#  Helping the Helper: Supporting Peer Counselors via AI-Empowered Practice
and Feedback

Shang-Ling Hsu<EMAIL_ADDRESS>0009-0005-1096-0405 Georgia Institute of
TechnologyUSA ,  Raj Sanjay Shah<EMAIL_ADDRESS>0000-0002-0847-8426
Georgia Institute of TechnologyUSA ,  Prathik Senthil<EMAIL_ADDRESS>0009-0007-7102-5773 Georgia Institute of TechnologyUSA ,  Zahra Ashktorab
<EMAIL_ADDRESS>0000-0002-0686-7911 IBM ResearchUSA ,  Casey Dugan
<EMAIL_ADDRESS>0000-0002-1508-2091 IBM ResearchUSA ,  Werner Geyer
<EMAIL_ADDRESS>0000-0003-4699-5026 IBM ResearchUSA and  Diyi Yang
<EMAIL_ADDRESS>0000-0003-1220-3983 Stanford UniversityUSA

(2022)

###### Abstract.

Millions of users come to online peer counseling platforms to seek support on
diverse topics ranging from relationship stress to anxiety. However, studies
show that online peer support groups are not always as effective as expected
largely due to users’ negative experiences with unhelpful counselors. Peer
counselors are key to the success of online peer counseling platforms, but
most of them often do not have systematic ways to receive guidelines or
supervision. In this work, we introduce CARE: an interactive AI-based tool to
empower peer counselors through automatic suggestion generation. During the
practical training stage, CARE helps diagnose which specific counseling
strategies are most suitable in the given context and provides tailored
example responses as suggestions. Counselors can choose to select, modify, or
ignore any suggestion before replying to the support seeker. Building upon the
Motivational Interviewing framework, CARE utilizes large-scale counseling
conversation data together with advanced natural language generation
techniques to achieve these functionalities. We demonstrate the efficacy of
CARE by performing both quantitative evaluations and qualitative user studies
through simulated chats and semi-structured interviews. We also find that CARE
especially helps novice counselors respond better in challenging situations.

dialogue generation, natural language processing, intervention, human-AI
collaboration, counseling

††copyright: acmcopyright††journalyear: 2022††doi:
XXXXXXX.XXXXXXX††conference: Make sure to enter the correct conference title
from your rights confirmation emai; October 13–18, 2023; Minneapolis,
MN††price: 15.00††isbn: 978-1-4503-XXXX-X/18/06††ccs: Human-centered computing
Collaborative and social computing systems and tools

##  1\. Introduction

Millions of people (nearly 1 in 5 American adults) are touched by mental
illness (Dieleman et al., 2016). The majority of people affected never access
care (for Behavioral Health Statistics and Quality, 2015), due to structural
barriers including the high cost of treatment and the lack of trained
professionals to meet the demand (Kazdin and Blase, 2011; Mojtabai et al.,
2011; Kantor et al., 2017). One promising remedy for under-treatment and to
meet the demand for mental health care is the use of online peer counseling
platforms, because of the benefits of anonymity, empowerment, and access
(Houston et al., 2002; Melling and Houguet-Pincham, 2011; Powell et al.,
2007). Online peer counseling platforms have been proven effective in reducing
people’s self-reported anxiety and depression and improving their quality of
life (Clarke et al., 2005; Eysenbach et al., 2004). Nevertheless, studies show
that online support groups are not always effective as expected (Hogan et al.,
2002), largely due to support seekers’ negative experiences with unhelpful
supporters (Smithson et al., 2011; Burke et al., 2010; Burke and Kraut, 2008).
Supporters are the key to the success of online peer counseling platforms (Ali
et al., 2015), and the quality of the support that seekers receive within a
community heavily depends on those providing support. Thus, finding ways to
help peer counselors be more helpful and supportive remains especially
important.

Evidence from volunteer-based support services indicates that even brief
training may improve a supporter’s ability to help seekers (Rodgers, 2010) and
may ultimately lead to improved mental health outcomes in seekers (Armstrong,
2010). Thus, there is a growing trend in scaling effective peer support
training online. For instance, 7 Cups (of Tea, 2021) provides users with
training on active listening techniques (Baumel and Schueller, 2016), and
SAHAR, an Israeli-based suicide-prevention initiative, trains its online
counselors extensively during weeks of in-person training to handle supportive
chats (Barak, 2007). However, supporters typically do not have the rigorous
professional training of their offline counterparts (Pruksachatkun et al.,
2019), with very abbreviated training or no training at all for most online
peer support groups (Wang et al., 2015; Gould et al., 2013). Thus, even if
supporters want to help others in need they might lack the necessary set of
skills or the knowledge to use evidence-based counseling strategies (Thériault
et al., 2009). Moreover, unlike traditional counseling settings, online
supporters often do not have systematic ways to receive supervision or
guidance, especially during their conversations with seekers and when there
are high levels of uncertainty about how to respond. Without appropriate
guidance, supporters might develop biased or even inappropriate helping skills
without being aware of it, based on their own experiences. This can lead to
ineffective, or at worst, harmful, support which might prevent seekers from
receiving other effective support options sooner (Reynolds and Perrin, 2004;
Søvold et al., 2021) and may also expose supporters themselves to self-doubts
(Thériault et al., 2009), increased stress and decreased self-efficacy (Curran
et al., 2019; Rozental, 2016).

Figure 1. Example interface of CARE to empower supporters by (1) diagnosing
which skill to use and (2) suggesting responses.

Existing mechanisms of training or scaffolding largely rely on human
supervision, which requires an extensive amount of resources such as cost,
time, labor, and expertise, making it hard to scale up to help a large number
of supporters (Atkins et al., 2014) who support millions of people in need of
care in text-based online peer counseling platforms. Social computing research
on online peer support groups mainly looked at how supporters’ language use or
counseling strategies are associated with better outcomes for seekers (Althoff
et al., 2016; Pérez-Rosas et al., 2019, 2017; Chikersal et al., 2020;
Pruksachatkun et al., 2019; O’Leary et al., 2017). In contrast, there have
been relatively fewer studies on building scalable systems and tools to
empower supporters to offer the most effective support in online peer
counseling platforms, with a few exceptions (Peng et al., 2020; Sharma et al.,
2022). More importantly, the majority of research has specifically been about
designing virtual counselors that emulate a human counselor in a chatbot-like
setting (Han et al., 2013, 2015). Different from previous work on chatbots
around virtual counselors or client-clinician interaction (Coyle et al.,
2012), we propose to build an automatic assistant that can augment or empower
supporters in terms of a wide range of counseling strategies.

Concretely, we propose to design an interactive training agent CARE to empower
peer counselors111In this work, we use peer counselors (or sometimes
supporters, or support providers) to refer to the individuals providing help
and support seekers (seekers) to refer to people seeking or receiving support.
in text-based, online peer-to-peer counseling platforms. Here, we use 7 Cups
as a research site and build contextualized language generation approaches to
provide tailored assistance for supporters by highlighting which counseling
strategies are needed in a given situation and suggesting example responses.
Building upon psychotherapy theories and empirical studies on Motivational
Interviewing (MI) (Moyers et al., 2014; Shah et al., 2022), we select a set of
counseling strategies from MI to be our main focus for empowering volunteer
counselors. We built machine learning classifiers on top of the large pre-
trained language model BERT (Devlin et al., 2018) to predict the most suitable
counseling strategies in a given context; we then develop language generation
approaches via a large pre-trained generation model DialoGPT (Zhang et al.,
2020) for generating example responses that counselors could further use and
edit. As shown in Figure 1, we integrate different components together into an
interactive interface for CARE to empower supporters at scale.

Furthermore, we use iterative, user-centered design principles in the
development process of CARE by working closely with stakeholders from 7 Cups.
To evaluate whether the proposed system CARE works for peer counselors, we
perform both quantitative and qualitative evaluations, including system log
analyses, questionnaires, and semi-structured interviews, to demonstrate the
impacts of CARE on counselors. We also find that CARE especially helps
beginner and novice counselors to better deal with these challenging
situations. To sum up, we make the following contributions:

  * •

Develop contextualized language generation techniques that provide tailored
assistance for peer counselors by highlighting which counseling strategies are
needed in a given situation and suggesting example responses.

  * •

Create an interactive tool CARE to empower peer counselors by offering
suggestions on the fly during practice sessions on an online peer counseling
platform.

  * •

Evaluate CARE via both quantitative system log analyses and qualitative user
studies to demonstrate the efficacy of CARE on a set of representative
counseling scenarios.

##  2\. Related Work

###  2.1. Peer Support and Online Peer Counseling Platforms

Peer support has been shown effective in a wide range of mental health
services (Repper and Carter, 2011), and is a major motivation for people who
go online for mental health support (Powell et al., 2007). Prior work has
provided some evidence for the effectiveness of online peer support groups.
For instance, a recent survey on 7 Cups indicated that users reported higher
satisfaction with the support provided by 7 Cups counselors, and users who
indicated receiving psychotherapy in the past marked the peer counselors’
support to be as helpful as psychotherapy (Baumel, 2015). Most studies on
online text-based peer counseling context have focused on the automatic
modeling of behaviors from peer counselors and seekers and their correlations
with successful outcomes (Klonek et al., 2015; Xiao et al., 2014; Lord et al.,
2015; Althoff et al., 2016) or user engagement (Sharma et al., 2022; Andalibi
et al., 2018). For instance, Can et al. (Can et al., 2012) used n-grams to
find the similarity between counselor and client speech to detect counselors’
reflective listening skills automatically. Sharma and de Choudhury (Sharma and
De Choudhury, 2018) classify messages into two categories of support:
emotional and instructional, to find the relationship between linguistic
accommodation and social support provision. Sharma et al. (Sharma et al.,
2021) develop a theoretically-grounded framework for empathy measurement on
peer counseling platforms.

