# How AI Processing Delays Foster Creativity: Exploring Research Question Co-
Creation with an LLM-based Agent

Yiren Liu<EMAIL_ADDRESS>University of Illinois Urbana-ChampaignUSA ,
Si Chen<EMAIL_ADDRESS>University of Illinois Urbana-ChampaignUSA ,
Haocong Cheng<EMAIL_ADDRESS>University of Illinois Urbana-
ChampaignUSA ,  Mengxia Yu<EMAIL_ADDRESS>University of Notre DameUSA ,  Xiao
Ran<EMAIL_ADDRESS>University of Illinois Urbana-ChampaignUSA ,  Andrew
Mo<EMAIL_ADDRESS>University of Illinois Urbana-ChampaignUSA ,  Yiliu
Tang<EMAIL_ADDRESS>University of Illinois Urbana-ChampaignUSA and  Yun
Huang<EMAIL_ADDRESS>University of Illinois Urbana-ChampaignUSA

###### Abstract.

Developing novel research questions (RQs) often requires extensive literature
reviews, especially for interdisciplinary fields. Leveraging Large Language
Models (LLMs), we built an LLM-based agent system, called CoQuest, supporting
RQ development through human-AI co-creation. We conducted an experimental
design with 20 participants to examine the effect of two interaction designs:
breadth-first and depth-first RQ generation. The results showed that
participants found the breadth-first approach more creative and trustworthy
upon task completion. However, during the task, they rated the RQs generated
through the depth-first approach as more creative. We also discovered that AI
processing delays allowed users to contemplate multiple RQs simultaneously,
resulting in more generated RQs and an increased sense of perceived control.
Our work makes both theoretical and practical contributions by proposing and
assessing a mental model for human-AI co-creation RQs.

Scientifc Discovery, Large Language Models, Co-creation Systems, Mixed-
initiative Design

††conference: ; ; Pre-print††ccs: Human-centered computing Empirical studies
in HCI††ccs: Human-centered computing Interactive systems and tools††ccs:
Computing methodologies Natural language processing

Figure 1.  CoQuest enables Human-AI co-creation for research questions (RQs)
using LLMs through a three-panel design: RQ Flow Editor, Paper Graph
Visualizer, and AI Thoughts. Major features of RQ Flow Editor panel include:
(a) offering either breadth-first and depth-first RQ generation (see Figure
4); (b) allowing users to provide feedback to AI based on a generated RQ, and
explore related papers in details on Paper Graph Visualizer panel; and (c)
presenting the rationale of RQ generation on a AI Thoughts panel when a link
between two RQs is clicked.

This is an overview of the CoQuest system interface. There are three panels.
The main panel is the RQ Flow Editor Panel with 2 design options, detailed in
Figure 4. There are six nodes presented on this panel: three on the left,
three on the right. The three nodes on the left are each connected to a node
not included in this diagram. The top left node is connected to three nodes on
the right. Each edge had text associated on it. The top right node is expanded
with more content and a different color. The top left node is denoted (a), the
expanded node on the top right is denoted (b), and one of the edges is denoted
(c). On the right of RQ Flow Editor Panel is the Paper Graph Visualizer Panel,
which presents an interactive graph. Each paper node on the network represents
a paper. One paper node is selected and the details of this paper is presented
below the graph on this panel. On the bottom-left of the RQ Flow Editor Panel
is the AI Thoughts Panel, which has three blocks of texts on it.

##  1\. Introduction

Identifying research questions (RQs) is a critical step of scientific research
and discovery (Elio et al., 2011). To formulate creative and valuable research
ideas, researchers often start with searching and scoping the relevant
literature, especially when the search involves works spanning multiple
domains (Kang et al., 2022). Developing research questions is also iterative,
that researchers would first have an initial idea or topic in mind, and then
conduct a literature search to refine the ideas, and repeat this process until
they have a satisfying research idea (Foster, 2004). Reading a large body of
literature, synthesizing them, and identifying the relevant work can be rather
time-consuming.

With the rapid development of Large Language Models (LLMs), scholars have
investigated the potential of harnessing the power of LLMs to support the
process of research literature discovery (Ought, 2023; Qureshi et al., 2023;
Consensus, 2023), which helps users significantly speed up the literature
discovery process. Meanwhile, the thriving of generative AI technologies has
also been widely used in various fields to promote creativity (Epstein et al.,
2023; Yuan et al., 2022; Mirowski et al., 2023). For example, a recent study
has found that more than 4.9% of users used ChatGPT to support creative
ideation (Fishkin, 2023). Although recent research has demonstrated the
potential of using smaller language models to generate novel RQs (Liu et al.,
2023b), there remains a lack of empirical understanding about how humans
evaluate AI-generated RQs. Given that LLMs are known to have problems with
hallucination and lack of factual accuracy (Ji et al., 2023), creating high-
quality RQs requires inputs from human researchers for their unique
backgrounds and expertise. Enabling humans and AI to co-create novel research
questions is particularly promising for conducting interdisciplinary research.

The concept of human-AI co-creation draws insights from pioneering research on
mixed-initiative systems, where human users and computer systems contribute to
a shared goal (Horvitz, 1999; Yannakakis et al., 2014). Recent academic
endeavors have also introduced a variety of design guidelines for human-AI co-
creation systems (Rezwana and Maher, 2022; Weisz et al., 2023; Lin et al.,
2023), further inspiring the intricate design considerations of mixed-
initiative systems. For instance, Rezwana and Maher (2022) explored whether
the exchange of initiatives between humans and AI should be designed as either
parallel or in a turn-taking manner. However, there remains a gap in empirical
research regarding the influence of AI-driven initiatives or creativity on
user experiences, such as their perception of the creative process, trust in
the AI, and sense of control. Moreover, the potential impact of these factors
on the results of human-AI co-creation, such as the quality of generated
content, has yet to be investigated.

To this end, we proposed a novel system called CoQuest, which allows an AI
agent to initiate RQ generation by tapping the power of LLMs and taking
humans’ feedback into a co-creation process. The system consists of three
panels: RQ Flow Editor for supporting RQ generation, Paper Graph Visualizer
for exploration of literature space, and AI Thoughts for explaining AI’s
rationales. The system design was informed by a formative study, where we
invited four researchers to verbalize their expected RQ-generation processes
with AI support. The formative study yielded an initial mental model of human-
AI co-creation for RQs. The system design was further evaluated with 20
participants through a within-subject experiment.

Our work makes novel and significant contributions as follows. Firstly, we
made a theoretical contribution by proposing and evaluating a new mental model
for human-AI co-creation in the context of RQ generation. Second, we proposed
a new agent LLM system and implemented two interaction designs (breadth-first
and depth-first), where the AI agent took different levels of initiative in
supporting users to develop RQs. Third, we gained new empirical understandings
of how AI’s different initiatives impact users’ perceived experiences and
outcomes through a within-subject experiment. Specifically, for overall
experience, the breadth-first design made users “feel” more creative and
gained more trust from users, though the effect varies by users’ research
background; but when rating individual RQs, users scored the depth-first
questions for higher creativity. Fourth, by closely observing participants’
interaction with AI, we discovered important co-creation behavior and proposed
a new design implication, namely, intentionally “slowing down AI”, giving wait
times for users to explore the co-creation space. This is especially
beneficial for users to gain a stronger sense of control. Last but not least,
we discussed the implications of our study for designing ethical human-AI co-
creation systems.

##  2\. Related Work

###  2.1. Humans Conducting Literature Discovery and Research Idea Creation

Scholars have sought to understand the process of how researchers conduct
literature discovery and formulate new research ideas (Glueck and Jauch, 1975;
Pertsas and Constantopoulos, 2017). Extensive works have strived to formulate
the model of researchers’ scientific activities (Palmer et al., 2009; Vilar,
2015; Benardou et al., 2010). For example, Foster (2004) proposed a framework
of idea formulation in academic research from an information behavior
perspective, which consists of three major components: Opening (initial
exploration), Orientation (problem definition and literature survey) and
Consolidation (knowledge creation). In this framework, literature discovery
and idea formulation are discussed as a recursive and iterative process. Many
studies have also been conducted to understand how researchers produce
innovative ideas for research purposes (Yang et al., 2016). For example, Jing
et al. (2015) proposed a system for research idea creation that incorporates
knowledge reuse through ontology construction. Later work by (Guo and Laidlaw,
2020) introduced a system for supporting research ideation through topic
modeling and visualization. Recently, Liu et al. (2023b) proposed to generate
new research questions using generative language models fine-tuned over
related works and explicitly written research questions from publications
within the HCI domain. But none of these works leverage large language models
(LLMs) or built an LLM-based system to examine human-AI co-creation RQ
processes.

###  2.2. Agent Large Language Model (LLM) Systems

Recent surge in the success of LLMs (OpenAI, 2023; Touvron et al., 2023) has
spurred strong interests in employing LLMs for solving complex tasks. There
has recently been a heated wave of explorations in building autonomous agents
using LLMs as a reasoning engine to solve different tasks, such as software
development (Shinn et al., 2023; Qian et al., 2023), gaming (Wang et al.,
2023b), and assisting social science research (Ziems et al., 2023).

