Intelligent Assessment Research Articles

A Multi-Satellite Space Environment Risk Prediction and Real-Time Warning System for Satellite Safety Management

In response to the need for a space security situation assessment during orbit, the multi-satellite space environmental risk prediction and early warning system is based on the detection results of the space weather payload of the Fengyun 4A and 4B satellites, as well as the prediction results of the National Space Weather Center, for the first time. By comprehensively utilizing some open-source data, it is the first time that we have achieved a 24 h advanced prediction of the space environment high-energy proton, low-energy particle, and high-energy electron risks for the safety of the Fengyun-series high-orbit satellites, and a real-time warning of satellite single-event upset, surface charging, and deep charging risks. The automation system has preliminarily achieved an intelligent space risk assessment for the safety of multiple stationary meteorological satellites, effectively improving the application efficiency of the space environmental data and the products of Fengyun-series satellites. The business status is stable in operation, and the resulting error between the predicted results of various risk indices and the measured data was less than one level. The warning accuracy was better than 90%. This article uses the system for the first time, to use Fengyun satellite data to, accurately and in a timely manner, predict and warn us about the low-energy particles and surface charging high-risk levels of the Fengyun 4A and 4B satellites during the typical space weather event on 21 April 2023, in response to the impact of complex spatial environmental factors on the safety of Fengyun-series high-orbit satellites. The construction and operation of a multi-satellite space environmental risk prediction and early warning system can provide a reference for the safety work of subsequent satellite ground system operations.

The intelligent prediction and assessment of financial information risk in the cloud computing model

cloud computing (cloud computing) is a kind of distributed computing, referring to the network "cloud" will be a huge data calculation and processing program into countless small programs, and then, through the system composed of multiple servers to process and analyze these small programs to get the results and return to the user. This report explores the intersection of cloud computing and financial information processing, identifying risks and challenges faced by financial institutions in adopting cloud technology. It discusses the need for intelligent solutions to enhance data processing efficiency and accuracy while addressing security and privacy concerns. Drawing on regulatory frameworks, the report proposes policy recommendations to mitigate concentration risks associated with cloud computing in the financial industry. By combining intelligent forecasting and evaluation technologies with cloud computing models, the study aims to provide effective solutions for financial data processing and management, facilitating the industry's transition towards digital transformation.

An Examination of Mental Stress in College Students: Utilizing Intelligent Perception Data and the Mental Stress Scale

In order to solve the problems of traditional mental stress detection in college students that are time-consuming, random, and subjective, this paper proposes an intelligent perception-driven mental stress assessment method for college students. First, we analyze the factors in SRQ and SCL-90, which can be measured by intelligent sensing methods, including sleep, exercise, social interaction, and environment, and then perform feature extraction. Secondly, we use machine learning methods to build a mental stress assessment model. The Shapley additive explanations (SHAP) model is used to explain the training results. Experimental results show that the model proposed in this article can effectively assess the mental stress state of college students. This means that the collection of intelligent perception data based on the mental stress scale can effectively evaluate the mental stress state of college students and provide a new research idea for further developing a non-intrusive and real-time mental stress assessment for college students.

Developing a smart green supplier risk assessment system integrating natural language processing and life cycle assessment based on AHP framework: An empirical study

Global carbon reduction drives the transformations of corporations, emphasizing carbon and environmental impact within supply chains. A critical challenge in risk management is converting unstructured data into quantifiable information, and ruining the conversion often leads to ineffective risk assessments. While artificial intelligence is increasingly used in supply chain risk management, previous studies seldom integrate text analysis for risk extraction and lack accurate risk assessment methods. This study pioneers an intelligent green supplier risk assessment system integrating natural language processing (NLP) and life cycle assessment (LCA) based on an analytic hierarchy process (AHP) framework. The proposed approach utilizes NLP keyword extraction to identify and assess risk and uses LCA to uncover environmental risks, minimizing negative impacts. The text classification model attains 81.7 % accuracy, ensuring a trustworthy risk assessment system. The proposed method could significantly improve risk analysis efficiency, foster sustainable procurement, and elevate enterprise competitiveness in sustainable business practices.

