Vol. 4 No. 1 (2026): Forthcoming Issue (April)

Background: Artificial Intelligence (AI) has transformed English language learning through personalized instruction and real-time feedback. However, current literature shows a critical gap: studies focus either on technological capabilities or pedagogical problems in isolation, lacking frameworks that balance innovation with humanistic teaching principles.
Aims: In order to investigate AI integration in English language learning, this systematic review will: (1) identify opportunities that AI presents; (2) analyze challenges and constraints; (3) develop a conceptual framework that strikes a balance between technological innovation and humanistic pedagogy; and (4) offer evidence-based recommendations for educators and policymakers.
Methods: Thirty peer-reviewed publications from Web of Science, Scopus, ERIC, and Google Scholar were systematically reviewed between January 2023 and August 2025 in accordance with PRISMA principles. A modified CASP checklist was utilized for quality assessment. Humanistic Learning Theory and TPACK frameworks served as the basis for the analysis.
Results: 73% of studies (n=22) found that accessibility and personalization improved writing accuracy and student motivation. But 50% (n=15) expressed worries about emotional engagement and ethical integrity. Problems included decreased personal connection, especially in collectivist societies (27%, n=8), algorithmic prejudice (17%, n=5), and plagiarism facilitation (30%, n=9). The four guiding concepts of the Innovation-Empathy Balance Model are educational intentionality, professional capacity building, ethical vigilance, and strategic complementarity.
Conclusion: AI-enhanced English language instruction has a number of prospects, but it necessitates striking a balance between humanistic pedagogy and technology innovation. The Innovation-Empathy Balance Model emphasizes intentional deployment, teacher preparation, and preserving human connection in education, positioning AI as a strategic supplement to human-centered teaching.

Background: This study looks at functional education as a means of lowering unemployment in developing nations. It investigates how functional education might improve employability skills (technical and soft), especially in developing nations. The contribution of functional education to the growth of entrepreneurship how to use Technical and Vocational Education and Training (TVET) as a long-term approach to young unemployment.

Aims: The aim of the study is to investigate whether functional education is a roadmap to reducing unemployment in the developing nations.                                               

Methods: It is a systematic review approach. Particularly in developing nations, government and policy assistance are crucial for establishing strong educational institutions, encouraging skill development, and generating job opportunities. Functional education faces difficulties in developing nations.

Results: This essay examines the main obstacles preventing functional education in developing countries from succeeding and makes reform suggestions. The idea that including functional education in national curricula greatly enhances employability outcomes and lowers unemployment is supported by studies reviewed from a number of emerging nations. The transformative significance of functional education in developing entrepreneurial capacities is affirmed by the empirical data presented in this research.

Conclusion: This study illustrates how incorporating TVET into national education systems can close the skills gap and dramatically lower young unemployment using empirical data, policy analysis, and international case studies. The study explore functional education as a means of reducing unemployment in the developing nations. It was done by the authors.

Background: The rapid emergence of artificial intelligence (AI)–assisted tools has begun to reshape English as a Foreign Language (EFL) teaching practices, raising new questions about teachers’ roles, autonomy, and professional growth. While previous research has addressed professional development (PD) needs in traditional and online contexts, limited attention has been given to AI-mediated teaching environments.

Aims: This study aims to explore EFL teachers’ perceptions and lived experiences of AI-assisted language teaching, particularly in relation to professional agency, identity, and emerging professional development needs.

Methods: This study adopted a qualitative interpretive phenomenological approach. Data were collected through in-depth semi-structured interviews with 10 experienced EFL teachers who had engaged with AI tools in instructional, assessment, or material development practices. The data were analyzed using reflexive thematic analysis.

Results: The findings reveal that teachers perceive AI as both a pedagogical support and a source of professional tension. While AI enhances efficiency, feedback provision, and instructional design, it also raises concerns related to loss of control, ethical responsibility, assessment validity, and role ambiguity. Teachers continuously negotiate their professional agency and identity when integrating AI into their practices. Furthermore, traditional PD frameworks are found to be insufficient in addressing these challenges.

Conclusion: The study highlights the need to reconceptualize EFL teacher professional development by emphasizing critical AI literacy, ethical awareness, and agency-oriented pedagogical decision-making. Professional development should move beyond technical training to support teachers as reflective and autonomous professionals in AI-mediated educational environments.

Background:As the database grows, predicting students' academic performance becomes more difficult. Traditional methods often overlook students with exceptional achievements and fail to fully track their progress. Although traditional assessments like exams and assignments provide valuable insights, they may not consider all factors affecting performance, such as socioeconomic status and engagement rates.

Aims: This study develops a predictive model aimed at classifying students' academic performance in higher education.

Methods: Using a combination of machine learning algorithms. Data collected from the Department of Computer Science and the Department of Mathematics at Tai Solarin University of Education was analyzed through the mutual information method to identify important factors. The model was created and tested using Google CoLaboratory, employing two algorithms: Support Vector Machines (SVM) and Decision Trees (DT). The accuracy of the models was measured using important indicators, including accuracy, precision, and the F-measure.

Results:This study shows that machine learning techniques can effectively identify student performance early, with SVM achieving 100% accuracy, enabling quicker involvements and better resource allocation.

Conclusion: Additionally, it supports evidence-based decision-making in educational institutions, which helps improve student encounter and enhances learners retention.