The use of Information and Communication Technology (ICT) in preschool education is an important research issue, and accordingly a wide range of research results have been published. This paper analyzes and compares the research findings on ICT use in preschool education in the past decade in Greece and China. The main analysis framework of this paper focuses on three aspects: (i) access to and use of ICT in preschool; (ii) ICT and preschoolers; (iii) ICT and preschool teachers. Finally similarities and differences of the ICT usage in preschool settings in the two countries are discussed and concluded.

Developmental domains like physical, verbal, cognitive, and social-emotional skills are crucial for monitoring a child’s growth. However, identifying neurodevelopmental deficiencies can be challenging due to the high level of variability and overlap. Early detection is essential, and digital procedures can assist in the process. This study leverages the current advances in artificial intelligence to address the prediction of neurodevelopmental disorders through a comprehensive machine learning approach. A novel and recently developed serious game dataset, collecting various data on children’s speech and linguistic responses, was used. The initial dataset comprised 520 instances, reduced to 473 participants after rigorous data preprocessing. Cluster analysis revealed distinct patterns and structures in the data, while reliability analysis ensured measurement consistency. A robust prediction model was developed using logistic regression. Applied to a subset of 184 participants with an average age of 7 years, the model demonstrated high accuracy, precision, recall, and F1-score, effectively distinguishing between instances with and without neurodevelopmental disorders. In conclusion, this research highlights the effectiveness of the machine learning approach in diagnosing neurodevelopmental disorders based on cognitive features, and offers new opportunities for decision making, classification, and clinical assessment, paving the way for early and personalized interventions for at-risk individuals.

Reading is a complex skill that requires accurate word recognition, fluent decoding, and effective comprehension. Children with dyslexia often face challenges in these areas, resulting in ongoing reading difficulties. This study systematically reviews the use of eye-tracking technology to assess dyslexia, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. The review identifies the specific types of eye-tracking technologies used, examines the cognitive and behavioral abilities assessed (such as reading fluency and attention), and evaluates the primary purposes of these evaluations—screening, assessment, and diagnosis. This study explores key questions, including how eye-tracking outcomes guide intervention strategies and influence educational practices, and assesses the practicality and time efficiency of these evaluations in real-world settings. Furthermore, it considers whether eye-tracking provides a holistic developmental profile or a targeted analysis of specific skills and evaluates the generalizability of eye-tracking results across diverse populations. Gaps in the literature are highlighted, with recommendations proposed to improve eye-tracking’s precision and applicability for early dyslexia intervention. The findings underscore the potential of eye-tracking to enhance diagnostic accuracy through metrics such as fixation counts, saccadic patterns, and processing speed, key indicators that distinguish dyslexic from typical reading behaviors. Additionally, studies show that integrating machine learning with eye-tracking data can enhance classification accuracy, suggesting promising applications for scalable, early dyslexia screening in educational settings. This review provides new insights into the value of eye-tracking technology in identifying dyslexia, emphasizing the need for further research to refine these methods and support their adoption in classrooms and clinics.

The aim of this study was to investigate the possibility of creating computerized application software to be used for e-learning activities for Greek preschoolers particularly to improve: (a) speech articulation problems and (b) language learning. The pedagogical model of the application was based on social learning theories and especially the nearest neighbour learning method. This study took place in Greece, during 2009 in a self selected group of children. The results of the study have shown that the learning outcomes of the integrated e-learning activities of language articulation in preschoolers were above average comparatively to other methods. Specially, we classified the learning processes according to the children's needs embedding the technological developments and the current needs of our society.