Keyword: academic achievement

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: Enhancing students' academic performance in higher education is a primary goal, necessitating a systematic and adaptable approach. Traditional methods for analyzing student performance data often struggle with complexity and unpredictability, making them inadequate for handling intricate educational patterns. As a result, the development of fuzzy logic-based decision-making systems has become increasingly important. This study aims to design an accurate fuzzy logic system for predicting first-year physics students' academic achievement at Dire Dawa University. The model leverages the fuzzy library, defining membership functions for participation in experiments and discussions (categorized as low, medium, and high). It then establishes rules to map participation levels to predicted grades (poor, average, good, and excellent). The analysis involved applying the fuzzy logic system to a dataset and validating the predictions against actual grades. The findings revealed that the model accurately predicted grades for medium participation (e.g., 50.00 for 50% participation) but tended to overestimate high participation levels (e.g., predicting 85.00 when the actual score was 67.51). This research contributes to educational technology by providing a flexible predictive tool that can be expanded to other STEM disciplines, enhancing data-driven decision-making in academic settings. These results demonstrate the effectiveness of fuzzy logic in managing educational uncertainties, though refinements are needed to address overestimation and incorporate additional variables such as study habits and prior knowledge.