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. 2026 May 20;14:1841351. doi: 10.3389/fpubh.2026.1841351

From information literacy to health literacy: AI-driven transformation in university libraries under digital public health—a perspective

Ya-xin Sun 1, Li-ying Wu 1,*
PMCID: PMC13229875  PMID: 42245363

Abstract

In the context of digital public health, the growing integration of artificial intelligence (AI) into health information environments challenges the adequacy of traditional information literacy frameworks in academic libraries. This perspective article argues for a transition from information literacy to AI-mediated health literacy education. It clarifies the conceptual distinctions between these two constructs, analyzes the AI-driven mechanisms reshaping educational paradigms, and identifies key institutional, technological, professional, and evaluative challenges. The article further proposes practical pathways that include mission reorientation, professional capacity building, platform development, collaborative governance, and evaluation system construction. This study advances existing literature by proposing an integrated, AI-mediated framework. This framework reconceptualizes health literacy within the context of digital public health. It also operationalizes this transformation through three interconnected dimensions: technological, educational, and governance-related. By outlining a theoretical framework and actionable strategies, it positions academic libraries as essential infrastructure for health literacy cultivation, contributing to health promotion and equity in an increasingly digital age.

Keywords: artificial intelligence, digital public health, health communication, health literacy, information literacy, university library

1. Introduction

The rise of digital public health marks a paradigm shift in health governance, transitioning from a disease-centered model to one focused on comprehensive health management (1). Within this evolving landscape, health communication and health literacy have become essential components of public health infrastructure (1). As individuals navigate an increasingly mediated information environment, their ability to access, interpret, evaluate, and apply health-related information plays a critical role in shaping health inequities (2). Academic libraries have traditionally served as key sites for information literacy instruction, fostering students’ critical information skills (3, 4).

However, the growing integration of artificial intelligence (AI) into the production, distribution, and interactive dynamics of health information has challenged the adequacy of conventional information literacy frameworks (5, 6). These frameworks, which primarily emphasize search skills, are ill-equipped to address the complexities of an AI-infused health information environment characterized by algorithmic mediation, multimodal content, and the convergence of credible and misleading information (7). While conventional information literacy frameworks remain effective in relatively stable knowledge environments, they are predominantly grounded in cognitive models centered on information retrieval and evaluation (8). Such models insufficiently account for the algorithmically mediated epistemic conditions in which information visibility, credibility, and relevance are continuously shaped by opaque computational processes (9, 10). As a result, a critical conceptual limitation emerges: the absence of a context-sensitive and action-oriented literacy framework capable of linking informational comprehension with real-world health decision-making in AI-driven environments (8, 11). This disconnect calls for a fundamental reassessment of the educational role of academic libraries and a deliberate shift from generic information literacy toward a context-sensitive health literacy paradigm.

From a digital public health perspective, this article proposes a framework for transforming information literacy into health literacy within academic libraries through AI-mediated approaches. It advocates for the comprehensive integration of technological considerations, health-specific contexts, and pedagogical strategies to develop a literacy model that meets the demands of contemporary health governance. The perspective article aims to clarify the logic underlying this transformation and to outline practical pathways for its implementation. As a perspective article, it does not seek to present empirical evidence but rather to encourage theoretical reflection and critical dialogue among researchers and practitioners regarding the future direction of academic library education, thereby providing a conceptual foundation for further inquiry and practice.

2. Logical foundations of the transition: from information literacy to health literacy

2.1. Relationship and distinction between information literacy and health literacy

A clear conceptual distinction between information literacy and health literacy is essential for understanding the rationale behind this transition. Information literacy traditionally emphasizes the ability to locate, retrieve, and evaluate information in general contexts (1). Health literacy, by contrast, extends beyond these cognitive skills to include the capacity to comprehend, critically appraise, and apply health-related information in ways that inform decisions and support health-promoting behaviors (12, 13). This distinction is especially important in an era marked by health information overload and the widespread proliferation of AI-generated content (14). The large volume and diversity of digital health information—much of which is algorithmically curated or synthetically produced—require not only evaluative skills but also contextual judgment and the ability to translate information into action (14). Health literacy therefore incorporates situational and behavioral dimensions that generic information literacy does not fully capture, making it a more suitable educational framework for navigating today’s complex health information environment (7) (Figure 1).

