# A Model for Integrating Generative AI into Course Content Development

Ethan Dickey  Purdue UniversityDepartment of Computer ScienceWest
LafayetteIndianaUSA47907<EMAIL_ADDRESS>0009-0007-3706-5253 and  Andres
Bejarano  Purdue UniversityDepartment of Computer ScienceWest
LafayetteIndianaUSA47907<EMAIL_ADDRESS>0000-0003-2611-2855

(Date: August 2023)

###### Abstract.

As Generative AI (GenAI) models continue to gain prominence, a new frontier is
emerging in the field of computer science education. Results from initial
anonymous surveys reveal that nearly half (48.5%) of our students now turn to
GenAI for academic assignments, highlighting its growing role in modern
education. With educators facing challenges in creating dynamic and unique
course content, the potential of GenAI becomes evident. It offers not only a
quicker method for content development but also paves the way for diversified,
high-quality educational resources, countering traditional cheating methods
and catering to varied student needs. Key questions thus arise: “How can GenAI
assist instructors in creating meaningful content and problems quickly, and
can it reduce the instructional staff’s workload?”

Addressing the first question, we unveil the “GenAI Content Generation
Framework”. This novel tool equips educators to tap into the prowess of GenAI
for course content design. The framework presents a systematic and practical
blueprint for generating university-level course material through chat-based
GenAI. Drawing from our first-hand experiences, we provide strategic guidance
on formulating inquiries and organizing GenAI sessions to elicit quality
content that aligns with specific educational goals and context.

Our work stands apart by outlining a specific workflow and offering concrete
suggestions for harnessing GenAI in course material development, backed by a
strong case for its adoption. Armed with the framework and insights presented
in this paper, educators and course content developers can move forward with
assurance, tapping into GenAI’s vast potential for innovative content
creation.

Generative AI (GenAI), course content development, content generation
framework, instructional workload reduction, instructional design

††ccs: Social and professional topics Computing education

##  1\. Introduction

In an era where academic integrity is challenged by the widespread
availability of unauthorized solutions online, it has become increasingly
important for instructors to generate novel and diverse content each semester.
However, in the past year, even freshly created content has come under threat
from GenAI models which purport advanced language comprehension and question-
answering capabilities. While opinions on the actual problem-solving
capacities of these models vary, as evidenced by both positive (Ouh et al.,
2023; Savelka et al., 2023; Wermelinger, 2023) and negative (Kabir et al.,
2023) reports, there is a consensus among instructors globally about GenAI’s
potential to significantly disrupt academia, especially in the realm of
Computer Science (CS) (Lau and Guo, 2023; Becker et al., 2023; Finnie-Ansley
et al., 2022; Moradi Dakhel et al., 2023).

A recent preliminary anonymous survey of our summer students enrolled in a
data structures and algorithms course revealed that at least 48.5% utilized
GenAI for homework assistance (Dickey et al., 2023). This figure might even be
an underestimation, considering potential hesitations in self-reporting.
Recent literature and news articles suggest that the actual number of students
using GenAI and the variety of their methods might be more extensive than
previously assumed by educators (Yilmaz and Karaoglan Yilmaz, 2023; Terry,
2023). Anecdotally, many students appear comfortable sharing insights about
their peers’ frequent use of GenAI. Their detailed knowledge of these tools
suggests familiarity, although direct admissions of personal use remain rare.
All of these factors underscore the growing influence of GenAI in the academic
realm but also hint at its potential applications for educators. As the
academic community grapples with the ever-growing demand for fresh, diverse,
and high-quality course content, the traditional methods of content creation
often fall short, being both time-consuming and occasionally monotonous.

