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Transforming customer experience in social robotics through explainable and interpretable artificial intelligence
Anshu S Arora1, Amit Arora1, John R McIntyre2
1School of Business and Public Administration, University of the District of Columbia, Washington, DC, United States.
Frontiers in Robotics and AI
|March 18, 2026
Summary
Explainable Artificial Intelligence (XAI) and Interpretable Artificial Intelligence (IAI) have significantly advanced social robotics over the past decade. These advancements enhance user and customer experiences by fostering trust and engagement in human-robot interaction.
Area of Science:
- Robotics
- Artificial Intelligence
- Human-Computer Interaction
Background:
- Social robotics has evolved significantly over the last decade (2015-2025).
- Early research focused on internal robot needs for self-explanation.
- Progress has shifted towards user-centered design and autonomous social behavior.
Purpose of the Study:
- To review a decade of progress in social robotics frameworks (2015-2025).
- To examine the integration of Explainable Artificial Intelligence (XAI) and Interpretable Artificial Intelligence (IAI).
- To assess the impact on user experience (UX) and customer experience (CX), prioritizing transparency, trust, and engagement.
Main Methods:
- Systematic literature review of research from 2015-2025.
- Analysis of frameworks for social robotics and human-robot interaction (HRI).
- Evaluation of studies on XAI/IAI applications in social robots.
Main Results:
- XAI and IAI integration has demonstrably improved UX and CX in social robotics.
- Explainable AI systems in robots lead to sustained user engagement.
- Current frameworks (by 2025) emphasize ethical considerations and societal implications.
Conclusions:
- The past decade shows remarkable progress in enhancing UX/CX in social robotics via XAI and IAI.
- Future frameworks integrate UX/CX with ethical and societal considerations.
- Transparency and trust are key drivers for successful human-robot interaction.
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