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Artificial intelligence (AI) plays an increasingly pivotal role in advancing sustainable economic development. While existing literature predominantly examines the environmental impact of AI technologies from national or sectoral perspectives, this study provides a micro‐level analysis of its effects on energy conservation and emission reduction (ECER) performance, utilizing a dataset of Chinese listed firms. We employ a large language model (LLM)‐based intelligent scoring system to capture firms' ECER performance from publicly available environmental disclosures, and construct two‐pronged measures of AI technological capabilities encompassing both innovation and adoption dimensions. The empirical analysis demonstrates that AI technologies significantly enhance ECER performance among Chinese listed firms, with results remaining robust to various alternative specifications and robustness tests. Mechanism analysis reveals that AI facilitates environmental improvements through the enhancement of productive efficiency and the promotion of green innovation. Heterogeneity analysis further indicates that AI‐driven environmental effects are more pronounced among state‐owned enterprises, mature‐stage firms, firms in polluting industries, sectors with lower competitive intensity, labor‐intensive and capital‐intensive industries, and firms located in cities with stringent environmental regulations. These findings offer novel firm‐level empirical evidence on AI's environmental implications, contributing to a more comprehensive understanding of the technology‐environment nexus in emerging economies and laying a theoretical foundation for targeted AI‐related environmental policy interventions.

Along with the progress of natural language processing technology and deep learning, the subjectivity, slow feedback, and long grading time of traditional English essay grading have been addressed. Intelligent English automatic scoring has been widely concerned by scholars. Given the limitations of topic relevance feature extraction methods and traditional automatic grading methods for English compositions, a topic decision model is proposed to calculate the topic relevance score of the topic richness in English composition. Then, based on the Score of Relevance Based on Topic Richness (TRSR) calculation method, an intelligent English composition scoring method combining artificial feature extraction and deep learning is designed. From the findings, the Topic Decision (TD) model achieved the best effect only when it was iterated 80 times. The corresponding accuracy, recall and F1 value were 0.97, 0.93 and 0.95 respectively. The model training loss finally stabilized at 0.03. The Intelligent English Composition Grading Method Integrating Deep Learning (DLIECG) method has the best overall performance and the best performance on dataset P. To sum up, the intelligent English composition scoring method has better effectiveness and reliability.

With the development and maturity of automatic scoring technology of English composition, the computer-aided composition marking system based on automatic scoring technology has begun to enter colleges and universities to assist English writing teaching. However, there are still many problems in the current scoring system, such as long running time and large deviation of scoring results, which requires us to design an English translation computer intelligent scoring system based on natural language processing. Through the system, we can reduce the workload of manual scoring, which will improve the efficiency of scoring. Therefore, we need to construct the structure of English translation scoring system, including translation data collection module, information feature extraction module, analysis model construction module and result feedback scoring module. By building a language model, the system can translate the probability distribution of specific sentences or word sequences. As an adaptive learning model, BP neural network has more advantages in dealing with the relationship between complex variables. Through BP network model, the system can extract the feature information of translation translation and translation training set. Through fitting calculation, the system will realize the intelligent scoring of English translation.

Polygraph tests have been used for many years as a means of detecting deception, but their accuracy has been the subject of much debate. In recent years, researchers have explored the use of neural networks in polygraph scoring to improve the accuracy of deception detection. The purpose of this scoping review is to offer a comprehensive overview of the existing research on the subject of neural network applications in scoring polygraph tests. A total of 57 relevant papers were identified and analyzed for this review. The papers were examined for their research focus, methodology, results, and conclusions. The scoping review found that neural networks have shown promise in improving the accuracy of polygraph tests, with some studies reporting significant improvements over traditional methods. However, further research is needed to validate these findings and to determine the most effective ways of integrating neural networks into polygraph testing. The scoping review concludes with a discussion of the current state of the field and suggestions for future research directions.

This chapter presents an introduction to the book titled “Intelligent Credit Scoring: Building and Implementing Better Credit Risk Scorecards” by Naeem Siddiqi. The book presents a business‐focused process for the development and usage of credit risk prediction scorecards, one that builds on a solid foundation of statistics and data mining principles. The application of business intelligence to the scorecard development process, so that the development and implementation of scorecards is seen as an intelligent business solution to a business problem. Credit scoring is now also being used increasingly in the insurance sector for determining auto and home insurance premiums. A unique study conducted by the Federal Reserve Board even suggests that couples with higher credit scores tend to stay together longer. One factor that users of credit scoring will need to be cautious about is the increasing knowledge of credit scoring in the general population. The amount of confidence in any model or scorecard must be based on both the quality and quantity of the underlying data, and decision‐making strategies adjusted accordingly. Models are very useful when used judiciously, along with policy rules and judgment, recognizing both their strengths and weaknesses.