While the challenges posed by GenAI to academic integrity are evident, it is
crucial to recognize the transformative potential these tools offer. The same
capabilities that enable students to seek unauthorized assistance can, if
channeled correctly, revolutionize the way educators create and deliver
content. Content Creators (CCs), in particular, find themselves uniquely
positioned in this landscape: they stand to benefit directly from GenAI’s
advantages in content generation, such as creating diverse, high-quality, and
relevant content, yet they also confront the challenges it presents to
academic integrity. This dual impact places CCs at a critical juncture.
Without a structured approach, they risk either not fully harnessing the
benefits of GenAI or inadvertently amplifying its challenges (Zamfirescu-
Pereira et al., 2023; White et al., 2023; Lau and Guo, 2023). Therefore, a
systematic method is essential to guide CCs in navigating the complexities of
GenAI, ensuring they can effectively leverage its benefits while being aware
of its potential pitfalls. This burgeoning potential brings forth a pivotal
question: how can educators harness the power of GenAI to create meaningful
course content efficiently? It’s worth noting that the challenge of
unauthorized student assistance with GenAI is a significant concern and is
addressed in detail in our separate parallel work (Dickey et al., 2023).

In light of the growing utility and student interest in GenAI tools, this
paper aims to develop a generalized approach for CCs in academia to harness
the potential of these tools. We begin by substantiating our observations on
GenAI’s utility and student interest, providing both rationale and
illustrative examples. After motivating why instructors should be interested
in mastering these tools, we introduce the GenAI Content Generation Framework.
Remarkably, up until the submission date of this paper, we have encountered no
preexisting framework specifically aiding educators in incorporating GenAI
tools within the content creation process. Most existing literature often
adopts a broader academic perspective, emphasizing empowering students to
critically assess these tools and fostering ethical conversations (Bull and
Kharrufa, 2023; Gutiérrez, 2023; Guerriero, 2023; Su and Yang, 2023). In
contrast, our approach stands out by specifying a particular workflow and
offering practical recommendations tailored for CCs. Furthermore, we provide
an explicit rationale for its adoption, ensuring that CCs not only understand
the ’how’ but also the ‘why’ behind each step. This framework, characterized
by a flow of steps and guiding perspectives, assists CCs in harnessing GenAI
efficiently to systematically and practically get high-quality results. While
each interaction with GenAI models is unique due to its dynamism and
creativity (and pseudo-random hallucinations), our framework serves as a
consistent guide, offering practical strategies to achieve precise content
outcomes. Subsequent sections delve into broader considerations for engaging
with GenAI and conclude with reflections on the framework’s implications and
potential future research directions.

##  2\. Why Should Educators Consider GenAI for Course Content Creation?

GenAI excels at assisting experts in executing simple tasks more efficiently
and, in many cases, with enhanced outcomes, as detailed in subsequent
subsections. While experts can harness the power of GenAI effectively, non-
experts (such as students) face distinct challenges (Lee, 2023). They may
struggle to verify, correct, and selectively use results, leading to potential
pitfalls such as the reinforcement of incorrect knowledge. Other significant
risks for non-experts include a lack of genuine skill development, diminished
problem-solving capabilities, and an over-reliance on the tool, which can
hinder self-sufficiency.

Recent studies and observations have underscored the multifaceted capabilities
of GenAI (Lau and Guo, 2023; White et al., 2023; Becker et al., 2023; Buriak
et al., 2023; Tlili et al., 2023; Shidiq, 2023). These models excel in
generating large volumes of content swiftly, drafting documents, automating
repetitive interactions, providing interactive explanations, generating and
evaluating code, and producing (arguably) creative content. For course CCs,
these capabilities translate into tangible benefits. They can produce content
more rapidly, brainstorm and ideate with the assistance of the model, and
refine content iteratively based on conversational or formal feedback. The
direct advantages of harnessing these properties include faster course
development, increased content flexibility (styles, levels, etc.), the ability
to update content frequently, scalability of personalized content, cost-
effective content creation, support for experimental approaches, and
automation of repetitive tasks.

While many of these benefits provide intrinsic motivation for course CCs to
explore these tools, we also present a student-centered perspective. We posit
that (a) students are highly likely to experiment with these tools, and (b)
these tools are here to stay. To truly understand the implications of these
tools within academia, instructors would benefit from firsthand experience
with them.

