Human-Computer Interaction Problem in Learning: Could the Key Be Hidden Somewhere Between Social Interaction and Development of Tools?

AbstractFirst-generation expert systems have significant limitations, often attributed to their not being sufficiently deep. However, a generally accepted answer to “What is a deep expert system?” is still to be given. To answer this question one needs to answer “Why do first-generation systems exhibit the limitations they do?” thus identifying what is missing from first-generation systems and therefore setting the design objectives for second-generation (i.e. deep) systems. Several second-generation architectures have been proposed; inherent in each of these architectures is a definition of deepness. Some of the proposed architectures have been designed with the objective of alleviating a subset, rather than the whole set, of the first-generation limitations. Such approaches are prone to local, non-robust solutions. In this paper we analyze the limitations (under the categories: human-computer interaction, problem-solving flexibility, and extensibility) of the first-generation expert systems thus setting design goals for second-generation systems. On the basis of this analysis proposed second-generation architectures are reviewed and compared. The paper concludes by presenting requirements for a generic second-generation architecture.

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Recommender Systems: Past, Present, Future

AI Magazine ◽

10.1609/aimag.v42i3.18139 ◽

2022 ◽

Vol 42 (3) ◽

pp. 3-6

Author(s):

Dietmar Jannach ◽

Pearl Pu ◽

Francesco Ricci ◽

Markus Zanker

Keyword(s):

Human Computer Interaction ◽

Recommender Systems ◽

Information Search ◽

Information Filtering ◽

General Purpose ◽

Learning Technology ◽

Computerized System ◽

Interaction Problem ◽

Selection Of ◽

Computer Interaction

The origins of modern recommender systems date back to the early 1990s when they were mainly applied experimentally to personal email and information filtering. Today, 30 years later, personalized recommendations are ubiquitous and research in this highly successful application area of AI is flourishing more than ever. Much of the research in the last decades was fueled by advances in machine learning technology. However, building a successful recommender sys-tem requires more than a clever general-purpose algorithm. It requires an in-depth understanding of the specifics of the application environment and the expected effects of the system on its users. Ultimately, making recommendations is a human-computer interaction problem, where a computerized system supports users in information search or decision-making contexts. This special issue contains a selection of papers reflecting this multi-faceted nature of the problem and puts open research challenges in recommender systems to the fore-front. It features articles on the latest learning technology, reflects on the human-computer interaction aspects, reports on the use of recommender systems in practice, and it finally critically discusses our research methodology.

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E-motion: Moving Toward the Utilization of Artificial Emotion

Informal Logic ◽

10.22329/il.v22i3.2593 ◽

2001 ◽

Vol 22 (3) ◽

Author(s):

Michael A. Gilbert ◽

T.J.M. Bench-Capon

Keyword(s):

Social Interaction ◽

Human Computer Interaction ◽

Real World ◽

Vital Role ◽

Emotional Content ◽

Human Interaction ◽

Coherent System ◽

Delicate Balance ◽

Topic Shift ◽

Computer Interaction

During human-human interaction, emotion plays a vital role in structuring dialogue. Emotional content drives features such as topic shift, lexicalisation change and timing; it affects the delicate balance between goals related to the task at hand and those of social interaction; and it represents one type of feedback on the effect that utterances are having. These various facets are so central to most real-world interaction, that it is reasonable to suppose that emotion should also play an important role in human-computer interaction. To that end, techniques for detecting, modelling, and responding appropriately to emotion are explored, and an architecture for bringing these techniques together into a coherent system is presented.

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The Psychologies of Human-Computer Interaction: An Integrative Approach

Proceedings of the Human Factors and Ergonomics Society Annual Meeting ◽

10.1177/107118139704100185 ◽

1997 ◽

Vol 41 (1) ◽

pp. 385-389 ◽

Cited By ~ 1

Author(s):

Douglas J. Gillan

Keyword(s):

Social Interaction ◽

Human Computer Interaction ◽

Perceived Control ◽

Transfer Of Training ◽

Graphical Representation ◽

Research Area ◽

Design Guidelines ◽

Integrative Approach ◽

Theoretical Approaches ◽

Computer Interaction

Human-computer interaction (HCI) has been identified as a rich task for the real-world study of psychology; however, the theoretical approaches to the psychology of HCI have narrowly focused on problem-solving (e.g., GOMS and CE+), memory (e.g., mental models and metaphors), and social interaction (e.g., perceived control). An attempt to create a broader theoretical framework integrates the three approaches to the psychology of HCI with a theory, IP3. This paper (1) discusses each of the three psychologies of HCI, (2) describes the integrative theory, IP3 (verbally, as well as by a graphical representation), (3) applies the theory to one representative research area—transfer of training, and (4) applies the theory to the interpretation of selected HCI design guidelines.

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