L’Intégration de l’Intelligence Artificielle dans la Pédagogie Numérique : Une Analyse des Défis et des Opportunités

Within the context of fast digital transformation, the integration of artificial intelligence (AI) in digital pedagogy opens new horizons for personalized learning, optimized school management, and a redefinition of the pedagogical relationship.
This article investigates how AI can support digital pedagogy not only through automation and increased efficiency, but also by assisting teachers in instructional decision-making and adapting learning paths to individual student needs.
Alongside the clear benefits, it also addresses significant ethical and socio-cultural challenges – such as algorithmic bias, the opacity of automated decisions, the impact on students’ digital identity, and the need for critical digital literacy among educators. Drawing from interdisciplinary theoretical frameworks and applied examples, the study advocates for a responsible and value-oriented integration of AI into educational systems.
The conclusion emphasizes that AI should be seen not as a substitute for teachers, but as a pedagogical partner – one whose success depends on balancing technological innovation with humanistic principles.

Keywords: artificial intelligence in education, digital pedagogy, digital identity, educational equity, algorithmic ethics, personalized learning, data-driven educational leadership

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Résumé :

Dans un contexte de transformation numérique rapide, l’intégration de l’intelligence artificielle (IA) à la pédagogie numérique ouvre de nouveaux horizons pour l’apprentissage personnalisé, l’optimisation de la gestion scolaire et une redéfinition de la relation pédagogique.
Cet article examine comment l’IA peut soutenir la pédagogie numérique, non seulement par l’automatisation et une efficacité accrue, mais aussi en aidant les enseignants à prendre des décisions pédagogiques et à adapter les parcours d’apprentissage aux besoins de chaque élève.
Outre ses avantages évidents, l’article aborde également d’importants défis éthiques et socioculturels, tels que les biais algorithmiques, l’opacité des décisions automatisées, l’impact sur l’identité numérique des élèves et la nécessité d’une culture numérique critique chez les enseignants. S’appuyant sur des cadres théoriques interdisciplinaires et des exemples concrets, l’étude plaide pour une intégration responsable et valorisante de l’IA dans les systèmes éducatifs.
La conclusion souligne que l’IA ne doit pas être considérée comme un substitut aux enseignants, mais comme un partenaire pédagogique, dont le succès repose sur l’équilibre entre innovation technologique et principes humanistes.

Mots-clés : intelligence artificielle dans l’éducation, pédagogie numérique, identité numérique, équité éducative, éthique algorithmique, apprentissage personnalisé, leadership éducatif axé sur les données

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Revista de Pedagogie Digitala – ISSN 3008-2013
2025, Vol. 4, Nr. 1, pp. 31-38
https://doi.org/10.61071/RPD.2543
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1. Contextual Framework for Integrating AI into Digital Pedagogy

Since the COVID-19 pandemic, schools and educators worldwide have had to rethink how teaching works, turning more and more to digital tools to keep students learning. In this changing landscape, artificial intelligence (AI) has started to play a bigger role – not just as a new technology, but as something that could really shift how learning and teaching are done (Holmes et al., 2021).

AI brings a paradigmatic shift to digital pedagogy through its capacity to rapidly analyze educational data, personalize teaching processes, and automate repetitive tasks. This evolution requires a deep rethinking of the teacher’s role, who increasingly becomes a facilitator of learning rather than a mere provider of information. Moreover, AI influences how students’ competencies are assessed and opens up opportunities for continuous, student-centered learning. As highlighted in the UNESCO report (2023), incorporating AI into education could play a valuable role in reaching the Sustainable Development Goals, particularly those related to equal access to learning. Still, the road ahead isn’t without challenges. Without clear policies and continued professional development for teachers, there’s a risk that we might lean too heavily on algorithms and lose sight of the human values that should remain at the heart of education.

Digital pedagogy represents an educational approach that merges technology with modern didactic methods, offering a flexible and adaptable framework for 21^st-century learning. In this context, artificial intelligence becomes a crucial catalyst, contributing to the personalization of the educational process and the reshaping of the relationship between teacher, student, and educational content.

