Automated AI Validation of Neutrosophic Plithogenic Hypotheses in Multigrade Literacy Improvement

Abstract

Literacy is acquired in multigrade classrooms in complicated scenarios because of varying literacy competencies and abilities and varied resources and materials. Thus, it's hard to determine if certain teaching interventions work. This is also a growing concern, a timely consideration, because as the institutions try to better the Quality of Education and prevent learning lags for multivariate classrooms are concerned. Yet the literature contains gaps where no direct attempt to stabilize teaching interventions is made despite the findings of many studies generating didactic interventions through the proceedings. Thus, this study fills the gap with an approach based upon hypothesis generation via neutrosophics plithogenic theory and invulnerability affirmation via non-programming AIs to simultaneously evaluate multiple, sometimes contradictory, findings for any teaching intervention. The results indicate that while combination reduces subjectivity at one level, a few levels up it correctly identifies A, B, and C as positive refinements for remediation toward more appropriate future refinements. Thus, this study presents a theoretically driven yet practically applicable avenue for better Educational intervention in the multi-grade classroom as well as AI exploitable steps for ANY subject area.