System designs are proposed for improving the method of prompting in Human-AI
interaction systems enabled by LLMs (Wu et al., 2022b, a; Dang et al., 2022).
To enhance the reasoning capabilities of LLMs, researchers proposed several
prompting frameworks to elicit reasoning in LLMs by decomposing and solving
the sub-tasks step by step by applying prompting techniques for general
purposes, such as Chain-of-Thought (Wei et al., 2022; Kojima et al., 2022),
Self-Consistency (Wang et al., 2023a), Least-to-Most (Zhou et al., 2023).
These prompting techniques focus on improving the task-specific performance of
LLMs. To build autonomous agents with LLMs, Yao et al. (2022a) proposed a
prompting framework named “ReAct” that unifies the ability of LLMs to reason,
take actions, and observe the results. Recent research on LLM-based prompt
engineering (Wang and Zhao, 2023) has explored a viable approach of
introducing humans’ mental model as prompting techniques to boost LLM’s
reasoning ability. These frameworks set a great foundation for Q&A reasoning
or general-purposed tasks (Liu et al., 2023c). In this paper, we applied LLM
to build an agent for specialized tasks in the context of literature discovery
and research ideation.

###  2.3. Human-AI Co-creation System Design

Many human-AI co-creation systems utilize generative AI technologies (Sun et
al., 2022; Louie et al., 2020). For example, Bilgram and Laarmann (2023)
showed that generative AI could augment the early phases of innovation, such
as exploration, ideation, and digital prototyping. Epstein et al. (2022)
discovered that discrepancies between users’ expected outputs and the actual
output from the system play a critical role in creating new ideas.

Scholars and practitioners try to develop design guidelines for using
generative AI (Weisz et al., 2023; Davis et al., 2015; Liu and Chilton, 2022;
Muller et al., 2020). From a role-based perspective, human-AI co-creation
systems are found to be different from prior systems where machines mainly
provide support to humans (Kantosalo and Jordanous, 2021). In co-creation
systems, both humans and AIs can be designed to take the initiative in
producing creative artifacts (Oh et al., 2018; Guzdial et al., 2019). Rezwana
and Maher (2022) introduced a Co-Creative Framework for Interaction design
(COFI), which categorizes mixed-initiative system designs into two types based
on their styles of participation: parallel and turn-taking. The evaluation of
mixed-initiative designs has been explored by recent research (Kreminski et
al., 2022; Withington and Tokarchuk, 2023) in application domains such as poem
writing and game design. Our research seeks to systematically model the co-
creation process and provide both theoretical and practical implications in
the scholarly research domain.

In this study, we propose and examine a new agent LLM system that helps
researchers formulate research questions by combining LLMs’ reasoning ability
with the mental model we discovered through a formative study with actual
researchers. We also discuss whether LLM-based co-creation systems can help
facilitate the process of information gathering and idea evolution using user
study results to provide new empirical understandings.

##  3\. Research Questions

Given the above literature and identified research gaps, we aim to address the
following research questions:

  1. RQ1 (perception & outcome): How do users perceive the co-creation experience (e.g., trust, control, feeling of being creative) and outcomes (creativity ratings of generated RQs) using the CoQuest system?

  2. RQ2 (behavior): How do users interact with AI when AI provides different levels of initiatives during the co-creation process?

  3. RQ3 (relationship): What behavioral factors are associated with users’ better perceived experience of human-AI co-creation, and outcomes?

In the remainder of this paper, we will first present the formative study,
where we developed an initial mental model for RQ co-creation between humans
and AI. We then introduce the system design and our experimental study, and
detail the findings in the order of the proposed research questions. We will
conclude with an in-depth discussion on the updated mental model informed by
our findings, and explore design implications for future work. Note that we
use RQ1, RQ2, and RQ3 to refer to our three research questions in the
remainder of the paper, whereas RQ and RQs are used to refer to the research
questions co-created by the user and AI.

##  4\. Formative Study

We conducted a formative study in order to understand the cognitive process of
researchers creating research questions. To understand how researchers create
RQs, we first conducted semi-structured interviews with 4 researchers about
how they formulate research questions; We then organized a focus group with
the same group of interviewed participants to identify their needs for an RQ
co-creation system.

We invited four researchers to participate in our semi-structured interviews
to understand their process of conducting research. All four participants were
doctoral researchers. We asked the interviewees to describe their most recent
experience starting a research project from scratch. Most participants
mentioned starting from a rough idea (e.g., domains, keywords, application
scenarios) before searching for related literature. Participants also
emphasized that formulating research ideas is often an iterative process.
Typically, participants used one or multiple hypothetical research
questions/ideas to facilitate the search for related literature and identify
research gaps. This process was described by the participants as both time-
consuming and labor-intensive. Participants also identified their process of
research question creation as hierarchical. One participant explicitly
mentioned that creating research questions naturally resembles the form of a
“mind map”, where the development of ideas gradually “narrows down” but could
have different branches. As described by the participants, the initial set of
RQs can often be broader and more general and can subsequently derive sub-RQs
under the topics of their predecessors, which helps facilitate the trade-off
between specific and general ideas as a common decision-making step during the
research ideation process. This process often resulted in the evolution of RQs
that, when visualized, could be drawn as a tree of RQs. This later informed
the design of our system to take the form of an interactive “mind map” that
preserved the provenance of RQ development.

(a) Feature Design.

The feature design screenshot was presented on sticky notes on this diagram.
The sticky notes were grouped into five themes, with different number of notes
in each group. There are also three yellow notes on top of the groups.

(b) Workflow Design.

The workflow design screenshot was presented on sticky notes on this diagram.
Six sticky notes on the right are inside a rectangular shape with text
”Iterative co-creation.” Five more sticky notes are on the left outside the
rectangular. All notes, except one white note on the left, are connected to at
least one other note.

Figure 2. Screenshots of the content created by participants using Miro during
the focus group study.

After the first interview study, the same participants were invited back to a
focus group session, where we aimed to identify their needs based on their
research workflow and propose interaction design to support the process. The
focus group went through two steps. First, participants were given three
questions created by researchers as cues, and then were asked to brainstorm
ideas and design expectations given the context of an AI-based system that
supports research question development. The three question cues were designed
as follows: 1) What questions will you ask the AI system? 2) What information
do you want to provide for the model to generate RQ for you? 3) How do you
produce RQ currently? And do you think the AI model can help you produce RQs?
The ideas created by participants are shown in Figure 2.

After discussion and summarizing themes, participants were asked to proceed to
the second step inspired by participatory approaches, where they discussed a
hypothetical workflow by contextualizing themselves using an AI-based RQ co-
creation system. During the focus group, we found that participants
highlighted several expectations for designing a human-AI RQ co-creation
system. Participants mentioned that the system should enable strong user
control by taking into consideration users’ inputs (e.g., ideas, keywords and
domain concepts), when generating new RQs. The ability for the system to
generate different variations of RQs with high diversity was also deemed a
preferred design, where the user should be allowed to choose from the outputs
based on their preference and expertise.

A Mental Model for Human-AI RQ Co-Creation After completing the formative
study, we summarized the findings and proposed a new mental model aiming to
capture the major interactions during the process of Human-AI Co-Creation for
RQs.

Figure 3.  Participants’ mental model of co-creating RQs with an LLM-based AI
agent. If an “action” is labeled together with an AI icon, it means that
participants perceived the ”labor” could be saved more by AI; a human icon
means that humans should evaluate AI generated RQs and provide feedback to
drive the iterative process; and it is hard to determine how human and AI
share the task of “refine and re-scope,” as it depends on individuals’
expertise and clarity of the expected research focus.

This is human’s mental model of RQ co-creation with the LLM-based agent. There
are four components inside this model: Present RQs and Explain AI Thoughts
(lower left, rectangular), Understand the Literature (center, rectangular),
Literature Space (top, with cylinder shape), and Refine and Re-scope (lower
right, rectangular). From Present RQs and Explain AI Thoughts to Refine and
Re-scope, there are two user-driven activities: Evaluate RQ creativity,
feasibility, etc., and Provide feedback. From Refine and Re-scope to
Understand the Literature, there is one user- and AI-driven activity:
Incorporate Human Feedback. From Understand the Literature to Present RQs and
Explain AI Thoughts, there is one AI-driven activity: Bootstrap Initial RQs.
From Understand the Literature to Literature Space, there is one AI-driven
activity: Search and select. From Literature space to Understand the
Literature, there is one AI-driven activity: Summarize and Integrate. The
model has ”Initial idea / keywords” as input and ”Well-defined High Quality
RQs” as output.

The proposed mental model consists of three major components: Understand
Literature, Present RQs and Explain AI Thoughts, and Refine and Re-scope. Past
research has provided in-depth discussion over how literature discovery plays
a major role in the scientific research process (Foster, 2004; Palmer et al.,
2009). However, such models were often discussed in a human-only context. Our
formative study results indicate the importance of considering AI as a
“collaborator” during the process of research ideation (Kim and Maher, 2023).
Refining research questions has also often been interpreted as a sub-step of
literature search and understanding. Although research ideation and literature
search are mutually dependent, our formative study results indicated a
distinction between the behaviors researchers conducted during the two
different stages. The process of literature discovery often involves reading
and summarizing a wide range of existing works. Participants emphasized their
challenges during the process of literature search and discovery, especially
for unfamiliar domains, and the need for AI to facilitate this process with
less human involvement. AI can be best used to perform factual summarization
and distillation of findings and knowledge before presenting them to humans.
On the contrary, proposing and refining research questions often requires more
creative thinking and generalization beyond past knowledge. In this scenario,
it is crucial to involve both humans and AI through the design of mix-
initiative co-creation systems in order to combine humans’ expertise and
preference with AI’s general world knowledge. Moreover, our formative study
findings explicate the evaluation of RQs as an additional component during the
RQ co-creation process. This process, as discussed during the formative study,
should utilize human expertise and the ability to conduct follow-up research
and validation.

