Aim and Scope Journal

Indonesian Journal of Artificial Intelligence in Education (IJAIE) publishes papers concerned with the application of AI to education. It aims to help the development of principles for the design of computer-based learning systems. Its premise is that such principles involve the modelling and representation of relevant aspects of knowledge, before implementation or during execution, and hence require the application of AI techniques and concepts. IJAIE has a very broad notion of the scope of AI and of a 'computer-based learning system', as indicated by the following list of topics considered to be within the scope of IJAIE:

1. Adaptive and intelligent multimedia and hypermedia systems

This area focuses on digital learning systems that can adapt content, presentation style, and difficulty level according to students’ needs and learning progress. Artificial intelligence is used to analyze learning behavior, preferences, and performance so that the system can provide personalized learning paths and feedback. The main goal is to create a more interactive, flexible, and student-centered learning experience.

2. Agent-based learning environments

This topic examines learning environments that use intelligent agents acting as tutors, learning companions, or observers of student activities. These agents can provide feedback, recommendations, and assistance automatically during the learning process. Agent-based environments encourage adaptive interaction, self-directed learning, and greater learner engagement.

3. AIE and teacher education

This field discusses how AI is utilized in teacher education and professional development. It includes digital pedagogical competence, assessment supported by learning analytics, and AI-assisted reflection on teaching practice. AI is positioned as a professional support tool that strengthens instructional decision-making rather than replacing the teacher’s role.

4. Architectures for AIE systems

This topic concerns the design of structures and components of AI-based educational systems so that they operate efficiently and in an integrated manner. It includes module organization, knowledge storage, data flow, and system interoperability. A well-designed architecture ensures stability, scalability, and sustainability of AIE systems.

5. Assessment and testing of learning outcomes

This area explores the use of AI in educational assessment, such as adaptive testing, automated scoring, and performance analytics. AI enables faster, more objective, and data-driven evaluation processes. Assessment results are also used to support remedial actions and instructional improvement.

6. Authoring systems and shells for AIE systems

This topic focuses on platforms that help educators create intelligent learning materials without requiring advanced technical skills. These systems support content generation, task design, and structured learning flows. Their purpose is to simplify and accelerate AI-based instructional development.

7. Case-based systems

This field discusses learning supported by previous cases or real-world experiences as a basis for problem-solving. Students learn by comparing new situations with prior cases and applying reasoning strategies. This approach strengthens analytical thinking and decision-making skills.

8. Cognitive development

This topic examines how AI supports students’ cognitive growth by helping them understand concepts, think critically, and build knowledge progressively. Learning systems are designed to strengthen mental processes rather than only measure outcomes. The emphasis is on meaningful and developmental learning.

9. Cognitive models of problem-solving

This field studies how AI models students’ thinking processes when solving problems. The information generated allows the system to provide guidance and hints that match learners’ needs. Through this approach, problem-solving support becomes more accurate and contextual.

10. Cognitive tools for learning

This topic focuses on AI-based tools that help students analyze information, interpret data, and develop structured ways of thinking. These tools support the learning process rather than replace human cognition. Their purpose is to enhance learning autonomy and reasoning ability.

11. Computer-assisted language learning

This area addresses the use of AI in language learning, including speech recognition, grammar analysis, and automated feedback. The system enables adaptive and interactive language practice. Learners benefit from real-time responses and personalized exercises.

12. Computer-supported collaborative learning

This topic examines how AI supports teamwork and online collaborative learning. The system monitors interaction patterns, distributes roles, and analyzes group contributions. This approach promotes effective communication and shared knowledge construction.

13. Dialogue (argumentation, explanation, negotiation, etc.)

This area studies intelligent dialogue systems that facilitate discussion, argumentation, and conceptual explanation in learning activities. AI helps structure conversations and support reflective thinking. Dialogue-based learning encourages deeper understanding and critical reasoning.

