Objective: The main goal of this study was to conduct a comprehensive review of empirical AIEd research from 1993-2020 to map the current state of research, identify AI technologies and applications, bridge gaps between innovations and educational implementations, and provide practical guidance for both technology experts and educators.

Methods:

• Comprehensive review of empirical studies from Web of Science database and specialized AIEd journals
• Multiple analysis methods including bibliometrics, content analysis, and categorical meta-trends analysis
• Selection criteria focused on peer-reviewed empirical studies with human participants in educational settings
• Total of 40 research articles met all selection criteria for full analysis

Key Findings:

• Research concentrated in USA (9 studies) and China (7 studies), with good representation from Global South
• Studies conducted across both K-12 (17 studies) and higher education (21 studies) settings
• Main AI technologies studied included:
  • Intelligent tutors/agents
  • Expert systems
  • Machine learning
  • Personalized learning systems
  • Chatbots
  • Visualizations and virtual environments
• Most studies were collaborative efforts involving multiple authors and disciplines
• Sample sizes varied greatly from 20 to 7,341 participants
• 25 of 40 studies were conducted in STEM fields

Implications:

• Provides framework for understanding current state of AIEd research and implementation
• Highlights need for more interdisciplinary collaboration between educators and AI developers
• Identifies gaps between AI technological capabilities and practical educational applications
• Offers guidance for both technological experts and educators on effective AI integration

Limitations:

• Limited to Web of Science database and selected journals
• Excluded conference proceedings and non-English publications
• Search terms may have missed relevant studies not explicitly labeled as AI
• Focus only on empirical studies with human participants
• Limited coverage of newer AI technologies and applications

Future Directions:

• Scale up research to examine AIEd at institutional, regional and national levels
• Conduct longer duration studies on AI implementation and impact
• Expand research on emerging AI technologies like chatbots and machine learning
• Address ethical concerns and privacy issues in AIEd
• Develop more comprehensive guidelines for educators
• Foster interdisciplinary collaboration between educators and AI developers
• Investigate AI integration with other emerging technologies like VR/AR