[call is now open] Educators’ digital competence in the age of Artificial Intelligence: Reconfigurations of pedagogical practice

Submission deadline: January 15, 2026

_________________________

In recent years, the integration of Artificial Intelligence (AI) in education has evolved from an emerging trend to an unavoidable reality, driving an exponential growth of research in the field (Crompton & Burke, 2023), particularly following the arrival of the first generations of chatbots powered by Generative Artificial Intelligence (GenAI). This heightened scientific interest reflects the transformative potential of AI across various academic domains. GenAI-driven digital tools have already demonstrated the capacity to personalise learning, provide immediate feedback, and support educators in adapting pedagogical strategies to diverse learner profiles  “Educational settings, increasingly embracing AI technologies, include intelligent tutoring systems, adaptive learning platforms, chatbots, automated grading systems, and data analytics tools” (Al-Zahrani & Alasmari, 2024, p. 2).

In addition, AI solutions have been deployed to automate high-volume administrative tasks such as admissions triage and chatbot-based institutional assistance, reducing repetitive workloads and enabling academic teams to concentrate on activities of greater pedagogical and scholarly value (UNESCO, 2023).

Similarly, within the domain of scientific research, intelligent algorithms support both the mining and processing of large data volumes and tasks such as literature review and bibliometric analysis, as well as the drafting of academic text (Baldrich & Domínguez-Oller, 2024). These tools have expanded methodological frontiers and accelerated knowledge production in both qualitative studies (Huang et al., 2024; Santos, 2024) and quantitative research (Hohenwalde et al., 2025; Naeem et al., 2025; Wachinger et al., 2024), albeit always under rigorous human supervision.

Despite recent advances, institutional attention to AI within educational organisations remains disproportionately limited when compared to its growing impact. Specifically in higher education, a recent study by Sánchez-Caballé and Santos (2025), conducted across the websites of 188 institutions in the Iberian Peninsula, identified only 19 publicly available documents offering formal guidance for educators or students on the use of AI, that is, in less than 10% of the sample. This finding reveals a governance gap and underscores the urgency of investigating how educational institutions can structure policies and develop mechanisms to promote digital competence and responsible practices that ensure the critical, ethical and legally compliant use of AI.

The adoption of AI introduces complex challenges that demand broad and rigorous investigation. Ethical and social concerns have come to the fore, including data privacy, algorithmic transparency, and the potential amplification of discriminatory biases by automated systems (Al-Zahrani & Alasmari, 2024). For instance, while algorithms may help reduce the effects of human subjectivity in decision-making processes (such as selection procedures or assessment), they can also perpetuate or even scale existing prejudices when trained on non-diverse datasets (UNESCO, 2023).

Inclusion and equity in access to AI-based innovations constitute another critical concern: institutions with fewer resources, or those serving traditionally disadvantaged social groups, risk being left on the margins of these technological advances, thereby deepening the digital divide. These challenges highlight the need for clear guidelines and educational policies that can orient the responsible and critical use of AI.

In the context of future employability and civic formation, AI is likewise exerting significant influence, reshaping the competences required in contemporary and emerging professional fields. Recent estimates suggest that approximately two-thirds of current occupations will be affected to some degree by automation enabled by AI-based systems (Hatzius et al., 2023), while new professional domains are emerging and demanding new forms of technological knowledge. The labour market is already registering an increased demand for AI specialists, and it is expected that all graduates, regardless of their disciplinary background, will require some level of AI literacy in order to participate in increasingly digital societies and economies (Al-Zahrani & Alasmari, 2024; UNESCO, 2023). This places pressure on higher education institutions to update curricula, foster lifelong learning (LLL), and prepare students not only in the technical dimensions of AI but also in transversal competences (such as critical thinking, creativity and ethics) that are essential for navigating human–machine interaction.

In light of this scenario, the urgency of interdisciplinary studies becomes evident—studies that investigate how to harness the opportunities afforded by AI in education, while simultaneously identifying and mitigating the risks associated with its use.

This special issue aims to bring together academic contributions that, whether collectively or individually, explore the key axes of the interrelationship between AI and education, including: innovations in teaching and learning; changes in university governance and management; the impact of AI on scientific knowledge production; and implications for academic training. Transversally, it also seeks to foster pedagogical, ethical, social and political reflection on the role of AI in education. By articulating these dimensions, this special issue intends to highlight the scientific relevance of the topic, providing robust theoretical and empirical foundations to inform decision-making processes undertaken by educational leaders, educators and researchers in constructing a future (in)formed by AI (Aguado-García et al., 2025; UNESCO, 2023).

For this special issue, we will welcome Literature Review articles (preferably Scoping Reviews, Systematic Reviews, or Meta-analyses) as well as original empirical studies that advance the scientific debate on the urgency of educators’ digital competence within the context of Artificial Intelligence.

This special issue encompasses a wide range of educational sectors, including primary education, where the foundations of digital competence are established; secondary education, which deepens students’ critical and autonomous development; vocational education, which seeks to align digital training with labour market demands; higher education, responsible for knowledge production and the preparation of highly qualified professionals; and corporate education, which promotes the continuous updating of digital competences within professional settings. In this sense, the issue aims to gather contributions that reflect the transversality and relevance of educators’ digital competence across all levels and contexts of education and training. Articles may be submitted in Portuguese, English, or Spanish, and should align with one (or more) of the six thematic axes outlined below:

