# Intelligent interactive technologies for mental health and well-being

arXiv: https://arxiv.org/abs/2105.05306 · HTML: https://mlsanity.com/paper/2105.05306/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 108 references that are in the map.
- First posted on arXiv 11 May 2021.

## Builds on (most cited first, 7 of 7)

- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision](https://mlsanity.com/paper/1904.12584/) (2019)
- [Techniques for Interpretable Machine Learning](https://mlsanity.com/paper/1808.00033/) (2018)
- [Semantics of the Black-Box: Can knowledge graphs help make deep learning systems more interpretable and explainable?](https://mlsanity.com/paper/2010.08660/) (2020)
- [Modeling emotion in complex stories: the Stanford Emotional Narratives Dataset](https://mlsanity.com/paper/1912.05008/) (2019)
- [Chatbots as conversational healthcare services](https://mlsanity.com/paper/2011.03969/) (2020)
- [A Multimodal Emotion Sensing Platform for Building Emotion-Aware Applications](https://mlsanity.com/paper/1903.12133/) (2019)

## Abstract (arXiv)

Mental healthcare has seen numerous benefits from interactive technologies and artificial intelligence. Various interventions have successfully used intelligent technologies to automate the assessment and evaluation of psychological treatments and mental well-being and functioning. These technologies include different types of robots, video games, and conversational agents. The paper critically analyzes existing solutions with the outlooks for their future. In particular, we: i)give an overview of the technology for mental health, ii) critically analyze the technology against the proposed criteria, and iii) provide the design outlooks for these technologies.