We discuss the new challenges and directions facing the use of big data and artificial intelligence (AI) in education research, policy-making, and industry. In recent years, applications of big data and AI in education have made significant headways. This highlights a novel trend in leading-edge educational research. The convenience and embeddedness of data collection within educational technologies, paired with computational techniques have made the analyses of big data a reality. We are moving beyond proof-of-concept demonstrations and applications of techniques, and are beginning to see substantial adoption in many areas of education. The key research trends in the domains of big data and AI are associated with assessment, individualized learning, and precision education. Model-driven data analytics approaches will grow quickly to guide the development, interpretation, and validation of the algorithms. However, conclusions from educational analytics should, of course, be applied with caution. At the education policy level, the government should be devoted to supporting lifelong learning, offering teacher education programs, and protecting personal data. With regard to the education industry, reciprocal and mutually beneficial relationships should be developed in order to enhance academia-industry collaboration. Furthermore, it is important to make sure that technologies are guided by relevant theoretical frameworks and are empirically tested. Lastly, in this paper we advocate an in-depth dialog between supporters of "cold" technology and "warm" humanity so that it can lead to greater understanding among teachers and students about how technology, and specifically, the big data explosion and AI revolution can bring new opportunities (and challenges) that can be best leveraged for pedagogical practices and learning.

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Our approach aims to provide a mechanism for recommending long tail items to knowledge workers. The approach employs collaborative filtering using browsing features of identified key population of the diffusion of information. We conducted analytic experiment for a novel recommendation algorithm based on the browsing features of identified selected users and discovered that the first 10 users accessing a particular page play the key role in information spread. The evaluation indicated that our approach is effective for long tail recommendation.

We approach the problem of rule extraction in its primary form. That is, given a trained artificial neural network, we extract rules classifying data set as correctly as possible. Attention is oriented toward extraction of fuzzy rules. The choice of fuzzy rules underlines the aim of balancing rule comprehensibility and complexity. To achieve higher comprehensibility of extracted rules, the formulated theoretical material is an extension of crisp rule extraction 1). A rule extraction algorithm is introduced. The presented algorithm for fuzzy rule extraction implies from the derived theoretical results rather than from heuristics. The rule extraction algorithm incorporates a ’built-in’ rule simplification mechanism. This feature is beneficial in cases when trained neural network structure is overdetermined for a given task. The rule extraction algorithm is experimentally demonstrated. Demonstrations incorporate both structure modification training and fixed structure training.

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