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https://doi.org/10.59654/btctmw45

Título a dos líneas

Línea 2

Two-line title

Line 2

Artificial intelligence literacy and content

curation: challenges and opportunities for

teachers and university students in France

Alfabetización en inteligencia artificial y curación de conteni-

dos: desafíos y oportunidades para docentes y estudiantes

universitarios en Francia

Abstract

Universities exist to produce science and create new knowledge. Therefore, the work of university professors is increa-

singly diversifying, and research is seen as an activity, a support tool for the improved development of the pedagogical

function. However, for some, research is viewed as complex, costly, and without implications for classroom teaching.

Given this reality, the objective of this research is to evaluate the quality of professor-researchers, based on the Efficiency,

Efficacy, and Effectiveness aspects of this doctoral thesis, which emerges from one of the dimensions of the research

project in Scientific Research Quality Management at UNAN-Managua. The methodology was characterized by a cons-

tructivist paradigm, a mixed approach, and an explanatory study type based on the time of occurrence of the events

and the recording of information. The study was retrospective, and, depending on the period and sequence of the

study, it was cross-sectional. Methods, techniques, tools, and instruments were used to collect and process data..

Keywords: quality, teacher-researcher, efficiency, efficacy, effectiveness, research.

Resumen

Las universidades están para producir ciencia, crear nuevo conocimiento, por lo cual el quehacer del docente univer-

sitario comienza cada vez más a diversificarse y la investigación es una actividad, un instrumento de apoyo para el

mejor desarrollo de la función pedagógica; pero para algunos la investigación lo ven como algo complejo, costoso y

sin implicaciones para la docencia en las aulas. Ante esta realidad el objetivo de esta investigación es evaluar la calidad

de los docentes en la investigación desde la eficiencia, eficacia y efectividad, que surge de una de las dimensiones de

la tesis doctoral en Gestión de la Calidad de Investigación Científica, UNAN-Managua. La metodología se caracterizó

por un paradigma constructivista, enfoque mixto, tipo de estudio explicativo, de acuerdo con el tiempo de ocurrencia

de los hechos y registro de la información, el estudio es retrospectivo y según el período y secuencia del estudio es

transversal, se utilizaron métodos, técnicas, herramientas e instrumentos para recolectar y procesar datos.

Palabras clave: calidad, docente investigador, eficiencia, eficacia, efectividad, investigación.

How to cite this article (APA): Hernández, C. T. R. (2026). Artificial intelligence literacy and content curation:

challenges and opportunities for teachers and university students in France. Revista Digital de Investigación y

Postgrado, 7(13), 111-128. https://doi.org/10.59654/btctmw45

Thais Raquel Hernández Campillo*

Professor, Department of Multimedia and Internet Professions, University Institute of Technology

of Blois, University of Tours, France..

Thais Raquel Hernández-Campillo

Instituto de Estudios Superiores de Investigación y Postgrado

112

Introduction

Artificial intelligence (AI) has been progressively integrated into various spheres of contemporary so-

ciety. Experts and scientists project that this technology will play an increasingly decisive role in sectors

such as the economy, health, and education. We are facing a technological revolution that demands

deep adaptations in social dynamics and in the automated processes that transform daily life. In this

context, diverse perspectives emerge: some seek to understand the scope of this revolution, while

others aim to guide the already visible changes.

Higher education constitutes one of the areas where these tensions manifest most intensely. AI is sig-

nificantly transforming teaching and learning, while simultaneously posing ethical and moral challenges

associated with its misuse. Hence, there is a need to promote training that fosters a critical and ethical

use of these technologies, both among university students and faculty.

The United Nations Educational, Scientific and Cultural Organization (Unesco) has emphasized the

uniqueness of AI compared to other digital tools applied in education. According to this agency, ar-

tificial intelligence is distinguished by its ability to mimic human behaviors, automatically generate

content from multiple sources, and raise moral and academic responsibilities. These particularities de-

mand specific competencies that transcend traditional digital literacy (Unesco, 2019, 2024a).

For its part, the European Union has oriented its approach to artificial intelligence towards fostering

scientific research and economic development (European Commission, 2025a). This framework rests

on two fundamental pillars: excellence, understood as the coordination of policies, resources, and in-

vestments to develop robust, high-performance systems; and trust, based on the creation of legal

frameworks that guarantee a safe and responsible use of AI. In this vein, the AI Act, the first European

legal framework on the subject, regulates associated risks and positions Europe as a global leader.

