Studies have shown that maintaining focus in the face of constant shifting priorities and interruptions in the workplace is a complex and important problem. Due to the ubiquity of multiple devices, including desktops, laptops, phones, surfaces, smart watches and speakers, notifications, messages and other kinds of disruptions have become a serious problem for keeping focused on tasks at work (Mark, Iqbal, & Czerwinski, 2017; Mark et al., 2015; Mark et al., 2008).
Information workers interrupted by conversation were more likely to return to work on more peripheral tasks such as emails and web searches, rather than resume their previous task.
Research suggests that more distractions can lead to higher reported stress and lower productivity in the workplace. The harmful effects of emails, notifications, face-to-face interruptions, messages and other kinds of distractions in terms of lowered productivity at work has been well documented (Mark, Iqbal, & Czerwinski, 2017; Mark et al., 2015; Mark et al., 2008).
How workers manage their tasks, attempt to stay focused, and deal with distractions and interruptions throughout the day has similarities to students’ distraction in the classroom.
Grover et al. (2020) reports on their design of two different conversational intelligent agents, one text-based (TB), similar to a standard conversational intelligent agent (also known as “chatbots”), and one virtual, embodied, conversational intelligent agent that responds to the user’s emotion (VA). This work builds on the design of a previous conversational intelligent agent named Amber by Kimani et al. (2019).
Grover et al. (2020) pointed out the features of Amber as they discussed the features of their new TB and VA prototypes.
Amber is a desktop conversational agent given a female gender with a user interface quite simple in its design, similar to a standard conversational intelligent agent (also known as “chatbots”).
Amber’s job is to help workers in four areas:
- scheduling high priority tasks,
- aiding workers in transitioning from one task to the next,
- avoiding and intervening with distractions, and
- reflecting on their work through a conversational AI interface (Kimani et al., 2019).
These two new software agents by Grover et al. (2020) extended the capabilities of Amber and were also given a female gender.
The text-based conversational interface prototype (TB) employs a
similar UI to Amber by Kimani et al. (2019). The VA prototype incorporates the ability to detect user emotions through video input and adapt its responses to be appropriate and congruent with the users emotional state (Grover et al., 2020).
What follows are the features and functionalities of the two new software agents by Grover et al. (2020) as described by the authors themselves.
In addition to the functionalities of Amber, the new prototype VA has a user interface that is more human-like with more emotional intelligence.
The VA prototype incorporated a video avatar of the software agent speaking to the user in addition to the text output from the software agent.
The words she would speak matched the text that was produced, providing more context in terms of emotional expressiveness and tone that is sometimes lost via text communication alone. To create the video clips used for the VA, they had an actor rehearse and film all 109 statements that the VA version would produce.
Daily user workflow
TA and VA provide a series of dialogues that starts at the beginning of the day with scheduling their tasks, helping users progress through these tasks until the end of the day.
When not active on the user’s screen, the software agent appears as an icon in the system tray of the desktop. When active, the agent (here for brevity, when we use agent we mean software agent ) would pop-up to the foreground of the user’s screen (usually as a panel to the right-hand side of their screen), and a notification would appear above the system tray in the lower-right corner of the screen.
First time dialogue
When users first installed the agent, the application window would appear in the foreground of the screen. The agent would introduce herself, her role and capabilities.
Morning dialogue
When the user unlocks their computer for the first time each day, the agent initiates a conversation with the user. She first asks how the user is feeling and the user would be given six different options in a drop-down menu to choose from (Happy, Sad, Stressed, Calm/Neutral, Focused, or Frustrated).
Next, the user is asked if they would like to schedule their high priority items on their agenda and the user is given a drop-down menu to choose from (Yes, No, or to remind them in 5, 10, or 15 minutes).
If the user chose yes to schedule tasks, the user is asked what time they plan to head home for the day.
Next the agent asks the user to enter their desired tasks in priority order and estimated duration of the tasks. The agent then proceeds to schedule the tasks and may ask the user additional questions, if necessary. (I am skipping details not relevant to our discussion).
Task Ramp-up dialogue
Three minutes before a task that was scheduled through the agent began, the application would appear in the forefront of the user’s screen, and the agent would inform the user of their next task (“Your scheduled focus time for one of your high priority tasks is about to begin. Are you ready to switch to it?“).
Task Ramp-down dialogue
Five minutes before a task that was scheduled through the agent ended, the application would appear in the forefront of the users screen and notify them that their scheduled focus time was about to end (“Your scheduled focus time for this task ends in 5 minutes. Now might be a good time to wrap up for a smooth transition…“)
Next, either right away or five minutes later if the user chose to let the agent remind them at the end of their task, the user was prompted with a question asking them how productive they felt during their scheduled task on a 5-point scale (Not at all, Slightly, Moderately, Very, or Extremely).
Distractions and Breaks dialogue
Grover et al. (2020) created a dialogue model that is triggered when the agent determined that the user was supposed to be in the middle of a task but the application detected that the user was distracted. The application is a sensing application developed by Grover et al. (2020) and integrated into both agent systems to enable the agent to monitor users’ windowing activity to initiate the distraction dialogue when appropriate.
The application monitored time the user was on social media sites (e.g., Facebook, Twitter, etc.) and certain applications such as shopping (e.g., Amazon), news (e.g., New York Times, CNN), and music streaming sites (e.g., Spotify, Soundcloud).
If the user spent more than 50% of the time on these sites, meaning that the user appears distracted, the distraction dialogue was initiated (“It looks like you may be taking a break. Would you like me to set a timer and remind you to get back to your tasks after a short break?“). Users would then be provided with a set of drop-down options to either set a timer for 5, 10, or 15 minutes, inform the agent that they are not taking a break, that they will get back to their task, or to ‘let me be’ (where the agent would not interrupt them again for the rest of their task).
The agents were designed to encourage the user to take short breaks after periods of extended focus. The sensing application incorporated the ability to detect and classify the user’s emotional state through video input from a webcam into four distinct categories (Happy, Focused, Frustrated, or Other (the default emotional state)). If the agent detected that the user had been in a Focused state continuously for the past hour, the user was prompted with a suggestion to take a short break.
End of Day dialogue
The End of Day dialogue model allow users to reflect on their day and
schedule any unfinished tasks for the next day.
30 minutes before their reported departure time, the agent prompts the user, asking them to reflect upon their day (“Hi again! Before you leave work, I would like to ask you to reflect on your day. Overall, how would you rate your day?“), where the user would be given five options (Very poor, Poor, Acceptable, Good, or Very good).
The user is then prompted to check off which tasks from the morning they did or did not complete, and if there are uncompleted tasks, the agent asks the user if they would like to save the tasks they did not complete for the next day, before wishing the user a good evening.