Generative artificial intelligence has ceased to be an emerging novelty and has become a factor of profound transformation in higher education, digital, open, and distance education, as well as in hybrid environments. Its presence affects teaching design, learning assessment, academic authorship, tutoring, personalization, institutional management, and quality assurance.
Having moved beyond the initial phase of technological astonishment, educational research now needs to advance toward studies capable of offering solid, verifiable, and relevant evidence. RIED – Revista Iberoamericana de Educación a Distancia invites submissions that do not merely describe possibilities, risks, or general perceptions regarding generative AI, but instead provide new, methodologically rigorous knowledge that is useful for guiding pedagogical, institutional, and policy decisions. In other words, contributions that generate interest and have an impact.
As is well known, RIED pays special attention to research conducted in higher education, which will be given priority. Likewise, priority will be given to studies focused on digital environments. Although, secondly, high-quality studies in other levels or contexts may also be considered when their findings are argued and justified in the sense that they can be clearly transferable to digital, open, hybrid, or distance higher education. International, comparative, and interinstitutional contributions will also be especially valued.
Within the proposed topic, this special issue will especially consider:
- original empirical research with well-justified quantitative, qualitative, or mixed-methods designs;
- experimental or quasi-experimental studies;
- longitudinal analyses; research with large and well-characterized samples;
- comparative studies;
- studies based on learning analytics, digital traces, relevant corpora, or institutional data;
- evaluations of interventions, tools, or pedagogical models; and qualitative studies of high analytical density, supported by consistent evidence.
Theoretical, conceptual, or review-based contributions may also be considered, but only when they offer an exceptional contribution that is clearly original and distinct from the existing literature. Systematic reviews, meta-analyses, or scoping reviews, exceptional in quality and interest, must follow rigorous protocols, address well-defined questions, and present original findings with real added value for the field.
The following will not be considered priorities and will be difficult to publish:
- manuscripts that are merely speculative, essayistic, descriptive, or based on general opinions;
- isolated experiences without rigorous evaluation;
- studies based solely on superficial perceptions or insufficient samples;
- proposals for untested models;
- narrative reviews without an explicit method or without exceptional novelty; or
- works that reiterate already widely known conclusions about AI in education.
Preferred thematic lines of the special issue
As a non-exhaustive guide, the following areas or lines of contribution are proposed, preferably in higher education contexts and in distance, online, hybrid, or bimodal environments:
1. Impact of generative AI on learning
- Teaching interventions mediated by generative AI and evaluation of their outcomes.
- Differential effects according to student profiles, disciplines, modalities, or levels of digital competence.
- Relationship between the use of generative AI, autonomy, critical thinking, self-regulation, and deep learning.
- Longitudinal studies on the adoption, sustained use, and cumulative impact of generative AI.
2. Learning assessment and formative feedback using AI
- Models of authentic, continuous, oral, process-based, competency-based, or performance-based assessment.
- Evidence on the reliability, validity, equity, and scalability of new forms of assessment.
- Use of generative AI for formative feedback and its impact on learning.
- Comparison between human assessment, AI-assisted assessment, and hybrid correction systems.
- Rubrics, portfolios, traceability, oral defense, peer assessment, self-assessment, and multimodal assessment.
3. Academic integrity, authorship, and responsible use of AI
- Actual behaviors of students and teachers regarding generative AI in academic tasks.
- Effectiveness of policies, protocols, declarations of use, task redesigns, or training strategies.
- AI literacy, academic integrity, and the development of critical judgment.
- Limitations, biases, and unintended effects of AI-generated content detectors.
- Authorship, transparency, traceability, and responsibility in AI-assisted academic productions.
4. Personalization, intelligent tutoring, and AI-supported guidance
- •ntelligent tutoring systems, chatbots, or conversational agents based on generative models.
- Adaptation of content, pathways, activities, and feedback according to students’ profiles or needs.
- Personalization aimed at inclusion, accessibility, and support for students with specific needs.
- Effects of automated tutoring on autonomy, satisfaction, achievement, retention, and teaching workload.
- Comparison between human tutoring, automated tutoring, and hybrid guidance models.
5. Quality, governance, and institutional conditions for AI use
Provided that they are supported by empirical evidence or rigorous analysis and not by general statements of principle, the following will also be considered:
- Research on organizational cultures, adoption criteria, academic leadership, responsible institutional uses, and the educational impact that all of this has on students.
- Research on policies, quality frameworks, equity, privacy, data protection, copyright, teacher training, and institutional sustainability.
CONCLUSION
With this special issue, RIED aims to continue contributing to a new stage of research on generative AI in education: one that is less focused on fascination with the tool and more committed to producing evidence, verifiably improving learning, protecting academic integrity, ensuring the quality of assessment, promoting equity, and supporting institutional responsibility.
This call is addressed to researchers and research teams capable of providing solid, internationally relevant, and methodologically demanding knowledge on the role of generative AI in digital, open, hybrid, and distance education.
In short, for this call, the journal will therefore give priority to rigorous and original empirical research. Theoretical, conceptual, or systematic review contributions will be considered exclusively when they provide a substantive, exceptional, and clearly differentiated advance with respect to the existing literature.
IMPORTANT DATES
- Submission of articles: during the month of November 2026, with a deadline of 01/12/2026, Madrid time. Please avoid submitting articles before November 2026.
- Official publication: This issue, Vol. 30(2), corresponds to 01/06/2027.
- OnlineFirst publication: Before that official date, articles will be published in OnlineFirst format, ready to read and cite, as they successfully complete the different stages of evaluation.
IMPORTANT
- Do not submit any work to RIED unless you are convinced that all the requirements set out in this document, in the links to which it refers, and in this call are fully met.
- All papers submitted under this call must address its topic and must be submitted to the “Special Issue” section.
- All articles not considered for publication in Vol. 30(2) will be rejected.
- Papers that successfully pass all stages of evaluation must be translated, with professional quality, into the second language: English, if the original manuscript was submitted in Spanish or Portuguese; and Spanish, if the original manuscript was submitted in English.
