Many web-based information-seeking tasks are set in a social context where other people's knowledge and advice improves success in finding information. However, when tightly-knit contacts (friends, family, colleagues) are not available, information seeking becomes more difficult. Inspired by previous work in social navigation, we developed WebWear, a system that collects and displays traces of activity for tightly-knit groups. WebWear allows people to use contextual knowledge of contacts' interests and activities to interpret the meaning of the traces, improving their usefulness. In a comparative study, we found that WebWear helped people complete information-seeking tasks more accurately, without requiring additional effort. A one-week field trial found that WebWear was both usable and useful, and that privacy concerns were reduced in the small-group context. WebWear shows that small-scale social navigation systems are feasible, and that they can improve the effectiveness of information seeking on the World-Wide Web.

This paper presents a questionnaire based intercultural study carried out to identify the differences observed in people when individually interact with the CM-ED Concept Map Editor. Each participant interpreted and localised to his/her own culture a Concept Map (CM) about a sensitive topic. The aim of the study was to identify what visible elements of a CM are meaningful from a cultural perspective and should be treated in a CM editor.

This paper presents the current state of our research project which aims to give users a simple, easy to use, and computationally light way of creating mashups of lecture content within the Opencast Matter horn lecture capture system. The system modifies the playback components of Matter horn to deliver thin and light video clipping functionality without requiring installation of any additional software. We plan to make use of the extensive logging framework built into Matter horn to examine the effects of this tool on learner engagement.

Most of the research in the area of expert finding focuses on creating and maintaining centralized directories of experts' profiles, which users can search on demand. However, in a distributed multiagent‐based software environment, the autonomous agents are free to develop expert models or model fragments for their own purposes and from their viewpoints. Therefore, the focus of expert finding is shifting from the collection at one place as much data about a expert as possible to accessing on demand from various agents whatever user information is available at the moment and interpreting it for a particular purpose. This paper outlines purpose‐based expert modeling as an approach for finding an expert in a multiagent portfolio management system in which autonomous agents develop expert agent models independently and do not adhere to a common representation scheme. This approach aims to develop taxonomy of purposes that define a variety of context‐dependent user modeling processes, which are used by the users' personal agents to find appropriate expert agents to advise users on investing strategies.

Social capital adds significant value to learning in virtual learning environments. Social capital is created when learners interact with each other, by exchanging rich and thoughtful experiences among themselves through storytelling. Little research has focused on how this stock of capital is valued in virtual environments. The goal of this paper is to describe how social capital is created, through storytelling. Trust is essential for building social capital in virtual learning environments. This paper presents a process model for creating and maintaining social capital in virtual communities based on trust.

In this paper we discuss a mechanism for modifying context in a tutorial dialogue. The context mechanism imposes a pedagogically motivated misrepresentation (PMM) on a dialogue to achieve instructional goals. In the paper, we outline several types of PMMs and detail a particular PMM in a sample dialogue situation. While the notion of PMMs are specifically oriented towards tutorial dialogue, misrepresentation has interesting implications for context in dialogue situations generally, and also suggests that Grice's maxim of quality needs to be modified.

A knowledge-based system for tutoring student physicians to diagnose bronchial asthma is presented. The system uses a case-based instruction methodology where the student learns how to diagnose bronchial asthma through practice on carefully selected cases. The tutoring system presents the case to the student as the student requests information, and is able to provide help if the student needs it. The dialogue is completely controlled by the student, who can use his/her own language in conversing with the system. Embedded in the tutoring system is an expert diagnostician for bronchial asthma, which is called upon to evaluate the student's attempted diagnosis and is used to provide a summary of the reasoning behind the expert's diagnosis. The system is fully implemented, has been tested with actual physicians, and is currently being field tested with physicians and residents in internal medicine. The system is robust and seems to be effective for the domain of bronchial asthma. Initial indications imply that the methodologies developed for this knowledge-based tutoring system will apply beyond the bronchial asthma domains as well. >

The SCENT (Student Computing ENvironmenT) project is concerned with building an intelligent tutoring system to help student programmers debug their LISP programs. The major thrust of current SCENT investigations is into the design of the SCENT advisor which is meant to provide debugging assistance to novice students. Six conceptual levels constitute the advisor. At the lowest level is the “raw data,” consisting of the student's (possibly buggy) program. This can be interpreted by a “program behaviour” level which can produce traces, cross‐reference charts, etc. from the student's program. These traces, etc. can be analyzed by “observers” for interesting patterns. At the next level are “strategy judges” and “diagnosticians” which determine which strategy the student has used in his/her program and bugs in this strategy. A “task expert” provides task‐specific input into the process of analyzing the student's solution, and a “student‐knowledge component” provides student‐specific input into this process. Information from the six levels interacts in a variety of ways and control is similarly hierarchical. This necessitates a blackboard‐style scheme to coordinate information dissemination and control flow. This paper discusses the objectives of SCENT and focusses on organizing the process of debugging student programs. A complete example is given to illustrate how entities at the six levels interact and to indicate the kinds of information sharing that occur in the SCENT advisor. The paper concludes with an evaluation of the strengths and weaknesses of this approach to automated debugging, and suggestions about directions for further exploration.

In this paper we argue that natural language interfaces to databases should be able to produce summary responses as well as listing actual data. We describe a system (incorporating a number of heuristics and a knowledge base built on top of the database) that has been developed to generate such summary responses. It is largely domain-independent, has been tested on many examples, and handles a wide variety of situations where summary responses would be useful.

Abstract Artificial intelligence (AI) research has recently captured media interest and it is fast becoming our newest “hot” technology. AI is an interdisciplinary field which derives from a multiplicity of roots. In this article we present our perspectives on methodological assumptions underlying research efforts in Al. We also discuss the goals (design objectives) of AI across the spectrum of subareas it comprises. We conclude by discussing why there is increased interest in AI and whether current predictions of the future importance of AI are well founded.