Digital pedagogy is not merely an extension of traditional educational practices through technology; rather, it represents a paradigm shift that involves a reconceptualization of the educational process. As Istrate, Velea, and Ceobanu (2025) point out, “digital pedagogy aims at developing an educational culture based on the conscious, creative, and critical use of technology for didactic and formative purposes”. In this regard, artificial intelligence should not be integrated merely as a tool for efficiency, but as part of a broader educational ecosystem in which values, relationships, and learning experiences are reimagined within a digital framework. The authors also emphasize that the transition toward authentic digital pedagogy entails a reorganization of the curriculum, didactic methodology, and institutional culture, focusing on autonomy, collaboration, personalization, and critical reflection. Within this framework, AI can play a central role – but only if guided by reflective teaching practice and in alignment with the ethical and educational principles embraced by the school community. This vision is essential to avoid a superficial instrumentalization of technology, where AI is applied without proper pedagogical reflection. On the contrary, as the authors argue, the integration of technology in education should aim at deep formative goals such as the development of critical thinking, creativity, and lifelong learning skills.

Within digital pedagogy, artificial intelligence plays a supportive – but not directive – role. Its capacity to streamline tasks and adapt instruction must be subordinated to pedagogical intentions. This ensures that AI augments rather than dictates learning design, preserving the integrity of educational goals and teacher agency. It also helps broaden access to customized educational materials. These developments signal a move away from the conventional teacher-led approach, encouraging instead a more interactive, reflective, and learner-focused model of education (Laurillard, 2021).

Beyond merely using technology, digital pedagogy calls for a transformation in how teachers and students interact in AI-enhanced learning spaces. This transformation includes reshaping the teacher’s role and developing digital abilities into purposeful teaching strategies.

While AI offers novel capabilities such as data-driven personalization, its integration into digital pedagogy must be evaluated through a pedagogical lens, not just a technological one. The essence of digital pedagogy lies in fostering critical thinking, learner autonomy, and transformative learning experiences – aims that can only be achieved if AI tools are contextualized within didactic frameworks and educational philosophies. As such, digital pedagogy should guide AI use, not the other way around.

2. Personalizing Learning through Artificial Intelligence

One of the most widely studied benefits of AI in education is personalized learning. Algorithms can analyze students’ preferences and difficulties, adapting the content in real time. For instance, adaptive systems like DreamBox Learning or Squirrel AI use predictive models to create individualized learning paths (Zawacki-Richter et al., 2019). This approach not only improves academic performance but also enhances students’ intrinsic motivation. A study by Chen et al. (2020) showed that students using AI-based platforms develop metacognitive skills more rapidly and become more responsible in managing their own learning. Although several studies highlight the potential of AI to improve learning outcomes (e.g., Chen et al., 2020; Luckin et al., 2021), recent meta-analyses (Zawacki-Richter et al., 2019; Williamson & Eynon, 2020) caution against overgeneralizing these findings. Many AI interventions show only marginal or context-dependent improvements, often mediated by factors such as teacher engagement, instructional design, and learner motivation. Moreover, the lack of long-term, large-scale randomized controlled trials limits the generalizability of these claims. Without rigorous empirical validation, there is a risk that AI’s “effectiveness narrative” becomes more aspirational than evidence-based. However, these findings are primarily based on short-term, small-sample studies, often conducted in controlled environments. The transferability to real-world classroom settings, especially in under-resourced schools, remains uncertain (Zawacki-Richter et al., 2019).

At the same time, AI can detect knowledge gaps or early signs of dropout risk. These functionalities allow teachers to proactively intervene and personalize the support offered to each student (Luckin et al., 2021). Furthermore, AI facilitates interdisciplinary learning through intelligent recommendations that connect content from various fields, depending on the student’s interests and skill level.

Personalization is a core principle of digital pedagogy, and AI supports it by providing tailored educational experiences. For example, on platforms such as Classcraft or Century Tech, AI analyzes student responses and suggests next steps based on their cognitive style and individual learning pace. This personalization supports the blended learning model, specific to digital pedagogy, in which students learn both in digital environments and face-to-face settings, benefiting from intelligent support. Thus, AI does not replace the teacher; rather, it offers data and tools that enhance the instructional decision-making process.

