Keyword: AI

ABSTRACT: As higher education faces the realities of technological advancements, institutions face the challenge of fostering high-level cognitive competencies while maintaining scalability. This paper proposes a holistic framework that integrates inquiry-based learning (IBL) with artificial intelligence (AI) to support learning. While traditional IBL is pedagogical and resource-intensive, the proposed model utilizes AI as a scaffolding layer, transitioning its use from an output generator to a metacognitive coach. Utilizing a conceptual framework methodology, the study maps the approaches between the stages of inquiry and AI interactions. In addition, the paper explores the institutional implications for teacher training and technological governance, arguing that the sustainability of such a system depends on shifting assessment from content mastery to measurable complex thinking skills. This contribution provides a cohesive foundation for administrators and educators seeking to implement active pedagogies in this era of AI.

ABSTRACT: Artificial intelligence (AI) is rapidly reshaping school leadership within Education 4.0, offering enhanced decision-making and organisational efficiency while intensifying ethical concerns regarding transparency, bias, and accountability. Existing research has largely treated these opportunities and risks as separate phenomena, overlooking the relational processes through which AI is enacted in practice. This paper advances a process-based conceptualisation by positioning trust as the central mediating mechanism in AI-enabled school leadership. It argues that AI does not produce outcomes directly; rather, its effects are contingent on how it is accepted, interpreted, and enacted within school contexts. The proposed framework shows that trust shapes whether AI leads to constructive outcomes, including ethical use, professional engagement, and improvement, or to disruptive consequences such as resistance and mistrust. Leadership is conceptualised as a key antecedent of trust, highlighting the centrality of relational governance in the effective and responsible integration of AI in schools.

ABSTRACT: This qualitative study investigates the use of educational technology (EdTech) to reach marginalized populations through non-formal education (NFE) in Zimbabwe. Grounded in Paulo Freire's Critical Pedagogy and employing an interpretive research philosophy, the study collected data from 50 educators using open-ended questionnaires distributed in November 2025. Thematic analysis of responses reveals four key findings. First, the digital divide is fundamentally economic and infrastructural: high data costs and unreliable electricity are critical barriers, necessitating investment in solar-powered community centers and school-based access points. Second, pedagogical relevance requires radical localization—incorporating indigenous languages, real-life examples, and practical, skills-based subjects delivered through low-bandwidth platforms such as WhatsApp and SMS/USSD. Third, sustainability depends on community ownership: projects must transition from foreign funding dependency to locally governed models, with trained community facilitators and traditional leaders ensuring ethical accountability. Fourth, inclusivity demands universal design principles that leverage built-in accessibility features for learners with disabilities, alongside robust data protection frameworks to safeguard against exploitation. The study theorizes a "Freirean EdTech" that positions technology not as a neutral tool but as a site of emancipatory practice, where success is measured by a project's ability to foster critical consciousness, economic agency, and collective self-determination. Implications for policy include regulatory interventions to lower data costs, curriculum reforms prioritizing localization, and governance frameworks that embed community oversight. The study concludes that sustainable EdTech in NFE requires moving beyond technological determinism toward socially embedded, ethically governed, and culturally responsive approaches.

ABSTRACT: Artificial intelligence (AI) has increasingly played an important role in educational development in the digital era. Teachers, therefore, need appropriate knowledge and perceptions regarding the use of such technology in instructional practices. This study aimed to: 1) develop and examine the construct of a scale measuring Thai teachers’ perceptions of the use of artificial intelligence in instructional management, and 2) investigate the level of Thai teachers’ perceptions regarding the use of artificial intelligence in teaching and learning. The research was conducted in two phases. Phase 1 involved instrument validation using Exploratory Factor Analysis with a sample of 353 teachers. Phase 2 examined teachers’ perceptions of the use of artificial intelligence in instructional management with a sample of 298 teachers. Data were analyzed using exploratory factor analysis and descriptive statistics. The findings revealed that teachers’ perceptions of AI in instructional management consisted of several components reflecting different dimensions of technology integration in teaching. Overall, Thai teachers demonstrated a moderate to high level of perception regarding the use of artificial intelligence in instructional practices. The results provide useful implications for promoting the effective integration of artificial intelligence in educational settings.

ABSTRACT: This study aims to examine the views of teachers working at various educational levels in Turkiye regarding AI-supported education, employing a qualitative approach. The study seeks to determine whether teachers perceive artificial intelligence as a tool for pedagogical transformation or as a potential risk leading to a loss of pedagogical control. Conducted within a phenomenological research design, data were collected through semi-structured interviews with 35 teachers working at primary, lower secondary, and upper secondary school levels. The data were analyzed using thematic analysis. The analysis showed that teachers’ views clustered around four main themes: perceived pedagogical opportunities and transformation, concerns about pedagogical control and professional autonomy, ethical and responsibility-related issues, and expectations regarding conditions of use and limitations. The findings indicate that teachers do not view AI-supported education as a one-dimensional technological innovation; rather, they conceptualize it as a multilayered phenomenon encompassing pedagogical, professional, and ethical dimensions. While teachers emphasized the potential of artificial intelligence to support personalized learning and instructional processes, they also expressed significant concerns related to algorithmic guidance, professional autonomy, and ethical responsibility. The results suggest that AI-supported education should be addressed within a framework that is teacher-centered, ethically sensitive, and grounded in pedagogical values.