A growing amount of automatic text-based analytical work has also been
conducted to identify behavioral codes derived from Motivational Interviewing
(MI) Skill Codes (Miller, 1996; Tanana et al., 2016; Pérez-Rosas et al., 2017,
2019; Schwalbe et al., 2014; Huang et al., 2018). For instance, Perez-Rosas et
al. (Pérez-Rosas et al., 2017) build a model to predict the MI strategies of
therapists using clinical audio transcripts. Shah et al. (Shah et al., 2022)
examine the relationship between the use of MI strategies and the satisfaction
ratings from support seekers in _online_ counseling sessions. Chen et al.
(Chen et al., 2021) explore challenges faced and MI strategies employed by
novice peer counselors via qualitative user studies and discuss design
implications to better prepare for the skill development of novice therapists.
Building upon these prior works, we focus on a subset of widely-used MI
strategies and leverage these theory-driven counseling strategies to guide us
in designing an interactive tool that can empower peer counselors.

###  2.2. Systems and Interventions for Peer Counseling

Interventions for support are generally valued by people with mental health
concerns (O’Leary et al., 2017). For example, people with schizophrenia showed
a positive attitude toward using technology for care (Gay et al., 2016).
Supporters show a substantial interest in learning helping skills and using
web-disseminated peer support interventions to help one another online
(Bernecker et al., 2017). Supportive chats guided by prompts in a design study
were found to be associated with reduced anxiety, perceived as “deeper” with
solutions to problems and new perspectives (O’Leary et al., 2018) compared to
unguided ones, calling for natural language processing empowered systems to
provide appropriate scaffolds at scale. This is in line with a recent study on
the impact of technology in psychotherapy (Imel et al., 2017), which has
identified the development of machine learning technologies for counselor’s
training and feedback as important needs where technology has a significant
impact. To address this, our work will leverage advanced state-of-the-art
natural language processing methods to provide peer counselors with
contextualized training to help them acquire or improve their counseling
skills.

There has been increased attention towards building automatic tools for online
health communities (Peng et al., 2020; Greer et al., 2019), however, the
majority of research has specifically been about designing virtual counselors
that emulate a human counselor in a chatbot-like setting. For instance, (Han
et al., 2013, 2015) introduced a counseling dialogue system that interacts
with users by recognizing what the users say and generating replies that
utilize language templates to incorporate basic counseling techniques. Shen et
al.(Shen et al., 2020) look at the generation of reflections using GPT-like
models, and (Saha et al., 2022) focus on empathic rewriting. In one recent
work, Sharma et al. (Sharma et al., 2022) provide HAILEY, an AI-in-the-loop
agent that provides just-in-time feedback to help participants who provide
support respond more empathically to support seekers. Different from previous
work on chatbots around virtual counselors or the facilitation of one single
counseling strategy such as showing empathy, our work builds an automatic
assistant that empowers counselors to help better train them with a wider
range of theory-driven counseling strategies.

##  3\. The Design of CARE

Realistic practice and tailored feedback are key processes for training
individuals with therapy skills (Reagans et al., 2005; Kluger and DeNisi,
1996; Bennett-Levy, 2006). As we discussed earlier, existing mechanisms of
providing feedback largely depend on human supervision. It often takes well-
trained human raters up to ten times as long as the duration of the session
itself to finish labeling that session on the involved counseling strategies
(Atkins et al., 2014), let alone providing detailed feedback, making it
difficult to scale up to help a large number of volunteer counselors who use
online communities. Our work aims to build upon advances from natural language
processing to automatically provide suggestions to assist volunteer counselors
during their practice sessions, especially for novices at scale (Hill et al.,
2007).

##### Research Site

Our design of CARE will use 7 Cups as a case study. 7 Cups is an online peer
support service, where support seekers with a variety of mental health
problems participate in text-based chats with peer counselors who have
completed active listening and other therapeutic training. As of February
2021, 7 Cups had over 200,000 trained peer counselors, supporting over a
million people a month.

##### Scope of CARE

Our work is mainly designed to be used in a training or simulated environment
where counselors can practice their skills in a safe environment without
harming real people, and where they can receive feedback or assistance. CARE
allows for real-time human oversight and control, and we require counselors to
review the suggested content before using it. Note that CARE will be mainly
used by counselors who have already received very abbreviated default training
on 7 Cups, not seekers or the vulnerable members who suffer from mental health
issues; thus, there is no direct communication between seekers and CARE. In
other words, counselors have full control and can decide whether and to what
extent they would use the suggested content; counselors can easily turn on or
turn off this feature. CARE is designed to _assist_ counselors in _training
environments_ by learning to improve their responses. We design CARE in a way
to empower counselors in practice chats, rather than replacing counselors in
real counseling sessions or posing any interferences to counselor-seeker
relations.

##### Overview of CARE

CARE is an interactive tool that works in a private text-based session
consisting of two speakers: a support seeker and a peer counselor. CARE aids
users by recommending up to three possible responses to counselors in a given
situation. We choose to display 3 responses on the basis of the interface
length and to allow for a good amount of flexibility for counselors. These
suggestions are built upon psychotherapy literature on Motivational
Interviewing (Moyers et al., 2014; Shah et al., 2022), which is a client-
centered counseling style for eliciting positive behavior change by helping
support seekers to explore and resolve ambivalence. We design the tool
interface to be the same as the UI of 7 Cups (of Tea, 2021) to simulate real-
life user studies and obtain genuine user feedback. CARE comprises a backend
system with suggestion-generation modules and a front-end user interface. They
together form a full stack system. Note that our backend system is designed to
be independent of the frontend client for generalizable deployment. The same
backend system can be connected to other front-end clients in a plug-and-play
fashion.

###  3.1. Motivational Interviewing Strategies

Table 1. Selected Motivational Interviewing strategies, their number of annotations from (Shah et al., 2022), and results of the most suitable counseling strategy prediction.  Using the human-annotated 7 Cups dataset from (Shah et al., 2022), to follow the distribution of counseling strategies on 7 Cups, we selected all 8 MI-consistent strategies from the 10 most frequent strategies on 7 Cups. Strategy, Description, and # Instances are distilled from Table 1 and 2 of (Shah et al., 2022); Accuracy and F1 Score are the evaluation results of the Strategy Suggestion models trained on 7C-HQ, reported on 7C-MI in Section 3.2.1.  Strategy |  Description |  | # Instances
---
Acc. | F1
Open Question |  Open-ended questions that leave room for a response. | 2,507 (15.29%) | 0.632 | 0.686
Closed Question |  Questions with short specific answer. | 1,954 (11.91%) | 0.612 | 0.672
Persuade with Permission |  Counselor explicitly tries to change member’s opinions, attitudes, or behavior based on logical arguments and facts. Counselor asks for permission first or emphasizes collaboration. | 1,918 (11.69%) | 0.692 | 0.719
Reflection |  Counselor captures the implicit meaning and feelings of client statements and returns it to the client through rephrases. | 1,697 (10.35%) | 0.648 | 0.695
Support |  Sympathetic, compassionate, or understanding comments encouraging client behavior. | 1,493 (9.10%) | 0.672 | 0.705
Introduction or Greeting |  Counselor and seeker greet each other, exchange names etc. | 1,260 (7.68%) | 0.867 | 0.874
Grounding |  Counselor facilitates conversation through acknowledgements. | 1,027 (6.26%) | 0.721 | 0.745
Affirm |  Counselor compliments the seeker. | 346 (2.11%) | 0.722 | 0.747

Motivational Interviewing (MI) Strategies are a set of client-centric
techniques used by therapists for client betterment (Moyers et al., 2014). The
underlying principle of MI techniques focuses on a therapeutic alliance
between a patient and a therapist with an emphasis on patient autonomy. These
techniques are suitable for online peer counseling platforms given the
established effectiveness in reducing drug and alcohol abuse (Burke et al.,
2003; Hettema et al., 2005; Jensen et al., 2011; Lundahl et al., 2010),
decrease in smoking (Gray et al., 2005), and reduction of sexual risk
behaviors (Fisher et al., 2006; Kuyper et al., 2009). We used the corpus
released by prior work on 7 Cups (Shah et al., 2022), which identifies and
annotates 17 categories of counseling techniques on 14,797 utterances from 7
Cups chats (referred to as _7C-MI_ hereafter). We selected a set of MI
strategies based on a few criteria: (1) there are a relatively good amount of
available annotations released by prior work so that we can build machine
learning models with reasonable accuracy; and (2) these MI strategies are
widely used by prior work with a sufficient grounding in the MI literature.
This led us to 8 strategies, as shown in 1. Future work can easily annotate
other types of counseling strategies to expand this set.

