Neutrosophic Stance Detection and fsQCA-Based Necessary Condition Analysis for Causal Hypothesis Assessment in AI-Enhanced Learning

The phenomenon of artificial intelligence (AI) use in educational settings has attracted increasing scholarly attention, although applicable empirical findings are sparse—and conflicting. This study seeks to resolve the ambiguities surrounding AI in education through a methodological contribution, merging neutrosophic stance detection and fuzzy-set Qualitative Comparative Analysis (fsQCA). Neutrosophic analysis allows for an explicit modeling of truth, uncertainty/indeterminacy, and falsity, while merging such findings through fsQCA creates a relative account of extant research findings. After assessing four causal hypotheses related to AI-based learning opportunities through the Consensus Meter, an investigatory survey with 24 university participants explored necessary conditions with respect to experiencing improvements in learning outcomes. The findings indicate that the digital divide is a necessary and sufficient condition for effective AI educational experiences. Additionally, necessity conditions emerge for AI feedback and usage of AI-based platforms; however, the effectiveness of those platforms generates high uncertainty. Ultimately, the neutrosophic-fsQCA framework provides a viable technique to synthesize ambiguous findings through a systematic approach. Empirically, results reveal that all stakeholders involved in potential AI-based learning need to ensure digital equity and high-quality design for interactive experiences to enjoy successful integration of AI in education.