Enhancing decision-making framework for talent cultivation quality evaluation using dual Hamy mean and prioritized aggregation operators

Abstract

At present, the solidification of disciplines and majors in higher education, unclear training objectives, and the need to strengthen teacher information literacy are practical problems that constrain the optimization of the “artificial intelligence+” talent training model. Scientifically adjusting training objectives, upgrading the “artificial intelligence+” talent training model in higher education by integrating multi-disciplinary curriculum systems, strengthening school enterprise cooperation, industry education integration, establishing credit recognition mechanisms and diversified evaluation mechanisms. The quality evaluation of “Artificial Intelligence+” talent cultivation in higher education in the era of education informatization 2.0 is looked as the multiple-attribute decision-making (MADM) issue. In this paper, we extended the dual Hamy mean (DHM) operator and prioritized aggregation (PA) operator to 2-tuple linguistic neutrosophic sets (2TLNSs) to propose the 2-tuple linguistic neutrosophic prioritized DHM (2TLNPDHM) operator. Finally, a decision example for quality evaluation of “Artificial Intelligence+” talent cultivation in higher education in the era of education informatization 2.0 is employed to show the 2TLNPDHM operator. The main contributions of this study are summarized: (1) the 2TLNPDHM operator is built; (2) the 2TLNPDHM operator is designed to cope with the MADM with 2TLNNs; (3) an empirical real-life example for quality evaluation of “Artificial Intelligence+” talent cultivation in higher education in the era of education informatization 2.0 is supplied to proof the designed method; (4) some comparative decision studies are used to show the rationality of the 2TLNPDHM.