In France, AI has decisively impacted the economy, society, and the educational sphere. Its application

in teaching is subject to respect for republican values, personal data protection, pedagogical freedom,

and environmental sustainability. The Ministère de l’Éducation nationale, de l’Enseignement supérieur

et de la Recherche (2025) acknowledges that AI poses challenges for traditional education by modif-

ying learning methods, lesson preparation, and assessment, although it also offers valuable opportu-

nities for teaching and institutional management.

In this line of thought, French researchers and authorities have explored multiple dimensions of AI use

among university faculty and students. Among recent work, notable studies include those analyzing the

degree of adoption of language models like ChatGPT (Agulhon & Schoch, 2023; Sublime & Renna, 2024),

the integration of AI into teaching and learning processes (Many, Shvetsova & Forestier, 2024; Modolo,

2025), and faculty preparation for its disruptive potential (Bidan & Lebraty, 2024). To these are added

official reports directed at the highest educational authorities—such as that by Pascal et al. (2025)—which

document the actual uses, challenges, and opportunities of AI in French higher education.

Another reference is the AI DL – Data Literacy in the Age of AI for Education project (France Éducation

International, n.d.), which seeks to strengthen digital citizenship through data and information literacy

supported by AI tools, especially generative AI. This program aims to equip educational stakeholders

with critical competencies to face contemporary challenges such as deepfakes and fake news.

The results of this research an these initiatives show that integrating AI into higher education opens

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opportunities to enrich teaching and institutional management, but also generates ethical dilemmas

and risks of bias that require rigorous attention. Therefore, it is essential to incorporate AI literacy into

university education, understood as the ability to understand its functioning, identify its biases, and

employ it critically and responsibly.

In a scenario of automated information production, content curation acquires a strategic role. This

practice allows for filtering, validating, and contextualizing information generated by artificial intelli-

gence systems, fostering more reflective and ethical learning. Integrating content curation into tea-

ching and student practices can strengthen skills in searching, analyzing, and verifying sources in an

informational environment increasingly mediated by AI.

However, academic literature often addresses AI literacy and content curation separately, limiting the

understanding of their combined potential. This theoretical gap constitutes the foundation and origi-

nality of the present study, whose objective is to analyze how content curation can be integrated into

the AI literacy of university faculty and students in France.

Methodology

The present study adopts a qualitative approach, given its interpretive nature and focus on understanding

phenomena through processes. This approach, with its non-linear and cyclical design, facilitates the fle-

xible organization of the researcher's work (Calle, 2023). According to Lim (2024), qualitative methodo-

logy is indispensable due to its capacity to offer information on complex social phenomena, generate

people-centered understandings, address real-world problems, and respond quickly to social changes.

As the main empirical method, a systematic literature review was applied, which allowed for examining,

evaluating, and synthesizing existing academic production to understand the context, establish ante-

cedents, and identify trends related to the object of study (Susanto et al., 2024). The methodology

proposed by Gómez et al. (2014) was followed, recognized for its applicability to diverse knowledge

areas and its usefulness for determining the relevance and originality of sources. This methodology

comprises four phases: problem definition, search, organization, and analysis of information.

The problem definition was articulated with the purpose of the study: to analyze the integration of

content curation within artificial intelligence literacy among teachers and students in higher education

in France. The review period was delimited between 2018 and 2025, coinciding with the start of Euro-

pean policies on artificial intelligence, including milestones such as the creation of the High-Level Expert

Group on AI, the European AI Alliance, and the Coordinated Plan on AI driven by the European Union.

The information search was conducted in scientific databases and academic repositories, including

ScienceDirect, Scopus, Google Scholar, HAL, and CAIRN, the latter two specialized in French research.

Following the principles of digital information retrieval, search operators and equations were applied

in French and English, such as: “higher education in Europe” + “artificial intelligence”; “AI literacy in

France” AND “content curation”; “content curation” AND “higher education”; as well as “artificial inte-

lligence” OR “generative artificial intelligence”.

As a result, 858 sources were retrieved. After applying exclusion criteria—removing citations, patents,

conference proceedings, duplicate records, and research unrelated to the French context—104 do-

cuments focused on artificial intelligence were obtained, although most addressed technical aspects

without reference to literacy or content curation. Finally, 20 sources were selected (see Appendix 1)

Thais Raquel Hernández-Campillo

Instituto de Estudios Superiores de Investigación y Postgrado

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based on the following criteria: (a) theoretical or empirical studies on AI in French higher education,

(b) primary sources (books, articles, reports, or theses), and (c) proposals aimed at acquiring digital

competencies among teachers or students.