To substantiate point (a), we highlight several observations. First, the
detection of GenAI tool usage for academic dishonesty has proven challenging,
given its capacity to produce seemingly original content (including craftily
rephrasing and editing) (Weber-Wulff et al., 2023; Dalalah and Dalalah, 2023).
This makes it arguably more elusive than traditional forms of academic
dishonesty. Second, conventional deterrents against academic dishonesty face
challenges in the context of GenAI due to its novelty and accessibility.
Unlike instances where students source content from the internet, GenAI-
generated content is unique, making detection and prevention more complex.
Lastly, there’s a growing consensus that these tools will be permissible and
even prevalent in future workplaces, a sentiment echoed by CS instructors
globally (Lau and Guo, 2023). We can see these conversations happening even
now, from legal perspectives (Grossenbacher, 2023; Bates, 2023) to strong
corporate stances on both sides of the line (Telford and Verma, 2023; DeRose,
2023). Given this trajectory, students often pose a challenging question: “Why
shouldn’t I use GenAI?” We delve deeper into this dilemma in a separate study,
where we introduce the “AI-Lab,” a method to address this very concern (Dickey
et al., 2023). In this paper, we contend that the rapid adoption of GenAI by
students and industries underscores the urgency for educators and course CCs
to fully grasp its academic ramifications. This ensures they remain at the
cutting edge of this dynamic academic environment. Presently, the most
effective method to achieve this comprehension is through direct engagement
and application of these tools.

To support (b), we share a quote from a recent investigation into GenAI by
McKinsey (Chui et al., 2023), one of the oldest and largest of the world’s
most prestigious management strategy consulting firms, which we believe aptly
summarizes current views on the future of GenAI: “All of us are at the
beginning of a journey to understand [GenAI’s] power, reach, and capabilities…
[This research] suggests that [GenAI] is poised to transform roles and boost
performance across functions such as sales and marketing, customer operations,
and software development. In the process, it could unlock trillions of dollars
in value across sectors from banking to life sciences.”

In light of the above discussions and the insights from McKinsey, it becomes
evident that GenAI is not just a fleeting technological trend but a
transformative force poised to reshape various sectors, including academia. As
educators and stakeholders in the academic community, it is incumbent upon us
to not only recognize the challenges posed by GenAI but also to proactively
engage with it. By doing so, we can harness its potential for positive
educational outcomes while mitigating risks. This proactive approach will
ensure that academia remains adaptive, relevant, and prepared for the evolving
landscape that GenAI presents. As we navigate this new frontier,
collaboration, continuous research, and open dialogue will be paramount in
guiding our path forward.

##  3\. GenAI Content Generation Framework

Properly motivated, we dive into the foundational principles and structure of
our GenAI Content Generation Framework. This framework is most succinctly
described as a sequence of steps, each accompanied by its respective
perspective, guiding the integration of GenAI in collegiate-level course
content development. While our approach mirrors traditional content
development processes, we have specifically aligned it with the Outcomes-Based
Course Design methodology. This choice is influenced by Ziegenfuss’s summary
of observed approaches to course design (Ziegenfuss, 2007) and the widely
recognized Backward Course Design Model (Hansen, 2011).

After designing course outcomes, the framework moves to course content draft
generation. This is followed by iterative refinement, initially on a macro-
scale and subsequently on a micro-scale. Recognizing the diverse nature of
course content and the versatile capabilities of GenAI, we’ve categorized our
approach into two primary content types: Lecture-Style and Problem Creation.
While our framework doesn’t adhere strictly to a specific design model, it
offers flexibility and adaptability to various educational contexts. We wrap
up this section with additional recommendations for harnessing GenAI, which,
while valuable, didn’t seamlessly fit within the main structure of the
framework.

It is important to note that our framework primarily targets the creation of
course content for undergraduate collegiate CS courses. Nevertheless, the
versatility of this framework allows for its adaptation to a variety of
educational contexts. As course material delves deeper and becomes more
advanced, there’s an increased likelihood of GenAI models producing inaccurate
or misleading content. Despite these potential pitfalls, GenAI models remain
invaluable tools for content design and ideation, even in advanced courses.

