Pattern 1D: Demonstrate possible system inputs

The user needs to understand what the system can do.

Show possible user inputs to demonstrate to the user what the system can do.

The system supports explicit user input.
It is hard for the user to discover the range of system capabilities.
It is desirable to promote consistency among user inputs.
It is desirable to influence the user’s input toward what is easier for the system to understand.

Show possible user inputs in one or more of the following forms:

Clickable preformulated user inputs.
Non-clickable user inputs, which are usually pre-populated in a user input field.
Fully preformulated possible user inputs.
Possible user inputs formulated during user interaction through auto-completion.
Static possible user inputs that do not change based on context.
Conditional possible user inputs that change in response to the user’s previous actions.

Select possible user inputs to display based one or more considerations:

Diversity – ensure the sample user inputs are sufficiently diverse to demonstrate the range of system capabilities.
Popularity – select among popular or trending user inputs.
Contextual relevance – for example, take into consideration current events or user goals.

Enables the user to discover non-obvious system capabilities.
Clickable preformulated user inputs enable the user to perform tasks efficiently.
Possible user inputs selected on the basis of popularity or contextual relevance may have informational value beyond learning what the system can do.

The system underperforms in response to a preformulated user input or to one that is similar.
Sample preformulated user inputs promote undesirable social biases or violate social norms.
Outputs resulting from preformulated user inputs promote undesirable social biases or violate social norms.
The pattern implementation creates expectations that the system can do more than it is capable of.

Note: Over-inflated user expectations have been shown to cause frustration and even product abandonment.

Over-inflated user expectations have been shown to cause frustration and even product abandonment:

Jan Hartmann, Antonella De Angeli, and Alistair Sutcliffe. 2008. Framing the user experience: information biases on website quality judgement. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ’08). Association for Computing Machinery, New York, NY, USA, 855–864. DOI:https://doi.org/10.1145/1357054.1357190
Jaroslav Michalco, Jakob Grue Simonsen & Kasper Hornbæk (2015) An Exploration of the Relation Between Expectations and User Experience, International Journal of Human–Computer Interaction, 31:9, 603-617, DOI: 10.1080/10447318.2015.1065696
Daniel S. Weld and Gagan Basal. 2018. Intelligible Artificial Intelligence
P. Robinette, W. Li, R. Allen, A. M. Howard and A. R. Wagner, Overtrust of robots in emergency evacuation scenarios, 2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI), Christchurch, 2016, pp. 101-108, doi: 10.1109/HRI.2016.7451740.