According to Istrate, “adaptive learning and personalized instruction acquire new dimensions with the help of AI, which enables a comprehensive customization of the learning experience. AI systems continuously analyze performance, learning style, and the individual pace of each student, dynamically adjusting educational content to optimally meet their specific needs” (Istrate, 2024b). Therefore, AI does not replace the teacher but rather provides data and tools that improve the instructional decision-making process.

Digital pedagogy also entails the development of critical thinking and metacognition. Through algorithms that encourage reflection (e.g., automated feedback systems), AI contributes to self-regulated learning – an essential element of effective digital education (Luckin et al., 2021). Moreover, in multicultural contexts or heterogeneous classrooms, AI supports inclusion through automatic translations, voice synthesis, or personalized visual aids, facilitating access to learning and promoting educational equity – a major goal of digital pedagogy.

A rapidly growing area of research involves conversational pedagogical agents – chatbots capable of interacting with students, offering immediate feedback, and personalizing educational content in real time. Triberti et al. (2024) emphasize that these tools can enhance the learning experience by adapting messages and feedback to each student’s cognitive style and study pace. Rather than replacing the teacher, AI can support learning by creating a personalized, interactive, and experiential environment, contributing to increased student motivation and better assimilation of content.

According to the authors, conversational agents have the ability to provide “intelligent, real-time personalized support,” which is essential in distance learning environments or asynchronous learning scenarios. Among the identified benefits are keeping students in the “flow experience” of learning, preventing conceptual misunderstandings, and providing information about related content or prerequisites (Triberti et al., 2024). These functionalities can significantly reduce extraneous cognitive load, facilitating deep learning and self-regulation.

Nevertheless, the article also highlights the necessity of a “human-in-the-loop” approach, in which the teacher continues to play an essential role in the educational process – guiding, coordinating, and intervening where AI cannot replace emotional intelligence and human ethical judgment. This perspective aligns with the principles of digital pedagogy centered on collaboration, autonomy, and critical reflection.

3. The School Ecosystem in the Algorithmic Era: AI-Supported Educational Leadership

More and more, schools are turning to artificial intelligence to help with administrative tasks and decisions grounded in data (Selwyn & Jandrić, 2020). With the help of predictive analytics, educators can spot trends in student behavior, which supports school leaders in managing resources and shaping more effective policies. Tools like Learning Analytics give principals a clear view of class progress, attendance rates, and how students engage in digital activities. When combined with academic results and social background data, these insights offer a richer, real-time picture of the educational landscape.

Artificial intelligence has become increasingly useful in managing various administrative tasks in education, such as staff recruitment, course scheduling, and optimizing the use of classroom space (Schiffner et al., 2023). By automating these operations, school administrators gain more time to focus on strategic planning and improving teaching quality. Still, it’s important to be cautious – when decisions rely entirely on algorithms, essential context may be lost, and transparency can suffer. That’s why AI should serve as a supportive tool rather than a replacement for human insight in the decision-making process.

In digital pedagogy, educational management is no longer a purely administrative process but becomes an active part of learning by generating useful data on student progress, learning styles, attendance, and motivation. AI plays a strategic role here, providing visibility and control over class progress. Educational dashboard systems enable teachers and administrators to make real-time pedagogical decisions: identifying struggling students, adjusting the curriculum, or reallocating digital resources. In this context, artificial intelligence serves as a valuable resource for data-informed educational leadership within digital pedagogy. It enables teachers to collaborate more effectively by drawing on predictive insights, which helps foster digital communities of practice – an essential feature of modern-day education (Selwyn, 2020).

Therefore, within the context of education’s digital transformation, it is essential to explore types of ethical and sustainable governance of intelligent agents. A recent contribution in this regard is offered by Chaffer et al. (2024), who propose a framework called ETHOS (Ethical Technology and Holistic Oversight System), based on Web3 technologies such as blockchain and smart contracts. This model supports the idea of a decentralized global register for AI agents, aimed at ensuring traceability, accountability, and ethical oversight of these autonomous systems’ behavior.

Applying such a framework in educational settings could increase the transparency of algorithmic decisions and provide a robust control mechanism over the use of student and teacher data. Furthermore, smart contracts could automate administrative processes – such as performance evaluation or resource allocation – reducing bureaucratic burdens and giving educational leaders more time for strategy and innovation. In this way, ETHOS becomes not only a technological solution but also a trust-building tool among educational stakeholders (Chaffer et al., 2024).

