Aims
Intelligent Education (IE) is an international, peer-reviewed, open-access journal dedicated to establishing intelligent education systems as a distinct interdisciplinary field integrating artificial intelligence, learning sciences, cognitive research, and data-driven educational transformation.
The journal focuses on how intelligent technologies reshape education across instructional design, learner support, and institutional governance. It welcomes work on intelligent learning systems, human–AI collaboration, learning analytics, AI ethics, teacher professional development, educational equity, inclusive design for diverse learners, and the broader digital transformation of teaching and learning.
The journal publishes high quality original research, theoretical advances, and empirical studies that offer rigorous and novel contributions to the field. It seeks work that is scalable, equitable, and responsible, with a particular interest in systems that support lifelong learning, foster human development, and contribute to sustainable educational progress across different cultural and socioeconomic settings.
Scope
The scope of this journal includes but not limited to:
1. Foundational AI Technologies for Education
- Educational knowledge graph construction, concept relationship reasoning, and cognitive mapping
- Large language model reasoning, hallucination mitigation, and output controllability in educational contexts
- Explainable and interpretable AI for education (e.g., why a learning path is recommended, why a score is assigned)
- Retrieval-augmented generationand knowledge-enhanced question-answering for education
- Benchmarking and adversarial testing of educational foundation models
- Causal inference methods for educational intervention evaluation
- Multimodal data fusion (g., speech, facial expression, gesture, EEG, eye-tracking) for learning analysis
2. Intelligent Learning Systems and Instructional Architectures
- Intelligent tutoring systems and conversational pedagogical agents
- Generative AI for instructional design, content generation, and automated assessment
- Adaptive learning technologies and dynamic personalized learning pathways
- AI-assisted curriculum design, textbook generation, and knowledge organization
- System-level instructional architectures and AI-native teaching–learning workflow reengineering
- Edge AI and on-device intelligence for offline, privacy-preserving educational applications
- Multi-agent systems for automated pedagogical strategy generation
3. Human–AI Collaboration, Cognition, and Immersive Environments
- Human–AI co-learning models and cognitive augmentation techniques
- AI-facilitated development of metacognition, critical thinking, and creativity
- Intelligent classrooms, XR (AR/VR/MR) immersive learning environments, and digital twin educational spaces
- Embodied cognition and multimodal interaction in education
- Next-generation AI-native learning ecosystems and adaptive learning ecologies
- Affective computing and emotional engagement in AI-mediated learning
4. Teacher Development and Professional Transformation
- Teacher AI literacy frameworks and professional development programs
- AI-assisted collaborative lesson planning, classroom diagnosis, and teaching reflection
- Role transformation and competence boundaries of teachers in human–AI co-teaching
- Teacher acceptance, trust, and adaptive use of AI tools
- Reform of pre-service teacher education and pedagogical training in the AI era
5. AI Ethics, Values Education, and Digital Citizenship
- Ethical governance frameworks for AI in education (bias, fairness, data privacy, algorithmic accountability)
- AI literacy education (K–12 to higher education): curriculum design and pedagogies
- Digital citizenship and responsible AI usage competencies
- Socio-emotional dimensions of human–AI relationships in learning (motivation, self-efficacy, sense of belonging)
- Value alignment and normative implications of AI-driven educational decisions
6. Educational Assessment Paradigm Shifts
- Process-oriented, authentic, and competency-based assessment powered by AI
- Automated essay scoring, spoken language assessment, and portfolio analysis
- LLM-driven adaptive testing and dynamic difficulty adjustment
- AI-mediated peer assessment, self-assessment, and collaborative evaluation mechanisms
- Assessment of higher-order competencies (critical thinking, creativity, collaboration) using AI methods
7. Educational Data Analytics and Data-Driven Decision-Making
- Multimodal learning analytics and behavioral modeling
- Educational data mining and learner profiling
- Dynamic academic early warning systems and personalized feedback generation
- Causal impact evaluation of educational policies and interventions
- Educational big data governance, standardization, and privacy-preserving sharing
8. System Transformation, Policy, Equity, and Inclusion
- Digital transformation strategies, policy modeling, and system dynamics for education
- AI-enabled educational governance (resource allocation, school management, quality assurance)
- Educational equity and inclusive AI design (underserved regions, disadvantaged groups, special educational needs)
- AI-empowered lifelong learning, vocational training, and future workforce development
- Cost–benefit analysis and sustainability assessment of smart education initiatives
9. AI-Supported Education for Special Needs and Diverse Learners
- AI-assisted identification and intervention for learning disabilities (dyslexia, dyscalculia, ADHD, etc.)
- Adaptive tools for students with autism spectrum disorder, visual/hearing impairments, and physical disabilities
- Talent identification and personalized development support for gifted learners
- Differentiated instruction for multilingual and multicultural learners
- Affective computing and AI-based early warning systems for mental health in educational settings
10. Empirical Studies, Case Implementations, and Cross-Cultural Comparisons
- Rigorous empirical studies (RCTs, quasi-experiments) on AI educational interventions
- Case studies spanning formal and informal education: K–12, higher education, vocational training, workplace learning, lifelong education
- Subject-specific AI applications (STEM, language, arts, physical education, etc.)
- Cross-cultural and cross-national comparisons of AI education practices
- Usability, user experience, and implementation effectiveness of AI educational products
- Failure-case analyses and lessons learned from real-world AI deployments in education
Publication Frequency
Quarterly.
Journal Abbreviation
Intell. Educ.
Publisher