Design Requirements Based on our findings from the focus group and the
proposed mental model, we also propose the following design requirements for
the system: The system should be able to 1) assist users’ brainstorming
process by automatically generating RQ candidates by taking human feedback; 2)
support users’ sensemaking of AI’s outputs by Explaining the rationale behind
the generation; 3) help users discover relevant literature and identify
research gaps.

##  5\. CoQuest System Design and Implementation

Based on our findings from the formative study, we designed and implemented an
LLM-based system that supports human-AI co-creation of creative research
questions. In this section, we provide details about the design of: 1) the
three-panel interface of the CoQuest system, including two different designs
to provide varied degrees of AI initiative; and 2) the agent LLM backend of
the CoQuest system.

###  5.1. CoQuest System Interaction Design

To support RQ co-creation, we designed features of the CoQuest systems around
the two-way communication between users and the AI, where users and AI take
turns during the communication process. As shown in Figure 1, our proposed
system consists of three major panels:

  1. (1)

RQ Flow Editor that facilitates a user’s major interactions, such as
generating RQs, providing input and feedback to AI, and editing the RQ flow
(e.g., drag and delete);

  2. (2)

Paper Graph Visualizer that displays the literature space related to each RQ;

  3. (3)

AI Thoughts that explains AI’s rationale of why each RQ is generated.

####  5.1.1. RQ Flow Editor: Two Design Options

CoQuest offers an interactive RQ Flow Editor panel that allows users to co-
create RQs with AI in an iterative manner. This panel is designed in a way to
resemble the design of a mind map, where each RQ node represents a generated
RQ (except the initial nodes, which only contain users’ initial ideas). This
allows users to more easily organize RQs under different topics while
preserving the hierarchical structure among different RQs, as suggested by the
findings from the formative study in section 2. Users can type in their rough
ideas or keywords by creating an initial node. When users click on one of the
nodes, the node expands and users can 1) type in textual user feedback to AI
in the text box; and 2) right-click on the node to generate more follow-up
RQs. The generated RQs will be connected with annotated edges to the source RQ
node. The RQ generation will result in one or several Directed Acyclic Graphs
(DAGs), which we will later refer to as RQ flows, that embed both the
hierarchical relations between generated RQs. Users can perform basic
interactions using the RQ flow editor, including zooming in/out and dragging
the nodes to organize the flows to aid their thinking process.

Two design options with different levels of AI initiative: Breadth-first and
Depth-first generation. One of the major design options we considered for the
CoQuest system is the degree of how much AI takes initiative during the co-
creation process. During the formative study, we obtained an understanding of
how users formulated their research questions in a hierarchical form, where
follow-up RQs are iteratively created based on previous predecessor RQs.
Intuitively, when formulating different RQs, the researcher might choose to
explore different topics in a broader sense, or to dive deep into a specific
topic. Thus, we consider two different designs when the system generates new
RQs, as shown in Figure 4: 1) breadth-first generation: When a user initiates
the generation of follow-up RQs, several RQs are produced simultaneously in
parallel. These new questions are all at the same hierarchical level following
the original question. 2) depth-first generation: In contrast, with this
method, when a user initiates the generation, follow-up RQs are created one
after the other, with each new question building upon the previous one in a
sequential manner.

The two designs impact the degree of initiative taken by AI in the CoQuest
system. Under the breadth-first generation, the user will have the freedom to
choose from multiple generated RQs and provide feedback at each turn of
generation, and then proceed to generate more RQs if desired. As in the depth-
first generation, the agent recursively generates a sequence of multiple RQs
without user input during the process. During the depth-first generation, the
agent needs to autonomously further “refine” the RQs multiple steps based on
the previous steps’ results, thus taking more initiative during the co-
creation. Although in both designs, AI actively participates in the co-
creation process by turn-taking with the human (Rezwana and Maher, 2022),
there exists a difference between the degree of initiative taken by AI, as AI
engages more actively during the depth-first generation compared to the
breadth-first generation. By carrying out the within-subject study under the
two designs, we aim to provide an empirical understanding of how initiative-
driven design options can impact users’ behavior and perception using human-AI
co-creation systems. To simplify the study design, we ensured that three RQs
were generated per turn in both designs.

Figure 4.  Two design options for the RQ Flow Editor panel in the CoQuest
system. The breadth-first generation triggers more RQs generated in one
iteration, whereas the depth-first generation triggers more iterations of RQ
refinement performed by the AI.

The two design options are presented side by side. On the left is Design
Option 1: Breadth-first Generation. From a Source RQ node on the left, three
generated RQ nodes on the right were connected with arrows. An additional
arrow pointing toward three Generated RQ nodes shows that these three arrows
are under iteration 1. On the right is Design Option 2: Depth-first
Generation. From a Source RQ node, a first Generated RQ node is connected with
arrow. An additional arrow pointing toward the first Generated RQ node shows
that this is iteration 1. From the first Generated RQ, a second Generated RQ
node is connected with arrow. An additional arrow pointing toward the second
Generated RQ node shows that this is iteration 2. From the second Generated
RQ, a third Generated RQ node is connected with arrow. An additional arrow
pointing toward the third Generated RQ node shows that this is iteration 3.

####  5.1.2. Paper Graph Visualizer: Interactive Literature Graph

The CoQuest system also provides an LLM-enabled literature discovery feature
to assist users in efficiently identifying and exploring existing works
related to each generated RQ. As shown in Figure 1, CoQuest’s literature
discovery feature is presented in the Paper Graph Visualizer panel.

When a user clicks on one of the generated RQ nodes, the Paper Graph
Visualizer panel reveals itself by visualizing the top-k most relevant papers
along with their citation relations retrieved from our citation graph. The
top-k papers are retrieved using our paper retrieval pipeline, as described in
section 5.2.2, using the text of the RQ clicked on by the user as the query.
In the displayed citation graph, each paper node represents a paper, and each
edge represents a citation relation. When the user hovers the cursor over one
of the paper nodes, a tooltip will appear with a quick preview of the paper’s
title information.

The paper nodes in the citation graph are designed to be interactive. When the
user clicks on one of the paper nodes , the the detailed information of the
selected paper will be displayed below. The information displayed includes the
paper’s title, author names, abstract, a TLDR summary provided by Semantic
Scholar API111https://www.semanticscholar.org/product/api, and a URL linking
to the page of the paper on Semantic Scholar. Upon the click on the paper
node, the Paper Graph Visualizer panel will also highlight the selected node
and its nearest neighbors (nodes and edges) to indicate which paper(s) have
directly cited the selected paper or been cited by the selected paper.

####  5.1.3. AI Thoughts: Explaining AI Rationale

The edges between RQ nodes represent the relation of which RQ the newly
generated RQ is based on. They also contain information about the results of
the actions undertaken by the agent leading up to the new RQ generated. When
the user clicks on an edge, the AI Thoughts panel appears that displays the
results of the agent’s action in a narrative format, as shown in the Figure 1.
Detailed implementations of the LLM-based backend for generating RQs and
rationales are provided below.

###  5.2. CoQuest System Backend and Implementation

The backend of the CoQuest system comprises an LLM-based agent that
automatically generates RQs based on users’ input and feedback, by performing
reasoning and executing actions that simulate a researcher’s mental model in a
semi-autonomous manner. Two major functions of the CoQuest backend include: 1)
the RQ generation ability of the LLM agent and 2) the related paper retrieval
module that supports literature discovery.

####  5.2.1. Generating RQs with LLM-based Agent.

The CoQuest system uses an LLM-based agent to generate creative research
questions (RQs) following the ReAct framework (Yao et al., 2022b), by adapting
the “Think-Act-Observe” framework when designing the prompting method. First,
the ”Think” step analyzes user input and context to decide an action,
resembling human research methods detailed in 2. During this round, the LLM
generates a chain of thought (following our designed prompt) before reaching
the conclusion of the action as the next step. The actions are executable sub-
processes whose results will be used as additional context to help the LLM
generate better RQs.The available actions include: 1) Search and summarize
related works (Literature Discovery); 2) Hypothesizing use cases
(Proposition); 3) Scoping/narrowing down (Refinement); 4) Reflection through
comparison with existing works (Evaluation). Next, during the “Act” and
“Observe” steps, the execution of actions is achieved in the format of API
calls through prompting, and can be later parsed and executed through one or
multiple pre-implemented Python functions (e.g., retrieve_papers and
summarize_papers). After the next action has been inferred during the “Think”
step, the agent executes the action and appends the results of the action to
the context. Finally, for the agent to generate RQs at each step, we added an
additional step of creating RQs at the end of each “Observe” step. At this
step, new RQs are generated based on the provided context and instructions
that combine the output from the performed action and pre-defined prompt.

####  5.2.2. Related Paper Retrieval.

In order to help users more easily identify related works, the CoQuest system
employs a retrieval pipeline to gather existing papers related to the RQs that
the users are developing. We curated a literature citation graph from an
existing pool of HCI papers, where the nodes represent papers and the edges
represent citation relations. The CoQuest system uses a sentence-based
semantic embedding model (Reimers and Gurevych, 2019) to obtain vector
representations of each paper in the citation graph. Given a paper’s title,
metadata, and abstract in text form and a given query (e.g., RQs and user
input), the sentence embedding model encodes them into semantic embeddings.
After obtaining the embeddings of papers and the query, the system calculates
the similarity between paper candidates and the query. This is done through
re-ranking using Maximal Marginal Relevance (MMR) (Carbonell and Goldstein,
1998). Then, the system ranks the paper candidates and select the top-k papers
with the highest similarity scores as the final related papers to visualize.
More details on the implementation of the retrieval pipeline are discussed in
5.2.3.

