Relationship Between Teachers’ Self-Efficacy And Their Use Of Ai-Based Assessment In Public Secondary Schools In Adamawa State, Nigeria

Isiaka Taiye OWONWAMI, Babale Danan FARANSA, John SAKIYO, Saratu YAKUBU

Abstract


This study investigated the relationship between teachers’ self-efficacy and their use of AI-based assessment among public secondary school teachers in Adamawa State, Nigeria. The study was anchored on five objectives and four hypotheses on technological self-efficacy, instructional self-efficacy, assessment self-efficacy and the combined effect of teachers' self-efficacy on AI-based assessment usage respectively. Social Cognitive Theory by Bandura formed the framework for this study in recognition of self-efficacy as an important determinant of behavior and technology adoption. The ex post facto research design was used in the conduct of this study. The population for the study consisted of about 1,050 Mathematics and Science teachers in public secondary schools in Adamawa State. The sample for the study was drawn from the population using multi-stage sampling techniques and consisted of 200 teachers. Two validated instruments; the Teacher Self-Efficacy Scale (TSES) and AI-based Assessment Usage Questionnaire (AIAUQ) were used to collect data. The reliability of the instruments is 0.84 and 0.86 respectively, indicating high internal consistency. Data were analyzed using descriptive statistics, Pearson Product Moment Correlation and multiple regressions at 0.05 level of significance were employed. The results indicated that teachers had relatively high levels of self-efficacy in technological (M=3.09), instructional (M=3.11) and assessment domains (M=3.04). A statistically significant positive correlation was established between technological self-efficacy (r = 0.62), instructional self-efficacy (r = 0.68), and assessment self-efficacy (r = 0.59) and AI-based assessment use (p < 0.05). In addition, multiple regression analysis indicated that the cumulative effect of teachers' self-efficacy had a significant prediction on AI-based assessment use (R = 0.74, R² = 0.55, F(3,196) = 79.12, p < 0.05), with technological self-efficacy being the most significant predictor. In conclusion, the study finds that teachers' self-efficacy is a significant predictor of teachers' use of AI-based assessment. The study recommends continued professional development and improvement of ICT infrastructure and training for teachers to increase their self-efficacy in AI-based assessment in secondary schools.

Keywords


Self-efficacy, Artificial Intelligence, AI-based assessment, technological self-efficacy, instructional self-efficacy, assessment self-efficacy

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DOI: http://dx.doi.org/10.52155/ijpsat.v58.2.8498

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