Search results

Education is increasingly using Intelligent Tutoring Systems (ITS), both for modelling instructional and teaching strategies and for enhancing educational programs. The first part of the paper introduces the basic structure of an ITS as well as common problems being experienced within the ITS community. The second part describes WITNeSS ‐ an original hybrid intelligent system using Fuzzy‐Neural‐GA techniques for optimising the presentation of learning material to a student. The original work in this paper is related to the concept of a “virtual student”. This student model, modelled using fuzzy technologies, will be useful for any ITS, providing it with an optimal learning strategy for fitting the ITS itself to the unique needs of each individual student. In the third part, experiments focus on problems developing a “virtual student” model, which simulates, in a rudimentary way, human learning behaviour. Part four finishes with concluding remarks.

A mathematical tool for the fuzzy identification of a system is presented. This hybrid mathematical method combines the fuzzy description of input space and the description of input‐output relations using the Bézier interpolation technique. This is a computationally simple method for designing a curve(surface). Its efficiency is expressed in its powerful application in controlling the shape of a described curve (surface), both simplifying the determination of the parameters of input‐output relations, qualititively by precision and quantitatively by decreasing the number of control points (values) required. Shows an application of the method to computer‐aided identification of the steady‐state operating points of a thermal engine.

Summary—A synthesis process of a multiple description is defined on a lattice of fuzzy sets which differs from the usual one. Connections between these lattices are stated. Mathematical definitions are correlated with concepts of decision and system theory.

The aim of this paper is to present the key areas of activity to be used for drafting service level agreements (SLAs) for electronic services and, at the same time, to present best practices and problems that arise from the application of this discipline.

A range of recently published (2000‐2005) works has been reviewed, in order to: a) analyse how an SLA has to be defined and applied b) identify the applicable best practices and c) identify the problem areas.

Provides guidance for the business and the Application Service Provider (ASP) when they want to engage in an outsourcing agreement by presenting best practices and problems that occur from the application of SLAs.

Suggests further research is needed in a number of research areas such as: development of semantic models for SLAs, development of flexible pricing models in relation with SLAs, definition of SLAs in cases of dynamic service creation, etc.

A useful source of information both for academia and the business.

Provides practical insight on a specialized topic and guidance to researchers.

The applications of Artificial Intelligence (AI) in various areas of professional and knowledge work are growing. Emotions play an important role in how users incorporate a technology into their work practices. The current study draws on work in the areas of AI-powered technologies adaptation, emotions, and the future of work, to investigate how knowledge workers feel about adopting AI in their work.

We gathered 107,111 tweets about the new AI programmer, GitHub Copilot, launched by GitHub and analysed the data in three stages. First, after cleaning and filtering the data, we applied the topic modelling method to analyse 16,130 tweets posted by 10,301 software programmers to identify the emotions they expressed. Then, we analysed the outcome topics qualitatively to understand the stimulus characteristics driving those emotions. Finally, we analysed a sample of tweets to explore how emotional responses changed over time.

We found six categories of emotions among software programmers: challenge, achievement, loss, deterrence, scepticism, and apathy. In addition, we found these emotions were driven by four stimulus characteristics: AI development, AI functionality, identity work, and AI engagement. We also examined the change in emotions over time. The results indicate that negative emotions changed to more positive emotions once software programmers redirected their attention to the AI programmer's capabilities and functionalities, and related that to their identity work.

Overall, as organisations start adopting AI-powered technologies in their software development practices, our research offers practical guidance to managers by identifying factors that can change negative emotions to positive emotions.

Our study makes a timely contribution to the discussions on AI and the future of work through the lens of emotions. In contrast to nascent discussions on the role of AI in high-skilled jobs that show knowledge workers' general ambivalence towards AI, we find knowledge workers show more positive emotions over time and as they engage more with AI. In addition, this study unveils the role of professional identity in leading to more positive emotions towards AI, as knowledge workers view such technology as a means of expanding their identity rather than as a threat to it.