Name
Abstract | ||
|---|---|---|
ABSTRACTThe advent of artificial intelligence (AI) and machine learning (ML) bring human-AI interaction to the forefront of HCI research. This paper argues that games are an ideal domain for studying and experimenting with how humans interact with AI. Through a systematic survey of neural network games (n = 38), we identified the dominant interaction metaphors and AI interaction patterns in these games. In addition, we applied existing human-AI interaction guidelines to further shed light on player-AI interaction in the context of AI-infused systems. Our core finding is that AI as play can expand current notions of human-AI interaction, which are predominantly productivity-based. In particular, our work suggests that game and UX designers should consider flow to structure the learning curve of human-AI interaction, incorporate discovery-based learning to play around with the AI and observe the consequences, and offer users an invitation to play to explore new forms of human-AI interaction. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1145/3411764.3445307 | Conference on Human Factors in Computing Systems |
Keywords | DocType | Citations |
Human-AI Interaction, Neural Networks, User Experience, Game Design | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jichen Zhu | 1 | 111 | 29.76 |
| Jennifer Villareale | 2 | 0 | 2.37 |
| Nithesh Javvaji | 3 | 0 | 1.01 |
| Sebastian Risi | 4 | 460 | 54.67 |
| Mathias Löwe | 5 | 3 | 1.78 |
| Rush Weigelt | 6 | 0 | 0.34 |
| Casper Harteveld | 7 | 77 | 19.17 |