####  5.2.3. System Implementation

The CoQuest system is implemented in Typescript as a web application using
ReactJS and TailwindCSS for the frontend. The interactive flow editor is
implemented using React Flow222https://github.com/wbkd/react-flow/. The
application backend uses Python with
FastAPI333https://github.com/tiangolo/fastapi/ as the RESTful API server
framework. We use AutoGPT444https://github.com/Significant-Gravitas/Auto-GPT/
as the foundation of our agent-based LLM implementation. We used OpenAI’s
gpt3.5-turbo-16k model as our LLM engine and text-embedding-ada-002 model as
the sentence embedding model through the cloud service API provided by
Microsoft Azure. We collected a fixed set of open-access publications through
the Semantic Scholar API covering several major HCI conferences (including
CHI, CSCW, UIST, Group, IMWUT, IJHCI, and IUI). The final collection of
publications includes 2,043 papers.

##  6\. User Study Evaluating Co-Creation with CoQuest

To further understand the effect of the CoQuest system and how two designs of
RQ generation impact users’ human-AI co-creation behavior, we conducted a
within-subjects user study with 20 participants from 8 different institutions
by asking the participants to create new research questions using the CoQuest
system. All participants were graduate students currently enrolled or just
graduated with prior experience with research. During the study process, we
collected participants’ behavior and perception (i.e., ratings towards RQs,
and ratings towards the CoQuest system) data for mixed-method (quantitative
and qualitative) analysis. All studies were completed remotely online over
video calls, where participants were asked to share their screens.
Participants were also free to withdraw at any point during the study. Study
procedures were approved by the IRB of the researchers’ institution. We
compensated participants $20 per hour for the user study. Studies lasted 1-2
hours, including two tasks using two designs (breadth-first and depth-first
generations), a survey after each task, and an exit interview.

###  6.1. Within-Subjects User Study - Two Tasks with Assigned Condition:
Breath-First vs. Depth-first

A within-subjects user study was conducted to understand the difference in
users’ behavior and perception of the system potentially brought by the two
different conditions using the two designs, referred to as breadth-first
condition and depth-first condition. Each participant was asked to complete
two tasks: During each task, we ask each participant to complete a task
designed by researchers to simulate real-life scenarios of research idea
formulation. The two task topics used in this study are: “AR/VR for education
and learning” and “AI and crowdsourcing”. The two topics were chosen since
they cover a wide range of specific domains and provide ample opportunities
for users to explore and drill down on related topics. To account for the
effect of the chronological order of both the task topics and conditions on
the results, we followed a counterbalanced design (Pollatsek and Well, 1995)
by randomizing the experiment conditions so that all possible orders and
combinations were randomly assigned to an equal amount of participants.
Participants were encouraged to think aloud during the tasks.

###  6.2. Data Collection and Analysis

We collected both users’ perception and behavior data to perform a
comprehensive evaluation of the CoQuest system. To understand users’
perception of the CoQuest system, we gathered two types of perception data
from participants: individual ratings for each generated RQ (RQ ratings),
which reflects users’ perception of their co-creation outcomes; and overall
post-task evaluations of the system (system ratings), which reflects users’
perception of their co-creation experience. Users’ behavior data was also
collected in the form of system logs and video recordings for later analysis.

####  6.2.1. Co-Creation Outcome: Rating AI-Generated RQs During Each Task

The RQ ratings are collected on the fly during each task of the study, where
participants were asked to rate at least six RQs of their choice. These
ratings are intended to capture the immediate perception of users towards the
co-creation outcomes (i.e., generated RQs). The during-task rating collection
was designed with the intention of nudging users to actively evaluate the RQs
during the co-creation process, and also as a way to reflect the accurate user
perception in real-time. We adopt Boden’s criteria (Boden, 2004) to measure
creativity from three different aspects- novelty, value, surprise, and
relevance. Participants can give ratings using a 5-point Likert scale slider
positioned under each RQ node.

####  6.2.2. Perceived Experience: Survey Scores collected at the End of
Either Task, and Exit Interviews

To analyze participants’ perceived experience using our system, we collected
their survey scores upon the completion of each task, and conducted exit
interviews before the end of each study.

Survey Scores The co-creation experience scores, however, were given by
participants at the end of each task through a survey. The survey contains
multiple 5-point Likert scale questions designed to measure their experience
from the aspects of: control, creativity, meta-creativity, cognitive load, and
trust. We designed 2 Likert-scale questions for each of the 5 aspects to avoid
potential bias and ensure a more comprehensive evaluation555The collected
survey ratings have an average Cronbach’s Alpha value of $\alpha=.85$,
suggesting good reliability.. The complete list of survey questions can be
found in Appendix A.1.

Exit Interview After the participant completed the post-task rating survey, we
also conducted a ten-minute semi-structured interview to obtain a deeper
understanding of the participant’s experience with the system. Interview data
was analyzed using open-ended coding, by having two researchers review the
interview transcripts and frequently discuss with each other (Khandkar, 2009).
The interview coding highlighted differences in perception between the two
conditions explicated by participants. To measure participants’ familiarity
with the task topic, we also asked participants how familiar they were with
the two topics with three choices: not familiar, kind of familiar, and very
familiar.

####  6.2.3. Behavior: Think-Aloud Data and System Log.

We annotated users’ behavior (think-aloud transcripts and system usage) to
understand how users utilized our system.

Think-Aloud During Co-Creation Think-aloud data was primarily used for
understanding how users generated and interpreted RQs. One researcher first
generated a codebook through open coding using videos and transcripts from
three randomly selected participants, and then three other researchers
independently coded the data of the same three participants, reaching an
inter-rater agreement of 0.83 in Krippendorf’s alpha. The annotators then
discussed and refined the codebook again until they reached full agreement.
Then, four researchers proceeded to annotate the remaining 17 participants’
behavior data separately. In the final codebook, whether users interacted with
the system was annotated and used for quantitative analysis in RQ3 as “Acted
During Wait”. The final codebook also included sense-making behavior (e.g.,
reasons for (not) waiting, reason for providing certain feedback) as
qualitative results.

System Log During Co-Creation We gathered multiple types of system logs for
subsequent analysis of user behavior. We collected and used the counts of
generated RQs and the lengths of user-typed feedback to AI for quantitative
analysis of RQ1 and RQ3. User interactions such as clicks on components like
RQ nodes and AI Thoughts were used for qualitative analysis along with the
think-aloud data. The text content of user-typed feedback was also used for
qualitative analysis in RQ2.

We used different notations throughout this paper to distinguish among the
different types of quotes presented in the results. For AI-generated RQs, we
use italic (e.g., AI-generated RQ); for feedback to the AI, we use double-
quotes (e.g., “feedback”); for interview and think-aloud quotes, we use
double-quoted italics (e.g., “interview quotes”).

##  7\. Findings

###  7.1. Perception of Co-Creation Experience and Outcome (RQ1)

The breadth-first condition allows users to generate multiple RQs in parallel
with one interaction, whereas the depth-first condition creates three RQs
sequentially, one after another. In this section, we analyzed how these two
different conditions impact participants’ perception towards both co-creation
experience and outcomes.

####  7.1.1. Experience: User Perceived Stronger Creativity and Trust Using
Breadth-first Condition

In total, 20 participants created 504 RQs throughout the study, with 276 RQs
(M=14.53, SD=6.19) co-created with CoQuest system under the breadth-first
condition and 228 RQs (M=12, SD=5.31) under the depth-first condition. To
evaluate the overall experience of the co-creation process, we asked our
participants to complete surveys upon finishing each task. A Mann-Whitney U
test666Power analysis conducted using G*Power(Faul et al., 2007). of the
survey results suggested that users perceived significantly stronger
creativity ($U=288.0$, $p=.015^{*},d=.68,Power=.83$) using the system under
the breadth-first condition (M=3.78, SD=0.82) than the depth-first condition
(M=3.18, SD=0.69). Similarly, the user-perceived trust under the breadth-first
condition (M=4.15, SD=0.76) was found significantly higher
($U=292.0,p=.011^{*},d=.67,Power=.81$) than the depth-first condition (M=3.5,
SD=0.86). The results of all 5 rating categories are shown in Figure 5.

Figure 5.  Users’ 5-point Likert scale survey ratings toward the two
conditions of the system; Users perceived stronger creativity and trust when
using the system in the breadth-first condition (statistically significant).

There are five violinplots (similar to boxplots) that presents users’ 5-point
Likert scale survey ratings toward five dimensions: control, creativity, meta
creativity, cognitive load, trust. Each violinplot has breadth-first and
depth-first side by side. For control, the two shapes are very similar with
the same median and upper limit. The lower limit is higher for breadth-first
than depth-first. For creativity, breadth-first had higher median and higher
upper limit than depth-first, and the lower limit is the same. For meta
creativity, the upper limit and lower limit are the same, but breadth-first
had higher median. For cognitive load, the shapes are very similar with the
same median, upper-limit, and lower-limit. For trust, breadth-first had higher
median, higher upper-limit, and higher lower-limit than depth-first.

During the interview, 12 out of 20 participants (60%) also mentioned that they
preferred the breadth-first condition. An example RQ flow generated by the
participant (P4) during one of the sessions under the breadth-first condition
is shown in Figure 6. The interview and think-aloud transcripts explained why
the breadth-first condition was perceived to create a better experience.

