Utilization of Artificial Intelligence-Based Applications in Instructional Delivery: Basis for Effective Teaching and Learning in Mathematics
DOI:
https://doi.org/10.5281/zenodo.23638868Keywords:
artificial intelligence in education, Mathematics instruction, AI-based applications, instructional delivery, teacher perceptions, student engagementAbstract
This descriptive-correlational study examined Mathematics teachers’ assessments of artificial intelligence (AI)-based applications in instructional delivery, teaching dynamics, and implementation challenges in two basic education institutions in Manila, Philippines, during School Year 2022–2023. Total enumeration included 31 Mathematics teachers: 19 from Holy Trinity Academy and 12 from St. Vincent Blessed School of Manila. A researcher-made questionnaire assessed AI utilization across six instructional areas, teacher-perceived student engagement, motivation and satisfaction, and six domains of implementation challenges. Weighted means summarized responses, while Pearson product–moment correlation tested relationships at the .05 significance level. Teachers rated instructional delivery highest (WM = 3.31), followed by lesson planning (3.25), test construction (3.24), curriculum design (3.19), performance evaluation (3.17), and classroom management (3.09). Teacher-perceived engagement and satisfaction each obtained a weighted mean of 3.20, while motivation obtained 3.13. AI utilization was significantly and positively correlated with engagement (r = .612, p < .001), motivation (r = .548, p = .001), and satisfaction (r = .576, p < .001). All six challenge domains were rated serious, led by ethical concerns (WM = 3.07) and accuracy/reliability (3.01); limited knowledge of suitable AI applications and insufficient technological resources were individually rated very serious (both 3.29). Findings informed a proposed, unimplemented intervention plan addressing teacher AI literacy, access, content verification, data protection, academic integrity, and workload. Results support context-sensitive, teacher-directed adoption of AI in Mathematics instruction but do not establish causal effects on learner outcomes.
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