Figure 1.

Flowchart illustrates the transition from information literacy to health literacy through an AI-mediated transformation. Information literacy includes search, evaluation, and general skills, which undergo an AI-driven shift of algorithmic mediation, personalization, and misinformation filtering, resulting in enhanced health literacy emphasizing contextual understanding, decision-making, and behavioral action.

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Conceptual transition from information literacy to Al-mediated health literacy.

From a theoretical standpoint, health literacy can be further conceptualized as a multidimensional construct encompassing functional, interactive, and critical levels, which correspond to progressively higher degrees of cognitive processing and social engagement (8). In the context of AI-mediated information ecosystems, these dimensions undergo substantive transformation (9). Functional health literacy extends beyond basic comprehension to include the ability to navigate algorithmically curated and dynamically personalized content environments (10). Interactive health literacy increasingly involves engagement with digital interfaces, including conversational agents and intelligent recommendation systems, requiring users to interpret and respond to system-generated outputs (9, 10). At the highest level, critical health literacy entails the capacity to interrogate underlying algorithmic structures, assess data provenance, and evaluate the credibility and potential bias of AI-generated information (9). This theoretical reconfiguration underscores a shift from static information processing toward adaptive, context-sensitive competencies, thereby providing a more robust conceptual foundation for understanding health literacy in digital public health settings.

2.2. Evolution of digital public health communication

This conceptual shift reflects broader changes in digital public health communication. Traditional models of health communication were largely based on one-way transmission of expert knowledge (15). In the current digital landscape, communication has become increasingly interactive, participatory, and tailored to individual needs and circumstances (16, 17). This evolution places new demands on educational institutions, which should now equip individuals with the skills not only to receive information but also to engage with, contribute to, and critically assess health information across diverse platforms and formats (18).

Importantly, these transformations give rise to a reconfiguration of literacy competencies that extends beyond conventional communication paradigms (8, 11). Individuals are increasingly required to interpret algorithmically personalized health recommendations, critically evaluate AI-generated content in terms of reliability and bias, and synthesize multimodal information streams—including text, visual, and interactive formats—into context-sensitive health decisions (9–11, 19). Such competencies reflect a shift from passive information processing toward active, judgment-oriented engagement, thereby necessitating corresponding pedagogical innovation in curriculum design and instructional strategies (8, 19).

2.3. Redefining the role of university libraries

These developments require a fundamental redefinition of the academic library’s role. Historically positioned as an information intermediary, the library is now called upon to serve as a hub for health literacy cultivation (12). This shift addresses a key concern of digital public health: the social determinants of health (20). By embedding health literacy education into their core mission, academic libraries can help address the structural factors that shape health outcomes, including disparities in access, understanding, and agency (21, 22). In doing so, they move beyond a transactional model of information provision toward a more integrative and socially responsive educational function (23, 24). This reorientation forms the logical foundation for the AI-mediated transformation of literacy education explored in this study.

3. AI-driven mechanisms of transformation: technological integration and educational restructuring

3.1. AI as a catalyst for transformation

AI acts as a critical catalyst in the transition from information literacy to health literacy within academic libraries (25). Its applications span several domains essential to health information management, including intelligent content filtering, personalized recommendation systems, automated detection of misinformation, and AI-mediated behavioral interventions (26, 27). These capabilities not only reshape the information environment but also profoundly influence the content and delivery of literacy education (28). By automating aspects of information triage and personalization, AI shifts the educational focus from foundational retrieval skills toward higher-order competencies such as critical appraisal, contextual judgment, and informed decision-making in health contexts (28).