Another key aspect of integrating AI into educational management is the profound transformation of school leadership. According to Doğan and Arslan (2025), artificial intelligence is redefining the role of educational leaders by offering real-time decision support, personalized learning, and improved administrative efficiency. The authors emphasize that AI should not be viewed solely as a technology but as a strategic partner in data-driven decision-making, enabling more responsive, equitable, and sustainable leadership.

In addition, effectively integrating AI into educational leadership demands both collaborative and ethically grounded strategies. Doğan and Arslan (2025) stress the importance of training school leaders not only in the technical aspects of AI but also in grasping its broader impact –particularly on educational equity and data privacy. Establishing well-defined policies rooted in accountability and digital inclusion is crucial within today’s post-digital learning environments. In this way, AI moves beyond its administrative role to become a dynamic force in digital pedagogy, shaping both institutional culture and the way innovation is navigated in schools.

At the same time, AI facilitates learning design – a central process in digital pedagogy – by helping teachers plan effective instructional sequences based on feedback and evidence. Digital pedagogy relies on transparency, responsibility, and collaborative learning – values that can be undermined by the uncritical use of AI. For instance, if students do not understand how the algorithms suggesting their content work, they may become dependent on technology without developing critical digital thinking.

Another major challenge is the need for AI literacy – an emerging support of digital pedagogy. Both students and teachers must understand not only how to use AI but also how to evaluate its impact on learning and everyday life (UNESCO, 2023). Furthermore, in a digital pedagogy centered on collaboration, AI must support – not isolate – learners. Without adequate pedagogical design, technology can lead to passive learning or excessive surveillance, which undermines students’ trust and autonomy (Williamson & Eynon, 2020). Therefore, the use of AI must be accompanied by clear ethical guidelines, teacher training, and student involvement in decisions regarding data and technology – key elements of a democratic digital pedagogy.

A significant contribution to understanding the impact of artificial intelligence in the educational environment is brought by Carmen Cioranu-Rădulescu (2024), who emphasizes that any attempt to sustainably integrate AI technologies should be underpinned by two fundamental principles: education and ethical awareness. The author highlights the contemporary paradox whereby technologies designed to support learning risk, in the absence of solid moral reference points, undermining the very core values of education – integrity, critical reflection, and personal effort.

From this perspective, AI emerges as a double-edged tool: on one hand, it offers rapid access to information, personalized learning processes, and support for adaptive learning; on the other hand, it may fuel the temptation for “quick fixes” and lead to a loss of the deeper meaning of learning, especially without a conscious pedagogical approach. According to Cioranu-Rădulescu, AI should not be demonized but understood as a tool that can become either a weapon or a support mechanism, depending on how it is implemented and contextualized.

Moreover, education is not, in its essence, a convenient or instantaneous process – it requires effort, time, and commitment. Attempting to substitute this effort with automated mechanisms, without concurrently developing a culture of integrity and ethical thinking, means sabotaging the very kind of formation we aspire to for future generations. From this perspective, AI must be accompanied by educational policies and curricular strategies that explicitly include the development of critical thinking, ethical awareness, and self-regulation.

Thus, the concept of “ethical awareness” becomes not just an educational ideal but a practical necessity for avoiding a narrow instrumentalization of AI technologies. In the end, as the author concludes, knowledge is not merely the accumulation of information but a process of human transformation – one that inevitably includes a moral dimension.

4. Ethical Dilemmas and Identity Implications: Risks of Integrating Artificial Intelligence into Digital Pedagogy

Alongside the benefits brought by AI, several major ethical challenges emerge. Algorithmic bias is one of the most pressing concerns, as AI models learn from historical data that may reflect social or cultural inequalities (Eubanks, 2018). This type of bias can perpetuate stereotypes, negatively affecting socio-economically or culturally disadvantaged groups. In the context of digital pedagogy, this issue becomes even more serious when AI is used to make decisions that influence students’ educational trajectories.

