Keyword: artificial intelligence in education

ABSTRACT: The integration of artificial intelligence into higher education has created opportunities for innovative instructional approaches that may enhance students' employability skills and academic achievement. This study investigated Artificial Intelligence-Supported Problem-Based Learning (AI-PBL) in relation to the self-reported employability skills and percentage academic achievement of Vocational Agricultural Education students in selected Nigerian universities. A quasi-experimental pretest-posttest nonequivalent control group design was adopted. The study involved 138 students drawn from two federal universities in South East Nigeria using multistage sampling procedures. The experimental group was taught using AI-PBL for six weeks with the support of ChatGPT, Microsoft Copilot, and other approved artificial intelligence resources, while the control group received instruction using the conventional lecture method. Data were collected using the Employability Skills Assessment Scale (ESAS) and the Agricultural Education Achievement Test (AEAT). Raw scores obtained from the AEAT were converted to percentage scores before analysis. The instruments were validated by experts, while reliability coefficients of 0.89 and 0.86 were obtained for the ESAS and AEAT, respectively. The mean and standard deviation were used to answer the research questions, whereas Analysis of Covariance was employed to test the hypotheses at the 0.05 level of significance after confirming that the assumptions underlying ANCOVA were satisfied. The findings revealed that students exposed to AI-PBL obtained significantly higher self-reported employability skills and percentage academic achievement than those taught using the conventional lecture method (p < .001), with large effect sizes observed for both outcomes. No statistically significant gender differences were found among students within the AI-PBL group. The findings suggest that AI-PBL is associated with higher workplace-related competencies and academic learning outcomes among Vocational Agricultural Education students under the conditions of the present study. The study recommends the integration of AI-PBL into university curricula alongside institutional support for the ethical and responsible use of artificial intelligence in teaching and learning.

ABSTRACT: Artificial intelligence (AI), particularly generative AI, changes assessment by altering the evidential relationship among student work, learning, and professional judgment. Teachers must design tasks when students can readily obtain machine-generated responses, interpret automated feedback and analytics whose validity is conditional, and justify consequential decisions under uncertainty. This critical integrative review synthesizes scholarship on AI in education, assessment, teacher learning, and emerging professional-development interventions. It proposes Assessment-AI Knowledge (A-AIK) as an assessment-specific configuration of professional knowledge rather than a replacement for assessment literacy, AI literacy, or technological pedagogical content knowledge. A-AIK comprises three interdependent capacities: design competence, which establishes conditions for eliciting meaningful evidence; interpretive competence, which calibrates the meaning, uncertainty, and evidential weight of AI-mediated information; and justificatory competence, which supports transparent, fair, and reviewable decisions. Disciplinary authenticity, human agency, equity, transparency and data governance, and epistemic humility operate as cross-cutting commitments. The reviewed literature suggests that promising interventions use scenarios, authentic assessment redesign, structured critique, peer deliberation, and iterative classroom trials. Recurrent limitations include small samples, reliance on self-report, short duration, and measures of confidence or acceptance rather than demonstrated assessment performance. A four-phase professional-learning model—calibrate, redesign, enact, and justify, is proposed as a synthesis-informed design hypothesis requiring empirical evaluation. The article concludes with provisional performance indicators, institutional conditions, and a research agenda for responsible AI-mediated assessment.

ABSTRACT: The global construction industry is undergoing a profound transformation driven by Construction 4.0, with Artificial Intelligence (AI) emerging as a pivotal technological force. Yet a critical skills gap persists in developing nations such as Nigeria, where the pace of educational curriculum development lags significantly behind industry demands. This study examines the integration of AI into Building Technology education programs in Nigerian universities and its implications for graduate employability. Employing an explanatory sequential mixed-methods design, the quantitative phase administered surveys to 120 final-year students, 40 faculty members across four universities in South-East Nigeria, and 60 construction industry employers, to assess the current level of AI integration and its perceived relationship with employability. A subsequent qualitative phase conducted semi-structured interviews with 12 faculty heads and 12 senior industry managers to identify the institutional barriers constraining AI-driven pedagogy. The study is theoretically anchored in the Construction 4.0 Maturity Model, adapted to evaluate educational program readiness. Findings reveal that Building Technology programs currently operate at a nascent 'Initial/Ad Hoc' stage of digital maturity. Institutional constraints, including inadequate infrastructure, limited faculty expertise, and absence of strategic vision were confirmed as significant predictors of low AI adoption. Employer data further revealed that graduates with AI competencies received significantly higher employability ratings than those with traditional skills alone. These findings provide a data-driven basis for curriculum reform, targeted policy investment, and strategic university-industry collaboration to equip Nigeria's construction workforce for the digital era of construction.

ABSTRACT: The development of artificial intelligence in education has outpaced the institutionalisation of policies governing its use, accountability, and implementation. Cross-national scholarship remains dominated by discussions of adoption, pedagogical opportunities, and ethical risks, while variation in policy enactment across national education systems remains underexplained. This study examines artificial intelligence policy enactment in Indonesia, Finland, and South Korea through a comparative qualitative policy analysis of 22 core and supporting policy documents. The analysis focused on policy orientation, the locus of enactment authority, regulatory-ethical institutionalisation, and implementation mode. The findings show that the main differences across the three systems do not lie in technology adoption itself, but in the logic through which policy is enacted. Indonesia demonstrates capacity-led enactment shaped by gradual implementation and teacher capacity building. Finland demonstrates regulatory-literacy enactment mediated by education providers and structured through regulation, ethics, and AI literacy. South Korea demonstrates scaled rollout enactment driven by state direction, infrastructure support, and personalised learning reform. The study proposes a three-part typology comprising capacity-led enactment, regulatory-literacy enactment, and scaled rollout enactment, and argues that cross-national variation in artificial intelligence policy in education is best explained through the arrangement of authority, regulatory-ethical institutionalisation, and implementation instruments.

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: 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: Students today learn mathematics in a world full of digital tools and instant access to information, yet many still find the subject difficult and overwhelming. This situation raises important questions about how learning is affected when technology becomes both a support and a source of confusion. The study used a structured, quantitative approach to examine how students experience mathematics in a digital learning environment, drawing on responses from first-year college students collected through a validated questionnaire. The study found that students showed strong engagement with technological and AI-based tools. However, their mathematical competence was weakened by high anxiety, low motivation, and limited confidence. Significant differences across eight dimensions revealed that emotional, environmental, and identity-related factors were the most vulnerable areas, compared to cognitive and technological strengths. These results show that improving mathematical readiness requires not only access to digital resources but also stronger support for students’ emotional well-being and learning environments.