For organizing and analyzing the documents, two content curation tools were used: Zotero and No-

tion. Zotero was employed as a bibliographic manager and PDF annotator, enabling the classification

of articles, creation of tags, and management of citations through its integration with Word. Notion

was used for note-taking and categorizing information according to the thematic axes of the review.

Its flexible interface allowed for the creation of a database with the retrieved articles and the extraction

of metadata (title, author, year, journal, and keywords).

Furthermore, theoretical methods were applied, such as analysis-synthesis, historical-logical, and in-

duction-deduction, which guided information processing and the construction of the theoretical fra-

mework. Analysis-synthesis allowed for deconstructing the contributions identified in the literature

(definitions, conceptual frameworks, experiences in France and Europe) to integrate them into an in-

terpretative model. Induction-deduction facilitated the identification of patterns in empirical studies

and their comparison with theoretical frameworks on digital and AI literacy. Finally, the historical-logical

method made it possible to trace the evolution of the concept of digital literacy towards AI literacy

and its relationship with content curation in the French context.

As a methodological instrument, a thematic guide for the literature review was developed (see Ap-

pendix 2). It allowed for organizing the selected articles into predefined categories: concepts, digital

competencies, experiences of teachers and students, and links between artificial intelligence and con-

tent curation. This tool facilitated the identification of patterns and theoretical gaps and ensured a

systematic review coherent with the study's objectives. Moreover, its application favors research re-

producibility and aligns with the logic of content curation by establishing filters and criteria that refine

and prioritize relevant information.

Finally, the study acknowledges some limitations. A deficit of research specifically focused on AI literacy

in French higher education is evident, as well as a lack of work addressing content curation in this

context. Furthermore, some of the French literature consulted is not indexed in international databases

like Scopus or Web of Science, limiting its visibility. On the other hand, the emerging nature of AI li-

teracy implies conceptual frameworks still under development. Lastly, although the thematic guide

contributed to a systematic organization, any classification carries a component of subjectivity. Con-

sequently, the results of this review should be interpreted as an initial approximation to the pheno-

menon and not as an exhaustive representation of the French higher education system.

Results and Discussion

Artificial intelligence literacy: Concept and relevance

Artificial intelligence is part of everyday life. Applications based on this technology directly influence how

we live and interact, both with technology and with other people. As AI evolves, the boundary between

humans and machines becomes increasingly blurred. Examples of this include smart home appliances,

voice recognition features on mobile phones, or applications that facilitate language learning. Virtual as-

sistants like Siri, Alexa, or Gemini respond to queries about the weather or news, while smartwatches mo-

nitor physical activity and well-being. The more integrated technology is in daily life, the less perceptible

its presence becomes, as its purpose is to minimize friction between the user and the device.

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In line with these advances, interest in the application of AI in education has grown significantly. Ho-

wever, "research on artificial intelligence in educational settings seldom defines the term" (Stolpe &

Hallström, 2024, p. 2).

Various international organizations have attempted to define this concept. Unesco (2024b) defines AI

as a digital system capable of processing and analyzing data from its environment to act autonomously

based on specific objectives. The European Parliament (2020) describes it as a machine's ability to

perform cognitive functions characteristic of humans, such as reasoning, learning, creating, and plan-

ning. In France, the Ministère de l’Éducation nationale, de l’Enseignement supérieur et de la Recherche

(2025) conceives it as a digital system based on probabilistic algorithms that uses datasets to produce

outcomes comparable to human cognitive activity. This organization distinguishes two main types of

AI: predictive, when models classify data, anticipate risks, or identify trends, and generative, when

models produce new content such as text, images, sounds, or videos.

Considering the potential of this technology, as well as the ethical and social implications of its use,

several authors argue that all citizens should receive training in artificial intelligence (Markus et al.,

2024; Olari & Romeike, 2024; Stolpe & Hallström, 2024). In this regard, education is needed that allows

teachers and students to understand what AI is, how it works, what its biases are, and how to interact

with it critically, ethically, and effectively.

From this perspective, artificial intelligence literacy emerges as an essential pathway for developing com-

petencies that facilitate leveraging its benefits and mitigating its risks in the educational and social spheres.

Capelle (2024) defines it as a set of competencies that enables people to critically evaluate AI systems,

as well as to communicate and collaborate effectively with them. This literacy is supported by other

competencies included in the European Digital Competence Framework, such as information and data

management, thus configuring a multiliteracy approach where various interrelated literacies converge.

