Regional innovation capability is an important indicator of both regional innovative and long‐term development. The purpose of this study is to build an evaluation index system for regional innovation capability in order to identify regional differences and support innovation more effectively. After establishing a reasonable evaluation value for regional innovation capability, a combination of simulated annealing optimized projection pursuit (SA–PP) and N‐layer nested fuzzy comprehensive evaluation models is used to assess China's regional innovation capabilities. The results show that the SA–PP model effectively mitigates the risk of erroneous evaluation results caused by index weight uncertainty, resulting in a more reasonable, robust, and intelligent assessment of regional innovation capability. Furthermore, the nested fuzzy comprehensive evaluation model is capable of easily resolving the evaluation factor set's heterogeneity and multilayer problems. The most significant influences on China's regional innovation capability are knowledge acquisition and enterprise innovation. The comprehensive score of the proposed combinational evaluation model manifests that provinces with strong regional innovation capabilities are mainly concentrated in the southeast coastal regions. The research results allow for precise weight determination and object ranking.
The continuous development and application of artificial intelligence (AI) technology greatly support education reform and profoundly influence the learning styles of learners. Artificial intelligence in education (AIED) can help teachers recognize teaching tasks explicitly and teach content accurately. Moreover, AIED can help students change the traditional learning styles according to their differences, thereby realizing intelligent teaching and meeting the learning needs of students. A good teachers’ perception of educational technology (ET) is beneficial for using AI technology to positively assist all teaching links, which in turn improves teaching effectiveness. In this study, five hypotheses concerning the influences of AIED on teaching effectiveness were verified. The teachers’ perception of ET was introduced as a mediating variable, and the mediating effect of AIED on the improvement of teaching effectiveness was analyzed. The influences of AI using the period of learners on teaching effectiveness were estimated. Results showed that the overall Cronbach α and Kaiser–Meyer–Olkin of the designed questionnaire were 0.907 and 0.878, respectively. Moreover, the χ2 of Bartlett’s test of sphericity reached the 0.01 significance level, indicating the considerable good reliability and validity of the questionnaire. All four aspects, namely, AI-assisted teaching, exercise, exam, and assessment, had significantly positive influences on teaching effectiveness. The teachers’ perceptions of ET played a partial mediating effect for AIED on the improvement of teaching effectiveness. Samples with different AI-using periods had significant influences on teaching (p < 0.01). Research conclusions can provide important references to teachers for making scientific use of AIED and propose more accurate teaching strategies according to the learning states of learners.
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