However, the integration of AI into health literacy education also introduces a set of structural and epistemic risks that warrant critical consideration (29, 30). Algorithmic bias embedded within data-driven systems may systematically distort information exposure, thereby reinforcing existing disparities in health knowledge access (29, 31). In addition, the opacity of algorithmic processes—often described as “black-box” decision-making—limits users’ ability to interrogate the provenance and reliability of recommended content (32, 33). Furthermore, excessive reliance on automated recommendations may attenuate learners’ independent evaluative capacities, potentially undermining the development of critical health literacy (30). Collectively, these limitations may compromise the trustworthiness of health information ecosystems and exacerbate existing inequities if not explicitly addressed within educational design and governance frameworks (31, 34).

3.2. Three dimensions of paradigm shift

This catalytic function gives rise to a threefold transformation in educational paradigms. First, in terms of content, instruction expands from generic information skills to include health information evaluation and health decision-making competencies (1, 12). This shift reflects the recognition that effective health literacy requires domain-specific knowledge and situational application. Second, with regard to methodology, education evolves from standardized training toward AI-assisted personalized learning pathways (35). Adaptive learning systems can tailor educational experiences to individual learners’ prior knowledge, skill gaps, and learning preferences, thereby enhancing engagement and outcomes (26, 35). For instance, AI-driven recommendation systems embedded within university library platforms can dynamically curate personalized health information resources based on users’ prior search behaviors and interaction patterns, thereby enabling more targeted and context-sensitive learning experiences (11, 19). Third, concerning context, educational delivery extends beyond traditional library services to achieve deeper integration with medical curricula, research processes, and community health initiatives (36). This contextual expansion embeds health literacy education into the academic and social environments where learners operate. In parallel, conversational agents and AI-enabled simulation tools can be employed to recreate health consultation scenarios, allowing learners to engage in interactive decision-making processes and apply acquired knowledge in practice-oriented contexts (37–39). Such implementations illustrate how the proposed three-dimensional framework can be operationalized within real-world educational settings, thereby enhancing both its practical relevance and pedagogical effectiveness (40, 41).

3.3. A data-driven closed loop in education

Underpinning these transformations is a data-driven educational loop. By establishing a closed-loop mechanism that includes identifying health information needs, delivering AI-assisted learning interventions, and systematically assessing health literacy outcomes, academic libraries can adopt a more responsive and evidence-informed educational model (25, 42). This cyclical process enables continuous refinement of instructional strategies based on real-world performance data, ensuring that health literacy education remains adaptive, effective, and aligned with the evolving demands of digital public health (17, 43) (Figure 2).

Figure 2.

Circular flowchart illustrating four steps: Step 1, Health Information Needs Identification; Step 2, AI-enabled Learning Intervention; Step 3, Health Literacy Outcome Assessment; Step 4, Feedback and Optimization, with directional arrows connecting each step sequentially.

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Al-driven closed-loop model for health literacy education in academic libraries.

To translate this conceptual model into practice, the operationalization of such closed-loop systems requires scalable data infrastructures capable of integrating heterogeneous learning and health-related data streams, alongside clearly defined governance frameworks to regulate data access, ownership, and accountability (44). This requirement becomes particularly critical in resource-constrained environments, where limitations in technical capacity and institutional support may hinder implementation and sustainability (45). Furthermore, embedding ethical principles into system design is essential to ensure responsible and trustworthy deployment; this includes safeguarding data privacy, establishing robust informed consent mechanisms, and promoting algorithmic transparency to mitigate risks associated with automated decision-making (44). Collectively, these considerations extend the closed-loop model from a purely pedagogical construct to an operational and ethically grounded framework suitable for real-world application.

4. Key challenges in the transition

The transition from information literacy to AI-mediated health literacy education in academic libraries faces multiple interrelated challenges across institutional, technological, professional, and evaluative dimensions.