In the French context, several studies have addressed the changes generated by AI in teaching and

learning processes, as well as concerns stemming from its indiscriminate use by students. Agulhon

and Schoch (2023) highlight the advantages of ChatGPT for supporting the drafting of academic pa-

pers and other educational tasks, but warn of the risks related to the reliability and quality of its res-

ponses. The authors emphasize the importance of combining AI's potential with human expertise to

avoid technological dependence and the weakening of critical thinking.

For his part, Modolo (2025) examines how the integration of AI transforms higher education by redefining

the traditional roles of teachers and students. From a critical perspective, he posits that this technology

acts as a disruptive tool capable of modifying pedagogical practices, generating new power dynamics,

and complicating learning assessment processes. Complementarily, Devauchelle (2025) analyzes the im-

pact of AI not only on teachers and students but also on the staff responsible for teacher training. Accor-

ding to the author, in France, the use of AI remains limited, primarily confined to the preparation of classes

and school assignments, although both its potential and the ethical challenges it entails are recognized.

The reviewed studies agree on the need for a reference framework to guide the integration of artificial

intelligence literacy in higher education. In response, Unesco (2025a) developed a Framework for AI

Competencies for Students, which aims to prepare students to become responsible and creative citi-

zens in the digital age, as well as to support teachers in its pedagogical integration. This document

defines 12 competencies organized into four dimensions and three levels of progression.

Thais Raquel Hernández-Campillo

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Figure 1

AI competency framework for students.

Note: Original elaboration based on Unesco (2025a).

Furthermore, Unesco (2025b) developed the AI Competency Framework for Teachers, aimed at those

who use this technology to enhance learning. This framework, structured around 15 competencies dis-

tributed across five dimensions and three levels, is founded on principles such as the protection of tea-

chers' rights and the strengthening of human agency, emphasizing that "human flourishing must remain

at the heart of the educational experience. Technology must not and cannot replace teachers" (p. 14).

Figure 2

AI competency framework for teachers

Note: Author's own elaboration based on Unesco (2025b)

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In line with this international interest, France has developed multiple initiatives to promote artificial

intelligence competencies among teachers and students, aiming to foster a safe, effective, and ethical

use of these tools. Principles and guidelines for the responsible use of AI at all educational levels have

been established (Ministère de l’Éducation nationale, de l’Enseignement supérieur et de la Recherche,

2025), along with practical resources for higher education: massive open online courses, manuals, di-

gital tools, national portals, guides of good practices, experimental experiences, and institutional trai-

ning programs (France Éducation International, n.d.; Université de Nantes, 2024).

These actions are complemented by funding initiatives under the France 2030 program, which allocates

54 million euros to the transformation of companies, educational institutions, and research centers.

Among the funded projects is AI DL – Data Literacy in the Age of AI for Education, focused on the

critical use of artificial intelligence in education and its integration into teaching practices (European

Commission, 2025). Furthermore, France participates in European projects such as Erasmus+, which

promote AI literacy in higher education.

Educational digital content curation as a key competency

Content curation constitutes an effective resource in the face of information overload. This concept,

originating in the fields of marketing, journalism, and communication, has been progressively incor-

porated into the educational context. According to Hernández et al. (2022), content curation in uni-

versity teaching work comprises the search, selection, and dissemination of relevant information for

a course, with the goal of facilitating the learning of disciplinary content. For students, this practice

plays an essential role in understanding a topic and in collaborative work, as it involves compiling, se-

lecting, organizing, editing, and sharing meaningful information (Ramírez, 2024).

In this way, content curation encompasses subprocesses such as the retrieval, storage, organization,

presentation, and dissemination of digital information. In a context where artificial intelligence has ex-

ponentially multiplied the production and circulation of data, curation is configured as a competency

for filtering and critical evaluation, enabling the distinction between reliable information and content

generated without quality control, the verification of sources and biases, and the selection of resources

aligned with specific informational objectives and needs. Consequently, it is constituted as an act of

advanced information literacy, indispensable in environments mediated by artificial intelligence.

Simultaneously, artificial intelligence can enhance the curation process. This approach has been ex-

plored in journalism, marketing, and advertising, where the adoption of intelligent tools for creating

personalized content is analyzed, redefining traditional communication practices (La-Rosa et al., 2025).

Codina and Lopezosa (2024) show how AI tools can streamline curation processes in journalism and

present AI-powered search engines applicable to academic contexts (Codina, 2023).

