PurposeRobots 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.Design/methodology/approachTesting 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.FindingsRespondents 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.Practical implicationsRobotization 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.Originality/valueWork’s meaningfulness in robotization is a novel research topic and a step toward socially sustainable robotization. (shrink)

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)