Recent years have seen increased interests in applying the latest technological innovations, including artificial intelligence (AI) and machine learning (ML), to the field of education. One of the main areas of interest to researchers is the use of ML to assist teachers in assessing students’ work on the one hand and to promote effective self-tutoring on the other hand. In this paper, we present a survey of the latest ML approaches to the automated evaluation of students’ natural language free-text, including both short answers to questions and full essays. Existing systematic literature reviews on the subject often emphasise an exhaustive and methodical study selection process and do not provide much detail on individual studies or a technical background to the task. In contrast, we present an accessible survey of the current state-of-the-art in student free-text evaluation and target a wider audience that is not necessarily familiar with the task or with ML-based text analysis in natural language processing (NLP). We motivate and contextualise the task from an application perspective, illustrate popular feature-based and neural model architectures and present a selection of the latest work in the area. We also remark on trends and challenges in the field.

In recent years, automated written feedback (AWF) has gained popularity in language learning and teaching as a form of artificial intelligence (AI). The present study aimed at providing a comprehensive state-of-the-art review of AWF. Using Scopus as the main database, we identified 83 SSCI-indexed published articles on AWF (1993–2022). We investigated several main domains consisting of research contexts, AWF systems, feedback focus, ways of utilizing AWF, research design, foci of investigation, and results. Our results showed that although AWF was primarily studied in language and writing classes at the tertiary level, with a focus on English as the target language, the scope of AWF research has been steadily broadening to include diverse language environments and ecological settings. This heterogeneity was also demonstrated by the wide range of AWF systems employed ( n = 31), ways of integrating AWF ( n = 14), different types of AWF examined ( n = 3), as well as varied research designs. In addition, three main foci of investigation were delineated: (1) the performance of AWF; (2) perceptions, uses, engagement with AWF, and influencing factors; and (3) the impact of AWF. We identified positive, negative, neutral, and mixed results in all three main foci of investigation. Overall, less positive results were found in validating AWF compared to results favoring the other two areas. Lastly, we grounded our findings within the argument-based validity framework and also examined the potential implications.

We present a method for assessing science inquiry performance, specifically for the inquiry skill of designing and conducting experiments, using educational data mining on students' log data from online microworlds in the Inq-ITS system (Inquiry Intelligent Tutoring System; www.inq-its.org ). In our approach, we use a 2-step process: First we use text replay tagging, a type of rapid protocol analysis in which categories are developed and, in turn, used to hand-score students' log data. In the second step, educational data mining is conducted using a combination of the text replay data and machine-distilled features of student interactions in order to produce an automated means of assessing the inquiry skill in question; this is referred to as a detector. Once this detector is appropriately validated, it can be applied to students' log files for auto-assessment and, in the future, to drive scaffolding in real time. Furthermore, we present evidence that this detector developed in 1 scientific domain, phase change, can be used-with no modification or retraining-to effectively detect science inquiry skill in another scientific domain, density.

The Writing Pal is an intelligent tutoring system that provides writing strategy training. A large part of its artificial intelligence resides in the natural language processing algorithms to assess essay quality and guide feedback to students. Because writing is often highly nuanced and subjective, the development of these algorithms must consider a broad array of linguistic, rhetorical, and contextual features. This study assesses the potential for computational indices to predict human ratings of essay quality. Past studies have demonstrated that linguistic indices related to lexical diversity, word frequency, and syntactic complexity are significant predictors of human judgments of essay quality but that indices of cohesion are not. The present study extends prior work by including a larger data sample and an expanded set of indices to assess new lexical, syntactic, cohesion, rhetorical, and reading ease indices. Three models were assessed. The model reported by McNamara, Crossley, and McCarthy ( Written Communication 27:57-86, 2010 ) including three indices of lexical diversity, word frequency, and syntactic complexity accounted for only 6 % of the variance in the larger data set. A regression model including the full set of indices examined in prior studies of writing predicted 38 % of the variance in human scores of essay quality with 91 % adjacent accuracy (i.e., within 1 point). A regression model that also included new indices related to rhetoric and cohesion predicted 44 % of the variance with 94 % adjacent accuracy. The new indices increased accuracy but, more importantly, afford the means to provide more meaningful feedback in the context of a writing tutoring system.

With the widespread adoption of wireless communication technologies in modern high-speed rail systems, the Train-to-Ground (T2G) communication system for Electric/Diesel Multiple Units (EMU/DMU) has become essential for train operation monitoring and fault diagnosis. However, this system is increasingly vulnerable to various cyber-physical threats, necessitating more intelligent and adaptive security protection mechanisms. This paper presents an intelligent security defense framework that integrates intrusion detection, risk scoring, and response mechanisms to enhance the security and responsiveness of the T2G communication system. First, feature selection is performed on the TON_IoT dataset to develop a Dream Optimization Algorithm (DOA)-optimized backpropagation neural network (DOA-BPNN) model for efficient anomaly detection. A Bayesian risk scoring module then quantifies detection outcomes and classifies risk levels, improving threat detection accuracy. Finally, a Q-learning-based reinforcement learning (RL) module dynamically selects optimal defense actions based on identified risk levels and attack patterns to mitigate system threats. Experimental results demonstrate improved performance in both multi-class and binary classification tasks compared to conventional methods. The implementation of the Bayesian risk scoring and decision-making modules leads to a 63.56% reduction in system risk scores, confirming the effectiveness and robustness of the proposed approach in an experimental environment.