An intelligent assessment method of criminal psychological attribution based on unbalance data

Criminal cases often exhibit imbalance and cannot be extended by data augmentation when classified into attribution types. To solve the problem of unbalance data in offenders’ attribution classification, this paper proposes a criminal psychological attribution assessment model by an improved Balanced TF-Distinguishing IDF method (B-TF-dIDF) and constructed a hybrid network with attention method to fuse numerical and text features for improving the accuracy. First, as a statistical method, B-TF-dIDF is presented to reduce the impact of class-imbalance for extraction of numerical features, in which a balanced element is added to reduce the effects of incorrect type keywords on classification, and a distinguishing element is added to discriminate the types of keywords. Then, an improved hybrid network model composed of Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) is constructed to balance the influence of different lengths of text samples for extracting the semantic features of criminal texts. For evaluating different feature weights by their importance, Spatial Attention is used to improve CNN in the feature maps. Moreover, the self-attention is also performed to re-evaluate the mixed features. Finally, the softmax classifier provides a scientific basis for developing a hierarchical treatment mechanism further. Additionally, we build a criminal data set with labels from real cases for testing. The experiment proved that the proposed model is better than other related methods in various evaluation indicators, including the micro and macro scopes. Moreover, the F1 of minority samples has increased by 6%–8%, indicating that the proposed method can reduce the impact of class-imbalance.

Digital twin‐driven online intelligent assessment of wind turbine gearbox

AbstractRemaining useful fatigue life monitoring of wind turbine drivetrains is important. However, the implementation of real‐time monitoring often faces efficiency and accuracy challenges. In order to resolve this, this paper proposes a vibration‐based damage monitoring digital twin (VBDM‐DT) that enables the online intelligent evaluation of wind turbine gearboxes, using gear tooth surface durability as an example fatigue mode. The VBDM‐DT integrates a random wind load model, a high‐fidelity dynamics model, and a fatigue damage model. The random wind load model takes the wind speed from the supervisory control and data acquisition (SCADA) as input to estimate the input torque of the drivetrain in real time. Simultaneously, VBDM‐DT uses the vibration signals from the condition monitoring system (CMS) to intelligently calibrate the dynamics model, allowing it to be continuously adjusted and optimized in response to actual vibrations. The fatigue damage model takes the real‐time dynamic loads estimated by the high‐fidelity dynamic model as input to achieve real‐time fatigue damage monitoring of key components in the wind turbine gearbox. Applying the VBDM‐DT model to a 2 MW wind turbine gearbox, the results indicate that the model provides satisfactory accuracy in estimating input loads and good adaptability in intelligent calibration of the dynamic model. Based on this model, the fatigue life estimation for gears and bearings is more credible and reliable.

Validation of the Quality Analysis of Medical Artificial Intelligence (QAMAI) tool: a new tool to assess the quality of health information provided by AI platforms.

The widespread diffusion of Artificial Intelligence (AI) platforms is revolutionizing how health-related information is disseminated, thereby highlighting the need for tools to evaluate the quality of such information. This study aimed to propose and validate the Quality Assessment of Medical Artificial Intelligence (QAMAI), a tool specifically designed to assess the quality of health information provided by AI platforms. The QAMAI tool has been developed by a panel of experts following guidelines for the development of new questionnaires. A total of 30 responses from ChatGPT4, addressing patient queries, theoretical questions, and clinical head and neck surgery scenarios were assessed by 27 reviewers from 25 academic centers worldwide. Construct validity, internal consistency, inter-rater and test-retest reliability were assessed to validate the tool. The validation was conducted on the basis of 792 assessments for the 30 responses given by ChatGPT4. The results of the exploratory factor analysis revealed a unidimensional structure of the QAMAI with a single factor comprising all the items that explained 51.1% of the variance with factor loadings ranging from 0.449 to 0.856. Overall internal consistency was high (Cronbach's alpha = 0.837). The Interclass Correlation Coefficient was 0.983 (95% CI 0.973-0.991; F (29,542) = 68.3; p < 0.001), indicating excellent reliability. Test-retest reliability analysis revealed a moderate-to-strong correlation with a Pearson's coefficient of 0.876 (95% CI 0.859-0.891; p < 0.001). The QAMAI tool demonstrated significant reliability and validity in assessing the quality of health information provided by AI platforms. Such a tool might become particularly important/useful for physicians as patients increasingly seek medical information on AI platforms.

