Examples of successful implementation of artificial intelligence tools and systems in education – organisational aspect

2. Key organisational factors

2.5. Evaluation of effectiveness and feedback

The successful introduction of AI in education requires continuous evaluation of its effectiveness to ensure that the tools and systems truly contribute to improving the learning and teaching process. Such evaluation must include not only technical indicators but also pedagogical aspects, user experiences, and real effects on the quality of education.

Implementation monitoring includes three interrelated goals:

  • Pedagogical effectiveness – measures the extent to which the use of AI improves students’ understanding, motivation, and achievement
  • User satisfaction – assesses the usefulness, accessibility, and support for all educational stakeholders
  • Identification of problems and opportunities for improvement – based on collected feedback, proposals for adaptation and system improvement are formulated

Example: 

Arizona State University (ASU) is one of the first universities to introduce ChatGPT Edu into teaching, research, and administration.

As part of the implementation, ASU systematically collects data on user experience, monitors tool usage, and evaluates its effects on learning outcomes. The data is analysed at multiple levels – from individual student engagement to the effectiveness of pedagogical models – to understand where AI truly adds value.

According to the university’s reports, the results show increased student engagement and a greater willingness among teachers to experiment with new teaching methods. It is emphasised that teacher feedback is key to further improving the system and developing ethically sustainable practices for AI application.

This example shows that AI in education is not a static tool but a dynamic system that requires constant reflection, measurement, and adaptation. Other universities can learn from ASU’s experience with an evaluation model that combines quantitative and qualitative data, involves all stakeholders, and builds a learning culture based on feedback.

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