###  3.1. Setup

####  3.1.1. Set Goals

The first and only offline (without GenAI) step in our framework is the
establishment of clear goals. These goals not only guide the narrative
presented to the GenAI model but also serve as a constant reminder of the
intended direction. For instance, when we were designing new homework and
tests for a summer class, one of our primary goals was: “to provide
meaningful, engaging, and fresh content for students in CS251.” While these
goals can be as broad or specific as desired, it is imperative that they
contain specific, measurable outcomes. To clarify, a measurable outcome might
be something like “students should be able to implement a basic sorting
algorithm” rather than a vague goal like “students should understand sorting.”
In the process of setting and working towards these goals, it’s also worth
noting the importance of self-reflection. Challenging one’s own assumptions
about what these models can achieve can be beneficial, as it fosters
adaptability and encourages a proactive approach to potential limitations and
innovations of GenAI. By regularly reassessing and adjusting one’s
expectations, educators can better harness the evolving capabilities of these
models. For our team, the challenge lay in the “fresh content” aspect. Given
the numerous semesters of content we had accumulated, devising non-repetitive
material had proven to be a significant hurdle. Contrary to our initial
assumptions, we found that these tools were particularly adept at generating
fresh content, often surpassing our own initial expectations.

####  3.1.2. Set Up the Context

Transitioning to the online component of our framework, the first essential
step with any GenAI session is to set up the context. Broadly, this involves
providing the model with details akin to what a new DTA111We refer to TAs who
develop content for courses as one of development TAs, dev TAs, or DTAs. would
require to design the course content. Specifically, to get quality results,
one should tell the model how to act (known as the ‘persona prompt pattern’
(White et al., 2023)), the topic under consideration, any prior knowledge the
students possess, and any pertinent student demographics (usually just their
academic year). For example, high schoolers in a collegiate summer program
possess a different skill set and background compared to sophomore CS majors.
Providing the model with such demographic details enables it to tailor content
with greater precision (additional benefits surrounding demographics and
perspectives are discussed in Section 4.1). Our approach to context setup
aligns with the ‘context manager prompt pattern’ introduced by White et al.
(White et al., 2023). However, our framework offers specific recommendations
on context details and their placement within the GenAI session.

####  3.1.3. Generate Learning Objectives

Concluding the setup stage, the generation of learning objectives (LOs) is
paramount. The centrality of LOs in guiding subsequent interactions with GenAI
cannot be overstated. This emphasis on LOs is rooted in our adoption of the
Outcomes-Based Course Design methodology (Ziegenfuss, 2007), which prioritizes
determining student outcomes from the outset. For a deeper exploration of LOs
as foundational to effective teaching, refer to (Felder and Brent, 2016).

When instructing GenAI to generate LOs, it’s essential to make specific,
measurable requests. Additionally, directing the model to employ professional
language, such as terms from Bloom’s revised taxonomy (Conklin, 2005), is
crucial. This not only implies a quality standard for the LOs based on other
LOs which use the same language but also communicates the desired specificity
and formality to the model. Below are sample prompts to guide GenAI: “Act as
an instructor of Computer Science in a university. The current topic we are
covering is Binary Search Trees. Students know discrete math, basic
programming, pointers, primitive data structures, and runtime algorithm
analysis. Students are in their second year of studies. Give me five LOs for
the current topic. Use Bloom’s revised taxonomy verbs for the objectives.”

re is the fill-in-the-blank version: “Act as a [insert top-level expert in the
field you are using GenAI for, with more qualifications or titles if
necessary]. The topic we are covering is [topic(s)]. Students know [insert any
relevant skills students have]. Students are in their [year] year of studies.
Give me [choose 5-10] learning objectives for the current topic. Use Bloom’s
revised taxonomy of verbs for the objectives.”

As we delve into design sessions with GenAI, it is important to grasp how best
to interact with the model. The generation of content, including LOs, is
iterative, with initial outputs often not aligning perfectly with the specific
course goals. This is a key reason for requesting a larger number of LOs than
might be immediately needed. From this broader set, educators can select a
subset that resonates more closely with their goals and the targeted student
profile. If the initial LOs don’t fully meet expectations, it is beneficial to
work with this chosen subset, providing the model with targeted feedback for
refinement. For instance, if an objective needs rewording to better fit within
Bloom’s revised taxonomy or to emphasize application over theory, such
iterative feedback can steer the model accordingly. An illustrative
interaction might be: “I like learning objectives 2, 3, and 7, but I want you
to reword 2 to be higher in the Cognitive Process dimension of Bloom’s revised
taxonomy and adjust 3 to prioritize application over theoretical
understanding.” Lastly, if you edit something the model gave you, it is
imperative to let it know what you did, even if you do not want its feedback.
This allows it to learn your preferences over a session.

