Intelligent Educational Technologies: Core Architectures, Algorithms, and Evidence-Based Systems
A special issue of Computers (ISSN 2073-431X). This special issue belongs to the section "AI-Driven Innovations".
Deadline for manuscript submissions: 31 October 2026 | Viewed by 515
Special Issue Editors
Interests: e-learning interoperability; computer programming education; gamification; GenAI
Special Issues, Collections and Topics in MDPI journals
Interests: didactics and training; applied linguistics; ICT applied to teaching
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The integration of Artificial Intelligence (AI) into educational ecosystems is catalyzing a paradigm shift in pedagogical methodologies through advanced computational frameworks. This Special Issue seeks original research at the nexus of information technology and education, focusing on the design, implementation, and validation of scalable AI systems for teaching innovation. Submissions must foreground technical rigor in addressing how algorithmic models, data infrastructures, and intelligent interfaces transform educational processes.
Potential topics include (but are not limited to) the following:
- AI System Architectures:
- Scalable frameworks for intelligent tutoring systems (e.g., NLP-driven feedback engines, reinforcement learning agents);
- Distributed computing models for real-time adaptive learning (edge/cloud-based deployment).
- Data-Centric Innovations:
- Multimodal learning analytics pipelines (sensor fusion, temporal pattern mining);
- Automated instructional design via generative AI (LLM-based content synthesis, RAG architectures).
- Algorithmic Applications:
- Personalization engines (collaborative filtering, knowledge tracing models);
- AI-augmented assessment (automated scoring, anomaly detection in evaluations).
- Human–AI Interaction:
- Conversational agents for teacher support (intent recognition, dialogue management);
- Explainable AI (XAI) interfaces for educational decision-making.
- DevOps for EdTech:
- MLOps practices in educational AI deployment;
- Ethical AI by design (bias mitigation algorithms, privacy-preserving federated learning).
We look forward to your contributions to this timely and impactful topic.
Prof. Dr. Ricardo Queirós
Dr. Mário Rui Domingues Ferreira da Cruz
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Computers is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- AI system architectures
- learning analytics pipelines
- adaptive algorithms
- generative AI in education
- educational data mining
- XAI for pedagogy
- MLOps in EdTech
- federated learning
- API interoperability
- NLP for tutoring systems
- algorithmic bias mitigation
- cloud-based AI services
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