####  3.1.1. Data Preparation

Table 2. Datasets used in this study. We obtained permission and release from (Shah et al., 2022) for 7C-MI. With the MI strategy classifiers from (Shah et al., 2022), we labeled 7C-HQ. Finally, we ran the model selection experiments on 7C-HQ-small, a 1-month subset. To evaluate CARE, we trained and fine-tuned models on 7C-HQ and reported results by evaluating them on 7C-MI. Abbreviation | Description | MI Strategy Annotations | # Utterances | Usage
---|---|---|---|---
7C-MI |  From (Shah et al., 2022) | Annotated by humans | 14,797 | Evaluate models
7C-HQ | Highly rated |  Labeled by classifiers from (Shah et al., 2022) | 20,445,517 | Train models
7C-HQ-small | Highly rated; 1 month | No | 344,335 | Select models

With written permission from (Shah et al., 2022), we assign MI strategy labels
to our data with their released strategy classifiers. The purpose of assigning
labels to our data instead of using their annotations directly is to obtain a
greater number of MI strategy annotations on highly rated conversations to
build our tool. For each strategy, we use the binary classifier of that
strategy to assign labels to 20,445,517 utterances from 7 Cups conversations
that receive a rating of 5 on a 5-point Likert scale from support seekers
(_7C-HQ_ hereafter). Lastly, we train and develop on 7C-HQ, setting aside
7C-MI as the ground-truth test set. We also created a 7C-HQ-small, a 1-month
subset of high-quality conversations for response generation model selection
(Section 3.2.2), which are explorative, computationally intensive experiments.
The information about the datasets is presented in Table 2. We received a
data-use agreement from 7 Cups for this research study and used dataset split
train/dev/test = 8:1:1 throughout this study for model development.

###  3.2. Generating Suggestions

CARE suggests counseling strategies and potential responses for a conversation
in real-time by using a multi-step process described as follows (Figure 2):

  * Step 1.

For each of the 8 counseling strategies described in Table 1, CARE predicts
the probability that which strategy should be used in the next peer counselor’
response using the 5 latest utterances in the chat and a strategy-specific
fine-tuned machine learning classifier.

  * Step 2.

CARE generates a potential counselor response for each of the top three most
probable counseling strategies. This generation is conditioned with the 5
latest utterances in the chat and uses a DialoGPT model fine-tuned with one of
the 8 counseling strategies.

  * Step 3.

CARE ensures another layer of support seeker safety by filtering all
potentially inappropriate generations using a fine-tuned HateBERT (Caselli et
al., 2021).

  * Step 4.

The de-duplicated responses are rendered on the front end in descending order
of the probabilities from Step 1.

We now describe in detail the machine learning models involved in Step 1, Step
2, and Step 3.

####  3.2.1. Step 1: Predicting the Most Suitable Counseling Strategy

Prior research shows that large pretrained language models (Devlin et al.,
2018; Liu et al., 2019) demonstrate state-of-the-art performances on a large
set of language-based downstream classification tasks (Wang et al., 2019).
Thus, we use one of the most widely used pretrained language models
Bidirectional Encoder Representations from Transformers (BERT) (Devlin et al.,
2018) to predict the most appropriate counseling strategy that a counselor
should use in response to the utterance from the support seeker.
Mathematically, we frame it as a multi-label classification task: given the
current conversation between a support seeker and a peer counselor, and a set
of possible counseling strategies, identify a set of strategies that are most
suitable for the given context. Practically, we will first use our automated
models of identifying MI strategies mentioned in Section 3.1, to identify
(i.e., label) the strategies used in counselors’ responses across all
conversations. Then, we treat pairs of contexts and counselors’ responses
identified as utilizing a certain set of strategies as positive instances of
those strategies and that are _not_ labeled with them as negative instances.

During the implementation stage, we transform this multi-label classification
task into eight binary classification tasks, following the binary relevance
paradigm for multi-label classification. We used the most recent five messages
as the context for the current conversation, as suggested by prior work (Shah
et al., 2022). To deal with any class imbalance issue, we used downsampling by
sampling the negative class size to be the same as the positive automatic
annotations. The final classifiers are built by fine-tuning a pre-trained
BERT-base-uncased checkpoint from Hugging Face (Wolf et al., 2020) on 7C-HQ.
The results are reported on 7C-MI, the human-annotated instances from (Shah et
al., 2022) as the test set. We show the number of instances, model accuracy,
and the F1 scores in Table 1. We found that our machine learning classifiers
performed reasonably well, with an overall F1 score larger than 0.705. Given
these reasonable predictions of the most appropriate counseling strategies, we
then can move forward with the detailed feedback generation.

####  3.2.2. Step 2: Generating Example Responses

Table 3. Comparison of different models conditioned on predicted strategies on
7C-HQ-small, and Results of strategy-conditioned next-utterance generation
(trained on 7C-HQ, reported on 7C-MI). _Positive_ indicates agreement of
predictors by Shah et al. (Shah et al., 2022) on the strategy of the generated
response. These BLEU scores are competitive to that of the related work
(Rashkin et al., 2019; Sharma et al., 2021; Zhang et al., 2020).

Models | Total # Instances | Avg # Words | ROUGE-1 | ROUGE-2 | ROUGE-L | BERT Score | BLEU | Positive
---|---|---|---|---|---|---|---|---
BART | 14,797 | 7.710 | 0.128 | 0.017 | 0.117 | 0.845 | 0.074 | NA
GPT-2 | 14,797 | 9.728 | 0.111 | 0.013 | 0.100 | 0.879 | 0.084 | NA
DialoGPT | 14,797 | 8.871 | 0.132 | 0.012 | 0.114 | 0.878 | 0.084 | NA
Strategy | # Instances | Avg # Words | ROUGE-1 | ROUGE-2 | ROUGE-L | BERT Score | BLEU | Positive
Open Questions | 1,853 | 7.991 | 0.164 | 0.034 | 0.155 | 0.876 | 0.188 | 0.930
Closed Questions | 1,785 | 10.754 | 0.138 | 0.020 | 0.124 | 0.870 | 0.185 | 0.880
Persuade | 1,883 | 13.809 | 0.111 | 0.010 | 0.093 | 0.861 | 0.177 | 0.680
Reflection | 1,638 | 13.417 | 0.107 | 0.010 | 0.090 | 0.863 | 0.167 | 0.642
Support | 1,364 | 11.042 | 0.172 | 0.037 | 0.156 | 0.872 | 0.167 | 0.809
Introduction | 214 | 4.734 | 0.248 | 0.092 | 0.244 | 0.879 | 0.191 | 0.925
Grounding | 960 | 1.984 | 0.143 | 0.035 | 0.143 | 0.883 | 0.098 | 0.948
Affirm | 324 | 12.320 | 0.172 | 0.040 | 0.155 | 0.872 | 0.183 | 0.766

Supporters, especially beginners, might not be able to identify how to respond
to a given situation (Naslund et al., 2019), or they may not feel confident
about providing an ideal response. To this end, we introduce the task of
contextualized suggestion generation with humans in the loop. The goal here is
not to replace counselors but to augment them when they experience high
uncertainty in conversation, as well as to provide help to these supporters in
formulating their responses. We formulate this task as a conditional text
generation problem: given an input (a seekers’ post and the context) and a
suitable strategy, we generate a statement that provides a sample response so
that volunteer counselors can review and use—or edit and then use—in a given
session. Similar to the setup in Section 3.1.1, we identified a set of pairs
(post, response) from high-quality conversations filtered by satisfaction
ratings as our corpus.

Auto-regressive models like GPT-2 (Radford et al., 2019) and BART (Lewis et
al., 2020) have shown great performance on generation tasks. Here, we choose a
variant of GPT-2, DialoGPT (Zhang et al., 2020) trained on multi-turn dialog
conversations, as its domain shares a considerable quantity of similarities
with the counseling setting. Specifically, our model generates responses
conditioned on (1) the counseling strategy, and (2) the most recent five
utterances in the conversation. We do this by appending the predicted MI
strategy token to the dialog history at both training and inference time. As
shown in Tables 3, overall, DialoGPT-2222Table 6 in Appendix provides a more
detailed comparison in different settings for different models. outperforms
the GPT-2 and BART in terms of our automatic evaluation metrics (Lin and Hovy,
2003; Zhang et al., 2019; Papineni et al., 2002). We then use this best-
performing model DialoGPT-2 to generate the example response when conditioning
on a specific counseling strategy. The lower half of Table 3 shows the
acceptable performances of our generation models, with an overall semantic
similarity score between a generated text and the ground truth text larger
than 0.87 (indicated by BERTScore). Furthermore, the column of _Positive_ ,
which refers to whether the generated text exhibits the type of counseling
strategy that it is supposed to have, demonstrates relatively good agreement
with an overall score of 78%. Table 3 also suggests that the quality of
generation differs in strategies, with _Grounding_ , _Open Questions_ , and
_Introduction_ being the strategies with the highest positive rates, and
_Reflection_ , _Persuade_ , and _Affirm_ given the lowest scores.

