Impact of AI-Driven Adaptive Learning Environments on the Success and Motivation of Students with Special Educational Needs: A Mixed-Methods Study

Ensuring that students with special needs learn on an equal basis as other students and participate fully in the educational process has become a global priority within inclusive education. The main goal of this movement is to introduce an inclusive pedagogical approach to the educational process that takes into account the special needs of students. For this reason, there is an increasing need for effective teaching methods used in sustainable and inclusive education. This need determined the purpose of the study. The purpose of this study is to investigate the impact of an adapted learning environment based on artificial intelligence on the academic performance and motivation of students with special needs. A mixed-methods approach combining quantitative and qualitative data was employed. Quantitative data were analyzed using ANOVA, RM-ANOVA, and ANCOVA methods. In addition, the Pearson correlation analysis was conducted to examine the relationship between learning achievement and motivation, and the Cronbach alpha was used to assess the reliability of measuring instruments. Qualitative data was collected through interviews and analyzed using thematic analysis. The findings suggested that the AI-based adaptive learning environment was associated with improvements in students’ academic performance and learning motivation. The interaction between time and group factors indicated that the experimental group demonstrated greater improvements compared to the control group. However, the main influence of the group has not reached statistical significance; therefore, in future studies it is recommended to increase the sample size and duration of the experiment. Qualitative analysis revealed that adapted materials supported the understanding of complex topics, but some participants faced technical difficulties and difficulties in adapting to the new format. In addition, since the thematic analysis was carried out by one researcher, it is recommended to involve several encoders in the future. In conclusion, the study suggests that AI-based adaptive learning environments may contribute to improving the academic performance and motivation of students with special educational needs. This contributes to the development of sustainable and inclusive education.