Hello.
My name is Chelsea Wang and I am a PhD student at Georgia Tech.
I am here to present our paper “Co-designing AI agents to support social connectedness
among online learners: functionalities, social characteristics and ethical challenges.”
This paper is co-authored with Shan Jing and Ashok Goel.
Online for-degree education programs have been in growing demand across universities
and colleges.
However, the lack of spontaneity and randomness in online interactions have made many online
learners in the online programs socially isolated.
Social isolation negatively impacts students’ mental well-being as well as their
online learning experience and outcome.
Improving students’ feelings of social connectedness is crucial to the success of online for-degree
programs.
AI agents offer great potential in supporting online learners’ social connectedness.
These agents have been commonly used in online learning context to sieve through large amount
of information to provide personalized learning support to each student.
AI agents have also been effectively used to deliver support to people experiencing
loneliness and social isolation.
So how should we design AI agents to help online learners feel socially connected?
There are a couple design factors to consider.
First, what can the agent do?
To offer social support, AI agents could be designed with functionalities like offering
well-being advice, facilitating therapy, or mediating social interactions.
Second, what social characteristic does the agent have?
For example, AI agents can be empathetic, or human-like during interactions.
Third, and most importantly, what are some potential negative consequence of this AI
technology and how can we mitigate it?
Understanding these design factors of AI agents that can promote online learners’ social
connectedness is crucial to design human-centered and socially responsible AI that can cater
to online learners’ social needs and preferences.
With this goal in mind, we seek to answer these three research questions through this
study: First, what functionalities should an AI agent possess to help online learners
feel socially connected?
And second, what social characteristics should an AI agent have to help online learners feel
socially connected?
Third, what are the potential social and ethical challenges of agent-mediated social interaction
in online learning?
To answer these questions, we took the approach of co-design to include online learners as
active participants to explore the design requirements of using AI agents to help online
learners feel socially connected
We conducted two virtual co-design workshop studies with 26 online students.
Each co-design study consisted of three workshop sessions with three different sets of participants.
We first conducted study 1 to understand the desired functionalities of the AI agent.
We then created an AI agent mockup based on study 1 findings.
We used this mockup in study 2 to probe for potential ethical concerns of the AI agent.
We also did additional co-design activities to understand students’ desired social characteristics
of the AI agent.
In both studies, we conducted a variety of co-design activities.
I won’t go into details for all of them here due to time constraint but you can find
all the details in our paper.
One of the activities we did with online learners was called storyboarding, where we gave students
the methods and tools to map out their desired version of an AI agent that could help online
students feel socially connected.
Through analyzing these storyboards, we found that almost all the agents in participants’
storyboards would continuously monitor students’ online activities and reach out when the agent
felt like the student needs them.
For example, in this storyboard, the AI agent, mini the chat cat reached out to the student
when it sensed that the student was feeling lonely.
We also found that the AI agents were often used to scaffold online learners’ remote
social interaction process by identifying and introducing online learners together.
This scaffolding functionality was explicitly called out in this storyboard.
In this storyboard, the AI agent was represented as a security blanket.
The student first “holds on” to the AI agent when they felt socially isolated, but
eventually “grew out of” the AI agent after the agent helped the student connect
with others.
Based on these findings from study 1, we built an AI agent mockup that could help online
learners feel more socially connected.
In this mockup, we demonstrated the agent’s ability to constantly monitor online learners’
online activities within the program to understand each students’ demographic information,
personality, goals, their emotion levels, etc.
The agent could also initiate interaction with the student when the student is feeling
lonely and introduce the student to other students with similarities.
We used this mockup in Study 2 to probe for online learners’ perceived social and ethical
concerns of agent-mediated social interactions.
The goal of study 2 was to understand students’ desired social characteristic of the AI agent
and their perceived social and ethical concerns of using an AI agent to facilitate their social
interaction process.
We examined these two questions by conducting two co-design activities.
The first activity was called design your agent.
In this activity, we presented five different AI agent dialogues taken and adapted from
the existing literature.
Together, these five dialogues demonstrates a wide range of functionalities and social
characteristics such as agent personalities, communication style, use of emoji, avatar,
conversation topics.
We asked participants to discuss what they liked and disliked about the agents in each
dialogue.
The second activity was called “challenge cards.”
In this activity, we divided participants into two teams: challenge team and solution
team.
After reviewing the AI agent mockup, the challenge team brainstorms the potential concerns of
the mockup agent and the solution team brainstormed desired features of the mockup agent.
The two teams then engaged in several rounds of competition, where the challenge team presented
challenge cards based on the potential concerns that came up during brainstorming sessions,
and the solution team came up with solutions to proposed challenge based on the desired
features that came up during their brainstorming session.
Through these co-design activities, we found that AI agents’ human-likeness could sift
through many aspects of the agent design and elicit discomfort among students.
For example, in the design your agent activity, students complained when the AI agent “pretends
to be human” by expressing the agent’s own preferences or feelings.
Other agent characteristics such as the use of emoji, human avatars and human names in
the design of AI agent could also trigger students’ discomfort.
We also identified other desired social characteristics such as intelligence and social etiquette,
you can find more details about these characteristics in our paper.
Participants also expressed a range of concerns regarding agent-mediated social interaction.
Participants displayed conflicted feelings surrounding the use of student data, expressing
their concerns about the continuous and large-scale data collection the agent in the mockup demonstrated
yet enjoying the highly personalized agent-mediated social interaction experience.
Students also worried that the AI agent could unintentionally add on to students’ emotional
burden.
The idea of using an AI agent to build social connections could make the students feel embarrassed
that they had to use an AI agent to make friends.
Concerns were also raised regarding the possibility of the AI agent misinterpreting students’
social needs or preferences, leading to undesirable outcomes.
Based on our findings, we distilled and summarized a set of design implications for AI agent
that could promote online learners’ social connectedness.
In our paper, we further examined the role of AI agent in promoting social connectedness
based on our findings and discussed why AI agent should act more as a facilitator instead
of offering social support on its own in online learning.
We also discussed the ethical design of agent-mediated social interaction, highlighting the unique
concerns raised by collecting and analyzing student data for social purposes in online
learning context and propose mitigation strategies.
Thank you for listening to this presentation.
Here is my contact information if you have any questions or comments.
Thank you.