Estimating Gender Differences in the Association between Cognitive Resilience and Mild Cognitive Impairment Incidence

Introduction: Recent evidence suggests that the influence of verbal intelligence and education on the onset of subjective cognitive decline may be modulated by gender, where education contributes less to cognitive resilience (CR) in women than in men. This study aimed to examine gender differences in the association between CR and mild cognitive impairment (MCI) incidence in an Australian population-based cohort. Methods: We included 1,806 participants who had completed at least the first two waves and up to four waves of assessments in the Personality and Total Health (PATH) Through Life study (baseline: 49% female, male = 62.5, SD = 1.5, age range = 60–66 years). CR proxies included measures of educational attainment, occupation skill, verbal intelligence, and leisure activity. Discrete-time survival analyses were conducted to examine gender differences in the association between CR proxies and MCI risk, adjusting for age and apolipoprotein E4 status. Results: Gender differences were only found in the association between occupation and MCI risk, where lower occupation skill was more strongly associated with higher risk in men than in women (odds ratio [OR] = 1.30, 95% confidence interval [CI] [1.07, 1.57]). In both genders, after adjusting for education and occupation, one SD increase in leisure activity was associated with lower MCI risk by 32% (OR = 0.76, 95% CI [0.65, 0.89]). Higher scores in verbal intelligence assessment were associated with reduced risk of MCI by 28% (OR = 0.78, 95% CI [0.69, 0.89]). Conclusion: Occupational experience may contribute to CR differently between genders. Life course cognitive engagement and verbal intelligence may be more protective against MCI than education and occupation for both men and women.

It is not all about you: Communicative cooperation is determined by your partner's theory of mind abilities as well as your own.

We investigated the relationship between Theory of Mind (ToM) and communicative cooperation. Specifically, we examined whether communicative cooperation is affected by the ToM ability of one's cooperative partner as well as their own. ToM is the attribution of mental states to oneself and others; cooperation is the joint action that leads to achieving a shared goal. We measured cooperation using a novel communicative cooperation game completed by participants in pairs. ToM was measured via the Movies for Assessment of Social Cognition (MASC) task and fluid intelligence via the Raven task. Findings of 350 adults show that ToM scores of both players were predictors of cooperative failure, whereas Raven scores were not. Furthermore, participants were split into low- and high-ToM groups through a median split of the MASC scores: high-ToM individuals committed significantly fewer cooperative errors compared to their low-ToM counterparts. Therefore, we found a direct relationship between ToM and cooperation. Interestingly, we also examined how ToM scores of paired participants determine cooperation. We found that pairs with two high-ToM individuals committed significantly fewer errors compared to pairs with two low-ToM individuals. We speculate that reduced cooperation in low-low ToM pairs is a result of less efficient development of conceptual alignment and recovery from misalignment, compared to high-high ToM dyads. For the first time, we thus demonstrate that it is not all about you; both cooperative partners make key, independent, contributions to cooperative outcomes. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The assessment of facial nerve function

The assessment of facial nerve function plays a crucial role in the diagnosis and treatment of facial nerve disorders.The assessment system for facial nerve function is primarily categorized into subjective and objective systems.While the subjective assessment system is relatively simple, it lacks accuracy as it can be influenced by the subjectivity of evaluator.Whereas, the objective system offers higher precision and stability, providing more quantitative information. In recent years, benefited with advancements in computer vision and artificial intelligence,we have witnessed increasingly accurate,stable and intelligent facial nerve assessment systems gradually implemented in clinical practice.When selecting a specific facial nerve assessment system,factors such as clinical scenarios,assessment objectives,patient characteristics should be considered.

Artificial Intelligence Assessment of Biological Age From Transthoracic Echocardiography: Discrepancies with Chronologic Age Predict Significant Excess Mortality

Age and sex can be estimated using artificial intelligence on the basis of various sources. The aims of this study were to test whether convolutional neural networks could be trained to estimate age and predict sex using standard transthoracic echocardiography and to evaluate the prognostic implications. The algorithm was trained on 76,342 patients, validated in 22,825 patients, and tested in 20,960 patients. It was then externally validated using data from a different hospital (n=556). Finally, a prospective cohort of handheld point-of-care ultrasound devices (n=319; ClinicalTrials.gov identifier NCT05455541) was used to confirm the findings. A multivariate Cox regression model was used to investigate the association between age estimation and chronologic age with overall survival. The mean absolute error in age estimation was 4.9years, with a Pearson correlation coefficient of 0.922. The probabilistic value of sex had an overall accuracy of 96.1% and an area under the curve of 0.993. External validation and prospective study cohorts yielded consistent results. Finally, survival analysis demonstrated that age prediction ≥5years vs chronologic age was associated with an independent 34% increased risk for death during follow-up (P<.001). Applying artificial intelligence to standard transthoracic echocardiography allows the prediction of sex and the estimation of age. Machine-based estimation is an independent predictor of overall survival and, with further evaluation, can be used for risk stratification and estimation of biological age.