First, the breadth-first condition results are easier to interpret and require
less wait time to obtain the same amount of results compared with the depth-
first condition. Participants appreciated that the AI was able to list the
three generated RQs in parallel “all at once” (P10), which allowed them to
easily “compare among the RQs” (P10) and “explore multiple potential research
questions” (P4). It is also easier to understand the reason behind the
generated RQs in breadth-first condition, as all three RQs shared the same
predecessor RQ and rationale. Although the depth-first condition also
generated three RQs using one click, participants had to wait longer to see
all three generated RQs than when using the breadth-first condition.
Therefore, some participants tended to either focus on the first RQ and ignore
the other two, or they would start with the last RQ and move to an earlier
generated RQ if the latter one was not deemed ideal.

Second, participants found that the breadth-first condition gave them more
control over which direction of RQs they would like to proceed with. With the
three options listed in parallel, participants were able to choose the RQ that
they preferred the most and generate more follow-up RQs based on it. The tree-
structured design also allowed participants to highlight RQs that were more
relevant, and then choose the branch they were more interested in. P14 found
the breadth-first condition to be “less cognitively demanding,” and P16
preferred to use the breadth-first condition for “brainstorming under one
topic.” The depth-first condition, on the other hand, may generate RQs in the
second and third iterations that were hard to understand how they were related
to the first iteration. For example, P6 started an RQ flow with feedback
“crowdsourcing and AI” using depth-first condition. While the first question
and their feedback did not mention the term medical diagnosis, this word
appeared after the second iteration but disappeared again after the third
iteration. During the interview, they mentioned that the depth-first condition
“sometimes go back and forth” and it was “confusing” to understand “the logic
of why these 3 are parent, child, and grandchild [nodes].”

Figure 6.  Part of P4’s RQ flow using breadth-first condition when exploring
topic “AI and crowdsourcing.” Note that the participant generated from two
different RQ nodes from the same iteration, and only one set of generated RQs
was presented. They used keywords such as “AI and crowdsourcing” and
“educational setting” as feedback to AI to generate more RQs. The third
iteration was not included in this figure.

The RQ flow of P4 using breadth-first condition. From the left, there is an
initial node with user feedback ”crowdsourcing and AI.” It leads to three RQ
nodes with the same text on each edge ”BreadthL search_and_summarize_papers.”
The generated RQs are: How can AI be used to improve the efficiency and
quality of crowdsourcing; What are the ethical implications of using AI in
crowdsourcing; How can crowdsourcing be used to improve the accuracy and
effectiveness of AI algorithms. The first RQ is connected with three edges,
but the three RQ nodes from it are not included in this diagram. The third RQ
is expanded with user feedback ”in an education setting.” This node further
leads to three RQ nodes on the right with the same text on edge ”Breadth:
narrow_down_rqs.” The three RQ nodes are: What are the potential ethical
concerns associated with using AI in grading and assessment in educational
settings; What are the ethical implications of using AI in crowdsourcing for
educational research; How can AI be used in educational settings to enhance
student learning while minimizing ethical concerns. The third RQ had three
more edges connected to it, but generated RQ nodes were not included in this
diagram.

Figure 7.  Part of P2’s RQ flow using depth-first condition when exploring the
topic “AI and crowdsourcing.” The dashed lines represent the second and third
iterations of RQ generation. From the first three iterations, the participant
chose to continue with the RQ generated in the first iteration and provide
feedback to ask the AI to be more specific. The AI then generated three more
RQs, which she again chose to continue with the first of the three (subsequent
RQs are not shown in this figure).

The RQ flow of P2 using depth-first condition. From the left, there is an
initial node with user feedback ”Crowd and AI collaboration for finding
relevant literature for research questions.” It leads to one RQ node with text
”depth: search_and_summarize_papers” on the solid line edge. The RQ is: How
can we design AI systems that effectively collaborate with human experts to
find relevant literature for research questions. This RQ node is expanded with
user feedback ”Can you be more specific about AI systems? this is just
repeating my initial thought.” This RQ node is connected to 2 nodes. The upper
node is connected to using dashed edge and text ”depth:
hypothesize_use_cases.” The generated RQ is: What are the most effective
strategies for incentivizing participation and engagement in a crowd-based
system for finding relevant literature for research questions. From this node,
there is another dashed edge connecting to another generated node with text
”depth: search_and_summarize_papers” on the edge. The generated RQ is: How can
we design a crowd and AI collaboration system for finding relevant literature
for research questions that is transparent, accountable, and inclusive of
diverse perspectives and expertise. The lower RQ node connected to the first,
expanded RQ node is connected with solid line edge with text ”depth:
hypothesize_use_cases.” The generated RQ is: How can AI systems be designed to
effectively integrate feedback from human experts to improve the quality and
relevance of the literature that is retrieved. This node is further connected
with two edges. The lower edge with solid line is connected to the outside of
the diagram. The upper edge with dashed line had text ”depth:
compare_rq_with_papers” and connects to the RQ: How can we design AI systems
that can effectively handle the diverse and complex needs of different
research domains and communities. This node is further connected to another RQ
node with dashed line and text ”depth: hypothesize_use_cases” on the edge. The
generated RQ is: What are the best ways to evaluate the accuracy and relevance
of AI systems that collaborate with human experts to find relevant literature
for research questions.

####  7.1.2. Outcome: Depth-first Condition Yields RQs with Higher-rated
Creativity

We measure the outcomes from the co-creation process by asking participants to
rate the RQs on the fly during the tasks. To account for the problem of
multiple comparisons, we conducted a MANOVA and found a significant difference
($F(4,209)=3.79,p=.0057^{**};Wilk^{\prime}s\ \Lambda=0.909$) in the user-
provided ratings for RQs between the two conditions (i.e., breadth-first and
depth-first). We conducted Mann-Whitney U tests with these ratings toward
generated RQs and found that both the novelty
($U=2199.5,p=.002^{**},d=.40,Power=.89$) and surprise
($U=2387.5,p=0.017^{*},d=.32,Power=.75$) of the RQs were rated higher when
using the depth-first condition (M=3.78, SD=1.29) compared to the breadth-
first condition (M=3.28, SD=1.22). In contrast to the post-task survey results
that suggested that the breadth-first condition was perceived to be more
creative, the RQ ratings suggested that the generated RQs using the depth-
first condition were more innovative instead. Figure 7 shows an example of
participants using the depth-first condition.

During tasks and interviews, participants explained why they found the
outcomes from the depth-first condition to be more innovative. First,
participants found that the depth-first condition tended to generate
surprising RQs with ideas that were not present previously or included in
their feedback to AI. Since the second and third RQs were generated based on
the first RQ without user feedback, the AI sometimes may add unexpected
keywords to the RQs. While a few participants found these unexpected additions
to be “surprising in a negative manner” (P3) or “distracting” (P12), more
participants described it in a positive way such as “insightful” (P18) or
“impressive” (P8) and found it interesting to see the AI “thinking when
generating the questions (P11).” This would be particularly useful if the
participant was less familiar with a certain topic, as it would be more
inspiring.

Second, the depth-first condition allows participants to dive deep into one
chosen RQ and improve its quality. While it requires “more patience” (P14) to
wait for and read through all three generated RQs from one interaction using
depth-first condition, participants found it easier to go “deeper” and form an
RQ that is “more specific” (P4). The depth-first RQs were also found to be
more “creative and unique” (P13). P17 also expressed a positive opinion about
having the freedom to choose and generate follow-up RQs from any AI-generated
RQs they would like to work on. In fact, participants could also utilize the
AI generation wait time to better interpret existing RQs and generate more
valuable RQs.

Summary (RQ1): According to the post-session survey results, the breadth-first
condition offered users a better co-creation experience, with higher
perceptions of creativity and trust. In contrast, RQ rating data showed that
the depth-first approach led to the generation of RQs deemed more creative by
participants, with a unique depth and unexpected novelty. This insight
indicates that while the breadth-first condition enhances user engagement, the
depth-first condition stimulates deeper and more novel outcomes, leading to
distinct advantages in different aspects of the co-creation process.

###  7.2. Users’ Co-creation Behavior with LLM-based Agent (RQ2)

To explore users’ co-creation behavior with AI, we examined how they
interacted with the CoQuest system through think-aloud comments, feedback to
AI, and activities performed while they were waiting for AI to generate the
results. In this section, we explain the findings to shed light on how
participants used the CoQuest system under the two conditions.

####  7.2.1. Depth-first Condition Stimulated More User Interactions During
Wait Time

The CoQuest system required users to wait for approximately 30 seconds for the
LLM inference to finish after each time the generation was triggered. During
this wait time, users can utilize other system features in parallel to the
generation, such as interpreting other RQs, generating additional RQs, viewing
AI Thoughts, and exploring the paper graph. By examining the behavior data of
participants, we found that most participants utilized the wait time during
the generation of RQs to perform other activities. In total, 12 out of 20
participants utilized the wait time to interpret and evaluate other existing
RQs on the RQ flow editor. Among them, 7 participants created new RQs while
waiting for prior RQs generation to be finished. We also found that more
participants explored other RQs during wait time under the depth-first
condition (N=12) than the breadth-first condition (N=6), with a two-proportion
z-test indicating significance ($z=-2.01$, $p=0.045^{*}$).

