Generative Artificial Intelligence - Based Scaffolding Activities with GeoGebra for Graphing Rational Functions in Online Remedial Learning

This study aimed to develop and validate the Generative AI–Based Scaffolding Activities with GeoGebra (GAI-SA-G) and examine the intervention’s potential effect on pre-service mathematics education students' skills in graphing rational functions in an online remedial learning context. The study employed a developmental research approach using the ADDIE instructional design model, a one-group pretest–posttest design, and a qualitative descriptive approach. The intervention integrated video-based instruction, Generative AI–based scaffolding, and GeoGebra within a Learning Management System. Expert validation confirmed that the learning material demonstrated high validity and was suitable for implementation. The intervention was implemented among second-year college pre-service mathematics education students at a public state university in Eastern Visayas, Philippines. Quantitative data were analyzed using descriptive statistics, the Wilcoxon signed-rank test, Hake's normalized gain, and effect size measures, while qualitative data from students' reflections were analyzed using thematic analysis. Results revealed statistically significant improvements in students' graphing skills, with a large effect size and a substantial increase in performance from pretest to posttest. Higher gains were observed for simpler rational function cases, whereas smaller gains were observed for more complex cases. Students performed better in identifying graph features than in integrating these features into complete graphs. Qualitative findings revealed that students reported greater confidence and perceived improvement in graphing rational functions after the intervention. Overall, the findings provide evidence supporting the effectiveness of the GAI-SA-G learning material as a scaffolded online remedial intervention. Further refinement and evaluation through experimental studies with larger samples are recommended.

Generative artificial intelligence; GeoGebra; graphing rational functions; scaffolding; online remedial learning; mathematics education; instructional design; pre-service teachers

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