ABSTRACT: Understanding mental health literacy is essential in equipping students with the knowledge and confidence to manage mental health concerns effectively. A structured survey-based approach was implemented to examine key dimensions of mental health literacy among secondary and post-secondary students in Bukidnon, Philippines. The study involved 3,397 legally aged students from secondary and post-secondary institutions, with most participants enrolled in undergraduate programs. Using validated measurement procedures, the findings confirm that mental health literacy is shaped by awareness of resources, help-seeking behavior, perceived stigma, self-efficacy in managing mental health, and cultural influences. Each dimension demonstrated stable measurement properties and meaningful relationships within the overall model. The developed questionnaire provides a reliable and multidimensional tool for assessing literacy patterns within academic settings. By identifying strengths and gaps across these domains, the instrument offers a structured basis for designing interventions that strengthen awareness, reduce stigma, promote help-seeking, and support student well-being within similar educational contexts.

ABSTRACT: This study examines the relationship between digital learning inclination and lifelong learning participation among Korean adults through generational and educational level analysis. Using data from the Korean Educational Development Institute's 2024 Individual Survey on Lifelong Learning (N = 30,829, ages 25-79), this research analyzed relationships between age, educational attainment, digital learning preferences, and participation rates through an ecological analysis approach using aggregated cross-sectional survey data. Digital learning inclination was operationalized using proxy indicators including learning media preferences, informal digital learning participation patterns, and information access pathways. Korea's overall lifelong learning participation rate was 33.1% in 2024, declining from 44.6% (ages 25-29) to 24.1% (ages 70-79). Educational attainment emerged as a critical moderating variable, with university graduates showing participation rates (40.4%) that were 17.8 percentage points higher than those with middle school education or less (22.6%). The Digital Learning Inclination Index revealed a five-fold difference between the youngest (81.2) and oldest (16.1) age groups, with age 50 emerging as a critical threshold. Statistical analysis revealed significant associations between age and educational level (χ² = 1,847.3, p < .001) and moderate correlations between digital learning inclination and participation rates (r = .52, p < .001). The findings highlight the necessity for digital literacy support policies tailored to specific generational and educational characteristics. This study provides a replicable methodological framework for contexts where comprehensive digital competency assessments are unavailable, offering valuable insights for policymakers and educators in nations facing similar demographic transitions and digital transformation challenges in adult education systems.

ABSTRACT: This study examined the relationship between AI-powered learning tools, student engagement, and academic performance in higher education, with a focus on differences across academic disciplines, age groups, and gender. The study employed a quantitative, correlational, and causal-comparative research design, involving undergraduate students from both STEM and non-STEM disciplines through a multi-stage sampling approach. Data were obtained from AI-generated learning metrics, specifically Time-on-Task, Interaction Frequency, and Knowledge Mastery, alongside a structured questionnaire measuring behavioral, cognitive, and emotional aspects of student engagement, as well as students’ self-reported academic performance. The findings revealed that student engagement varied according to the type of AI learning tool utilized. Tools designed to support knowledge mastery were associated with higher levels of engagement compared to those focused primarily on interaction frequency or time spent on tasks. Students in STEM-related disciplines generally demonstrated stronger engagement than those in non-STEM fields, although the pattern of association between AI tool use and engagement was consistent across disciplines. Knowledge Mastery also emerged as the most influential factor in predicting academic performance across different age groups, with older students tending to achieve better academic outcomes. Additionally, gender differences were observed in how students benefited from specific AI tools, suggesting varying learning preferences and responses to AI-supported instruction. Overall, the study highlights the significant role of AI-powered learning tools in shaping student engagement and academic performance. It emphasizes the need for mastery-oriented, learner-sensitive, and discipline-responsive AI interventions to optimize learning outcomes in higher education.

