Artificial Intelligence (AI) has rapidly become a significant part of modern life, influencing various sectors including education. The new research by Panagiotes Anastasiades (2024) and colleagues highlights the necessity of adopting a human-centered approach to AI in education, emphasizing the critical role of the educational community in this transition.1

Human-Centered AI (HCAI) focuses on the needs and well-being of humans, ensuring that AI applications support rather than replace human capabilities. This approach is essential for developing trustworthy and socially responsible AI systems. According to the research, HCAI should:

  1. Contribute to human well-being.
  2. Be governed by social responsibility.
  3. Ensure data privacy.
  4. Involve human participation in design and evaluation.
  5. Emphasize transparency and environmental protection.
  6. Foster public understanding of AI functions.

Historical Context of AI in Education

AI’s integration into education began in the early 20th century with pioneers like Sidney Pressey and B.F. Skinner. Pressey developed a machine that guided students to correct answers, while Skinner’s machine provided immediate feedback, acting as a personal tutor. These early efforts laid the groundwork for modern Intelligent Tutoring Systems (ITS), which adapt to individual student needs.

Adaptive Learning Systems: These systems, developed in the 1950s, tailored educational content based on student responses. Norman Crowder’s programmed instruction and Gordon Pask’s SAKI machine are notable examples.

Computer-Aided Instruction (CAI): In the 1960s and 1970s, CAI systems like PLATO provided interactive educational materials. However, these early systems lacked personalization.

Intelligent Tutoring Systems (ITS): ITS models simulate human teaching by providing personalized learning experiences. They include domain models (subject knowledge), pedagogy models (teaching methods), and learner models (student data). ITS can function as intelligent teachers, learners, educational tools, or policy advisors, adapting to individual student needs and enhancing learning efficiency.

Educational Data Mining (EDM): EDM uses machine learning algorithms to analyze student data, predict learning outcomes, and provide personalized support. This helps identify areas where students need additional help, improving overall learning efficiency.

Learning Analytics (LA): LA involves collecting and analyzing data from student activities to create personalized learning experiences. By understanding student behavior, LA can provide insights for educators and tailor educational content to individual needs.

The Role of Robots in Education

Robots, powered by AI, can assist or even replace teachers in certain tasks, making learning more personalized and engaging. They can adapt to each student’s learning pace, keeping them motivated and focused.

A pilot study conducted by the University of Crete revealed mixed feelings among teachers about AI in education. While 71.5% of teachers felt they had general knowledge about AI, many expressed concerns about its social implications and job security. However, 61.9% of respondents were optimistic about AI’s potential in education.


The research underscores the necessity of developing a comprehensive pedagogical framework for HCAI in education. This framework should prioritize:

  1. Informed and responsible use of AI, emphasizing its social and cultural implications.
  2. Open dialogue with educators, students, and parents about AI’s challenges and opportunities.
  3. Training teachers to integrate HCAI into their teaching practices, fostering critical thinking, collaboration, and creativity.

Reviewed by the Psyhologer Editorial Team

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