1. Innovations in Teaching and Learning; University Governance; Impact on Scientific Knowledge Production; Implications for Academic Training: This axis examines how the integration of AI and emerging technologies is transforming pedagogical practices, management processes, and models of scientific knowledge production. It explores innovations in teaching and learning mediated by GenAI, the use of AI-driven educational data and analytics, and the implications of these transformations for academic training, institutional culture, and the evolving roles of educators and researchers.
2. Institutional Policies and Programmes for Educators’ Digital Competence and/or the Integration of AI-based Solutions: This axis examines how public policies, institutional plans and regulatory documents define goals, indicators and professional development programmes aimed at fostering educators’ digital competence for the critical and creative use of technological solutions and/or for the integration of systems associated with Artificial Intelligence. It investigates governance strategies capable of transforming innovation agendas into sustainable mechanisms for professional capacity-building and organisational improvement.
3. Critical Digital Competence - Prompt Engineering and Output Verification in GenAI: This axis focuses on the digital competences required to operate GenAI in a deliberate and informed manner, including designing, drafting and iterating prompts, fact-checking AI outputs, identifying biases, and ensuring pedagogical relevance. It also examines approaches to teaching these competences to students, promoting data and AI literacy that supports collaborative, creative, self-regulated and responsible learning on an ongoing basis.
4. Educators’ Digital Competence in Pedagogical Design with GenAI: This axis explores the digital competences required to (re)design learning scenarios using GenAI, including selecting appropriate tools, orchestrating hybrid learning activities, co-creating multimodal resources, and applying educational analytics to provide personalised feedback. It encompasses research on intelligent tutoring systems, adaptive learning platforms, automated assessment, and performance dashboards that enhance learner engagement and academic outcomes.
5. Digital Competence for Research with GenAI: This axis interrogates the digital competences that enable teacher-researchers to integrate GenAI across the entire scientific cycle: literature prospecting and synthesis, data curation and cleaning, analytical modelling, AI-assisted writing and revision, and open-access dissemination. It examines AI simultaneously as an object of study and as a methodological tool, foregrounding its epistemological and ethical implications.
6. Pedagogical, Ethical, Social and Political Reflection on GenAI in Pursuit of Equity and Inclusion: This axis examines the digital competences that enable educators to use GenAI in ethical and inclusive ways, including the protection of educational data, algorithmic transparency, bias detection, the design of culturally and socially sensitive prompts, and pedagogical mediation. It also analyses policies that ensure justice, participation and social inclusion, as well as the full exercise of citizenship in AI-mediated educational contexts.

 

References

Aguado-García, J. M., Alonso-Muñoz, S., & De-Pablos-Heredero, C. (2025). Using Artificial Intelligence for Higher Education: An Overview and Future Research Avenues. SAGE Open, 15(2). https://doi.org/10.1177/21582440251340352

Al-Zahrani, A. M., & Alasmari, T. M. (2024). Exploring the impact of artificial intelligence on higher education: The dynamics of ethical, social, and educational implications. Humanities and Social Sciences Communications, 11(1). https://doi.org/10.1057/s41599-024-03432-4

Baldrich, K., & Domínguez-Oller, J. C. (2024). El uso de ChatGPT en la escritura académica: Un estudio de caso en educación. Pixel-Bit, Revista de Medios y Educación, 71, 141-157. https://doi.org/10.12795/pixelbit.103527

Crompton, H., & Burke, D. (2023). Artificial intelligence in higher education: the state of the field. International Journal of Educational Technology in Higher Education, 20(1). https://doi.org/10.1186/s41239-023-00392-8

Hatzius, J., Briggs, J., Kodnani, D., & Pierdomenico, G. (2023). The Potentially Large Effects of Artificial Intelligence on Economic Growth. Global Economics Analyst. https://www.gspublishing.com/content/research/en/reports/2023/03/27/d64e052b-0f6e-45d7-967b-d7be35fabd16.html

Hohenwalde, C. E., Leidecker-Sandmann, M., Promies, N., & Lehmkuhl, M. (2025). ChatGPT’s potential for quantitative content analysis: categorizing actors in German news articles. Journal of Science Communication, 24(2), 1-27. https://doi.org/10.22323/2.24020201

Huang, Y., Wu, R., He, J., & Xiang, Y. (2024). Evaluating ChatGPT-4.0’s data analytic proficiency in epidemiological studies: A comparative analysis with SAS, SPSS, and R. Journal of Global Health, 14(1088), 04070. https://doi.org/10.7189/jogh.14.04070

Naeem, M., Smith, T., & Thomas, L. (2025). Thematic Analysis and Artificial Intelligence: A Step-by-Step Process for Using ChatGPT in Thematic Analysis. International Journal of Qualitative Methods, 24. https://doi.org/10.1177/16094069251333886

Sánchez-Caballé, A., & Santos, C. (2025). Perspectives of Higher Education in Spanish and Portuguese Institutions on Artificial Intelligence: A Content Analysis. Edutec, Revista Electrónica de Tecnología Educativa, 92, 253-269. https://doi.org/10.21556/edutec.2025.92.3879

Santos, C. (2024). Artificial Intelligence in the Analysis of Educational Research Quantitative data: Reliability of Data Analyst GPT (ChatGPT) Compared to SPSS end Jamovi. Nuances: Estudos Sobre Educação, 35(00), e024013. https://doi.org/10.32930/nuances.v35i00.10682

UNESCO. (2023). Harnessing the Era of Artificial Intelligence in Higher Education: A Primer for Higher Education Stakeholders. https://unesdoc.unesco.org/ark:/48223/pf0000386670

Wachinger, J., Bärnighausen, K., Schäfer, L. N., Scott, K., & McMahon, S. A. (2024). Prompts, Pearls, Imperfections: Comparing ChatGPT and a Human Researcher in Qualitative Data Analysis. Qualitative Health Research. https://doi.org/10.1177/10497323241244669

 

Cassio Santos, Ilka Serra, Neuza Pedro and Francesc Esteve-Mon

[Guest editors of Sisyphus special issue on ‘Educators’ Digital competence in the age of Artificial Intelligence: Reconfigurations of pedagogical practice’]