Assessing the impact of industrial intelligence on urban carbon emission performance: Evidence from China

With the growing emphasis on sustainable development, there has been increasing attention given to measures aimed at promoting environmental improvements and reducing carbon emissions, including the adoption of intelligent industry. Recent studies have analyzed the influence of industrial intelligence on urban carbon emission performance while ignore the spatial spillover effects and lack in-depth discussion of the mechanisms, which reduces the reliability of the assessment of industrial intelligence's impact on carbon emission performance. To address this issue, the paper examines direct effect, spatial spillover effects, and mechanisms, utilizing a balanced panel data from 2008 to 2019 for 238 Chinese cities. The findings reveal that a 1 % improvement in industrial intelligence results in a 2.747 % enhancement of local carbon emission performance. Moreover, through the spatial spillover analysis, we determined that industrial intelligence has a notable negative impact on the carbon emission performance of surrounding areas. The mechanism analysis demonstrated that industrial intelligence affects the carbon emission performance of local and neighboring areas by influencing the agglomeration of productive services. Furthermore, our study illustrates that the industrial intelligence's enhancement effect on carbon emission performance shows more significantly in eastern, resource-dependent, and ordinary prefecture-level cities. Finally, endogeneity and robustness tests conducted yielded consistent conclusions. Our study provides a new perspective on industrial intelligence's carbon reduction effect and contributes to the development of policies related to industrial upgrading and green development.

The Artificial Intelligence Assessment Scale (AIAS): A Framework for Ethical Integration of Generative AI in Educational Assessment

Recent developments in Generative Artificial Intelligence (GenAI) have created a paradigm shift in multiple areas of society, and the use of these technologies is likely to become a defining feature of education in coming decades. GenAI offers transformative pedagogical opportunities, while simultaneously posing ethical and academic challenges. Against this backdrop, we outline a practical, simple, and sufficiently comprehensive tool to allow for the integration of GenAI tools into educational assessment: the AI Assessment Scale (AIAS). The AIAS empowers educators to select the appropriate level of GenAI usage in assessments based on the learning outcomes they seek to address. The AIAS offers greater clarity and transparency for students and educators, provides a fair and equitable policy tool for institutions to work with, and offers a nuanced approach which embraces the opportunities of GenAI while recognising that there are instances where such tools may not be pedagogically appropriate or necessary. By adopting a practical, flexible approach that can be implemented quickly, the AIAS can form a much-needed starting point to address the current uncertainty and anxiety regarding GenAI in education. As a secondary objective, we engage with the current literature and advocate for a refocused discourse on GenAI tools in education, one which foregrounds how technologies can help support and enhance teaching and learning, which contrasts with the current focus on GenAI as a facilitator of academic misconduct.

Assessment of adult cognitive abilities in Greece: A differential item functioning study of the General Ability Measure for Adults (GAMA)

The purpose of this study was to evaluate the generalizability and possible adaptation for use of a non-verbal measure of intelligence developed in the United States, the General Ability Measure for Adults (GAMA; Naglieri &amp; Bardos, 1997) in Greece. As a validity evidence the study examined the differential item function of its questions in order to explore potential item bias utilizing the disproportionate group probabilities of participants’ correctly endorsing test items. The analysis was performed using a logistic regression procedure with samples from the United States (n = 2,369) and Greece (n = 1,273). The findings indicate a small (&lt;1%) number of items that work differentially between these two cultural groups. Implications for the development and weighting of cross-cultural intelligence assessment tests using non-verbal measures are discussed.