###  3.2. Course Content Rough Draft

Once refined LOs are established, the focus shifts to the specific course
content. While course content can take various forms, for the purposes of this
framework, it is categorized into two primary types: Lecture-Style and Problem
Creation. A rough draft of the former refers to an outline that should be
correct only from a very high-level view, while a rough draft of the latter
refers to lists of potential problems, from which only a select subset will be
chosen for iterative refinement. Following the creation of these rough drafts,
we move to iterative refinement from a macro perspective.

###  3.3. Macro Refinement

It is critical to understand the distinction we make between macro and micro
refinement. Macro refinement focuses on adjusting the content draft
holistically, refining entire sections to ensure alignment with the
established expectations, goals, LOs, and context. Conversely, micro
refinement zeroes in on specific parts of the draft, addressing them with
precision and individualized context. At the macro stage, it is acceptable if
certain components don’t fully meet expectations, as long as they are broadly
correct; finer adjustments are reserved for the micro refinement phase.

During macro refinement, interactions with the model should mirror discussions
with a dev TA11footnotemark: 1, employing technical terminology and offering
feedback on the draft’s strengths and weaknesses.

With this foundation, we present considerations for each category and provide
guidance on when to transition to micro refinement.

####  3.3.1. Lecture-Type

In any content type which requires a larger flow and organization, outlines
are typically a good starting place. Request an outline appropriate for your
content and specify which LOs you wish to use in it. From there, you should
iteratively refine the outline as a whole. Here are some things to consider:

  * •

Duration: How long is the lecture?

  * •

Associated Tasks: Are there any associated tasks outside the lecture, such as
grading in-class activities or preparing materials?

  * •

Pre- and Post-Lecture Activities: Are there any activities before or after the
lecture?

  * •

Subtopics: Are there any subtopics that are crucial to cover?

  * •

Specific Activities: Are there particular activities, such as quizzes or group
work, that are planned?

Each of these considerations, if relevant, should be communicated to the model
during the refinement process. Addressing all considerations simultaneously or
tackling them individually are both valid approaches, with both methods
showing comparable success rates in our experience.

Additionally, it can be helpful and incredibly insightful to let GenAI
brainstorm on different lecture components. Moreover, encouraging the model to
deviate from routing approaches aligns with the active learning principle of
unpredictability (Felder and Brent, 2016). For a deeper dive into
brainstorming and alternate perspectives, refer to Section 4.1.

Once the outline aligns with the lecture’s objectives and approach, it’s time
to delve into specific sections, similar to the process for problem sets. At
this juncture, managing expectations is crucial. The model might produce a
near-perfect outline or offer just a few valuable section ideas. Regardless,
these outputs serve as a foundation for the micro refinement phase, where the
lecture content can be further tailored for a complete draft or cherry-picked
for integration into an existing draft.

####  3.3.2. Problem Creation

For content types that result in lists, such as problems or activities, the
emphasis is on the diversity of responses and the general applicability of the
items, hereafter referred to generically as “problems.” Unlike Lecture-Type
content, only a subset of the generated list will be included in the final
version. During the micro refinement phase, the focus will be on a select few
top problems rather than an exhaustive review of every item as is done for
outlines.

To generate a draft, ask the model to generate a specific number of problems,
ideally more than required, based on the selected LOs. If the generated
problems do not align with the intended goals and context, provide feedback to
the model, emphasizing the desired attributes. Key variables to consider
include:

  * •

Answer style: Multiple choice, short answer, etc.

  * •

Depth: Desired level(s) within Bloom’s Revised Taxonomy (Conklin, 2005) or
Webb’s Depth of Knowledge (Webb, 2002).

  * •

Theme: Leveraging GenAI’s strength in creativity.

  * •

Topical theme: Ensuring the problems address specific skills within the topic.