####  3.2.3. Step 3: Filtering Out Inappropriate Responses

As an extra layer of safety protection, CARE filters out inappropriate and
undesired responses before presenting them to peer counselors. A response is
defined as _inappropriate_ if it consists of abusive behavior, the inquiry of
unnecessary support seeker personal information/ personal identifiers, or
contains unnecessary and excessive swear words. To avoid outputting
inappropriate example responses, CARE employs the inappropriate classifier
(Shah et al., 2022), which is a HateBERT model (Caselli et al., 2021)
finetuned on human annotations. The classifier is recall-heavy to ensure that
CARE refrains from suggesting possibly inappropriate responses. It gives an F1
score of 84.21% on the human-annotated test set of 7C-MI.

Figure 2.  CARE system architecture. CARE consists of 8 next-strategy
predictors, one next-response generator, and one inappropriate response
classifier. It predicts the probability distribution of the next counseling
strategies and generates suggested responses for each confident strategy
independently. In the end, the list of strategies and responses is filtered
and ordered decreasingly by the confidence of the predictors.

###  3.3. System Development

Building upon the aforementioned steps, we design an interactive system CARE
to empower volunteer counselors during their chats with support seekers. CARE
integrates iteratively developed front-end and back-end components to optimize
the user experience and runtime efficiency. Specifically, the system is hosted
on an Amazon AWS server with an Ubuntu Operating system (32 Threads, 480GB
RAM) having 8 Tesla k80 GPUs (12 GB RAM). These specifications enable CARE to
interact seamlessly with humans. The back end is implemented with a Flask API
that uses WebSockets to enable continuous bi-directional communication with
the chat client. The functions provided by the back-end API fulfill the
requirements of the system such as posting and pushing log-in credentials,
posting chat logs, and pushing generations from the back-end models. Server-
side event handlers are invoked through API function calls made by the front-
end interface. All models (DialoGPT, BERT) are hosted on the same server to
enable faster communication. The Server-side event handlers are flexible to
connect with any type of front-end client. This allows CARE to be used with
any 7 Cups-like online mental health platform and provides training to peer
counselors in a platform-agnostic manner. A client-side API reference is used
to facilitate the processing of function calls to the back-end API. This
enables functionalities such as the initialization of new chat sessions, the
addition of new messages to the chat log, and the clearing of sessions. With
this architecture, the chat system has the ability to return suggestions and
generations in real-time. We ensure that the client-side API is lightweight
for quick loading of the system on the client end.

####  3.3.1. User-Centered Design of the System Front-End

Figure 3. The peer counselor side frontend view. We design CARE as a
Suggestion Panel. The other components (Chat Information, Chat Window, and
Text Input Box) mimic the frontend view of 7 Cups. CARE is comprised of three
elements: 1) The blue words: Suggested MI counseling strategy to be used in
the next peer counselor response. Users can hover over the strategy to see its
1-sentence description. 2) Black sentences in boxes: A generated next peer
counselor response that utilized the MI strategy above it. Users can click the
response to auto-fill the input text box for modification before sending. 3)
Hide/Show example button: Toggle for collapsing and expanding Suggestion
Panel.

The front end has two views, one for the support seeker and one for the peer
counselor. The view for the peer counselor is given in Figure 3. The UI
contains the four major components to replicate a chat environment like 7
Cups. These components are the chat information toolbar on the top, the chat
window, the collapsible panel for suggestions and responses generated by CARE,
and the text input box. The support seeker view of the platform is the same as
the peer counselor view except for the absence of the collapsible panel.
Feedback on the interface was obtained by working with domain users from the 7
Cups platform to simulate the look and feel of the platform. Key features
added from the iterative development design through user feedback are (1) _a
user is typing_ indicator, and (2) the option to hide the tool window. When a
peer counselor clicks on one of the potential responses in the tool, the text
input box gets populated with the response. The peer counselor can then choose
to modify the response before sending it.

####  3.3.2. Logging of peer counselor actions

We log333During the user studies, we ask for written consent from the
participants before doing so.  peer counselor actions on the suggested
responses to understand the impact of our tool in chats. For each utterance,
we log the CARE suggestions, the peer counselor’s click (choice) in the
suggested responses, the peer counselor’s click to show and hide the
collapsible panel, and the final peer counselor response. We use these logs
for our subsequent quantitative evaluation.

##  4\. Evaluation of CARE via User Studies

Evaluating the proposed system CARE requires rigorous examination and ethical
consideration based on its sensitive and high-stakes cases. We use iterative,
user-centered design principles in the development of the interfaces, by
working closely with stakeholders from 7 Cups who best know the domain for
interface development and by conducting user studies to test our interfaces so
that our tool may be used widely in practice.

Note that although CARE is designed to be used for training in mock chats
only, we also collect participants’ opinions on the possibility of using CARE-
like systems in real-time chats to understand its potential and risks more
comprehensively, as an additional analysis.

Table 4. Participant Information. ID is the ID of the participant in this study. Tenure denotes the length a participant has been a peer counselor on 7 Cups by the date they complete their user study session. These quantities are rounded to the closest integer in the largest non-zero unit. CARE Chat marks whether the participant sees CARE’s AI assistance in the first (1) or the second (2) chat in our user study session, which is randomly assigned. Category is the more familiar or comfortable category the participant chose from Anxiety and Relationship Stress for the mock chats. Device(s) Used for 7 Cups is collected because 7 Cups provides UIs for computers, tablets, and cellphones, but for consistency, we conducted our user studies with solely computers. Most Recent Background denotes the participant’s most recent, external, related academic or professional background. Given the variety of responses and the sample size, we include all essential participant information here but do not analyze the relations between these variables and the participants’ perceptions of CARE. ID | Tenure |  CARE Chat | Category | Device(s) Used for 7 Cups | Most Recent Background
---|---|---|---|---|---
P1 | 3 months | 2 | R | Mostly computers | Degree in Sociology
P2 | 5 years | 1 | A | Always computers | No
P3 | 3 years | 2 | A | Mostly computers | Degree in Psychology
P4 | 7 years | 2 | R | Always computers | Degree in Counseling
P5 | 8 years | 2 | R | Half-and-half | Unknown
P6 | 4 years | 1 | A | Always cellphones/tablets | Student in Clinical Social Work
P7 | 2 years | 1 | R | Mostly computers | No
P8 | 4 years | 1 | R | Mostly computers | Student in Psychology
P9 | 8 years | 2 | A | Half-and-half | Crisis Counselor
P10 | 3 years | 2 | A | Half-and-half | No
P11 | 1 year | 2 | A | Mostly computers | Student in Psychology
P12 | 5 days | 1 | A | Always cellphones/tablets | No
P13 | 8 years | 1 | R | Always computers | Unknown
P14 | 1 month | 2 | R | Always cellphones/tablets | No
P15 | 3 months | 1 | R | Half-and-half | No

###  4.1. Recruitment of Participants

This study has been approved by the Institutional Review Board (IRB) at the
first author’s institution. By working with community moderators on 7 Cups, we
recruit 15 peer counselors who are 7 Cups users located in the United States
(11 females, 3 males, 1 unknown, age 18–69). Note that as CARE is designed for
peer counselors, no support seekers. All of the participants were recruited
via notifications on the 7 Cups mobile/ web app and were compensated $20 for
their time (the average time duration of the study was a little under 1 hour).
Our recruitment criteria allowed only those participants who have completed at
least 25 conversations on 7 Cups to ensure platform familiarity. All peer
counselors in the study had a peer counselor rating of 4+ (out of 5). Lastly,
we set an exclusion criterion for any peer counselor who had been previously
blocked by a support seeker for researcher safety. This set of recruitment
strategies led to 15 virtually-conducted participant studies. The experience
level of these recruited participants ranged from a tenure of 5 days to 8
years on the platform by the time of their interview. We aggregate their non-
identifiable information in Table 4.

###  4.2. User Study Details

We test how a system like CARE empowers volunteer counselors by conducting
user studies with two conditions: (1) no support, and (2) supported by CARE.
We now describe in detail the different components involved in this user
study. Note that since CARE is designed to assist _peer counselors_ in
training, the user study and analysis do not involve support seekers.

####  4.2.1. Procedure and Method

For each user study session, a participant (peer counselor) first selects a
chat category between _anxiety_ and _relationship stress_ based on their own
familiarity and comfort. The participant then tests the system by interacting
with a researcher who acts as a support seeker. Both of the systems mimic the
chat interface of 7 Cups as described in Section 3.3.1. The order of the chats
is randomly assigned using a computer randomizer: one with CARE, and the other
without CARE.

####  4.2.2. Participants Onboarding with CARE

To get participants familiar with CARE, we briefly introduce the tool prior to
their experimentation with the system. The participants are also asked to
watch a tutorial video emphasizing the voluntariness of the use of CARE and
explicitly teaching how to hide and show CARE’s AI assistance. The video also
describes the 8 counseling strategies along with their examples. We clarify
all doubts and confusions a participant may have about the study. During the
chat, when CARE shows AI assistance for the first time, we verbally instruct
the participants again that they may show/hide the tool, ignore/modify
generated responses, or use it as they prefer.