The findings of this research are transferable to higher education, where teachers and students can

apply AI tools in content curation. At this educational level, managing reliable information to support

an argument or develop a viewpoint constitutes a common practice, which corresponds to the cura-

tion process, whether as part of learning activities or teaching preparation.

The following table presents artificial intelligence tools applicable to each phase of the content curation

process, highlighting that AI does not replace curation but enhances its value through the interpre-

tation, contextualization, and ethical re-reading of information:

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Instituto de Estudios Superiores de Investigación y Postgrado

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Table 1

Integration of artificial intelligence tools in content curation phases

Nota: Elaboración propia.

Most of the identified tools offer free or academic versions, facilitating their integration into university

projects without requiring major investments. However, the limitations of freemium plans (number of

searches, storage space, or advanced features) demand strategic and mindful use.

In France, research on content curation in higher education is still scarce, and as of this review, no

studies explicitly linking it to artificial intelligence or AI literacy have been recorded. Nevertheless, re-

levant work providing valuable information to the academic community has been identified, such as

Knauf and Falgas (2020), who integrate content curation into a master's-level communication course

on information search and retrieval, and Kemp (2018), whose doctoral thesis proposes a system based

on curation and big data exploration services to facilitate digital information retrieval. Other significant

studies were excluded from the analysis for not meeting the methodological selection criteria.

In the age of artificial intelligence, educational digital content curation is established as a key compe-

tency, not only for its instrumental value but also for its critical dimension. Teachers and students must

be able to identify and manage the risks associated with the intensive use of intelligent tools, including

Process phase Main objective Recommended AI tools Potential uses by teachers/

students

Seaarch Locate relevant and

up-to-date information

Perplexity AI, Elicit, Seman-

tic Scholar (IA Search),

Consensus

Formulate questions in natural lan-

guage or specific prompts; identify

relevant scientific sources; com-

pare evidence or study results.

Selection Evaluate and filter the quality

of information.

Scite.ai, Scholarcy, Research

Rabbit, Explainpaper

Summarize scientific articles; verify

whether a study has been cited

positively or critically; compare dif-

ferent sources on the same topic.

Storage and

organizations

Classify, tag, and preserve

curated content.

Notion AI, Symbaloo AI Obsi-

dian + plugins IA, Diigo IA

Save articles and notes with auto-

matic metadata; create connected

knowledge bases; tag and relate

key concepts.

Creation (with added

value)

Reinterpret and contextua-

lize curated information;

generate educational ma-

terials.

ChatGPT, Copilot, Claude, Ge-

mini, Canva Magic Write,

Gamma App, Notion AI.

Its use should be combined

with the content curation te-

chniques proposed by Guallar

(2021).

Write interpretive and critical texts;

design infographics, presentations,

or teaching materials; recontextua-

lize texts according to students'

level.

Dissemination

Share curated content in

digital or academic envi-

ronments

LinkedIn + IA, Medium, Subs-

tack con asistencia IA, Padlet,

Wakelet, Pearltrees, Moodle

con IA plugins

Publish annotated resource collec-

tions; generate automatic summa-

ries or visualizations; create

repositories or collaborative lear-

ning spaces.

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technological dependency, algorithmic biases, and information overload (infoxication). These pheno-

mena threaten cognitive autonomy and learning quality, but they justify the need to strengthen cu-

ration as a reflective practice, ensuring training in how to filter, contextualize, and transform

information, thereby reintroducing human judgment into an increasingly automated environment.

Intersection Between AI literacy and content curation

Content curation occupies an intermediate position between traditional digital literacy (searching,

using, and communicating information) and artificial intelligence literacy (understanding how AI

systems function and are trained). It also teaches how to formulate questions, prompts, or search cri-

teria strategically, involves interpreting algorithmic results by recognizing their non-neutral nature,

and fosters ethical responsibility in the selection and dissemination of AI-generated information. In

this sense, content curation can be understood as a practice that develops the critical evaluation of

artificial intelligence systems.

On the other hand, content curation enables the exercise of AI literacy as part of the learning and

knowledge production process. In this context, teachers can design personalized learning environ-

ments based on materials filtered, validated, and adapted with the help of ChatGPT, Perplexity, or Se-

mantic Scholar. Students, in turn, train in the critical selection of results from search engines or

generative assistants, evaluating those most pertinent to their learning and academic projects.