What’s more, continuously monitoring student activity through tools like learning analytics can lead to increased anxiety, reduced autonomy, and a heightened feeling of being overly controlled. In educational settings meant to nurture trust and responsibility, gathering too much data risks undermining the teacher–student relationship and contributing to an emotionally taxing environment. In the context of digitally mediated education, safeguarding students’ personal data is a critical responsibility. Information related to academic records, learning preferences, and online behavior must be handled with care and discretion. Although legislation such as the GDPR outlines stringent requirements regarding informed consent and data minimization, these provisions are often misunderstood or inconsistently enforced across educational institutions.

Beyond issues of bias, transparency, or data protection, another frequently overlooked dimension in critical AI analysis is its impact on personal identity, particularly for students raised in digitized educational environments. As Viorel Cioară (2025) points out, digital technology is not merely a neutral tool but a “distinct way of understanding and relating to reality”, and this reconfiguration significantly reshapes how we construct identity in the posthumanist era.

In this new educational framework, ongoing exposure to and interaction with algorithmic entities – such as chatbots or digital pedagogical agents – can lead to the fluidization of the self, blurring the boundaries between what is authentic, projected, or artificial. According to Cioară (2025), identity in the digital age risks becoming “so fluid that it loses any defining contour”, which may seriously affect young learners’ self-awareness and social integration. This meta-narrative of digital identity calls for a reevaluation of digital pedagogy’s foundational principles. If AI is used for personalization and efficiency, we must ask: What kind of self is it helping to become?

In the absence of an educational vision rooted in humanistic values and the holistic development of individuals, AI might unintentionally contribute to identity fragmentation and the “externalization” of personal reflection – a trend already observable in today’s digital cultures. As such, education for digital identity becomes a key component of media and digital literacy.

Students should be encouraged to critically reflect on their relationship with AI and the way they construct their virtual presence and self-narratives, in order to avoid alienation, uncritical imitation, or the replacement of inner dialogue with algorithmically generated responses. Digital pedagogy must therefore support the conscious and coherent reconstruction of identity in the algorithmic era, as the aforementioned author emphasizes.

One pressing challenge in the use of AI in education is the lack of transparency surrounding how these systems make decisions. Teachers and students alike need to be able to understand the reasoning behind algorithmic outputs and have the opportunity to question or contest them when necessary. If this level of transparency is missing, AI tools risk becoming so complex and opaque that they function as unquestionable authorities – what some refer to as the “black box effect.”

Furthermore, teacher training remains essential to the successful integration of AI into education. A lack of digital and pedagogical competencies may result in superficial applications or outright implementation failures. As Istrate also points out: “what skills do teachers need to effectively integrate AI agents into their teaching? This question concerns the necessary changes in professional development as education becomes increasingly digitized” (Istrate, 2024a). He argues that intelligent agents should not be seen merely as functional tools but as “emerging educational actors” who interact with values, norms, and school culture. This perspective aligns with the principles of digital pedagogy, which promote a values-based, equitable, and autonomous approach. AI can support these objectives only when accompanied by clear regulations, institutional support, and a school culture open to critical innovation.

A key dimension of modern digital pedagogy lies in the teachers’ capacity to integrate generative AI agents effectively into their teaching process. As outlined in the Innovating Pedagogy 2024 report, educators must acquire not only technical but also pedagogical and ethical competencies to make productive use of these emerging tools: “Generative Artificial Intelligence (GenAI) tools like ChatGPT and Gemini simulate Socratic questioning, fostering dialogic learning by providing real-time answers and guidance” (Kukulska-Hulme et al., 2024). This type of interaction promotes dialogic learning and supports reflective, personalized education, with the potential to replace rigid forms of teaching.

At the same time, the report warns of limitations: the uncritical use of AI can lead to overreliance on algorithms, reduced critical thinking, and the perpetuation of algorithmic biases. This is why developing digital literacy and prompt design skills becomes essential in ongoing teacher training. It’s not enough for educators to know how to use AI – they must also understand why, when, and under what conditions it best serves educational goals.

The report also presents an optimistic outlook on co-creating educational resources with AI, suggesting that AI can become a pedagogical partner in designing content, interactive simulations, and adaptive formative assessments. For these benefits to be achieved, AI must be integrated into a well-defined pedagogical framework grounded in democratic values, inclusion, and transparency.

