Home⇛Journal of Interdisciplinary Perspectives⇛vol. 4 no. 6 (2026)

Teachers’ Perceptions and Utilization of Generative AI in Assessment and Feedback: Evidence for Policy Development in Basic Education

John Cliford M Alvero | Marierose P. Saldua

Discipline: Education

 

Abstract:

The increasing integration of generative artificial intelligence (GenAI) in education presents both opportunities and challenges for assessment and feedback, particularly in basic education, where empirical evidence remains limited. This study examined teachers’ perceptions and utilization of GenAI for assessment and feedback, identified key concerns and institutional gaps, and developed policy recommendations for its responsible and pedagogically sound integration. A convergent parallel mixed-methods design was employed, involving 39 basic education teachers selected through stratified proportionate sampling. Quantitative data were collected using a structured survey and analyzed using means, standard deviations, one-way ANOVA, and Pearson correlations, while qualitative data were analyzed using thematic analysis. Results revealed that teachers perceived GenAI as highly useful and appropriate for assessment (M = 3.32), yet its use for personalized, constructive feedback remained moderate (M = 3.05). No significant differences were found across age, sex, and educational attainment (p > .05), indicating consistent perceptions and practices across demographic groups. A strong positive relationship (r = 0.71, p < .05) was identified between perception and utilization, highlighting the role of teacher beliefs in AI adoption. Qualitative findings underscored concerns about academic integrity, reliability, fairness, overreliance, and the need for human oversight, as well as institutional gaps, including unclear policies, insufficient training, and limited support systems. The integration of findings suggests that while teachers are receptive to GenAI, its effective implementation requires comprehensive policy frameworks, sustained professional development, ethical guidelines, and institutional support mechanisms. This study contributes to theory by reinforcing perception–utilization linkages in technology adoption and to practice by offering evidence-based policy recommendations that promote responsible, equitable, and pedagogically grounded use of AI in assessment.



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