The intersection between AI literacy and content curation redefines informational competencies in

higher education. It is no longer just about accessing or communicating information, but about un-

derstanding the algorithmic mediations that structure knowledge production and circulation. From

this perspective, the curation process becomes a metacognitive exercise: by interacting with AI tools,

the user learns to reflect on their own processes of search, selection, and creation, developing a critical

awareness of technology's role in knowledge construction.

Integrating content curation into AI literacy also entails rethinking the ethical and formative role of

the university. Institutions can leverage curation practices to promote a responsible and transparent

use of artificial intelligence, fostering source traceability, authorship attribution, and respect for epis-

temic diversity. In this way, curation ceases to be an individual practice and transforms into an institu-

tional competency that upholds academic integrity in AI-mediated environments.

This convergence between AI literacy and content curation also opens the possibility of transforming

pedagogical practices. Instead of focusing solely on transmitting information, teachers can guide stu-

dents towards the collaborative construction of knowledge through the critical interpretation of AI-

generated results. Curation, in this context, acts as a bridge between the technical understanding of

artificial intelligence and its reflective application in real learning contexts.

Challenges of AI literacy in the french higher education context

In France, the deployment of artificial intelligence literacy faces several structural obstacles. One of

the main ones is the digital divide, highlighted by the Conseil économique, social et environnemental

(CESE), which warns that approximately one-third of the population feels disconnected from digital

technologies, including young people and inhabitants of areas with limited internet access (Meyer &

Tordeux, 2025). Furthermore, OECD reports on the digital divide in education point to inequalities in

connectivity, available digital resources, and competencies, which prevent all students from having

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equitable access to AI-mediated educational practices (Burns & Gottschalk, 2019; OECD, 2023).

Secondly, the training of teachers and students is insufficient to meet emerging challenges. A report

by the Commission on Economic Affairs presented to the French Senate notes that the training offering

in AI is modest, both in initial and continuous training systems, and that existing programs do not

adequately cover the ethical, technical, and pedagogical dimensions of artificial intelligence (Hoffman

& Golliot, 2024). Nevertheless, projects like AI4T seek to fill this gap through open manuals and

MOOCs aimed at teachers, but their scale is still too limited to impact the entire higher education

system.

Finally, there is a clear need for integrated educational policies that embed AI literacy and content

curation within university curricula. The frameworks for the use of AI in education, established by

Unesco and the Ministère de l’Éducation nationale, de l’Enseignement supérieur et de la Recherche

in France, set out principles and guidelines for the responsible use of artificial intelligence. While these

documents are the result of extensive international and national study, it is considered pertinent to

move from principles to practical implementation in specific curricular modules.

Similarly, the report on artificial intelligence in higher education presented by the Minister responsible

for Higher Education and Research identifies several priority actions to transform French universities

into active agents of this change, including institutional structuring, specialized teacher training, and

the social appropriation of knowledge in artificial intelligence.

Conclusions

The review conducted confirms that artificial intelligence literacy is emerging as a new axis of digital

competence in higher education. Beyond the instrumental acquisition of technological skills, it involves

understanding how AI systems are designed, trained, and operated, as well as the ability to critically

analyze their impact on knowledge production and circulation processes. Its relevance lies not only in

technical mastery but in the development of an ethical and critical awareness that enables teachers

and students to act as informed digital citizens in algorithm-mediated environments.

Within this framework, educational digital content curation emerges as a key competency comple-

mentary to artificial intelligence literacy. Far from being a merely technical task, curation constitutes a

cognitive and pedagogical practice that involves the ethical search, selection, evaluation, contextua-

lization, and dissemination of information. In the age of artificial intelligence, this practice acquires a

new dimension: it allows for filtering informational overabundance, identifying algorithmic biases, and

adding value through human interpretation, thereby contributing to the formation of critical and au-

tonomous thinking.

The intersection between artificial intelligence literacy and content curation constitutes a space for

active learning where interaction with intelligent tools becomes a formative opportunity. When tea-

chers use artificial intelligence to design personalized materials or students learn to formulate prompts

and evaluate results generated by automated systems, both exercise a practical, situated, and critical

literacy. This convergence redefines the pedagogical function: educational actors cease being passive

consumers of information and transform into reflective curators and creators of knowledge, aware of

the technological mediations involved in its construction.

In the French context, artificial intelligence shows significant advances and challenges. France has a

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solid institutional foundation, including ministerial plans, frameworks for AI use, and innovation projects

like AI4T, which aim to guide the integration of AI into the education system. However, digital divides,

access inequalities, and deficits in teacher and student training persist, limiting a critical and equitable

appropriation of these technologies. The institutional reports reviewed underscore the urgency of ar-

ticulating public policies that integrate AI literacy within university curricula, ensuring its teaching is

not limited to technical competencies but incorporates ethical, epistemological, and pedagogical di-

mensions.