4.1. Conceptual and organizational barriers

At the institutional level, conceptual and organizational barriers remain significant. The role of academic libraries in health literacy education has yet to be fully recognized within the broader university structure (46). Health literacy is often viewed as belonging to medical or public health schools, leaving libraries on the periphery (1, 12). This ambiguous position is worsened by the lack of established mechanisms for cross-departmental collaboration (1, 47). Without formal partnerships with academic departments, health services, and research units, libraries struggle to integrate health literacy instruction into the institutional framework in a sustained and scalable way (47).

Beyond these observable structural constraints, the barriers also reflect deeper governance-level challenges embedded within institutional systems (48, 49). Specifically, fragmented authority across administrative and academic units leads to unclear responsibility allocation, while competing disciplinary priorities hinder the alignment of shared educational objectives (48). In addition, the absence of effective incentive mechanisms—such as performance evaluation metrics or dedicated funding structures—further constrains cross-sector collaboration and limits the long-term sustainability of integrated health literacy initiatives (48). Collectively, these governance dynamics not only impede coordination but also weaken institutional capacity to operationalize interdisciplinary educational transformation (48).

4.2. Risks in AI technology application

Technological challenges arise from the use of AI in health education contexts. Algorithmic bias poses a significant risk, as AI systems may perpetuate or even amplify existing disparities in health information access and representation (50, 51). These risks extend beyond technical limitations and have systemic implications for health equity, as algorithmic decision-making processes may encode and reinforce pre-existing social and structural inequalities (52). Privacy concerns are equally pressing, especially regarding the collection and use of personal health data in learning environments (53). Inadequate data governance mechanisms may further exacerbate vulnerabilities, particularly for populations with limited digital literacy or reduced capacity to control personal data use (52).

In addition, the spread of AI-generated content raises fundamental questions about information authenticity, requiring learners to exercise greater critical judgment (54). Unequal access to AI-enabled educational resources may also contribute to a widening digital divide, whereby already disadvantaged groups face compounded barriers in acquiring reliable health information and developing critical evaluation skills (55). These technological risks introduce new ethical considerations that educational frameworks should explicitly address (26). Collectively, these dynamics may erode public trust in digital health systems, thereby undermining the effectiveness and legitimacy of AI-mediated health education initiatives if not proactively addressed through equitable design and governance strategies (56, 57).

4.3. Insufficient professional competencies of librarians

The professional capacity of library staff represents another critical challenge. Effective health literacy education requires a combination of competencies that go beyond traditional librarianship (47, 58). These include foundational knowledge in health sciences, proficiency in AI technologies, and skills in health communication (59). The current shortage of professionals with such interdisciplinary expertise limits the scope and quality of health literacy initiatives (58, 60). Addressing this gap requires structured training pathways, interdisciplinary curricula, and institutional investment in continuous professional development. These elements are necessary to sustain competency transformation.

4.4. Absence of health literacy assessment frameworks

Finally, the absence of robust evaluation frameworks hinders progress. There is a notable lack of validated assessment tools designed specifically to measure health literacy outcomes in higher education settings (1). Without reliable instruments to assess learning effectiveness, institutions struggle to demonstrate impact, secure support, and refine instructional approaches (2, 61). Addressing these multifaceted challenges is essential to realizing the full potential of AI-driven health literacy education in academic libraries.

5. Practical pathways for digital public health

Realizing the transition from information literacy to AI-mediated health literacy education requires a coherent set of practical pathways addressing institutional strategy, professional development, resource infrastructure, collaborative governance, and evaluation mechanisms. To enhance practical applicability, each pathway can be operationalized through clearly defined stakeholders, implementation strategies, and measurable outcomes.

5.1. Pathway one: redefining institutional mission

The first step is to embed health literacy into the core mission of academic libraries (62). This involves a strategic shift from viewing the library as an information center to establishing it as a hub for health literacy cultivation (63). Such reorientation requires institutional recognition, leadership commitment, and alignment with broader university priorities related to health and well-being.

