International students face unique challenges in pursuing higher education in a foreign country. To address these challenges and enhance their academic experience, higher education institutions are increasingly exploring the use of artificial intelligence (AI) applications. This research essay aims to investigate the impact of AI on the education of international students. Instead of a traditional literature review, it employs a research approach to examine the potential applications of AI and discuss associated concerns. The research paper explores various AI applications, such as personalized learning experiences, adaptive testing, predictive analytics, and chatbots for learning and research. By analyzing the role of AI in education for international students, this research paper sheds light on how AI can improve learning efficiency and provide customized educational support. Additionally, it identifies significant risks and limitations, including privacy concerns, cultural differences, language proficiency, and ethical implications, which must be effectively addressed. The findings contribute to a better understanding of the potential impact of AI on international students’ educational experiences and offer insights into the integration of AI into educational administration and learning processes.

Distance education is now a reality introducing a “specific methodology of flexible and interactive multiform learning”. Due to its characteristics, different instructional design models apply to distance education as guidelines of the design thinking process pursuing specific learning outcomes. This study refers to the investigation of good teaching practices and approaches in relation to the ADDIE model in distance online environments. The purpose of this paper is to investigate both the effectiveness of the ADDIE model in distance education and its contribution to the online teaching process. Meta-analysis is chosen as the research methodology. Specifically, we export a total of 58 articles referring to the ADDIE model. From these, we find that only 23 articles are appropriate for the meta-analysis. According to the results of this study, we observe that the ADDIE model applies to meet different teaching requirements in all online educational environments. In this study, we observe that good practices of teaching are the multimedia presentation, feedback, variety of interactive exercises or activities, combined learning strategy (individualized and collaborative), and role of educators. Then, an asynchronous approach was preferred in distance education. Finally, the ADDIE model is considered as a valuable source of additional information by providing good teaching practices.

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.

The use of artificial intelligence (AI) applications in education has the potential to revolutionize the learning experience for international students, who face unique challenges when studying in a foreign country. This paper explores various examples of AI applications in education and their potential impact on international students. AI applications such as personalized learning experiences, adaptive testing, predictive analytics, and chatbots for learning and research are examined for their potential to improve learning efficiency and provide customized education support. It also explores the significant risks and limitations associated with AI technologies, such as privacy, cultural differences, language proficiency, and ethical implications. To maximize the potential benefits of AI applications in higher education, it is crucial to implement appropriate safeguards and regulations. This paper provides a starting point for research on the potential impact of artificial intelligence on international students’ educational experiences and how AI may be integrated into educational administration and learning processes.

Educational Design and Evaluation Models are important factors in e-learning as they provide guidance information for proper strategy organization pursuing both specific learning outcomes and ensuring the main elements of e-learning, such as self-regulation and collaborative learning. The examined educational models of ADDIE, Bloom, and Kirkpatrick are widely known and recognized as models for design and measuring the effectiveness of learning in order to achieve the best possible learning outcomes based on the needs of a specific target group in a specific educational context whether traditional or digital. Specifically, the ADDIE Model is a widely known learning design model used by many educational designers and training programmers to develop education and training programs. The Bloom Taxonomy is a method of building learning goals that follows the process of cognition. The Kirkpatrick Model is a method of evaluating the effectiveness of e-learning and educational programs in general, both in terms of training and business performance of learners. The purpose of this paper was both the investigation of the academic performance, the self-regulated learning and the collaborative learning in relation to the models of ADDIE, Kirkpatrick and Bloom in distance online environments and their effectiveness to the learning process. Meta-analysis was applied for research methodology. After a systematic literature review, we found that only 37 articles were appropriate for meta-analysis. Especially, 23 articles were on the ADDIE model, 9 articles were on the Kirkpatrick model and 5 articles were on the Bloom model. According to the results of this study, we found that all models apply to online process and meet different learning requirements. Regarding the cognitive performance of the trainees, all models supported the effectiveness of distance education. Moreover, the self-regulated learning and the collaborative learning, as factors inextricably linked to the effectiveness of the distance education, were examined in a small number studies in the above models. Finally, all three examined models reinforced students’ positive attitudes and perceptions, even while transferring the acquired knowledge to the workplace.