Collectively, the results of this research suggest that artificial intelligence literacy, understood through

the practice of content curation, can become a transformative axis for higher education. Integrating

both competencies into the training of teachers and students would foster the development of a

critical academic citizenship, capable of using artificial intelligence not as a substitute for human

thought, but as an instrument to enhance understanding, creativity, and responsibility in the collective

construction of knowledge.

Privacy: Not applicable.

Funding: This work did not receive any funding.

Declaration on the use of artificial intelligence: The author of this article declares that no Ar-

tificial Intelligence was used in its preparation.

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Article received: June 27, 2025.

Article accepted: August 1, 2025.

Approved for layout: August 15, 2025.

Publication date: January 10, 2026.

About the author

* Thais Raquel Hernández Campillo is a Professor in the Department of Multimedia and Internet Professions at the Uni-

versity Institute of Technology of Blois, University of Tours, France. She is a Researcher at the Laboratory for Information

and Mediation Practices and Resources (EA 7503) at the University Institute of Technology of Tours, University of Tours,

France. Email: thais.hernandez@univ-tours.fr

REDIP, Revista Digital de Investigación y Postgrado, E-ISSN: 2665-038X

Artificial intelligence literacy and content curation: challenges and opportunities for teachers and

university students in France

125

Appendix

Appendix 1

Academic publications on content curation and artificial intelligence literacy included in the review

Author /

Year

Country or

context Tipe of study Objective Key findings o

r contributions

Relevance to the

review

Stolpe y

Hallström

(2024)

Sweden

Europe Theorical

To analyze and cri-

tically discuss the

components of AI

literacy in relation

to technological li-

teracy.

AI literacy integrates scien-

tific-technological know-

ledge and socio-ethical

understanding. A concep-

tual framework for AI lite-

racy is proposed.

Fundamenta la nece-

sidad de alfabetiza-

ción en IA.

Ministère

de l’Éduca-

tion natio-

nale, de

l’Enseigne-

ment supé-

rieur et de

la Recher-

che (2024)

France Theorical

To provide a fra-

mework for the use

and understanding

of AI in education

in accordance with

ethical, legal, and

environmental

principles.

It defines objectives, princi-

ples, obligations, and ethical

guidelines for the educatio-

nal use of AI.

Conceptualization

and challenges of AI

literacy in France.

Markus,

Pfister, Ca-

rolus,

Hotho y

Wienrich

(2024)

Germany

Europe Theorical

To design online

training to improve

the understanding

of AI in relation to

virtual assistants.

Increased understanding

and critical use of AI, as well

as positive attitudes towards

virtual assistants.

It reinforces the need

for AI literacy.

Olari y Ro-

meike

(2024)

Germany

Europe Mixto

To enable stu-

dents to unders-

tand how AI

systems work.

A compendium of key

concepts for designing

AI learning plans.

It proposes con-

ceptual competen-

cies for AI literacy.

Capelle

(2024) France Mixed-

Methods

To analyze the

relationship bet-

ween data lite-

racy and AI

literacy in teacher

training.

It identifies data literacy

as an essential compo-

nent of AI literacy.

Necessary compe-

tencies for teachers

and students.

Unesco

(2025a) International Theorical

To define the

knowledge, skills,

and values that

teachers must

master in the age

of AI.

AI competency frame-

work for teachers.

A central reference

on AI literacy and

teaching.

Thais Raquel Hernández-Campillo

Instituto de Estudios Superiores de Investigación y Postgrado

126

Unesco

(2025b) International Theorical

To define the

knowledge, skills,

and values that

teachers must

master in the age

of AI.

AI competency frame-

work for teachers.

A central reference

on AI literacy and

teaching.

Agulhon &

Schoch

(2023)

France Theorical

To examine the be-

nefits and challen-

ges of ChatGPT in

higher education

Rational use of ChatGPT;

risks linked to the reliability

of information.

Benefits and challen-

ges of using AI in hig-

her education.

Modolo

(2025)

Morocco, De-

mocratic Re-

public of the

Congo, and

Cameroon.

Empirical

To analyze how AI

is transforming

higher education

and its social impli-

cations.

Redefinition of teacher and

student roles; inequalities in

access to AI.

Changes and challen-

ges arising from AI in

higher education.

