International Journal of Artificial Intelligence and Education Technology

The paper presents a novel approach for predicting the efficiency of engineering higher educational institutions in India. The prediction model uses data envelopment analysis to solve linear programming problems and support vector regression as a supervised machine learning algorithm. DEA is a nonparametric tool for computing the relative efficiency of HEIs across multiple inputs and outputs. A total of 25 featured variables is considered, out of which 15 are input-oriented, whereas 10 are output-oriented. Input-oriented features are normalised using the standard z-score method, whereas output-oriented features are normalised using the Min–Max normalised method. A sample of 7432 engineering institutions is considered for the featured dataset. The featured dataset is split at a 90:10 ratio for training and testing. The hybrid model, combining traditional DEA with a supervised machine learning SVR, is trained on a training dataset. The prediction model is tested for estimating 743 engineering institutions. The model provides higher accuracy, along with a precision score, in the confusion matrix comparing the actual and predicted HEI performance categories. The best-fitting hybrid model also yields a predicted efficiency index for engineering HEIs, along with MAE, MSE, and RMSE, which are 0.0703, 0.0080, and 0.0894, respectively. The R-squared score of the prediction model is 0.7642. The developed predictive model can be generalised across sectors such as agriculture, banking, industry, and education.

Driven by the global informatization wave, AI technology is leading a profound change in education, and the traditional higher education model is facing unprecedented opportunities and challenges. Firstly, the advantages of the comprehensive integration of “AI+ Education” are sorted out from the two aspects of students and teachers. Secondly, it analyzes the transformation of teachers from classroom manager to learning ecological designer from three aspects: education personalization and fairness, man-machine teaching collaboration and interdisciplinary scene innovation. Next, the construction of student evaluation system is discussed from four dimensions of thinking visualization. Finally, the author expounds the characteristics of the teaching profession that cannot be replaced by AI.

In this paper, the effect of visual arts usage on national identity among first cycle students in the United Arab Emirates will be evaluated. It should be noted that today visual arts became an effective way of informing youth about their cultural values, sense of belonging to their nation, national heritage and values. Nowadays when there are a lot of social and cultural problems, schools should make great efforts in helping students to establish their national identity through creativity. This research is conducted using the methodological framework of descriptive-analytical approach and explores the effect of visual arts on students’ national identity. For the purpose of this study, a questionnaire has been created and distributed among the target audience of first cycle students in order to analyze students’ perspectives on visual arts effects on their sense of national identity, their culture and national values. Moreover, the impact of certain demographic factors like gender, grade, and academic background has been explored. The results have been obtained using statistical tools like frequencies, percentages, means, standard deviations, t-test, one-way ANOVA and Pearson’s correlation coefficient. The research suggests that visual arts have a positive effect on students’ national identity as it encourages students to appreciate and recognize their national culture, belonging to their nation and values, as well as create something based on those.

This study examines the effectiveness of artificial intelligence (AI)–based language translation in comparison with human translation, with particular attention to accuracy and contextual sensitivity in educational settings. As higher education institutions increasingly integrate AI tools into language instruction and multilingual course delivery, evaluating their pedagogical reliability and cultural appropriateness has become essential. Using a mixed-methods design, the study analyzes translation outputs produced by leading AI systems and professional human translators across academic texts, lectures, and instructional materials in multiple language pairs. Quantitative measures assess linguistic accuracy, while qualitative analysis focuses on cultural nuance, disciplinary appropriateness, and contextual meaning. Semi-structured interviews with university lecturers and students further explore perceptions of the benefits and limitations of AI-assisted translation in academic contexts. Findings indicate that although AI tools offer substantial advantages in speed and accessibility, they remain less effective in maintaining disciplinary precision and cultural depth. The study recommends a complementary model in which AI technologies support, rather than replace, human expertise in multilingual academic communication.

Background: The rapid development of artificial intelligence (AI) is changing the sphere of higher education, creating a pressing need for graduates with competencies related to AI. Conventional degree-based credentialing models are difficult to adapt to the rate, interdisciplinary, and job alignment demanded in AI-driven economies. Because of this, the other forms of AI credentials, including micro-credentials, certificates, and stackable learning pathways, have become of worldwide significance. This review critically evaluates international views of AI credentialing and reviews the effects of these credentials on changing the teaching and learning practices in higher education. Methods: Narrative review methodology was used to summarize peer-reviewed articles, policy reports, and international frameworks published in 2015-2025. The important sources were Scopus, Web of Science, ERIC, UNESCO, OECD, and publications of the World Economic Forum. Thematic analysis was employed to determine the global trends, pedagogical changes, and policy implications. The review shows an increasing convergence through competency oriented, flexible, and industry-oriented AI credentials in the regions. Such qualifications facilitate curriculum modularization, experiential and personalized learning, and graduate  employability. Nevertheless, there are issues of quality assurance, standardization, fair use of AI, and fair access. Conclusion: AI credentials are both a great offer to curriculum innovation, personnel growth, and lifelong learning, and a strong governance and collaboration across sectors. AI credentials are a disruptive process of rethinking teaching and learning in higher education. Their implementation should take place in strategic, ethically based, and globally coordinated action so as to achieve their full potential.

This paper investigates the readiness of pre-service teachers towards ICT integration in teaching at federal universities in the North-Eastern Nigeria. A descriptive survey design was adopted for the study. The population of the study consist of 7885 pre-service teachers of Federal Universities in the North-Eastern Nigeria. The sample size is 381 pre-service teachers selected across the Federal Universities in North-Eastern Nigeria using a random sampling technique. Three research questions and three hypotheses were generated and answered in the study. The instrument for data collection in this study was ICT Attitude, Access and Competency (ICT-AAC). The instrument was validated by four (4) experts who checked the suitability and clarity of these items. The Cronbach Alpha reliability coefficient was computed for the instrument (ICT-AAC) which yielded an internal consistency reliability index of 0.82, 0.88 and 0.73 for cluster A, B, and C respectively, with an overall reliability index of 0.89. The research questions were answered using descriptive statistics and Spearman’s correlation coefficient using SPSS version 25. Findings of the study showed that most of the pre-service teachers have positive attitude towards ICT integration in teaching. It was also found that pre-service teachers have no Access to most of the ICT facilities. It was further confirmed that the pre-service teachers were Competent in manipulating ICT facilities. The study further indicated a significant relationship between pre-service teachers’ Attitude, Accessibility and ICT Competency. Finally, a number of challenges that might hinder the integration of ICT facilities in teaching were found and recommendations proffered, which include; improvement of pre-service teachers’ Access to ICT facilities such as projector, smart/interactive white boards and the software required for the effective use of the boards. This should be aimed at increasing their Competency of the newly acquired or available facilities and how to integrate them in teaching.