####  4.2.3. Conducting Simulated Chats

We select a larger set of representative seeker-supporter conversations from 7
Cups, remove any personal identifiers in these conversations, and manually
paraphrase and fuse them, to obtain a smaller set of representative chats. In
total, we devise four representative scenarios, two for each category and
their corresponding scripts. In each simulated chat, the researchers act as
support seekers by sending messages modified from a pre-defined script. In
detail, for our focused two categories: relationship stress and anxiety, the
research team read roughly 30+ conversations from actual chats in 7 Cups.
Researchers involved in the user study in our team have signed up as volunteer
counselors and support seekers and conducted observations with more than 100
cumulative hours of counseling time on the platform. This enables us to be
well-equipped in interacting with participants while conducting the studies.
While the prepared scripts help maintain a coherent theme, we also allow for
variability in the conversation due to the variability in the peer counselor’s
responses. The scenarios are summarized in Table 5.

###  4.3. Measure and Evaluation

We perform quantitative analyses on counselors’ use of suggested counseling
strategies when they interact with seekers, and qualitative analyses on the
participants’ feedback on what they liked and disliked about each session. To
deeply understand the participant’s perceptions of CARE, we designed a post-
task, anonymous questionnaire (Table 8 in Appendix), and also conduct a semi-
structured interview. The questionnaire does not ask for any personal user
identifiers to allow participants to freely disagree/ agree to the use of CARE
and leave their opinions and comments. The questionnaire contains a mixture of
Likert scales, checkboxes, and an open-ended response textbox, depending on
the nature of the question. The participants complete the questionnaire during
the session and may opt to unmute and ask questions whenever the questionnaire
is unclear to them. As for the semi-structured interview, we prepare a list of
questions and asked follow-up questions whenever we identify a potential
theme.

###  4.4. Safety Protocol

We take careful steps to ensure the safety of our participants. First, CARE is
designed for _training_ and not for real-time uses; thus, supporters are
involved in a simulated environment with little risk. We collect feedback and
refine CARE in an interactive iterative process based on user insights to make
sure it can work as expected. Second, at all times, the participants were free
to leave the study (and will receive full payment) if they feel uncomfortable
with the topics discussed in the chats. Third, our research team was involved
in every support provision conversation transcript; if there are any signs of
negative impacts (e.g., negative mood, frustration), our team will talk to
participants immediately to discuss the issue and take appropriate actions
such as referring them to the consulting clinician and sharing information
about available national resources.

Table 5. Summary of the mock chat scenarios. Category | Order | Scenario Summary
---|---|---
Anxiety | 1 | A college student is worried about an upcoming admission exam and school-life balance.
2 | A person feels lonely and seeks to take their friendship to a deeper level.
Relationship Stress | 1 | A college freshman struggles with a newly long-distance relationship.
2 | A person feels financially stressed after their lover becomes unemployed for 3 years.

##  5\. Results

As described in Section 4.3, our participants fill out an anonymous
questionnaire after they complete the first 30 utterances of each of the two
system-testing chats. There are 30 system testing chats from the 15 user
studies, which result in 926 utterances sent in those mock chats, where 470
utterances (50.76%) are sent by peer counselors, and the remaining 456
utterances (49.24%) are sent by us, the simulated help seekers. On top of the
obtained numerical data from log analysis (Section 5.1) and Survey Responses
(Section 5.2), we also conduct semi-structured interviews to understand the
impact of CARE (Section 5.3). The quantitative and qualitative results of our
study are given below.

###  5.1. Log Analysis

We analyze the system logs to understand whether CARE has effects on peer
counselors and show that peer counselors actually make use of CARE when it is
provided. We report the aggregated numbers at both, the utterance level and
the chat level, to give a sense of the overall statistics and account for the
difference between individual preferences.

####  5.1.1. CARE provides assistance 84% of the time.

CARE’s AI assistance is frequently provided to peer counselors during the
experiment chats. CARE starts suggesting strategies and responses when a chat
reaches at least 5 utterances. After that, CARE presents potential responses
if the confidence score of the next strategy prediction is greater than a
threshold of 0.5. Our analysis shows that across all utterances, CARE gives
suggestions for 374 out of 467 utterances (80.09%) that were sent in chats,
which is 84.38% of the time if we compute the median at the chat level.

####  5.1.2. Peer counselors choose to see CARE 93% of the time.

Despite being told that they have the choice to see the tool or not see the
tool, most peer counselors almost always keep the CARE panel open during the
mock chats. The peer counselors choose to turn on CARE for 340 utterances out
of 374 utterances (90.91%) when CARE is available during the experimental
chats, which takes up 93.20% of the time span on average across chats
(std=23.376%, median=100%). Notably, 13/15 peer counselors keep the AI
assistance panel shown throughout their experimental chat.

####  5.1.3. Peer counselors check CARE’s suggestions before sending in around
47% of responses.

We join system logs on clicks and messages together to analyze their usage of
CARE. When it is the peer counselors’ turn and they see CARE’s suggestion,
they click them before sending their response 75 out of 199 times, making the
total click-through rate 37.69%. The median across the 15 experimental chats
with CARE yields a 46.67% click-through rate. Appendix Section B and C
summarize the situations where CARE is clicked and not clicked, respectively.

####  5.1.4. Peer counselors use a CARE’s response directly or edit it.

We compare the text similarity between what CARE suggests and what peer
counselors finally send out. From the utterances peer counselors sent after
clicking responses suggested by CARE, we found that most (45/75; 60%) of the
responses were sent without modification from CARE’s suggestion, while the
others were altered before being sent. For the 30/75 (40%) edited responses,
we calculate the length of the Longest Common Subsequence (LCS) between the
clicked suggested response and the actual utterance sent by the peer
counselor. This calculation yields a median difference of 41.5 characters
between the two strings. On one hand, if we divide the length of each LCS by
the length of its corresponding generated response, we get the median 99.3%
(mean=87.7%, std=19.6%). On the other hand, if each LCS length is instead
divided by the length of the corresponding actual response peer counselors
sent, the resulting median is 47.7% (mean=50.8%, std=20.6%). These two ratios
together advise that when peer counselors modify a CARE’s suggestion, they
usually build upon it by preserving almost the entire suggestion and adding
more content to it.

####  5.1.5. Peer counselors send longer responses with CARE

The median length of peer counselors’ responses increases with CARE compared
with its counterpart without CARE. Concretely, when peer counselors see CARE’s
suggestions, the median length of responses (77 characters) is significantly
longer than the response length without CARE (61 characters) (N = (199, 271),
p ¡ 0.01; Mann-Whitney U rank test).

###  5.2. Survey Responses

This section summarizes the responses to the questionnaires in terms of Likert
chart questions (Section 5.2.1) and check box questions (Section 5.2.2 and
5.2.3). Responses to the open-ended questions are rather free of form and
highly relevant to the interview contents, so they are coded with interview
transcripts in Section 5.3.

####  5.2.1. Overall Perceptions

(a) Perceptions of Models and Future Usage

(b) Perceptions of CARE

Figure 4. The responses to perception questions. a) A bar chart of the
participants’ answers to Likert scale questions regarding their perception of
the suggested counseling strategies (strategies), example responses
(examples), and future usage of CARE. b) A bar chart of participants’ overall
perception of CARE. The dashed lines mark the medians of responses.

As shown in Figure 4(a), the responses to the model perception questions show
that for most participants, the strategy prediction and suggestion generation
of CARE are helpful in terms of whether the counseling strategies suit the
situation, and whether the generated responses fit the conversation topic, and
help peer counselors. The figure also shows that most participants feel that
the model suggestions look natural for a 7 Cups-like peer counseling platform.
As a whole, shown in Figure 4(b), all participants agree that CARE is
straightforward to use, while more than half of them think it is overall
helpful. The median response to “Will you use CARE” (If the tool is provided
to you, how often do you think you will make use of it? in the questionnaire)
tilts towards positive. Lastly, responses to the question “Do the examples
contain *harmful* message” are: Never (4/15), Very Rarely (6/15), Rarely
(3/15), Occasionally (1/15), Frequently (1/15), and Very Frequently (0/15).
This indicates that peer counselors generally think the example responses CARE
suggested can hardly cause harm.

####  5.2.2. Like & Dislike

(a) Like Aspects

(b) Dislike Aspects

(c) Helpful Occasions

Figure 5. The responses to multi-select checkbox questions regarding
preferences. The dashed lines mark the medians of responses.

To deeply understand what features or aspects of CARE are more appreciated by
peer counselors, we visualized their preference responses in Figure 5. We find
that more than half of the participants agree that CARE reminds them of
counseling strategies (12/15), inspires them to better responses (10/15), and
increases their confidence (8/15). While about half of the participants think
CARE can disrupt the thought process during conversations, we believe that the
seamlessness of the tool increases with greater familiarity and use.

####  5.2.3. When Is CARE Helpful?

As shown in Figure 5(c), for the question of when CARE is helpful, we saw from
the top three selections to our question “CARE helps more when…?” that, 13/15
said YES to the peer counselor is new to listening chats, 10/15 mentioned the
peer counselor feels more stressed, and 10/15 emphasized when the peer
counselor is less confident in the chat’s category. This result suggests the
effectiveness of CARE in helping, especially novice peer counselors.

###  5.3. In-depth Interview Results

To provide an in-depth understanding of our findings from the log analyses and
questionnaires, this section takes a deep dive into what participants shared
with us during their semi-structured interviews.

####  5.3.1. How peer counselors use CARE

##### Preference for and against showing CARE

Participant P7 attributed why they keep CARE open throughout the simulated
chat to potential inspirations. This might explain the result about the major
user preference of showing CARE more often than hiding it in Section 5.1.2.