Participants were observed to switch among different threads of RQs during the
wait time of generation. P16, for example, utilized wait time under depth-
first conditions to start generating another thread of RQs. The participant
further explained during the interview that if the RQs were generated quicker,
“probably would just end up exploring one at a time. But because it was taking
a while, I was like, oh, I can write another one.” The participant found the
generation wait time beneficial for brainstorming new ideas, as it offered a
good opportunity for exploring different directions in parallel. In addition,
we also found a positive correlation, shown by Pearson’s correlation test
($r(392)=0.18,p<0.001^{***}$), between whether a user made use of the RQ
generation wait time and the length of the user’s feedback to AI. The test
result indicated that users utilized the RQ generation wait time to
contemplate more detailed feedback for AI, suggesting higher engagement in
human-AI co-creation.

During the wait time for generation, participants were also found to use the
“AI thoughts” feature to understand the relationship between different RQs and
the rationales behind generated RQs. Though it was confusing for some
participants, most participants were observed to have read the “AI thoughts,”
especially while waiting for all three RQs to be generated under the depth-
first condition (14 out of 20 users clicked and viewed the “AI thoughts” more
than 10 times). More specifically, in the depth-first condition, each click
generates three different “AI thoughts” compared to breadth-first condition,
where each click only generates one “AI thoughts.” In addition to
understanding the logical relation and reasoning behind RQs, participants also
used “AI thoughts” for other co-creation purposes that were shared between the
two conditions. For instance, users have found AI thoughts to be helpful both
in terms of understanding the rationale of how RQs are generated, and also
providing additional ideas for users to elaborate on. Some participants used
the AI thoughts for sense-making of the generated RQs. When they recognized
the AI thoughts were connected with the paper graph, they expressed that they
trusted the system more because it seems like the AI thoughts are “from actual
papers” that “are peer-reviewed” rather than “coming from large language
model, which is not necessarily [factually] true” (P10). Among our different
types of “AI thoughts,” one participant who could tell such differences (P16)
said they liked the “summary of existing work” explanation type because it
seems like this type “builds on existing works” and makes it more trustworthy.
Some other participants also used the terms and keywords that appeared in “AI
thoughts” to help develop their feedback to further guide RQ generation. For
example, P10 wrote the feedback to AI “Explore feature selection for non-
experts” by taking inspiration from the AI’s thoughts about a summary of works
related to feature selection techniques for data mining.

####  7.2.2. Users Have Different Strategies for Providing Feedback to AI

The CoQuest system allows users to type in textual feedback to AI under each
RQ before triggering the generation of new RQs. Overall, 20 participants wrote
119 pieces of feedback in total using both conditions (M=7.44, SD=5.25) during
the co-creation process. To find whether the participants had different ways
of communicating with the AI using text feedback, we first analyzed the
difference among users’ input lengths (word counts) by fitting a mixed-effects
model that considers both the random effect from different users and the
potential fixed effect from the two conditions. Details of the model and
results can be found in Appendix A.2. A likelihood ratio test was then
conducted to validate the significance of the random effect brought by users
($\chi^{2}(1)=9.189,p=0.002^{**}$). The result of the likelihood ratio test
indicated a statistically significant difference in feedback lengths among
different users regardless of the condition. However, we found no significant
association between the system condition and users’ feedback lengths.

To obtain a deeper understanding of how participants employ different
strategies in providing feedback to AI, we further reviewed and coded
participants’ feedback to AI during the co-creation process, and observed that
participants tend to provide feedback to the AI in different ways. More
specifically, we found three main themes.

The most common theme of giving AI feedback is to provide keywords (N=59),
which is shorter in length. Among these feedback, 61.0 % (N=36) were used to
start a new node using the keywords that the participant would like the AI to
start with. From a generated RQ, participants may also provide new keywords
and instruct the AI to include these keywords in the next iteration. Search
and summarize was frequently used by AI to generate RQs based on the new
keywords. For example, P4 started a node using the breadth-first condition
with “AI and crowdsourcing,” as shown in Figure 6. The participant then
continued with the generated RQ What are the ethical implications of using AI
in crowdsourcing? and wrote “in an educational setting.” The AI then generated
three new RQs with the new keyword.

The second theme is asking AI specific questions (N=40). These questions are
usually longer and in full sentences that either ask the AI to explain terms
used in a generated RQ, to lead the AI toward a new direction, or to ask the
AI to review more related literature. For example, based on the RQ What is the
impact of medical simulations on medical student learning and skill
development?, P17 wrote, “It good question to research into, can you give me
some information that is common among the papers.” The participant was able to
proceed with one of the three generated RQs.

A third theme is asking the AI to be more specific on an RQ (N=55). These
feedback usually starts with the phrase “be more specific.” In 45 out of the
55 cases, participants would also include the direction that they would like
the AI to continue on. In response, the AI usually narrows down RQs or
performs search and summarization. For example, after seeing the RQ How can we
design AI systems that effectively collaborate with human experts to find
relevant literature for research questions? using depth-first condition, P2
wrote, “Can you be more specific about AI systems? this is just repeating my
initial thought.” The AI then generated three more RQs, as shown in Figure 7.

Summary (RQ2): Participants were found to have employed different strategies
when co-creating research questions (RQs) with AI. Participants mainly
provided feedback by listing keywords, posing additional questions, or
requesting specificity. While waiting for the AI’s response, users often
explored other RQs, showing greater engagement in conditions with longer wait
times. The AI Thoughts panel, which offers insights into the AI’s reasoning,
was found beneficial in enhancing trust and inspiring feedback, especially
when grounded in peer-reviewed literature.

###  7.3. Relationship between Users’ Persona/Behavior and Their Perceptions
(RQ3)

To better understand the factors influencing participants’ perceived
experiences and outcomes, we performed two linear regression analyses, aiming
to associate user perceptions with their behaviors and backgrounds. The two
sets of regressions aim to yield insights about users’ perceptions from two
perspectives: 1) the first set of regressions adopts participants’ ratings for
RQs (i.e., novelty, value, surprise, and relevance) as dependent variables; 2)
another set of regressions participants’ post-session survey scores for the
system (i.e., control, creativity, meta creativity, and cognitive load) as the
dependent variables.

We chose factors that could represent users’ behavior as predictors based on
our previous findings777 To examine the potential issue of multicollinearity,
we calculated the Variance Inflation Factor (VIF) for each predictor. All
predictors yielded VIFs lower than the common threshold of 5 (James et al.,
2013), indicating no substantial collinearity problem. . We first considered
the conditions of the system (breadth-first or depth-first) experienced by the
user, as indicated by the results in 7.1 that the two conditions might have a
difference between their impact over the user’s perception of co-creation
experience and outcomes. We constructed the predictor as a categorical
variable, with value 0 standing for breadth-first condition and value 1
standing for depth-first condition. We also considered factors related to user
engagement, including the length of user feedback to AI, which was found to
vary across different users as mentioned in 7.2.2, and the count of total RQ
nodes created. Additionally, we included whether users performed any actions
while waiting for results from an already triggered generation as a potential
factor, as discussed in 7.2.1. Participants’ familiarity with task topics was
also considered as a predictor, measured through the familiarity ratings
collected through the post-session surveys. The familiarity variable was
constructed as an ordinal variable taking three possible integer values from 0
(not familiar) to 2 (very familiar).

Table 1.  Regression Results with RQ ratings as dependent variables and users’ behavior data as predictors; Each column in the table represents one regression performed with the corresponding rating item as the dependent variable.  | Dependent Variables — Outcomes (RQ Ratings)
---|---
Predictors |  Novelty* |  Value* |  Surprise* |  Relevance**
|  $\beta$ (S.E.)* |  $\beta$ (S.E.)* |  $\beta$ (S.E.)* |  $\beta$ (S.E.)**
Condition (Depth-First=1) |  1.53 (.63)* |  1.54 (.64)* |  1.52 (.60)* |  1.70 (.68)**
Feedback Length |  .15 (.06)* |  .17 (.06)* |  .15 (.06)* |  .18 (.06)**
Total # of RQs Created |  -.01 (.03)* |  -.03 (.03)* |  -.02 (.03)* |  -.04 (.04)**
Acted During Wait |  -.06 (.07)* |  -.07 (.07)* |  -.06 (.06)* |  -.08 (.07)**
Familiarity |  .19 (.14)* |  .18 (.14)* |  .20 (.13)* |  .22 (.15)**
*: p$<$0.05, **: p$<$0.01, ***: p$<$0.001
Table 2. Regression Results with survey ratings as dependent variables and users’ behavior data as predictors; | Dependent Variables — Experience (Survey Scores)
---|---
Predictors |  Control** |  Creativity |  Meta Creativity |  Cognitive Load |  Trust
|  $\beta$ (S.E.)** |  $\beta$ (S.E.) |  $\beta$ (S.E.)** |  $\beta$ (S.E.)*** |  $\beta$ (S.E.)
Cond. (Depth-First=1) |  .34 (.71)** |  -.41 (.46)* |  -.74 (.23)** |  .82 (.36)*** |  -.58 (.88)*
Feedback Length |  -.03 (.04)** |  .02 (.05)* |  -.01 (.03)** |  .07 (.01)*** |  .03 (.04)*
Total # of RQs Created |  -.07 (.03)** |  -.01 (.01)* |  .03 (.01)** |  -.01 (.01)*** |  .02 (.05)*
Acted During Wait |  .10 (.04)** |  -.06 (.07)* |  -.03 (.04)** |  .06 (.05)*** |  -.03 (.04)*
Familiarity |  -.22 (.08)** |  .04 (.09)* |  -.09 (.05)** |  -.08 (.04)*** |  .07 (.08)*
*: p$<$0.05, **: p$<$0.01, ***: p$<$0.001

Positive perceptions of generated RQs associated with longer feedback to AI.
As shown by the results in table 1, a positive association has been identified
between all four dimensions of user-perceived RQ ratings and the average
lengths of users’ input feedback lengths. The finding suggests that increased
user engagement in the human-AI co-creation process through providing textual
feedback might improve the perceived outcome quality. In addition, we found
that the RQ ratings are positively associated with whether a user used the
system under the depth-first condition. Namely, users perceived the RQs
generated by AI under the depth-first condition to be of better quality than
the breadth-first condition. This also corresponds to our findings in 7.1.2.
From Table 1, it can also be seen that participants’ feedback length is
positively associated with perceived cognitive load. The results indicate
that, although providing longer feedback required users to invest more
thoughts, it also improved the co-creation outcomes. The finding also aligns
with our earlier qualitative results in 7.1. The interview result further
reveals that providing feedback promoted their reflection on themselves and
trying to understand how AI works, although tiring, simultaneously helped them
improve and manage their own creative thinking processes. More specially, some
participants were observed during think-aloud performing “reverse engineering”
to interpret how AI works and even try to replicate the generation process in
a new thread.