5.2. Pathway two: rebuilding professional competencies

Effective implementation depends on a reconfigured workforce. A competency framework combining librarianship, health sciences, and AI literacy should be developed to guide professional development (64, 65). Creating dedicated positions focused on health literacy education can help institutionalize this function and attract interdisciplinary talent (64, 66).

5.3. Pathway three: developing resources and AI-driven platforms

Developing specialized infrastructure is essential. This includes curating authoritative health information repositories and designing AI-enabled learning platforms that deliver personalized, adaptive support (26). Such platforms can tailor content to individual learners’ needs, track progress, and provide timely interventions, thereby enhancing engagement and learning outcomes (67, 68). The successful development and implementation of these platforms require the coordinated involvement of key stakeholders, including library administrators, information technology specialists, public health experts, and academic faculty (69). The effectiveness of such infrastructure should be evaluated through measurable outcomes, encompassing user engagement metrics, health literacy assessment scores, and system usability indicators (19).

5.4. Pathway four: fostering collaborative governance

No single unit can accomplish this transition alone. A networked governance structure involving libraries, schools of public health, university health services, and student affairs departments is needed to form a health literacy education consortium (1, 70). This collaborative model enables resource sharing, curriculum integration, and coordinated outreach, ensuring that health literacy education is both comprehensive and sustainable (36).

5.5. Pathway five: establishing evaluation systems

Progress should be measured with appropriate assessment tools. Developing validated instruments to evaluate health literacy outcomes in higher education settings is a priority (71, 72). Moreover, integrating health literacy into the broader student competency assessment framework signals institutional commitment and encourages learner engagement (2, 73). Together, these pathways offer a systematic approach to advancing health literacy education in alignment with the principles and demands of digital public health (Supplementary Table 1).

6. Summary

This perspective article has argued that the transition from information literacy to AI-mediated health literacy education in academic libraries is both a necessary response to the evolving landscape of digital public health communication and a key direction for the innovation of library functions. As AI increasingly shapes the production, dissemination, and consumption of health information, traditional information literacy frameworks are no longer sufficient to equip learners with the competencies needed to navigate this complex environment. Health literacy, with its focus on contextual judgment, decision-making, and behavioral application, offers a more suitable educational paradigm.

By outlining the logical foundations of this transition, examining AI-driven mechanisms of transformation, identifying key challenges, and proposing practical pathways, this paper provides a conceptual framework for reimagining the role of academic libraries within the digital public health ecosystem. This framework makes a unique contribution by integrating AI-mediated mechanisms with a health literacy paradigm within academic library systems. It offers a structured yet adaptable model for digital public health education. The analysis offers insights for institutional policy development, educational reform, and professional capacity building.

Looking forward, there is a pressing need for interdisciplinary research to explore best practices in AI-enhanced health literacy education. Future research should focus on empirical validation and cross-institutional implementation models. It should also aim to develop standardized evaluation tools that can help translate this conceptual framework into scalable practice. Such efforts should aim to establish academic libraries as essential infrastructure for public health communication, positioning them to contribute meaningfully to health promotion and health equity in an increasingly digital age.

Funding Statement

The author(s) declared that financial support was not received for this work and/or its publication.

Footnotes

Edited by: Laura Maaß, University of Bremen, Germany

Reviewed by: Santy Irene Putri, Politeknik Kesehatan Wira Husada Nusantara Malang, Indonesia

Data availability statement

The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.

Author contributions

Y-xS: Resources, Writing – original draft, Writing – review & editing, Data curation, Conceptualization, Visualization, Methodology, Validation. L-yW: Writing – original draft, Writing – review & editing, Investigation, Data curation, Project administration, Conceptualization, Validation, Visualization, Supervision.

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that Generative AI was not used in the creation of this manuscript.

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpubh.2026.1841351/full#supplementary-material

Table_1.DOCX (12.1KB, DOCX)

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Table_1.DOCX (12.1KB, DOCX)

Data Availability Statement

The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.

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