IMPACT OF GENERATIVE AI ON EDUCATIONAL SECTOR

This study looks into how generative artificial intelligence (AI) is affecting the field of education. The study examines the possible advantages of using generative AI into instruction, such as more individualized learning opportunities, enhanced accessibility, and creative teaching strategies. The study also recognizes and examines the moral issues raised by the application of AI in education, highlighting the significance of upholding academic honesty and openness. It draws attention to the necessity of cautious application in order to guarantee that AI fosters fruitful learning results. The suggested research technique describes a mixed methods strategy that combines quantitative data analysis with qualitative information from surveys and interviews. The goal of the project is to provide best practices and standards for the appropriate integration of generative AI in education, while also identifying the potential and problems that this technology presents. The anticipated results include a thorough comprehension of how generative AI will affect education and how it might improve learning opportunities, ethical issues for responsible usage, and the identification of possible problems and solutions. In the end, the research hopes to promote a more inclusive and productive learning environment by aiding in the creation of useful frameworks for incorporating AI into educational settings. The education industry has drastically evolved in recent years, thanks to the remarkable advancements made by artificial intelligence. With the integration of generative AI technology, educators and students are now presented with a wealth of possibilities and opportunities. As AI continues to advance further, it holds great potential for revolutionizing education as we know it through improved efficiency measures that personalize learning experiences while enhancing overall student outcomes. Harnessing this power-packed tool in teaching environments creates an opportunity for educational institutions across various sectors allowing them efficient intelligent assessment tools coupled with personalized tutoring provisions towards addressing classroom weaknesses resulting in custom-made lesson plans aimed at betterment of individual academic progress developed using predictive models creating ample time such that teachers can focus solely on providing tailored instruction &amp; support based exclusively around their Student's requirements&amp; specifications. This groundbreaking technological development provided meaningfully leveraging Generative Intelligence efforts promises nothing short but will be instrumental when shaping how Education is structured from henceforth enabling automation capabilities within the Administrative tasks management system,&amp; content-building-The advantages come twofold-Enhancement not only resides generally concerning Upgrade Learning Experience situations; there exist tailor solutions projecting Speaker understanding regarding distinctive needs pupils have always had throughout all levels ultimately leading to Developmental Progression heights hitherto attainable anticipated sooner than later.

A Hierarchical Approach to Efficient Assessment of Functional Intelligibility: The Modular FITI (Functional Importance to Intelligibility) Phrase List

Purpose: Gurevich and Kim (2023a) developed speech stimuli with full phonetic coverage and phonemic balance to comprehensively examine production and perception of consonants. They identify a hierarchy of positional variants with functional importance to intelligibility (FITI). The FITI stimuli are well suited for assessing intelligibility. Multiple studies report that clinicians prefer nonstandard materials (e.g., reading passages, word lists) over formal tools (e.g., Frenchay Dysarthria Assessment–Second Edition [FDA-2]; Enderby &amp; Palmer, 2008), citing time constraints among reasons. Having analyzed the phonetic coverage of the three standard passages (Gurevich &amp; Kim, 2022), the purpose herein is to provide a similar analysis for the FDA-2 and to construct an alternative list of stimuli that achieves strategic coverage of priority intelligibility targets and is adaptable to time constraints. Method: The FDA-2 word list was analyzed in terms of its coverage of the 466 allowable contexts for the 24 English consonants and its coverage of priority FITI targets. A subset of the stimuli from Gurevich and Kim (2023a) was used to construct phrases organized in modules from highest to lowest FITI priority. Results: The full FDA-2 word list covers 35.41% of the allowable 466 contexts, compared to 83.05% by the 308 FITI word list from Gurevich and Kim (2023a). It also underperforms in covering priority FITI targets but does capture some of the hierarchy if all 115 words are used. Fifteen modules were constructed from the 308 FITI words with one to five phrases per module, presented in order of highest to lowest FITI priority. Conclusions: The newly constructed list of phrases has comprehensive phonetic coverage, follows the FITI hierarchy, and is built in autonomous modules. Using the modular FITI phrases to investigate speech is adaptable to time constraints in a manner that prioritizes intelligibility targets.

Full-coverage unobtrusive health monitoring of elders at homes

Due to the fast increase of the elderly population, in-home eldercare has emerged as an important topic of research. Existing studies in intelligent health monitoring have yielded promising outcomes. Nevertheless, these approaches fall short of achieving round-the-clock, unobtrusive surveillance. Herein, we introduce a system offering full-dimensional and unobtrusive in-home health monitoring for elderly individuals living alone. We deploy smart sensors, including infrared, bed, sound, and physiological sensors, to provide all-around coverage of the elderly living space. The proposed system proficiently identifies activities such as cooking, coughing, snoring, talking, listening to music, and walking, while concurrently estimating the indoor location and providing intelligent sleep monitoring and assessment of physiological parameters. Our proposed algorithm has undergone evaluation using real-world data, encompassing a 30-day activity record of ten subjects aged 65 and older. The results demonstrate the remarkable effectiveness of our method, showcasing a high degree of accuracy in recognizing a diverse range of activities. This substantiates the practical implementation of our system in authentic homecare environments.