Factors associated with users’ perception of co-creation experience. Table 2
shows the regression results with participants’ survey rating responses as
dependent variables and user-specific factors as predictors. We found a
statistically significant positive association between whether a participant
has utilized the RQ generation wait time to perform other activities and the
participant’s perceived control for the system. This indicates that users tend
to perceive higher control of the system when better utilizing the generation
wait time beyond merely waiting for the generated results. There was also a
negative association between the total number of nodes generated by
participants and their perceived control, which indicates that as users
generate more RQs during each session, they perceive to have weaker control of
the system.

Participants’ familiarity with the task topics was also found to be negatively
associated with their perceived control. This was also reflected by
participants during their think-aloud process, as users who were more familiar
with the task topic had stronger expectations for the generated RQs: “… in
educational research, we don’t say crowd-sourcing anymore. We say learner
sourcing … ” (P4). We also noted that more experienced researchers would
explicitly specify their needs for the agent to generate RQs in directions
using terms related to research methodology. For example, P17 wrote in one of
his feedback to AI, “What the metrics for effectiveness and engagements.”
Several participants reflected that they were having a rough initial thought
in their mind when being assigned a topic that is familiar to them, and what
AI does is only help them externalize the thoughts. P4, for example, found
several generated RQs to be “surprising in a negative manner” as he would
evaluate whether an RQ “is something interesting, something novel, and …
relates to some of the prior literature.”

Summary (RQ3): Factors related to participants’ system usage were found to be
associated with users’ perceptions of co-creation experiences and outcomes.
The depth-first condition was also associated with better-perceived outcome
quality in AI-generated RQs than the breadth-first condition. Users’
familiarity with task topics, interestingly, led to a reduced sense of
control, indicating users with different levels of expertise may have specific
expectations unmet by the AI system. Additionally, we found that users
providing longer feedback to AI, although introducing a larger cognitive load,
led to outcomes with better quality.

##  8\. Discussion

In this section, we explore how the insights from our user study align with
established theories and previous research. Furthermore, we offer design
implications for future co-creation systems using Large Language Models.

###  8.1. Enriching our Proposed Mental Model for Human-AI Co-Creation of
Novel Research Questions

Existing studies (Foster, 2004; Palmer et al., 2009) have mostly been focused
on the research lifecycle as a whole when discussing the models of the
research process. Our user study findings shed light on additional factors to
consider when designing future human-AI co-creation systems for research
question generation, as shown in Figure 8.

Figure 8.  Updating the human-AI RQ co-creation mental model with additional
factors identified through the experimental study.

This is the updated human’s mental model of RQ co-creation with the LLM-based
agent. The following description are the same as the initial model in Figure.
There are four components inside this model: Present RQs and Explain AI
Thoughts (lower left, rectangular), Understand the Literature (center,
rectangular), Literature Space (top, with cylinder shape), and Refine and Re-
scope (lower right, rectangular). From Present RQs and Explain AI Thoughts to
Refine and Re-scope, there are two user-driven activities: Evaluate RQ
creativity, feasibility, etc., and Provide feedback. From Refine and Re-scope
to Understand the Literature, there is one user- and AI-driven activity:
Incorporate Human Feedback. From Understand the Literature to Present RQs and
Explain AI Thoughts, there is one AI-driven activity: Bootstrap Initial RQs.
From Understand the Literature to Literature Space, there is one AI-driven
activity: Search and select. From Literature space to Understand the
Literature, there is one AI-driven activity: Summarize and Integrate. The
model has ”Initial idea / keywords” as input and ”Well-defined High Quality
RQs” as output. The following description are the differences from the initial
model. There are three additional blue text highlighting the additional
factors added to the model. From Present RQs and Explain AI Thoughts to Refine
and Re-scope, there is blue text: User persona. From Refine and Re-scope to
Understand the Literature, there is blue text: Intentional wait-time. From
Understand the Literature to Present RQs and Explain AI Thoughts, there is
blue text: AI Initiatives: breadth/depth.

####  8.1.1. Wait Time in AI-based Co-Creation System as an Opportunity to
Promote Creativity

Our observation of users’ behavior patterns when using the CoQuest system
sheds light on the possibility of utilizing the AI system’s processing wait
time as a design opportunity for users’ exploration in the context of human-AI
co-creation systems. The RQ3 findings revealed that when users utilized wait
time to perform other activities, it improved their perceived control of the
co-creation experience. This is contradictory to the common belief that the
response delay in AI-based systems only leads to a negative impact on users’
experience. Our RQ2 results showed that users utilized the time waiting to
jump across different threads of RQs and perform exploration of ideas in
parallel, or to articulate more detailed feedback for AI. The utilization of
wait time was found to be prominent especially under the depth-first
condition, which could be one of the reasons leading to its higher perceived
co-creation outcome quality.

Most recent studies on LLM optimization focus on reducing the inference time
and speeding the generation wait time needed for general purposes (Liu et al.,
2023a; Leviathan et al., 2023; Dettmers and Zettlemoyer, 2023). Our findings,
however, provided a unique perspective that in the context of LLM-based co-
creation systems, the generation wait time can be exploited, or even purposely
introduced, to promote users’ creative activities. This can be achieved using
different design techniques, such as tree-based visualization that highlights
concurrency, or incorporating interactive nudges that guide users towards
reflection, ideation, or brainstorming during these pauses. Such techniques
can not only enhance user engagement but also maximize the cognitive benefits
derived from the wait time breaks, potentially leading to more innovative and
diverse co-creation outcomes.

####  8.1.2. Aligning Users’ Perception towards Experience and Outcomes of
Generated RQs for Improved Creativity

In addition to prior studies’ understanding of mix-initiative system design
(Rezwana and Maher, 2022; Weisz et al., 2023), our study provides empirical
findings to support the need to consider the degree of initiative taken by AI
as a design option in further human-AI co-creation system design. Our RQ1
findings indicate that the degree of initiative taken by AI during co-creation
could affect users’ perception of both co-creation experience and outcomes: If
AI takes less initiative and gives users more freedom to choose from various
generation outputs, it improves the users’ co-creation experience. Contrarily,
if AI drives deeper thoughts by taking more initiative, it leads to co-
creation outcomes with higher quality and creativity. Based on our study
results, this can be implied through lower user-given ratings (e.g.,
creativity and trust) towards their experience during the co-creation process.
The findings lead us to believe that there exists a balance between user
agency and AI initiative that can be aligned to better support co-creation. An
ideal design would likely involve a dynamic adjustment of AI’s role based on
the user’s expertise, background, and desire for control, allowing for both an
engaging co-creation experience and innovative outcomes. Future designs should
prioritize user feedback and adaptability, ensuring that the AI system can
recognize and respond to user needs and preferences throughout the co-creation
process.

####  8.1.3. Customizing Co-Creation based on Researcher’s Persona

The findings of RQ3 revealed that the background of researchers, including
factors such as domain knowledge and research experience, were found to
influence their interaction with our system and their evaluation of generated
RQs. Additionally, a user’s preference for the diversity and specificity of
the model’s outputs can vary depending on their research stage. For instance,
during earlier stages of research, users tend to prefer the generated RQs to
be more explorative and cover a broader perspective, while they might value
more relevant and specific outputs during later stages of research where the
research topic or idea has been scoped down to a certain degree. Although
prior research has described the information-seeking behavior of researchers
as a non-linear and dynamic process (Foster, 2004), our observations suggest
that users’ expectations of the system diverge based on their individual
backgrounds and the progression of their research. The newly identified
factors highlight the significance of personalization and adaptability to
users’ evolving needs in co-creation systems, particularly in the context of
scholarly research. Recent research in aligning user persona with LLMs (Hwang
et al., 2023; Salemi et al., 2023) has provided viable means for future
designs of personalized research co-creation systems. It’s crucial that later
designs emphasize adaptability to ensure that system outcomes align with the
individual researcher’s background, progress, and specific research goals.

###  8.2. Design Implications

####  8.2.1. Explaining LLM Rationales Through Mind-Map-Styled Design

Besides harnessing the text generation capability of LLMs, our study also
highlighted the importance of utilizing the Chain-of-Thoughts prompting
ability to not only improve LLMs’ task-specific performance (Wei et al.,
2022), but also as a way to enhance the explainability of AI-based systems and
bridge the gap for users to understand the rationale of AI. Prior research has
explored using mind-map-like design (Kang et al., 2021) to support users’
creative ideation process. Our CoQuest system explored another viable design
of using mind-map-styled visualization to facilitate a natural communication
of LLMs’ chain of thoughts towards humans in addition to the modality of text,
most importantly through the AI Thoughts feature. It was also noted that some
participants encountered difficulty interpreting AI rationales even after
reading the explanation provided through AI Thoughts, as they could not ask
for further explanations as in common chat-based interfaces. We argue for
future designs of AI-based co-creation systems to provide explanations of AI
rationales interactively through graphical designs, such as dynamic
exploration features where users can prompt for further explanations or ask
questions directly within a mind-map-like interface.

