Input-level control over user profiles in an educational recommender system suffices to boost perceived control and positively shapes transparency, trust, satisfaction, and perceived quality, while further controls mainly reinforce impressions.
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A qualitative study of a provotype shows that adding transparency and control features to AI recommender interfaces helps users understand personalization, address filter bubble concerns, and build trust.
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Investigating the Effects of Different Levels of User Control in an Interactive Educational Recommender System
Input-level control over user profiles in an educational recommender system suffices to boost perceived control and positively shapes transparency, trust, satisfaction, and perceived quality, while further controls mainly reinforce impressions.
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Rethinking User Empowerment in AI Recommender System: Innovating Transparent and Controllable Interfaces
A qualitative study of a provotype shows that adding transparency and control features to AI recommender interfaces helps users understand personalization, address filter bubble concerns, and build trust.