> “I think I would probably have it on more often than not, I’m just in case
> it suggested something that I hadn’t that hadn’t popped into my mind. ” – P7

Alternatively, P6 and P14 prefer to use CARE as a safety net and refer to CARE
when there are uncertainties.

> “Only if I’m really on, like, lost on what to say, I don’t have any ideas or
> strategies to go, I would just go there [CARE’s suggestions]. Other than
> that, I prefer to go with my train of thought without looking at it.” – P14.

##### Importance of Optionality

Although most (13/15) participants chose to show CARE instead of hiding it,
giving peer counselors an option to hide the tool is still essential. Some
peer counselors (P3, P4, P6, P10) pointed out the need for this option to
switch the tool on/off.

> “You can switch it on and off as needed. If you pick up a chat with a
> subject you’re not as familiar with, if you decide [that] you want to put it
> on, you have that option. If you don’t want to, you don’t have to.” – P4

##### Adopting a CARE Response

Section 5.1.3 shows that peer counselors click CARE’s suggestion before
sending in around 47% of responses. P7 demystifies this behavior by
illustrating their usage of CARE:

> “So you know, I might take a quick glance at it [CARE] and see if it’s the
> same thing I was going to type anyway. And then click on it [the
> suggestion], that’s definitely a time saver.” – P7

In terms of intentions behind the modification, P8 mentioned their rationale
for doing so is because “There were some open-ended questions that the tool
suggested that I didn’t think of that I thought is really good, and sometimes
I use exactly what was suggested.”. At other times, P8 modifies the suggestion
for personalization: “The personalization part, I still think that the
listener would have to be able to write the words out themselves.” Thus, when
P8 wants to make the response conform to their style more, they “fix up the
wording to make it a bit warmer and less cold” once in a while.

####  5.3.2. Peer counselors think CARE is straightforward and helpful.

Participants’ interview comments on the straightforwardness and helpfulness of
CARE are consistent with the survey response results in Section 5.2.1.

##### Straightforwardness of CARE

When being asked to comment on the UI/UX of CARE, P10 compliments on the UI
design as they “like how the suggestions are right above the chat bar.”. P12
describes the intuitive nature of CARE as “Even if you didn’t know about it
[how to use the tool] before, you can [directly] use this technology here.”.

##### Helpfulness of CARE

Many participants (P1, P6, P7, P8, P9, P15) also agree that CARE is helpful
and also mention the utility of the strategies. Others show satisfaction in
the quality of CARE’s suggested responses.

> “I think it could be a great tool. I really do. I think it’s something that
> we need in 7 Cups because we don’t really train listeners very much. We it’s
> very minimal training. So we kind of grab listeners once problems are
> occurring. So if we could put this at the front end, that would be
> phenomenal.” – P1

##### Risks of CARE

While CARE is perceived rarely harmful (Section 5.2.1), peer counselors who
see CARE’s mistakes think CARE may not actually cause negative consequences:

> _“I did notice just a couple of suggested responses that didn’t quite fit.
> But um, I mean, I think the average listener would know not to click on
> them. I don’t think that they would just randomly start clicking.” – P7_

####  5.3.3. Interpretation of Likes

This part extends Section 5.2.2 from what to why to gauge the reason behind
peer counselors’ preference for CARE.

##### Reminds peer counselors of counseling strategies.

The top questionnaire answer for what peer counselors like about CARE finds
that it reminds peer counselors of counseling strategies (Section 5.2.2).
$80\%$ of peer counselors like the fact that CARE reminds them of MI
strategies, indicating that the tool’s ability to suggest relevant MI
strategies is a helpful feature that supplements the short training these
counselors receive. Peer counselors P6 and P12 mentioned in their interview
how CARE helped them with learning MI strategies.

> _”The tool is using what the member [support seeker] is saying to come up
> with these prompts, and it’s giving me open question options, it’s giving me
> the different active listening skills that you’re supposed to use when
> you’re listening. And people often forget that as listeners. They get stuck
> on asking questions all the time. Or just validating all the time or just
> reflecting all the time. And I think this gives me the confidence to explore
> the different skills that you should have as a listener.” – P6_

> _”I think there were a lot of good [strategy] suggestions. And it helped
> remind me of different coping mechanisms that were appropriate to like,
> mention in the use with the member [support seeker], for example, the whole
> challenging negative thought thing. The suggestions, you know, reminded me
> of that, because with affirmation and as being one tool, for example, it
> really helped me.” – P12_

##### Inspire better responses

In Section 5.2.2, we bring out that 10/15 of the surveyed participants
reported that they liked how CARE inspires better responses. CARE’s dual-
functioning of providing the MI strategy and potential response of what to say
is a helpful feature. Even if peer counselors know which MI strategy they want
to use, their training and experience may not be extensive enough to
adequately apply the strategy to the situation. This is where CARE is able to
support counselors by providing examples of using MI in the specific scenarios
at hand. Even if counselors do not directly use the suggested utterance, it
gives insight into what an alternative/better response might be. Peer
counselor P6 mentioned multiple times how CARE helped write better responses,
even in situations where the generated responses are not a perfect fit, they
inspire the choice of response and the listener can then modify the
suggestions, as P6 mentioned: _”[it] inspires me to use different strategies
and gives me ideas and then I take that and modify it.”_

##### Increases peer counselors’ confidence

More than half of the participants mentioned that using CARE increases their
confidence (8/15). During the interviews, many peer counselors (P1, P3, P6,
P7, P8, P9, P10, P11) support that the tool would be greatly beneficial to
novice counselors by empowering them in new situations and would help build
confidence. Peer counselor P3, who often coaches peer counselors to be better,
also mentioned how it helps in new situations. Peer counselor P10 stated that
CARE would help provide better service to the support seekers.

> _”I liked it honestly, I do listener coaching and mentoring. And I liked
> that this really prompted I think a nice variety of responses depending on
> how comfortable the person is and really diving more deeply into whatever
> the issue that the member [support seeker] has. So I thought that as I was
> going through, I thought, this is a great tool for listeners who are newer
> or are not feeling comfortable with the topic.” – P3_

> _”I feel like it [deploying CARE on 7 Cups as part of the training program]
> would make them [new peer counselors] more knowledgeable. It would make it
> easier for them to provide better service in a way for the members [support
> seekers].” – P10_

####  5.3.4. Interpretation of Dislikes

Similar to Section 5.3.4, this section measures why some peer counselors have
the inclination to disfavor CARE. Example chats are listed in Appendix Section
C.

##### Disrupts thinking

Some participants (8/15) pointed out that the addition of an interactive tool
may distract a peer counselor from the actual conversation. The same peer
counselor, P6, who said that the tool gave ideas, also thinks that it leads to
an unconscious comparison between the peer counselor’s response and the
suggested response, which can lead to distractions— _”I am always comparing
what it was saying to what I was going to say. And just like having another
thing to see.” _ However, a few participants (P3, P8) disagree. This is
supported by prior research that shows similar user feedback on the
introduction of any new AI assistance tools: users feel that the tool disrupts
thinking initially; over time, they feel that the tool is indispensable as
they get used to it (Sinha et al., 2001; Jakesch et al., 2019). P3 attributes
this to friction during the process of getting used to AI assistance.

> _“Well, you know, the thing is, it’s interesting that you say that because
> at first, I was thinking, Oh, this is going to be distracting. But then when
> I didn’t have it I was like well, I have to work a little harder because I
> have to come up with everything on my own.” – P3_

##### Does not capture nuances.

Several participants (P1, P2, P3, P6, P8, P10, P11) mention responses not
apprehending the message underlying the conversation. While P1 thinks _“it
just tha-, it just seemed that there were certain situations or certain, like,
nuances of the conversation that the AI just didn’t really pick up on.”_ , P6
sees the difficulties as an inherent property of AI – _“As a listener, we’re
able to detect what this person might mean based on what they’ve already said,
based on our whole conversation, but it’s more difficult for AI to do that.
They [AI] just take the words interpreted differently.”_ Despite this, P10
still thinks _“having that general basis of knowledge is useful for new
listeners when they are building their confidence”_, and P3 also believes that
for new peer counselors CARE is already more humanlike than the existing
assistance provided by 7 Cups, which are a set of pre-defined, generic tips
randomly prompted to peer counselors during chats:

> _“ In 7 Cups, we have something sort of like this [CARE], but it’s always
> the same and people complain that it sounds like a bot because it doesn’t
> sound very authentic. It [the random tip suggestion in 7 Cups] sounds
> pretty, you know, like a scripted type of thing. And people will always say
> like don’t send those bot messages, and so it’s hard when you’re new, for
> someone [new] to be comfortable enough to do this. And I liked that these
> messages [from CARE] sounded very genuine and related to the issue and not
> so abstract almost.”_ – P3

##### Contains made-up information.

A few participants (P8, P9, P11) noticed some false information in CARE’s
suggestions. For example, the suggested responses may be sometimes based on
inaccurate assumptions. This may be a consequence of the insufficient dialogue
context given to CARE. For example, if the gender of the support seeker is not
mentioned in the latest-message context, CARE may infer one. However, peer
counselors can refer to the earlier context of the chat and correct this.
Future work may incorporate a dialog summarization model (Chen et al., 2022)
into the context.