####  8.2.2. Sharing of Expertise: Steering the Direction of Outputs by
Injecting Meta-Research Knowledge

The CoQuest can not only be used for co-creation, but also for educational
purposes that transfer knowledge about meta-research practices among
researchers of different levels of experience. During our user study, we
observed that while some researchers focused on the novel elements AI provided
in newly generated RQs in a brainstorming manner, researchers with more
experience tended to explicitly indicate their needs for AI to generate
results from a more “technical” perspective, such as providing ideas related
to evaluation metrics or surveying existing works regarding certain research
methodology. Although users often have different specific topics of interest
during RQ co-creation, the higher-level research thinking and skills, as
discussed in existing meta-research works (Weissgerber, 2021; Ioannidis et
al., 2015; Stevens et al., 2023), can be beneficial in general when shared
across users, especially for novice researchers or researchers new to certain
fields. Past research has explored designs to facilitate the cultivation of
new researchers’ research skills, such as story-telling (Schrum and
Bogdewiecz, 2022). New understandings unveiled by this study about
researchers’ behavior using LLM-enabled co-creation provide novel implications
that can pave the way for a more collaborative and educative approach in
future system designs. Experienced researchers’ interaction with the co-
creation system can provide pathways from which less experienced researchers
can learn and benefit. Integrating this understanding, we envision a design
where the AI serves not just as a tool for co-creation, but also as a mediator
in the knowledge transfer process between researchers. This integration can
potentially bridge the gap between research idea formulation across domains by
utilizing user-sourced expertise of meta-research.

####  8.2.3. Utilizing Personalized Design to Harness “Surprising” Outputs

Hallucination is a well-recognized challenge in many task-oriented LLM system
designs (Singhal et al., 2022; Li et al., 2023; McKenna et al., 2023).
Previous HCI studies suggest that while users might sometimes view unexpected
outputs favorably (Epstein et al., 2022), they can also find them unhelpful at
times (Lee et al., 2022). Our findings echo these observations. While certain
users viewed AI’s topic drift-off unfavorably, others appreciated the fresh
content introduced by the AI. This points to a subjective user preference.
Additionally, our findings in RQ3 reflected that users’ background influences
their expectations for outputs generated by AI. Thus, we argue that in the
future design of human-AI co-creation systems, the decision to allow models to
introduce potentially out-of-context content (often labeled as
“hallucination”) should be seen as a design choice rather than a blanket
problem. Furthermore, user backgrounds, such as domain familiarity, should be
taken into account as they could influence the optimal design options. Future
designs might consider how to detect users’ different intents via their
feedback. One possible approach could be to utilize implicit preference
probing, where the system actively gauges the user’s reaction to certain
outputs and adjusts its responses accordingly. For instance, if a user
consistently displays positive engagement with unexpected outputs, the AI
could be more inclined to provide similarly “out-of-context” suggestions in
future responses. Similar designs can also be applied to identify which stage
of the research or creative process a user is in, so that the AI can tailor
its responses.

###  8.3. Ethical Concerns and Potential Biases

In our study, we found that users’ expertise influenced their sense of control
when co-creating with AI. Specifically, users felt less control and lower
cognitive demand when working on familiar topics. Interviews with participants
revealed that they had taken cognitive shortcuts by having intrinsic
expectations about the generated outcomes. Their sense of control also
decreased when these expectations were not met, indicating the presence of
confirmation biases. This aligns with the growing research on understanding
cognitive biases in human-AI interactions (Boonprakong et al., 2023b, a; Wang
et al., 2019; Chen et al., 2022). Our research further highlights the impact
of confirmation biases on users’ perceived control in human-AI co-creation
tasks. Moreover, our research suggests that these biases can operate through
the lens of human expertise, potentially creating blind spots. Future research
should explore diverse strategies for mitigating bias in co-creation with AI,
such as drawing from crowd-sourced ideas, as demonstrated in (Yen et al.,
2023).

Another ethical concern with our system is users blindly accepting AI-
generated outcomes or relying too heavily on them, as pointed out by (Buçinca
et al., 2021). To address this, we implemented two effective approaches in our
design. First, we introduced an RQ rating during the generation process to
encourage users to evaluate AI-generated content. Second, we incorporated AI
thoughts to assist users in understanding the literature space and the
generation process. Users frequently utilized AI thoughts during wait times,
which enhanced their perceived control in co-creation. These two strategies,
utilizing metrics to promote human active evaluation of AI-generated content
and providing explanations to enhance human understanding of the AI-generation
process, offer valuable insights for those seeking to mitigate bias in human-
AI collaboration. Our work builds upon prior research (Vasconcelos et al.,
2023) by providing options for users to check for explanations in order to
reduce AI over-reliance. We argue that future research should explore more
ways of explaining AI outputs and promoting active human thinking to advance
the understanding of the role of explanations in research and learning.

##  9\. Limitations and Future Work

We acknowledge that our study has several limitations. First, our system
currently relies on a fixed and relatively limited set of publications as its
source pool. This design limitation often results in users finding themselves
constrained to a narrow segment of literature after a few iterations of RQ
generation. Ideally, the system should be integrated with online publications
databases to harness a broader and continuously updated spectrum of
publications. Trust in the system also emerged as a concern. Some users
hesitated to utilize the generated RQs directly with concern about their
originality and fearing potential overlaps with pre-existing research.
Addressing these concerns is crucial for enhancing user confidence and the
overall effectiveness of the system. Furthermore, the time constraints imposed
on the tasks might not have been optimal for all participants. Those less
familiar with the task might require more time before they get acquainted with
the research space and can effectively generate RQs. This “warm-up” period
could be considered more thoughtfully in future studies. Moreover, our
evaluation focused solely on doctoral students who, while having some research
background, are still in the early stages of their research careers. The
results might differ when evaluating seasoned researchers or even
undergraduates. Expanding the participant pool in future studies can offer a
more comprehensive understanding of the system’s effectiveness and user
experience across varying expertise levels.

##  10\. Conclusion

In this study, we introduced an agent LLM system aiming to support researchers
in research idea creation. Through a formative study with actual researchers,
we proposed a mental model combining the process of literature discovery and
research ideation, and applied it to the design of the CoQuest system. We
introduced two interaction design options for CoQuest: breadth-first and
depth-first generations, diversifying the degree of AI’s initiative during co-
creation. A within-subjective study with 20 participants revealed that a
higher degree of AI initiative can lead to co-creation outcomes with higher
creativity while dampening co-creation experience. We also found that users’
better utilization of generation wait time resulted in outcomes with higher
quality and fostered a stronger sense of control.

##  11\. Acknowledgements

This material is based upon work supported by the National Science Foundation
under Grant No. 2119589. Any opinions, findings, and conclusions or
recommendations expressed in this material are those of the author(s) and do
not necessarily reflect the views of the National Science Foundation.

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##  Appendix A Appendices

###  A.1. Post-Session Survey Questions

Table 3. Survey Questions for User Experience Evaluation Aspect |  Question
---|---
Control |  How much control did you feel you had when using the system?
|  Did the system allow you to make choices and decisions while creating RQs?
Creativity |  How would you rate the creativity of the research questions generated by the system?
|  How creative did you feel about yourself when using the system to create
RQs?
Meta-Creativity |  Did the system inspire new ways of thinking or approaching RQ creation for you?
|  Did the generated RQs make you think or reflect about the topic in a new
way?
Cognitive Load |  How mentally demanding was the task using the system?
|  Did you feel overloaded with information or options while using the system?
Trust |  How confident were you in the RQs generated by the system?
|  Would you trust the system’s generated RQs to be used in a real research
scenario?

###  A.2. Mixed Effect Model of User Feedback Length

We observe that users tend to provide human feedback to the system in distinct
ways. In terms of linguistic styles, we investigated the difference among
users’ input lengths (word counts) by fitting a mixed-effects model by
considering both the random effect from different users (i.e., $b_{0i}$), and
the potential fixed effect from the two conditions (i.e., $\beta_{1}$):

(1) $Y_{ij}=\beta_{0}+\beta_{1}\times\text{condition}+b_{0i}+\epsilon_{ij}$

Where $Y_{ij}$ is the length of text feedback for the $j^{th}$ observation of
the $i^{th}$ participant; $\beta_{0}$ is the intercept; $\beta_{1}$ is the
effect of the two conditions (i.e., depth-first and breadth-first); condition
is a categorical variable (binary) indicating the user’s condition; $b_{0i}$
is the random effect for the $i^{th}$ user; $\epsilon_{ij}$ is the residual
error for the $j^{th}$ observation of the $i^{th}$ user.

| Coef. | Std.Err. | z-value | $P(>|z|)$ | [0.025 | 0.975]
---|---|---|---|---|---|---
Intercept | 10.119 | 0.908 | 11.147 | 0.000 | 8.339 | 11.898
condition (depth-first=1) | -0.637 | 1.034 | -0.616 | 0.538 | -2.663 | 1.389
Group Var | 4.411 | 0.405 |  |  |  |
Table 4. Mixed linear model regression results for feedback lengths

The results of the mixed-effect model are shown in Table 4.