Theoretical frontiers in building a machine tutor
Artificial intelligence and instruction: Applications and methods
Pixie: a shell for developing intelligent tutoring systems
Artificial intelligence and education; vol. 1: learning environments and tutoring systems
Coding, Analysis, Interpretation, and Recognition of Facial Expressions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Affective computing
IEEE Transactions on Pattern Analysis and Machine Intelligence
The friendly intelligent tutoring environment
ACM SIGCHI Bulletin
Expression glasses: a wearable device for facial expression recognition
CHI '99 Extended Abstracts on Human Factors in Computing Systems
ITS '02 Proceedings of the 6th International Conference on Intelligent Tutoring Systems
Multimodal Human Emotion/Expression Recognition
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Toward computers that recognize and respond to user emotion
IBM Systems Journal
The Virtual Human Interface: A Photorealistic Digital Human
IEEE Computer Graphics and Applications
Towards Emotionally-Intelligent Pedagogical Agents
ITS '08 Proceedings of the 9th international conference on Intelligent Tutoring Systems
Linking perception and action through motivation and affect
Journal of Experimental & Theoretical Artificial Intelligence
Towards Systems That Care: A Conceptual Framework based on Motivation, Metacognition and Affect
International Journal of Artificial Intelligence in Education
Fifteen years of constraint-based tutors: what we have achieved and where we are going
User Modeling and User-Adapted Interaction
Computer Methods and Programs in Biomedicine
| Hi-index | 0.00 |
There is a growing body of evidence that supports the claim that affect plays a critical role in decision-making and performance as it influences cognitive processes [1], [2], [3]. Despite this body of research the role and function of affect is not generally recognized by the disciplines that address the broad issues of understanding complex systems and complex behavior, especially in the presence of learning. The innovative models and theories that have been proposed to facilitate advancement in the field of human-computer interaction (HCI) tend to focus exclusively on cognitive factors. Consequently, the resulting systems are often unable to adapt to real-world situations in which affective factors play a significant role. We propose several new models for framing a dialogue leading to new insights and innovations that incorporate theories of affect into the design of (affect-sensitive) cognitive machines.