heightens the stakes of the algorithmic age and framing highlights the urgent need for critical engagement with how information’s value is constructed, bargained, and moves.

Attribution and Authorship in AI-Generated Content

AI tools disrupt a well-established set of guidelines regarding authorship. Traditional citation methods function to trace the origins of information. Using AI as an information source, however, complicates the ability to connect ideas to their authors. Free AI tools have challenges with citations not frequently experienced in subscription-based tools. With free tools, AI’s output does not consistently direct the user back to identifiable sources with information often emerging from the unknown. Asking AI for the origins of its information may result in misleading citations, but the unreliability of these citations, and their associated information, may be unclear. AI tools with clear citation tracing methods often require a subscription or are embedded within high-cost databases, commodifying information and pricing out users.⁹

Without clear education on how AI tools operate, students may assume AI generations are original. When using AI for writing assignments, questions emerge. Can AI be cited as an author, and what risks are associated with the inability to critically analyze AI outputs? These issues lead to broader ethical conundrums relating to ownership and labor. If a student has used AI extensively throughout the writing process, are students the sole author? Should AI be listed as a contributor, and will students take the steps to list AI as a contributor? How can the voices of the unattributed original text be credited and/or compensated? AI assistance complicates our understanding of intellectual labor and associated costs.¹⁰ It is imperative that students rethink author responsibility in relation to AI. To guide students, information professionals must develop pedagogical practices that consider these issues. Using responsible AI disclosures and statements should be nonnegotiable. As revisions to the framework are considered, incorporating references to AI tools is necessary.

Teaching Strategies: Helping Students Navigate Information as Currency

AI content reshapes the economy by placing value on their product but not the individual creators. Source transparency regarding AI should be coupled with traditional teaching methods for source attribution, and readers should be informed when they are engaging with AI-generated or AI-assisted content. Citing AI and the differences between citing and disclosing should also be explicitly taught. Limitations of the information resulting from AI output should be shared and discussed so individuals can make informed decisions about usage. These dynamics reveal AI-mediated information systems as economies where participation is compulsory, valuation is uneven, and legitimacy is algorithmically assigned. Information circulates not only as knowledge but also as cultural, social, and economic currency. A currency framework shifts the instructional focus from information being used correctly to discourse about how value, labor, and authority are created, exchanged, and unclear in AI systems. Helping students develop a critical and ethical relationship with generative AI requires instructional approaches that ground information’s role as economic, social, and cultural currency. The following strategies offer a pedagogically grounded framework for supporting students as they navigate AI-mediated information environments.

Transparency and Disclosure

Educators can model responsible AI engagement by designing assignments that prompt students to clearly articulate their use of AI tools. Students should describe whether they used AI, how they used it, and why they selected that approach. This practice demystifies the role of AI in the writing process and reinforces the understanding that disclosure is part of ethical academic participation. Requiring students to disclose AI tool usage exposes hidden stages of information exchange, making circulation visible. Transparent reporting also supports conversations about authorship, intellectual labor, and the boundaries between assistance and creation. As educators, it is also important to practice our own teachings and be transparent about our own usage of AI tools.

Critical Evaluation Frameworks

Critical evaluation teaches students to identify and consider whether information is inflated, devalued, or unstable. Students benefit from structured processes that teach them to interrogate AI outputs rather than accept them at face value. This includes examining model limitations, using structured evaluation processes like lateral reading to analyze content against verified sources, identifying incomplete or biased responses, and asking whose voices may be missing from the dataset. Evaluation activities strengthen research skills and reinforce that AI tools reflect human choices, absences, and inequities.

Information Literacy Activities

Hands-on information literacy activities allow students to trace the transformation of information as it moves through AI systems. Integrating AI-focused activities into information literacy instruction helps students practice metacognitive awareness. Useful exercises include comparing AI-generated text to scholarly sources, analyzing available training data statements, and examining how generative tools paraphrase, compress, or distort work. These activities help students recognize the mechanics of AI transformation and implications for accuracy and integrity.

Ethical and Responsible Use

Ethical instruction requires students to question labor, ownership, and accountability within information exchanges. Educators should emphasize intellectual honesty, fairness, and attribution when discussing AI. This includes teaching when not to use AI. Reflective prompts can help students consider which values are upheld or violated through their AI use and how their decisions impact the broader knowledge ecosystem.

Conclusion

In AI-mediated systems, information functions as cultural, economic, and social currency, regulating circulation and assigning value across knowledge ecosystems, causing learners to reevaluate how to navigate these environments. As the landscape evolves, students need guidance in evaluating AI-generated information, recognizing undisclosed labor, and understanding how information is valued and functions. Librarians and educators play a critical role in encouraging transparency, ethical engagement, and critical information literacies and in empowering students to make informed decisions before they prompt, create, and share.

Notes

1. “Framework for Information Literacy for Higher Education | Association of College and Research Libraries,” accessed March 5, 2026, https://www.ala.org/acrl/standards/ilframework#value.

2. Tobin Hart, “Chapter 2: The Currency of Information,” Counterpoints 162 (2009): 15–31.

3. “Framework for Information Literacy for Higher Education.”

4. Arne Bewersdorff et al., “AI Advocates and Cautious Critics: How AI Attitudes, AI Interest, Use of AI, and AI Literacy Build University Students’ AI Self-Efficacy,” Computers and Education: Artificial Intelligence 8 (2025): 100340, https://doi.org/10.1016/j.caeai.2024.100340.

5. Yunjo An, Ji Hyun Yu, and Shadarra James, “Investigating the Higher Education Institutions’ Guidelines and Policies Regarding the Use of Generative AI in Teaching, Learning, Research, and Administration,” International Journal of Educational Technology in Higher Education 22, no. 1 (2025): 10, https://doi.org/10.1186/s41239-025-00507-3.

6. Helen Pearson, “Can AI Review the Scientific Literature — and Figure out What It All Means?” Nature 635, no. 8038 (2024): 276–78, https://doi.org/10.1038/d41586-024-03676-9.

7. Aurora Cuevas-Cerveró, María-Jesús Colmenero-Ruiz, and Daniel Martínez-Ávila, “Critical Information Literacy as a Form of Information Activism,” The Journal of Academic Librarianship 49, no. 6 (2023): 102786, https://doi.org/10.1016/j.acalib.2023.102786.

8. Ron Darvin, “Identity and Investment in the Age of Generative AI,” Annual Review of Applied Linguistics 45 (2025): 10–27, https://doi.org/10.1017/s0267190525100135.

9. Elizabeth Gibney, “Open-Source AI Tool Beats Giant LLMS in Literature Reviews — and Gets Citations Right,” Nature, February 4, 2026, https://doi.org/10.1038/d41586-026-00347-9.

10. Sarah Moore and Kathryn Lookadoo, “Communicating Clear Guidance: Advice for Generative AI Policy Development in Higher Education,” Business and Professional Communication Quarterly 87, no. 4 (2024): 610–29, https://doi.org/10.1177/23294906241254786.