A novel generalized orthopair fuzzy CURLI MCDM framework for smart art communication integrating emotional intelligence

The capacity to identify, adjust to, and interact with human emotions has become a crucial component of effective art communication in digital and interactive environments. Measuring smart art communication through an Emotional Intelligence (EI) lens is not easy since human emotional reactions are unpredictable, biased, inexplicable, and hard to measure. To overcome this problem, this paper suggests a q-Rung Orthopair Fuzzy Collaborative Unbiased Rank List Integration (qROF-CURLI) multicriteria decision-making (MCDM) model where uncertainty in expert judgments is modelled by a q-rung orthopair fuzzy set (qROFS) and the CURLI mechanism combines the rankings of individuals to produce a final and unbiased overall ranking. Expert linguistic assessments are converted into qROF numbers and aggregated to construct the decision matrix, after which the CURLI procedure produces the final ranking of alternatives. The applicability of the proposed framework is demonstrated through a decision-making case study related to smart digital art communication platforms, involving fifteen alternatives evaluated across nine criteria by four decision-makers. The criteria of benefits include Emotional Recognition Capability, Emotional Adaptability, Audience Engagement, Empathy Enhancement, Technological Integration, and Cultural Relevance, whereas the costs criteria include Implementation Cost, Technical Complexity, and Ethical Risk. According to the results, the gamified and AI-adaptive communication platforms receive the best rankings. The sensitivity analysis and benchmarking on the existing qROF-MCDM approaches prove the stability and reliability of the model. The suggested framework offers a usable support to assess emotionally intelligent digital art communication systems and offers a unique combination of CURLI rank aggregation with the MCDM using qROF in order to enhance the reliability of collective decisions that are made under uncertainty.