Another important theoretical approach to AI’s ethical challenges concerns the moral development of AI systems. According to Cocioabă (2025), AI systems currently lack intrinsic morality but can be programmed to operate within moral value frameworks defined by humans. He distinguishes between two architectures – Combinatorial-State Automata (CSA) and Level of Abstraction (LoA) – based on their ability to respond morally or adhere to ethical thresholds in decision-making. He explains that, unlike CSA systems – which carry out preset goals without considering ethical implications – LoA systems can be designed with “threshold functions” that stop them from crossing moral boundaries. This distinction is especially important in education, where interactions between students and AI should reflect core values like honesty, accountability, and academic integrity.

Through an analysis of concrete examples – such as the CSA-type robot “Cătălin” used in call centers and the Character.ai platform as a LoA system – Cocioabă shows how the presence or absence of a moral database can significantly influence interactions between users and AI. The first system may exhibit manipulative behavior or even programmed deceit (“No, I am not a robot”), while the second can offer personalized yet ethically constrained responses.

These insights suggest that AI in education should not only be seen as a functional tool but also as a behavioral model for students. An educational AI that adheres to ethical norms becomes a formative example in itself. Conversely, if students witness deceptive behavior from AI, they may internalize the notion that “the ends justify the means” – a concept fundamentally at odds with democratic education.

In conclusion, the moral development of AI should be considered an integral part of its educational design, not an optional add-on. As Cocioabă (2025) accurately observes, “the pedagogical impact is already present and becoming increasingly evident as artificial intelligence advances”.

Conclusion

Artificial intelligence is on track to reshape education by making learning more personalized, school operations more efficient, and classrooms more inclusive. But unlocking these benefits depends on having a clear strategy, solid ethical foundations, and the full involvement of everyone in the education community.

Digital pedagogy must integrate AI in a responsible manner, preserving the human dimension of learning. AI-supported education does not mean less humanity, but rather more adaptability, empathy, and innovation. For AI to truly contribute to the advancement of education, it must be perceived as an ally to both teachers and students – a tool that supports authentic learning and character development.

Artificial intelligence plays a vital role in strengthening digital pedagogy, providing not only technological support but also a context for redefining the educational relationship. Modern digital pedagogy is undoubtedly enriched by AI, but the success of its integration depends on the education systems’ ability to maintain a balance between innovation, equity, and responsibility. Rather than replacing teachers, artificial intelligence should complement their work – helping tailor instruction to individual students and fostering greater inclusivity in the classroom. As schools continue to embrace digital tools, the teacher’s role remains essential in guiding not just academic growth, but also ethical reflection, empathy, and meaningful social engagement.

Digital pedagogy enhanced by AI holds exciting potential – offering continuous, flexible learning experiences that adapt to each student’s needs. But these advantages can only be fully realized when teachers, students, and school leaders collaborate to create a learning environment rooted in trust, meaningful data use, and shared human values.

Ultimately, the most promising applications of AI in education will be those designed with pedagogy as the primary framework – where the goal is not merely efficiency or personalization, but meaningful human development. AI should serve as an instrument within the educator’s toolbox, embedded in reflective, evidence-informed teaching practices. As such, its impact should be continuously assessed not only in terms of outcomes, but also in how it reshapes the core values and purposes of education.

Bibliography

Chaffer, S., Kaas, L., Pavone, V., & Stahl, B. C. (2024). ETHOS: Towards an Ethical Technology and Holistic Oversight System for the Development and Use of Autonomous Agents. arXiv. https://arxiv.org/abs/2412.17114

Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: a review. IEEE Access 8, 75264-75278. doi: 10.1109/ACCESS.2020.2988510

Cioară, V. (2025). Identitatea în mediile virtuale. Problema meta-narațiunii în domeniul metamorfozelor digitale. Revista de Pedagogie Digitală, 4(1), 13-18. https://doi.org/10.61071/RPD.2522

Cioranu-Rădulescu, C. (2024). Educația și conștientizarea etică a tehnologiilor IA și folosirea lor în mediul academic. Revista de Pedagogie Digitală, 3(1), 3-9. https://doi.org/10.61071/RPD.2420

Cocioabă, A.-D. (2025). Dezvoltarea morală a inteligenței artificiale [The Moral Development of Artificial Intelligence]. Revista de Pedagogie Digitală, 4(1), 19-22. https://doi.org/10.61071/RPD.2524

Doğan, M., & Arslan, H. (2025). The Role of Artificial Intelligence in School Leadership. Revista de Pedagogie Digitală, 4(1), 23-30. https://doi.org/10.61071/RPD.2531

Eubanks, V. (2018). Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor. St. Martin’s Press.