ABSTRACT: The rapid rise of generative AI tools such as ChatGPT has introduced new dynamics into higher education, particularly in the domains of academic writing and scholarly communication. This study investigates how ChatGPT influences three interrelated dimensions: productivity, inclusivity, and academic integrity. A qualitative content analysis of forty peer-reviewed articles, conference proceedings, and professional reports published between November 2022 and March 2024 was conducted to synthesize emerging perspectives. The analysis reveals that ChatGPT enhances productivity by streamlining drafting, editing, and citation processes, and it fosters inclusivity by reducing linguistic barriers that often marginalize non-native English speakers in global academic publishing. At the same time, significant concerns arise regarding plagiarism, authorship attribution, and the erosion of critical thinking skills, underscoring the need for clear ethical and institutional guidelines. This paper contributes to the literature by reframing ChatGPT not merely as a technological tool, but as a systemic force reshaping scholarly communication. By highlighting both opportunities and risks, the study underscores the importance of developing balanced strategies—integrating AI literacy, institutional policy frameworks, and responsible usage practices—to ensure that ChatGPT strengthens, rather than undermines, the integrity and equity of higher education.

ABSTRACT: Amid the post‑COVID‑19 expansion of e‑learning, this study developed and validated a survey instrument to assess e‑learning competencies among science and mathematics supervisors in Kuwait. After expert review using the Lawshe–Tristan method, an exploratory factor analysis on data from 345 supervisors confirmed an eight‑factor structure encompassing computer literacy, computer skills, educational planning, program design, pedagogical practice, assessment, professional development, and ethical/legal awareness. Confirmatory factor analysis produced an acceptable fit for teachers but a weaker fit for students, underscoring the need for further refinement. Cronbach’s alpha and McDonald’s omega coefficients indicated excellent internal consistency. Descriptive analyses revealed that male supervisors, those with advanced technology expertise and higher English proficiency, and those at intermediate or secondary levels scored higher across domains. The validated instrument is a valuable tool for diagnosing training needs and guiding e‑learning professional development in Kuwait and similar contexts. The instrument thus provides a psychometrically robust tool for assessing readiness to learn and highlights demographic differences that can be used to target professional development. The study concludes with a recommendation to conduct larger sample sizes and additional validation phases to refine the instrument further and improve its applicability in different educational contexts.

ABSTRACT: The high cost of tuition and other educational resources makes it difficult for students in Nigeria to access postsecondary education, placing a financial strain on both the students and their parents. Due to difficulties brought by the high cost of tuition, the Nigerian government established the Student Loan Program to assist students who are unable to pay for tuition and other educational expenses. Despite the Nigerian government’s efforts, the country’s student loan approval and uptake rates are still shockingly low, which raises a number of concerns about the factors compromising the loans’ ability to effectively address educational disparities. This study, grounded on the Human Capital Theory employs a binary logistics regression to model the loan approval rate for Nigerian students enrolled in higher institutions. Data utilized in this study was sourced from the Nigeria Education Loan Fund (NELFUND) online database. This study found that under graduates and students with high Credit Information Bureau (India) Limited (CIBIL) score were more likely to get a student’s loan request approved than graduated students with low CIBIL scores. The study also revealed that the students’ income per annum, loan amount and bank asset value had a positive and insignificant influence on students’ loan approval. Recommendation from the study’s findings was that NELFUND should take into account the knowledge gathered to improve their loan approval procedure by concentrating on the applicant’s credit score and modifying the educational status requirements to attain a more precise and equitable loan distribution.

ABSTRACT: In this study, the relationships between Personality Trait (PT), Emotional Intelligence (EI), and Intrapreneurial Self-Capital (ISC) are examined. A modified version of the Intrapreneurial Self Capital and Trait Emotional Intelligence Questionnaire was administered to 200 first-year and 200 third-year undergraduate students. Hierarchical regression analysis was used to explore relationships. The study found that a significant change in one’s ISC occurs with a unit increase in EI and PT. Also, in reference to respondents aged less than 18, none of the age groups exhibited a positive EI. It was also established that females exhibited a positive EI as compared to their male counterparts, whereas level 300 students also exhibited a positive EI ahead of their juniors in level 100. This study recommends that students should take an interest in non-cognitive workshops and seminars to improve their EI to increase their capabilities to cope with their future careers and work life. It is also recommended that students understand their personality traits to interact well with their colleagues at the workplace after graduation. Lastly, this study recommends that the content of courses taught in our universities should be tailored to enhance EI since the current situation is not too good.

ABSTRACT: The study aimed to examine the role of teacher quality on students’ mathematics interest as facilitated by students’ perception of mathematics. The participants were 300 students from three senior high schools. The study was purely a quantitative method that employed a questionnaire as a data collection tool. The data was analyzed using Structural Equation Modeling (SEM) to estimate the result for the hypothesized paths. The findings from the study revealed that teacher-student collaboration and teacher empathy had a direct positive and statistically significant effect on student’s mathematics interest. On the other hand, student’s perception in mathematics partially facilitates the relationship between teacher empathy and the student’s mathematics interest. Moreover, the perception of mathematics partially facilitates the relationship between teacher-student collaboration and student’s mathematics interest. The study recommended that mathematics teachers must collaborate with students in terms of classroom teaching and learning and work more practical mathematics examples with students in the class in order to enhance student’s mathematics interest.