Adaptive interfaces and agents

By Anthony David Jameson. 2002

469 citations recorded by the source corpus.

Subfields: Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/10c6626c44569770c7593ad96395fc5926ca060b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Industrial and Manufacturing Engineering

1. Cristina Conati
2. Heather Maclaren

Citations in the corpus

• Guidelines for Human-AI Interaction (2019, background): “Others recommended managing expectations so as not to mislead or frustrate users during interaction with unpredictable adaptive agents [16, 20, 33].”
• Designing Contestability: Interaction Design, Machine Learning, and Mental Health (2017, unknown): “With regard to machine learning algorithms, this is typically described in terms of “transparency”, or of “opening the black box” (e.g. [15]) to reveal to users the inner mechanisms that drive computer prediction.”
• When do we interact multimodally?: cognitive load and multimodal communication patterns (2004, background): “Adaptive interface design is viewed as one direction for managing sources of increased cognitive load in future interfaces [ 10 , 21].”
• Empirically building and evaluating a probabilistic model of user affect (2009, background): “agent can also explain the rationale underlying its interventions, a feature that can contribute to increase the user’s trust and confidence in the system (Jameson 2003).”
• The effects of transparency on trust in and acceptance of a content-based art recommender (2008, background): “In spite of these potential problems, previous authors have emphasised the importance of transparency (Jameson 2003; Höök et al. 1996).”
• Power to the People: The Role of Humans in Interactive Machine Learning (2014, background): “Others have stated that artificially intelligent and machine-learning-based systems should manage expectations to avoid misleading or frustrating the end user during interaction (e.g., Norman, 1994; Höök, 2000; Jameson, 2009).”