The macro refinement process can vary based on the alignment of the generated
problems with the goals. For instance, if only a few questions are relevant,
instruct the model to generate problems similar to the relevant ones. If the
model’s responses become repetitive or misaligned, providing a sample problem
can be particularly helpful in resetting its misconceptions about what is
desired. Several other examples exist, but we leave it to the discretion of
the reader to respond to the model within the spirit of this framework.

The boundary between macro and micro refinement in Problem Creation can be
nebulous. A good indication of the transition to micro refinement occurs when
the focus shifts to refining a select subset of problems, while disregarding
the rest.

###  3.4. Micro Refinement

Before delving into micro refinement, educators should be satisfied with the
overall structure and general alignment of the content draft with the intended
goals and context. From there, the focus shifts to perfecting and elaborating
on each segment of the content. This involves examining each section,
subsection, or question individually, informing the model of the specific
focus on that part.

During these small, focused refinements, it is crucial to maintain context
specificity. For instance, if refining a problem on frequency analysis in
Huffman Coding emphasizes the theoretical aspect over the practical skill,
this context should not inadvertently influence a subsequent question on
creating the Huffman Coding Tree, where hands-on application is integral to
understanding the theory.

Alongside specifying the content segment for refinement, provide the model
with clear modification instructions. The granularity of these requests can
vary widely. For instance, feedback can range from broader directives like,
“make this question more challenging; it’s currently too straightforward for a
homework assignment,” to more specific directives such as, “I find the word
’target’ unsuitable; could you rephrase that sentence, please?” Such detailed
interactions ensure the content aligns closely with the educator’s vision and
objectives.

The micro refinement process is inherently flexible, adapting to the
educator’s vision for the final content. While there’s no singular approach,
we offer two potential workflows for each content type.

####  3.4.1. Lecture-Type

Once satisfied with a particular segment of the outline, educators can request
the model to generate a detailed script or essential talking points. These
scripts serve as a roadmap for the lecture, ensuring a coherent flow and
comprehensive coverage of the topic. As the script is generated, it’s crucial
to assess its alignment with the LOs and its potential to engage students. Any
misaligned or inaccurate sections should be refined iteratively. Once sections
are polished, the model can merge the script cohesively. This refined script
can be paired with lecture slides, multimedia, or classroom activities to
enrich the educational journey.

####  3.4.2. Problem Creation

In the refinement of individual problems, several strategies emerged as
particularly effective. Among these, iterative rewording of the problem by the
model, adjusting the problem’s difficulty, and embedding problems within a
narrative stood out. Notably, while most narrative integrations are typically
part of micro refinement, exceptions arise when a global storyline is
employed, where each problem contributes to a larger narrative.

During problem refinement, educators can instruct the model to answer the
question from an undergraduate’s perspective (or some other demographic(s)).
This approach can reveal common pitfalls and misconceptions, guiding further
refinement. Such insights are invaluable, especially for problems where
students must comprehend and act without instructional support.
Misunderstandings can arise in activities without instructional support, often
over aspects not intended for assessment. Rewording the problem can clarify
these points, enhancing comprehension.

As the refinement progresses, educators should request the correct answer
(usually requesting in a brief or concise way to avoid the overly-wordy
responses certain models are prone to). If accurate, this stage is suitable
for generating a rubric. While not always standard practice, rubrics ensure a
consistent and fair assessment. For a deeper understanding of rubric benefits,
Ragupathi and Lee (Ragupathi and Lee, 2020) provide valuable insights.

If the model’s answer is incorrect, educators can guide it towards the right
solution. Persistent errors can highlight tasks that challenge the model,
incredibly powerful information which offers unique teaching opportunities
about the limitations of GenAI. Regardless, perfection in answers isn’t the
goal. The quality of a rubric doesn’t hinge on the exact correctness of the
provided answer, but rather on sound evaluation criteria.

While the iterative refinement process may seem labor-intensive, it’s
essential to note that these steps are integral to traditional content
creation. GenAI often streamlines this process, reducing the time and effort
typically required.

