Branch Mathematics and Statistics Faculty and Staff Publications
Document Type
Article
Publication Date
2025
Abstract
This study addresses the ambiguities in empirical findings on artificial intelligence (AI) in education by proposing a new methodological framework that combines neutrosophic stance detection and Fuzzy Set Qualitative Comparative Analysis (fsQCA). This approach explicitly models truth, indeterminacy, and falsity, allowing for the synthesis of contradictory research. The authors evaluated four causal hypotheses related to AI-based learning, using a survey of 24 university participants to explore the necessary conditions for perceived learning improvements. The results indicate that the digital divide is a perfectly necessary condition for effective AI-enhanced learning. The findings also reveal that AI feedback and AI platform use are necessary, though not sufficient, for learning improvements, but generate significant uncertainty. The study concludes that the neutrosophic-fsQCA framework is a viable technique for synthesizing ambiguous findings and provides empirical evidence that digital equity and high-quality design are crucial for the successful integration of AI in education.
Language (ISO)
English
Keywords
Neutrosophic Logic, Artificial Intelligence In Education, Digital Divide, AI Feedback, Necessary Condition Analysis, Educational Technology, fsQCA
Recommended Citation
Hechavarría-Hernández, Jesús Rafael; Maikel Y. Leyva Vazquez; and Florentin Smarandache. "Neutrosophic Stance Detection and fsQCA-Based Necessary Condition Analysis for Causal Hypothesis Assessment in AI-Enhanced Learning." (2025). https://digitalrepository.unm.edu/math_fsp/818
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.