AI-Driven Educational Technologies: Architectures, Algorithms, and Evidence-Based Systems
A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".
Deadline for manuscript submissions: 30 May 2026
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 and 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 invites 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 research areas:
- AI System Architectures:
- Scalable frameworks for intelligent tutoring systems (e.g., NLP-driven feedback engines and reinforcement learning agents).
- Distributed computing models for real-time adaptive learning (edge/cloud-based deployment).
- Data-Centric Innovations:
- Multimodal learning analytics pipelines (sensor fusion and temporal pattern mining).
- Automated instructional design via generative AI (LLM-based content synthesis and RAG architectures).
- Algorithmic Applications:
- Personalization engines (collaborative filtering and knowledge tracing models).
- AI-augmented assessment (automated scoring and anomaly detection in evaluations).
- Human–AI Interaction:
- Conversational agents for teacher support (intent recognition and 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 and privacy-preserving federated learning).
We look forward to your contributions exploring this timely and impactful topic.
Prof. Dr. Ricardo Queirós
Prof. 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 100 words) can be sent to the Editorial Office for announcement on this website.
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. Information 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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