Matthew L. Bernacki
Author directory2026
Automated Evaluation of Mathematical Equivalence Between Personalized and Standard Word Problems
Burcu Arslan | Ikkyu Choi | Jesse R. Sparks | Reginald M. Gooch | Candace Walkington | Matthew L. Bernacki
Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Coordinated Session Papers
Burcu Arslan | Ikkyu Choi | Jesse R. Sparks | Reginald M. Gooch | Candace Walkington | Matthew L. Bernacki
Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Coordinated Session Papers
Generative AI enables real-time and scalable context personalization of mathematics word problems (MWPs) based on students’ self-reported interests during assessment. However, a question arises: are personalized and standard MWPs mathematically equivalent? In this paper, we present two Natural Language Processing pipelines for evaluating mathematical equivalence between personalized and standard MWPs.