Artificial intelligence is a useful predictive tool for a wide variety of fields of knowledge. Despite this, the educational field is still an environment that lacks a variety of studies that use this type of predictive tools. In parallel, it is postulated that the levels of self-esteem in the university environment may be related to the strategies implemented to solve problems. For these reasons, the aim of this study was to analyze the levels of self-esteem presented by teaching staff and (...) students at university and to design an algorithm capable of predicting these levels on the basis of their coping strategies, resilience, and sociodemographic variables. For this purpose, the Rosenberg Self-Esteem Scale, the Perceived Stress Scale, and the Brief Resilience Scale were administered. The results showed a relevant role of resilience and stress perceived in predicting participants’ self-esteem levels. The findings highlight the usefulness of artificial neural networks for predicting psychological variables in education. (shrink)

Our synthetic review of the relevant and related literatures on the ethics and effects of using AI in education reveals five qualitatively distinct and interrelated divides associated with access, representation, algorithms, interpretations, and citizenship. We open our analysis by probing the ethical effects of algorithms and how teams of humans can plan for and mitigate bias when using AI tools and techniques to model and inform instructional decisions and predict learning outcomes. We then analyze the upstream divides that feed into (...) and fuel the algorithmic divide, first investigating access and then representation. After that, we analyze the divides that are downstream of the algorithmic divide associated with interpretation and citizenship. At present, lacking ongoing reflection and action by learners, educators, educational leaders, designers, scholars, and policymakers, the five divides collectively create a vicious cycle and perpetuate structural biases in teaching and learning. However, increasing human responsibility and control over these divides can create a virtuous cycle that improves diversity, equity, and inclusion in education. We conclude the article by looking forward and discussing ways to increase educational opportunity and effectiveness for all by mitigating bias through a cycle of progressive improvement. (shrink)

With the rapid development of big data, artificial intelligence teaching systems have gradually been developed extensively. The powerful artificial intelligence teaching systems have become a tool for teachers and students to learn independently in various universities. The characteristic of artificial intelligence teaching system is to get rid of the constraints of traditional teaching time and space and build a brand-new learning environment, which is the mainstream trend of future learning. As the carrier of students’ autonomous learning, the artificial intelligence teaching (...) system provides a wealth of learning resources and learning tools on the one hand, and on the other hand, it gradually accumulates more and more learning behaviors, learning status, and other large amounts of data, which is an in-depth study of online learning and provides valuable and generative dynamic resources. Based on relevant researches on domestic and foreign related learning analysis and common big data analysis methods, combined with actual learning evaluation goals, this paper proposes an artificial intelligence teaching system using big data analysis methods and a modeling process framework for online learning evaluation and uses student data to carry out predictive evaluation modeling to evaluate student learning outcomes. The evaluation results can enable teachers to predict whether students can successfully complete the course of learning after a period of teaching. Through the final evaluation, students’ learning problems can be discovered in time based on the evaluation results, and targeted interventions can be made for students who are at risk. The scientific and objective learning evaluation obtained in this study through data analysis can not only provide teachers with relevant information and provide personalized guidance to students, but also improve the adaptive and personalized service functions of the learning platform of the artificial intelligence teaching system, greatly reducing teachers teaching burden. Artificial intelligence teaching evaluation can help educators understand the problems in teaching, adjust teaching strategies in time, and improve teaching results. (shrink)

Robots have a history of replacing human labor in undesirable, dirty, dull and dangerous tasks. With robots now emerging in academic and human-centered work, this paper aims to investigate psychological implications of robotizing desirable and socially rewarding work.,Testing the holistic stress model, this study examines educational professionals’ stress responses as mediators between robotization expectations and future optimism in life. The study uses survey data on 2,434 education professionals.,Respondents entertaining robotization expectations perceived their work to be less meaningful and reported more (...) burnout symptoms than those with no robotization expectations. Future optimism about life was not affected by robotization expectations alone, but meaninglessness and burnout symptoms mediated the relation between expectations of robotization and future optimism.,Robotization may be viewed as challenging the meaningfulness of educational work by compromising ethical values and interaction. To prevent excess stress among personnel, robotization should be planned together with employees in co-operation negotiations. This implicates the need for co-designing technological changes in organizations especially in the cases of social use of robots.,Work’s meaningfulness in robotization is a novel research topic and a step toward socially sustainable robotization. (shrink)