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TERTIARY EDUCATION TEACHERS’ PERCEPTION AND UTILIZATION OF ARTIFICIAL INTELLIGENCE TOOLS IN EVALUATING STUDENTS’ PERFORMANCE IN HOME ECONOMICS

Sep 2026 · Nigeria Journal of Home Economics (ISSN: 2782-8131) · 0 citations

TL;DR

It was concluded that AI adoption is hindered by infrastructural and training gaps, and institutional investment in AI infrastructure and capacity-building programs to enhance integration into assessment practices is recommended.

Abstract

The study investigated tertiary education teachers’ perception and utilization of Artificial Intelligence (AI) tools in evaluating students’ performance in Home Economics. The primary aim was to examine lecturers’ awareness, perceptions, extent of usage, and challenges in integrating AI tools for assessment purposes. The study was guided by four research questions and two null hypotheses. Specifically, it sought to assess whether lecturers’ perception significantly influenced utilization, and whether experience level accounted for differences in AI usage. The study employed a cross-sectional survey design. The target population comprised all Home Economics lecturers in Nigerian Colleges of Education, Polytechnics, and Universities offering Home Economics or related programs. A total of 150 participants were selected using a multi-stage sampling technique combining purposive, convenience, and quota methods. Data were collected using a 31-item structured questionnaire, validated by three experts and pilot-tested with 20 lecturers. The reliability coefficient was 0.90, confirming internal consistency. Descriptive statistics (mean, SD) were used to analyze research questions, while Spearman Rank-Order Correlation and One-Way ANOVA tested the hypotheses at 0.05 significance level. Findings revealed that while lecturers had moderate awareness and positive perceptions of AI, actual usage remained low, especially in evaluating practical components. A significant relationship was found between perception and usage, and less experienced lecturers used AI tools more frequently. The study concluded that AI adoption is hindered by infrastructural and training gaps. It recommends institutional investment in AI infrastructure and capacity-building programs to enhance integration into assessment practices.

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