> _“At one point I remember during the chat it said the person was saying how
> they were stressed and the prompt, the suggestion was, ‘your anxiety and
> depression seem really bad,’ which-, and if a listener selects that, that is
> kinda assuming that the person who’s talking to, who has anxiety and
> depression, which is probably not good.”_ – P9

####  5.3.5. CARE helps both new and experienced peer counselors.

#####  CARE helps inexperienced peer counselors learn and grow.

As most peer counselors (13/15), experienced and inexperienced, believe that
CARE helps more the peer counselor is newer to listening chats as it “gives
them suggestions about what to say or how to respond” (P1). _Aiding new peer
counselors_ is also a common theme mentioned in interviews and open-ended
questionnaire responses.

> “New people haven’t really dealt with the situation before. And they’re not
> listening and they don’t really know exactly what to say yet. [CARE] can be
> quite a guide.” – P2

A couple of peer counselors, especially those who are involved in the new peer
counselor training and mentoring efforts, think CARE will be useful in
training new peer counselors. Some compare CARE with the current training
modules and reach-out resources on 7 Cups, saying that learning through real-
time experience with CARE is more natural and practical than current static
training modules:

> “With this tool, I think it’ll be really helpful because this helps like
> when they are taking the chat instead of like help before or after that
> because this is real-time help. So it will be a lot more helpful for the new
> listeners to get familiar with the process.” – P8

> “As most listeners, you know, they don’t devote the time [on training
> modules]. Maybe not most, but some of them at least don’t. So this way,
> you’ll just, it’s much better to pick up through experience.” – P12

Others even make recommendations on how to incorporate CARE into the peer
counselor training process. P3 mentions how suggested responses give new peer
counselors a sense of the expected language and the degree of formality on 7
Cups, making it a helpful tool for new peer counselors to refer to during the
practice chats before they are verified. Still, others, especially those who
join 7 Cups newly, think other than helping them to learn, hypothetically, the
fact that CARE is available on 7 Cups will itself increase their willingness
to contribute more to chats. This can potentially help mitigate the high
dropout rate or low engagement of peer counselors.

> “It makes the whole process likely easier. It could only be helpful. So I
> guess it would make me feel more open to doing more chats than I usually
> do.” – P15

Peer counselors also foresee that new peer counselors’ dependence on CARE can
decrease over time since they gradually pick up what and how to use different
strategies and respond with CARE, which meet our goal of helping new peer
counselors to grow. One listener depicts their personal experience with CARE
during the mock chats.

> _“When I started listening, it was helpful because when when you first get
> into the listening, it’s kind of hard to figure out immediately what you’re
> going to say. So I did read the suggestion that was there. But once I’m more
> comfortable, I was more comfortable listening and I kind of got the idea of
> how to be empathetic and how to ask open or closed questions or just
> sympathize.” – P14_

#####  CARE helps experienced peer counselors in difficult situations.

In terms of the CARE’s effect on experienced peer counselors, other than the
aforementioned responses in the questionnaire, peer counselors think of other
occasions where CARE can help, such as dealing with culture/ language
barriers, unfamiliar topics, or context switches when being in multiple chats.
By suggesting suitable responses, CARE can help mitigate the language barrier
or age gap between a peer counselor and a help seeker.

> “It [CARE] could be really helpful. So I know for some people on 7 Cups,
> they don’t always have the same native language. Sometimes there are
> language differences or, like slang words that aren’t always understood.” –
> P1

> “Sometimes you have trouble connecting with people who are not your age or
> even closer I want to feel like a, like a colleague and not a mother. You
> know, so maybe the tool would help in that in that instance.” – P4

Peer counselors often specialize in a set of topics, yet they often have to
converse on topics beyond that. A few participants describe difficulties in
new topics which CARE helps mitigate: “[listeners often] feel stuck in a chat,
which happens quite often” (P9). Specifically, P4 encountered this situation
during the simulated chat and reflects that:

> “You know what my specialty is grief. Relationship stress, [the category of
> the simulated chat], is not something that I’m as familiar with. I mean, I
> am but I’m better on certain topics. So I think it would help me you know,
> with the category being as not as familiar so it would help me like that.” –
> P4

Many peer counselors manage multiple chats concurrently, P3 talks about how
CARE can help in such context switches of peer counselors by hinting according
to their experience from peer counselor coaching.

> “I have some people that I coach who take multiple chats and have a hard
> time managing them. This [CARE] would allow them I think to manage them more
> properly because they would have some kind of preset options to choose from
> and not have to come up with everything originally.” – P3

To sum up, via our system log analyses, questionnaires, and semi-structured
interviews, we found that:

  * •

General Perception: Overall, peer counselors think CARE is helpful and often
use the tool when provided. The tool also leads to longer peer counselor
responses on average. Peer counselors also agree that the tool is easy to use
and these suggestions are natural. Analysis of system logs and surveys both
showed that users choose to use CARE more often than not when given an option.

  * •

Situations where CARE helps: Peer counselors think that CARE inspires better
responses and reminds them of relevant counseling strategies. This helps
novice counselors gain experience and confidence. Additionally, experienced
peer counselors agree that the agent will help them with difficult and new
topics. Semi-structured interviews also revealed that CARE holds the
possibility to help bridge language, age, and cultural barriers.

  * •

Potential concerns towards CARE: Some peer counselors report it to be
sometimes distracting, therefore, including an option to show or hide CARE is
important for better training.

##  6\. Conclusion

This work develops an interactive training agent CARE for empowering volunteer
counselors on online peer-to-peer counseling platforms For any given chat
between a support seeker and a peer counselor, CARE automatically suggests
counseling strategies and provides tailored responses based on the
conversation context to assist counselors. We built upon the motivational
interviewing framework and focus on a set of representative counseling
strategies. We then develop contextualized classifiers to highlight which
counseling strategies are needed in a given situation and scalable language
generation approaches to generate example responses on the fly. We integrate
different components together into the interactive counseling support tool
CARE to enable a realistic user experience and adopt a user-centered design
principle by working with domain users. Through both quantitative analysis and
qualitative user studies, we demonstrate the efficacy of CARE on a set of
representative counseling scenarios.

###  6.1. Implications and Design Recommendation

Theoretically, this work creates a novel synthesis of psychotherapy theories
and advances in natural language processing to build an interactive assistant
CARE that empowers peer counselors on an online peer counseling platform. Our
contributions consist of operationalizing theory-driven motivational
interviewing strategies, developing contextualized natural language models,
designing an interactive HCI system, analyzing observational data from system
logs and questionnaire results, as well as performing semi-structured
interviews—all of these demonstrate a user-centered example paradigm on how to
build both theory-driven and model-driven systems to augment human
capabilities via AI.

One straightforward design recommendation is to use CARE to complement the
current training programs on 7 Cups and many other similar online peer
counseling platforms. As we discussed earlier, existing mechanisms of training
or scaffolding largely rely on human supervision, which is costly and
difficult to scale. Volunteer counselors often do not have the rigorous
professional training as their offline counterparts and most online counseling
platforms only provide very abbreviated training. By evaluating it with 15
stakeholders both quantitatively and qualitatively, we demonstrate the
possibility of CARE as an initial and effective effort to improve these
training mechanisms.

Our work sheds light on how CARE may be integrated into platforms such as 7
Cups to better support peer counselors. It has great potential to be deployed
for real time scaffolding that supporters can turn to during their
interactions with seekers, to obtain an actionable suggestion on which skill
best fits in a given scenario, and on how to formulate what they are going to
say, towards the long-term goal of increasing the number of people they can
help and the effectiveness of their interactions in addressing the mental
health needs of support seekers.

We design CARE to be primarily used as a training or simulated environment for
support providers so that they can practice their skills in a safe
environment, without harming real people. While the user studies and findings
are grounded in the context of 7 Cups as the primary platform, findings on the
impact of such an AI tool, design recommendations for the deployment of the
tool, and the general perception of the tool are broadly applicable to other
online health communities with similar setups of private support seeker peer
counselor chat rooms. In other words, this system is generic and agnostic to
any online peer counseling platforms such as MellowTalk (MellowTalk, 2021),
thus it can be used in many similar environments, not only specific to 7 Cups.

From a technical perspective, CARE consists of two main functions:
highlighting which counseling strategies are most suitable in a given context
and suggesting example responses in real-time. Each function can be used as an
individual sub-tool to help specific use cases in online support groups. For
instance, the resulting machine learning classifiers in detecting the most
suitable strategies can also be used to understand the relationship between
different strategies uses and their influence on conversation success or the
well-being of support seekers. This work also provides insights and raises
questions for the field of natural language processing and machine learning on
how to generate responsible and contextualized responses in high-stakes
scenarios.

###  6.2. Limitations and Future Work

This work is subject to a few limitations. First, given the sensitive nature
of this study on online peer counseling platforms, any use of AI models in
providing assistance might introduce concerns about user safety and the
inherent biases of these AI models. While we have taken very careful steps
such as implementing an inappropriate content filer as shown in Figure 2 and
maintaining safety protocols as described in Section 4.4, there might still be
cases that are potentially problematic and undesirable. Future work can help
mitigate this concern by developing better algorithms for debiasing and
content filtering as well as introducing more real-time human oversight and
control.