Devauche-

lle (2025) France Theorical

To explore the im-

pact of AI on tea-

ching and teacher

training.

Tensions and perceptions of

French teachers regarding

the integration of AI.

Challenges and im-

pact of AI in French

higher education.

France Édu-

cation In-

ternational

(s.f)

France Theorical

To promote data

literacy and the cri-

tical use of AI in

education.

The "AI-DL: Data Literacy in

the Age of AI for Education"

project.

AI literacy initiatives in

France.

Universidad

de Nantes

(2024)

France Practical

To offer AI training

resources for uni-

versity teachers.

Resources, events, articles,

courses, and training tools.

Institutional resour-

ces for teacher lite-

racy.

European

Commis-

sion (2025)

France

Europe Theorical

To present projects

promoted by

France in the field

of educational AI.

Funding for AI innovation

and training projects.

Financial and institu-

tional support for AI

literacy.

Hernández,

Legañoa &

Campillo

(2022)

International Theorical

To analyze the inte-

gration of content

curation into tea-

chers' informatio-

nal competencies.

Content curation is confir-

med as an informational

competency that strengt-

hens teachers' digital lite-

racy

Content curation as a

key teaching compe-

tency.

Ramírez

(2024) International Empirical

To examine the be-

nefits of content

curation in collabo-

rative learning.

Implementation of content

curation in students' colla-

borative learning.

Content curation as a

key student compe-

tency.

La-Rosa,

Ortega-Fer-

nández &

Perlado

(2025)

Spain

Europe Empirical

To analyze the

scientific produc-

tion on generative

AI in journalism,

marketing, and ad-

vertising.

Predominance of marketing

in publications; Spain leads

research on AI applied to

journalism.

Application of AI in

content curation and

personalization.

REDIP, Revista Digital de Investigación y Postgrado, E-ISSN: 2665-038X

Artificial intelligence literacy and content curation: challenges and opportunities for teachers and

university students in France

127

Codina &

Lopezosa

(2024)

Spain

Europe Theorical

To demonstrate

the application of

AI tools in the

phases of con-

tent curation.

Identification of search

engines and prompts for

digital curation processes

Integration of AI

into the phases of

content curation.

Codina &

Lopezosa

(2024)

Spain

Europe Theorical

To demonstrate the

application of AI

tools in the phases

of content curation.

Identification of search en-

gines and prompts for digi-

tal curation processes.

Integration of AI into

the phases of content

curation.

Codina

(2023)

Spain

Europe Empirical

Comparative

analysis of alterna-

tive search engines

to Google with ge-

nerative artificial

intelligence.

General characteristics of

types of search engines.

Functional and interface

analysis of search engines;

recommendations for aca-

demic use.

AI tools applied to in-

formation curation.

Knauf &

Falgas

(2020)

France Empirical

To strengthen digi-

tal skills through

curation and infor-

mation manage-

ment.

Experiments with master's

students in communication

on digital content monito-

ring.

Intersection between

AI literacy and con-

tent curation.

Kemp (2018 France Empirical

To propose a ser-

vice-based system

for curating and

exploring big data.

"CURARE" model for infor-

mation exploration and ex-

traction through data

analysis.

Thais Raquel Hernández-Campillo

Instituto de Estudios Superiores de Investigación y Postgrado

128

Appendix 2

Thematic guide to the documented bibliographic review

1. Artificial intelligence literacy in higher education.

1.1. European context.

1.2. Concept and relevance.

1.3. Necessary competencies for teachers and students (frameworks and theoretical proposals).

1.4. Recent initiatives in Europe and France (state programs, universities, policies).

2. Content curation as a key competency.

2.1. Definition and phases.

2.2. Integration of ai into content curation phases: use of tools.

2.3. Risks: Dependence, bias, information overload.

2.4. Incorporation into the training of university teachers and students.

3. Intersection between AI Literacy and Content Curation.

3.1. Conceptual Approach: Curation as a Bridge between Digital Literacy and AI Literacy.

3.2. Practical-Pedagogical Approach: How Teachers and Students Practice this Literacy.

3.3. Epistemological or Formative Approach: Why Does This Intersection Redefine Informational

Competence in Higher Education?

3.4. Institutional or Ethical Approach: How Can Content Curation be Integrated into University AI

Literacy Policies or Strategies?

4. Challenges of AI literacy in the context of higher education in france.

4.1. Digital divide and access inequalities.

4.2. Insufficient training of teachers in ai and curation.

4.3. Need for educational policies that integrate content curation and ai literacy into curricula.