14. Discovery environments and microworlds

This topic focuses on exploratory learning environments and simulations that allow learners to experiment and discover concepts independently. Students can test ideas in a safe virtual setting. The approach supports inquiry-based and conceptual learning.

15. Distributed learning environments

This field examines learning systems delivered through networks and cloud-based platforms that enable remote and flexible learning. AI manages interaction, coordination, and integration of learning resources. The goal is to support accessible and collaborative learning environments.

16. Educational robotics

This topic explores the integration of robotics in education to develop logical reasoning, creativity, and problem-solving skills. AI enables interactive learning experiences with robotic systems. This field contributes to technological and computational literacy.

17. Embedded training systems

This area involves training systems embedded directly within tools, devices, or real-world environments. Learners receive automatic guidance while performing tasks. AI ensures that learning occurs contextually and in real time.

18. Empirical studies to inform the design of learning environments

This topic emphasizes empirical research as a basis for designing AI-supported learning environments. Data from studies are used to improve features, strategies, and user experience. This ensures that systems remain relevant to real educational needs.

19. Environments to support the learning of programming

This field focuses on AI-enhanced platforms that assist students in learning programming through feedback, error analysis, and guided practice. The system helps learners progress step-by-step. The aim is to make programming more accessible and understandable.

20. Evaluation of AIE systems

This topic examines the effectiveness, usability, and educational impact of AI-based learning systems. Evaluation is conducted using quantitative and qualitative approaches. Findings are used to improve system quality and implementation.

21. Formal models of components of AIE systems

This field studies the mathematical and computational modeling of AIE components such as knowledge representation and inference processes. These models ensure precision, consistency, and reliability. They also support system transparency and traceability.

22. Human factors and interface design

This topic highlights user experience and human interaction in AI-supported learning systems. Interfaces are designed to be accessible, intuitive, and inclusive. Human-centered design is viewed as a key success factor.

23. Instructional design principles

This area focuses on learning design principles that integrate pedagogy and AI technology. It covers goal setting, learning strategies, and activity sequencing. The goal is to ensure learning remains meaningful and pedagogically grounded.

24. Instructional planning

This topic discusses AI-assisted lesson planning that adapts learning materials and activities to student needs. Data-driven insights support instructional decisions. The approach strengthens personalization in teaching.

25. Intelligent agents on the internet

This field explores the use of intelligent online agents to support information navigation, content recommendation, and automated learning assistance. These agents function as digital learning companions. They facilitate efficient access to knowledge resources.

26. Intelligent courseware for computer-based training

This topic studies digital learning systems equipped with adaptive and analytical AI capabilities. The system monitors learning progress and adjusts content delivery. It enhances the effectiveness of technology-based training.

27. Intelligent tutoring systems

This field focuses on AI-driven tutoring systems that simulate the role of a teacher by providing personalized guidance and feedback. The system models student performance and learning behavior. Its aim is to improve individual learning quality.

28. Networked learning and teaching systems

This topic examines network-based learning ecosystems connecting learners, instructors, and digital resources. AI supports coordination, interaction, and communication across learning communities. The approach reinforces collaborative learning practices.

29. Neural models applied to AIE systems

This field analyzes the use of neural networks to study learning behavior, predict performance, and recommend learning strategies. These models strengthen educational data analysis. They play an important role in AI-driven learning analytics.

30. Practical, real-world applications of AIE systems

This topic covers the implementation of AI in real educational contexts such as schools, universities, and vocational training environments. The focus is on effectiveness, challenges, and institutional readiness. It highlights the practical impact of AIE technologies.

31. Situated learning and cognitive apprenticeship

This field studies learning that occurs in real contexts through guided practice and authentic tasks. AI helps support experience-based learning activities. The approach strengthens knowledge transfer to real-world situations.

32. Student modelling and cognitive diagnosis

This topic focuses on modeling student learning profiles to identify abilities, difficulties, and learning patterns. AI provides detailed diagnostic information and targeted interventions. The goal is to support more precise and individualized instruction.