Pilot Research for the Assessment of Neuroanatomical Correlates in Relation to Brain Talents Identified Through the Six Seconds Emotional Intelligence Assessment

The current research is centered on investigating the neurophysiological correlates associated with various talents, with the goal of determining whether specific brain areas are activated during the expression of these talents. The study specifically investigates individuals with different occupational roles, distinguishing between social/healthcare and non-healthcare (administrative) positions. The research was conducted in three phases. The initial phase involved the selection of 60 employees from the Piccola Opera Caritas of Giulianova. Participants were administered the Brain Talent Profile questionnaire to gather information about their talents. The second phase focused on identifying neuroanatomical correlates using the BrainBit helmet. Participants engage in various brief activities while wearing the EEG headband, suitable for both neurofeedback and brain activity measurements. The final phase was dedicated to analyze the data collected through the Brain Brief Profile, which comprises three domains: Perception, Evaluation, and Action. Our hypothesis suggests significant correlations between participants&amp;apos; talents and recorded EEG patterns. To validate this hypothesis, the outcomes derived from the Social and Emotional Intelligence (SEI) test were compared with EEG measurements. This research holds potential applications in the recruitment phase, providing insights into the neurological basis of talents in prospective employees. It may offer advantages in training initiatives dedicated to enhancing talents and skills across diverse organizational roles. The emphasis on various job roles, particularly in the social/healthcare and administrative sectors, strengthens the practical relevance of the study for talent assessment and development within organizational contexts. In summary, our research aims to bridge the gap between talents and neurophysiological correlates, with potential implications for recruitment and training strategies within organizational settings.

Emotional Intelligence, Perceived Stress and Life Quality Assessment Among Dental Students.

The capacity to grasp and recognize one's own and human feelings, use cognitive awareness to control actions and behavior, and modify moods in response to difficult situations is known as emotional intelligence (EI). Perceived stress has been linked to higher levels of depression among healthcare students. This study set out to assess EI, perceived stress (PS), and life quality assessment (LQA) among dental undergraduates and to ascertain how these factors are linked to one another. The self-reported cross-sectional research was organized among preclinical and clinical years of dental students to examine their emotional intelligence and stress-coping capacity. About 146 students participated in the study, with ages ranging from 19 to 25, irrespective of gender. The students were given the Schutte Emotional Intelligence and Perceived Stress Scale tools (PSS10) by Cohen, and with World Health Organization Quality of Life (WHOQOL-BREF) tool examined the characteristics that contribute to life quality among students. The study included 146 individuals, 38 males (26.02%) and 108 females (73.97%), with an unresponsiveness rate of 2.66%. The correlation revealed statistically high significance among emotional intelligence and perceived stress (P = 0.000), perceived stress and life quality (P = 0.02), and emotional intelligence and life quality (P = 0.008). The statistical analysis of the influence of EI, PS, and LQA on academic year-wise analysis determined non-significant (P > 0.05). Due to the time constraints of their job, dentistry training may be highly stressful, yet regular evaluations of student performance and the accompanying factors would help us to understand how students behave in challenging circumstances.

THE DEVELOPMENT OF THE MATHEMATICAL LOGIC INTELLIGENCE ASSESSMENT INSTRUMENT

Mathematical logic intelligence leads to the ability to think systematically, logically, and scientifically. This intelligence supports learning outcomes not only in the realm of mathematics but also in other fields such as physics, chemistry, statistics, and so on. So far, there has yet to be a valid and reliable mathematical logic intelligence assessment instrument for students in elementary schools or Madrasah Ibtidaiyah. This study aims to develop a valid and reliable mathematical logic intelligence assessment instrument for Madrasah Ibtidaiyah students. The research method is research and development with nine steps: (1) determining the purpose of preparing the instrument, (2) looking for relevant theories or material coverage, (3) preparing indicator items, (4) preparing instrument items, (5) validating the content, (6) revision based on validator input, (7) conducting trials on appropriate respondents to obtain participant response data, and (8) conducting analysis, and (9) assembling the instrument. The research instruments used tests and validation sheets. Instrument validation in the form of content validation and construct validation by three experts in mathematics learning at the Madrasah Ibtidaiyah. The test was conducted on grade V students doing the limited experiment. The study's results produced a test to measure mathematical logic intelligence in as many as nine items, which were valid and reliable items with a coefficient of 0.793. The mathematical logic intelligence assessment instrument consists of five indicators, namely: logical thinking with the ability to organize ideas systematically, abstract thinking with the ability to understand complex mathematical concepts, deductive thinking with the ability to conclude from given premises, inductive thinking with the ability to collect data, and analytical thinking using the ability to analyze and solve mathematical problems.