Second, we design CARE as a training tool primarily for one-to-one
conversations and evaluate it with relatively short conversations. However,
some online support provisions might happen in multi-user chat forums like
Reddit or public chat groups. Furthermore, conversations between support
seekers and peer counselors can be very long in the real world; such long-term
dependencies may not be captured well in the current setup when providing
assistance. How to generalize CARE to multi-party and long conversations
requires further investigation.

Third, although we utilized the current state-of-the-art models for
recommending counseling strategies and example responses, some generation
performances are still unsatisfactory as pointed out by participants. Such
limited quality of generations may negatively affect the training or
suggestion provision to peer counselors. Future work can further improve these
models by performing extensive parameter searches and optimizing model
architectures.

Fourth, as an initial effort to empower peer counselors, our work mainly
focuses on the development and design of CARE, and the evaluation of it with
domain users via interviews and system logs in a simulated one-time chat
environment. Our work has not evaluated any _long-term_ effects of CARE, such
as its influence on certain skill acquisition of peer counselors, on the
mental health outcome changes in support seekers, or the change in listener
engagement to a conversation with the availability of CARE. We plan to
continue improving CARE and evaluate its long-term impact by working with more
domain users in a longitudinal way.

Next, in terms of multiple design choices such as counseling strategies, we
mainly focus on a subset of motivational interviewing strategies, which
provide a solid foundation for CARE. However, there are other types of MI
strategies or other prominent counseling techniques such as cognitive
behavioral therapy which could be strong alternatives for empowering peer
counselors. While the chosen subset of strategies covers most messages in the
7 Cups dataset, its potential influence on the trainees’ variety of strategies
remains unknown, such as whether they may bias against the strategies
exclusive of our chosen subset. Similarly, for multiple thresholding choices
such as the number of responses to show on the CARE interface, we chose them
based on the researchers’ expertise in design and feedback from stakeholders.
We plan to iteratively refine them and perform more robustness checks for the
next version of CARE.

Last but not least, our user studies involve only 15 peer counselors, who are
located in the United States and who use English in their counseling sessions.
During our user studies, we only asked peer counselors to work on two of the
most popular topics: relationship stress and anxiety. However, in practice,
topics discussed on online peer counseling platforms cover a wide variety of
situations. Some examples of popular situations and topics are relationship
stress, anxiety, depression, health, LGBTQ, dissociative identity, home life,
self-improvement, etc. Future work can explore the efficacy of our system
across more topics for better generalization. Moreover, the participating peer
counselors vary in length of service, and most of them have not received
formal counseling training. Nonetheless, expert opinions on the quality of
CARE may be more aligned with the actual potential impacts of CARE. Extending
this work to more participants with diverse peer counselor backgrounds,
language use, locations, and expertise can bring in more nuanced findings.

##  7\. Ethical Considerations

The Institutional Review Board has been approved for this study at the
researchers’ institution. The data for the study was collected in partnership
with 7 Cups, following Health Insurance Portability and Accountability Act
(HIPAA) and confidentiality data use agreements. To protect the privacy of the
participants, the data has been anonymized and additional steps have been
taken to ensure that it cannot be linked to any specific user. At all times,
actual user data was present in secure servers, which could be only accessed
by the researchers of this study. All the researchers involved in this study
have completed CITI Program certifications on responsible code of conduct in
research. All the participants in the user study are older than 18 and have
signed a consent form stating their explicit consent. The researchers
acknowledge and address the potential safety issues for support seekers and
peer counselors when using such a system for training in the Safety Protocol
in Section 4.4 and the Limitations in Section 6.2.

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##  Appendix A Appendix

Table 6. Comparison of different models on the two conditioning scenarios on 7C-HQ-small.  | Conditioned on
---
MI Strategies
Model | ROUGE-1 | ROUGE-2 | ROUGE-L | BERT Score (F) | BLEU | Avg Length
No | BART | 0.118 | 0.015 | 0.111 | 0.841 | 0.061 | 7.269
No | GPT-2 | 0.119 | 0.018 | 0.110 | 0.839 | 0.058 | 7.496
No | DialoGPT | 0.119 | 0.015 | 0.111 | 0.841 | 0.060 | 7.127
Yes | BART | 0.128 | 0.017 | 0.117 | 0.845 | 0.074 | 7.710
Yes | GPT-2 | 0.111 | 0.013 | 0.100 | 0.879 | 0.084 | 9.728
Yes | DialoGPT | 0.132 | 0.012 | 0.114 | 0.878 | 0.084 | 8.871
Table 7. Results of the next utterance strategy predictions (trained on 7C-HQ, reported on 7C-MI). Strategy | # Instances | Accuracy | Recall | Precision | F1 Score
---|---|---|---|---|---
Open Question (QUO) | 3,586 | 0.632 | 0.802 | 0.599 | 0.686
Closed Question (QUC) | 3,714 | 0.612 | 0.795 | 0.582 | 0.672
Persuade (PR) | 3,778 | 0.692 | 0.788 | 0.661 | 0.719
Reflection (RF) | 3,312 | 0.648 | 0.804 | 0.613 | 0.695
Support (SUP) | 2,742 | 0.672 | 0.783 | 0.640 | 0.705
Introduction/Greeting (INT) | 430 | 0.867 | 0.916 | 0.835 | 0.874
Grounding (GR) | 1,926 | 0.721 | 0.817 | 0.685 | 0.745
Affirm (AF) | 666 | 0.722 | 0.820 | 0.686 | 0.747
Overall | 20,154 | 0.664 | 0.801 | 0.631 | 0.705
Table 8. Questionnaire presented to study participants. The participants
completed this questionnaire during the user studies. In addition to the
questions in this table, screenshots of individual UI components are also
included in the questionnaire as a visual aid to help participants identify
which component of CARE a question refers to.

Questions | Question type
---|---
How often did the suggested strategies (blue hints) suit the situation? |  | Likert scale with six options
---
Never, Very Rarely, Rarely, Occasionally, Frequently, Very Frequenty
How often did the suggested strategies (blue hints) help? |  | Likert scale with six options
---
Never, Very Rarely, Rarely, Occasionally, Frequently, Very Frequenty
How often did the example responses (white buttons) look natural on 7 Cups? |  | Likert scale with six options
---
Never, Very Rarely, Rarely, Occasionally, Frequently, Very Frequenty
How often did the example responses (white buttons) fit the conversation topic? |  | Likert scale with six options
---
Never, Very Rarely, Rarely, Occasionally, Frequently, Very Frequenty
How often did the example responses (white buttons) help? |  | Likert scale with six options
---
Never, Very Rarely, Rarely, Occasionally, Frequently, Very Frequenty
How often did the example responses (white buttons) contain _harmful_ message? |  | Likert scale with six options
---
Never, Very Rarely, Rarely, Occasionally, Frequently, Very Frequenty
As a whole, was the tool straightforward to use? |  | Likert scale with five options
---
Strongly Disagree, Disagree, Undecided, Agree, Strongly Agree
As a whole, was the tool helpful? |  | Likert scale with five options
---
Strongly Disagree, Disagree, Undecided, Agree, Strongly Agree
What do you like about the tool? |  | Check box with the following options
---
Saves time
Saves typing
Simplifies decision process
Increases listeners’ confidence
Reminds listeners of counseling strategies
Inspires better responses
Improves the overall quality of your responses
None of the above
Other (also has a text box)
What do you dislike about the tool? |  | Check box with the following options
---
Takes more time to read examples
Requires more decisions
Disrupts thinking
Limits the variety of counseling strategies
Limits the diversity of responses
Reduces the overall quality of your responses
Interferes in listener-member communication
None of the above
Other (also has a text box)
This tool helps more when |  | Check box with the following options
---
the Listener is newer to Listening chats.
the Listener is less confident in the chat’s category.
the Listener is less familiar with the Member’s chief complaint.
the Member is less communicative.
the Listener feels more stressed.
None of the above
Other (also has a text box)
If the tool is provided to you, how often do you think you will make use of it? |  | Likert scale with six options
---
Never, Very Rarely, Rarely, Occasionally, Frequently, Very Frequenty
Any other comments? | Open-ended text box

##  Appendix B Positive Example Chat Excerpts

We selected one excerpt for each common type of situation in which CARE was
shown to the participant, and the participant checked it. The text in the
bottom input box is the actual response the participant sent. Texts adopted by
the participants are highlighted in yellow or green.

Figure 6. The participant selected and sent a response from CARE without any
modification.  Figure 7. The participant added to a CARE response by
completing it. That CARE response was truncated because of the generation
length limit. Figure 8. The participant combined two suggestions from CARE by
concatenating them with a comma. Figure 9. The participant added their
feelings (about being honored) to the suggestion provided by CARE. Figure 10.
The participant personalized CARE’s suggestion to fit their style.

##  Appendix C Negative Example Chat Excerpts

We selected excerpts for situations in which CARE was shown to the
participant, but the participant did not select it.

Figure 11. CARE’s suggestions were valid, but the participant made a different
point from the suggested ones. Figure 12. CARE’s suggestions were valid, but
the participant combined more than one strategy in their response. Figure 13.
CARE replied based on the literal message without considering the underlying
sentiment and implications. Figure 14. Neither the identity of the “he” nor
the reason of the calls was given to CARE, making it challenging for CARE to
respond accordingly. On the other hand, a human counselor can remember or
refer to the previous messages before responding.