Holmes, W., Bialik, M., & Fadel, C. (2021). Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Center for Curriculum Redesign.

Istrate, O. (2022). Digital Pedagogy. Definition and Conceptual Area. Journal of Digital Pedagogy, 1(1) 3-10. Bucharest: Institute for Education. https://doi.org/10.61071/JDP.0313

Istrate, O., Velea, S., & Ceobanu, C. (2025). Pedagogie digitală. Polirom.

Istrate, O., Velea, S., & Ștefănescu, D. (2022). Ariadna Experiment. The Role of Artificial Intelligence in Education Sciences. Journal of Digital Pedagogy, 1(1) 15-22. Bucharest: Institute for Education. https://doi.org/10.61071/JDP.0300

Istrate, O. (2024a). AI Agents in Education: An Early Systematic Review of Emerging Roles, Potential, and Limitations. Revista de Pedagogie Digitală, 3(1), 24-30. https://doi.org/10.61071/RPD.2496

Istrate, O. (2024b). Inovare în educație cu ajutorul inteligenței artificiale. iTeach: Experiențe didactice. https://iteach.ro/experientedidactice/inovare-in-educatie-cu-ajutorul-inteligentei-artificiale

Kukulska-Hulme, A., Ferguson, R., Sharples, M., Henderson, M., Holmes, W., & Aristeidou, M. (2024). Innovating Pedagogy 2024: Exploring new forms of teaching, learning and assessment, to guide educators and policy makers. The Open University. https://iet.open.ac.uk/file/innovating-pedagogy-2024.pdf

Laurillard, D. (2013). Teaching as a Design Science: Building Pedagogical Patterns for Learning and Technology. Routledge.

Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2021). Intelligence Unleashed: An Argument for AI in Education. Pearson Education.

Selwyn, N., & Jandrić, P. (2020). The Human Labour of School Data: Exploring the Production of Digital Data in Schools. Learning, Media and Technology, 45(3), 270-283.

Schiffner, D., Selby, J., & McRae, K. (2023). AI for Administrative Innovation in Higher Education. Journal of Educational Administration and History, 55(1), 75-91.

Triberti, S., Di Fuccio, R., Scuotto, C., Marsico, E., & Limone, P. (2024). “Better than my professor?” How to develop artificial intelligence tools for higher education. Frontiers in Artificial Intelligence, 7. https://doi.org/10.3389/frai.2024.1329605

UNESCO (2023). Artificial Intelligence and Education: Guidance for Policy-makers. https://unesdoc.unesco.org/ark:/48223/pf0000376709

Williamson, B., & Eynon, R. (2020). Historical threads, missing links, and future directions in AI in education. Learning, Media and Technology, 45(3), 223–235. https://doi.org/10.1080/17439884.2020.1798995

Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – Where are the educators? International Journal of Educational Technology in Higher Education, 16, 39. https://doi.org/10.1186/s41239-019-0171-0

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Author:

Cristina-Georgiana Voicu
voicucristina2004@yahoo.fr – corresponding author
Titu Maiorescu Secondary School, Iași, Romania
https://orcid.org/0000-0001-9299-6551

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Primit: 7.04.2025. Acceptat pentru publicare: 9.04.2025
© Cristina-Georgiana Voicu, 2025. Acest articol cu acces deschis este publicat în termenii Creative Commons Attribution Licence CC BY, care permit utilizarea, distribuirea și reproducerea liberă, cu condiția menționării autorului și sursei:

Citare:

Voicu, C.-G. (2025). Integrating Artificial Intelligence into Digital Pedagogy: An Analysis of Challenges and Opportunities. Revista de Pedagogie Digitala, 4(1) 31-38. București: Institutul pentru Educație. https://doi.org/10.61071/RPD.2543