###  3.5. Maintaining Contextual Integrity in Iterative Refinement

In the process of iterative refinement, two critical concerns arise: context
blending and loss of focus. The term ‘context blending’ pertains to the
merging of contexts between different steps that do not inherently share the
same context. On the other hand, ‘loss of focus’ denotes the model’s
diminishing capacity to execute the tasks and refinements as directed. To
mitigate context blending, one should delineate clear transitions between
sections and intermittently reinforce context-specific labels, such as LOs,
sections, or parts under consideration. Furthermore, prompting the model to
reiterate these context-specific labels as it transitions to a subsequent
section can enhance its adherence to the expected context. Most crucially, if
a loss of focus is observed, it may be beneficial to initiate a complete
context reset. This can be achieved by starting a new session, reintroducing
the context, and specifying the current stage of the process—by providing, for
instance, the working outline, LOs, global context, and the specific section
in focus.

###  3.6. Consolidating Generated Content

Given the intricacies of generating comprehensive course content, there is a
high likelihood that it will be necessary to initiate multiple sessions with
GenAI. Due to this segmented approach, the model may not seamlessly provide an
overarching summary of all content components in a singular response. As a
practical measure, CCs are encouraged to maintain a dedicated document to
collate and refine the selected outputs, ensuring a cohesive and well-
structured course assembly.

####  3.6.1. GenAI in Comprehensive Course Planning

While the primary focus of this framework is not on constructing an entire
course from the ground up using GenAI, the potential of these models in the
realm of course planning warrants attention. Once LOs, course outlines, and
assessments are established, GenAI models demonstrate a commendable
proficiency in devising comprehensive course activity plans within a single
session. This includes detailed time allocations and other integral components
of a structured plan. Leading chat-based GenAI enterprises, such as OpenAI,
Anthropic, and Google, have underscored the prowess of these tools in
planning, brainstorming, and feedback solicitation. Notably, the planning
capability of GenAI shines when tasked with orchestrating plans for cohesive
sets of course content, rather than isolated components.

##  4\. General Considerations for Engaging with GenAI

In this section, we offer various insights and recommendations pertinent to
interacting with GenAI. These considerations, while valuable, do not align
explicitly with any specific segment of the framework.

  1. (1)

Perspective. A useful analogy for understanding a GenAI model likens it to a
child aged 6-8 years. Such children excel at executing tasks when given clear
instructions, provided they possess the requisite knowledge. In the case of
GenAI, instead of drawing from 6-8 years of human experiences, it taps into
the vast expanse of the internet. However, akin to children of this age, GenAI
models often lack initiative and may not perform tasks without explicit
direction. Absent specific guidance, the assumptions made by the model could
diverge significantly from the intended objectives of the session.

  2. (2)

Brainstorming. One of GenAI’s notable strengths lies in its creative prowess.
Rather than providing explicit specifications for assessments or activities,
CCs might find it beneficial to solicit recommendations from GenAI for various
course content elements. This approach can yield diverse and innovative ideas,
enriching the educational experience.

  3. (3)

Embracing Imperfection. While the pursuit of excellence is commendable, it’s
essential to recognize the inherent limitations of GenAI. Continuously
striving for perfection can lead to diminishing returns. Instead, it’s often
more productive to acknowledge areas where the model may falter and focus on
harnessing its strengths. In essence, avoid getting mired in endless
refinements and capitalize on the valuable insights it provides.

  4. (4)

Optimizing Content Generation. When soliciting GenAI for new content, always
request a more extensive set than required. This approach allows for a
selection process, ensuring the final subset aligns closely with goals and
context. Additionally, by providing specific directives, such as “with varying
levels of difficulty” or “answer in a brief way,” one can enhance the utility
and precision of the generated output.

###  4.1. Diversity of Perspectives

In the realm of education, understanding and addressing the diverse
backgrounds and experiences of learners is paramount. One of the unique
capabilities of GenAI is its ability to simulate a multitude of perspectives,
which can be instrumental in illuminating potential blind spots in course
content, especially when creating content for unfamiliar demographics.

A particularly effective strategy involves employing the phrase “act as” when
interacting with GenAI (known as the ‘persona prompt pattern’ (White et al.,
2023)). By instructing the model to “act as” a particular demographic or adopt
a specific perspective, educators can gain insights that might otherwise
remain obscured. For instance, asking GenAI to “act as a student from a non-
technical background” or “act as an international student” can yield content
and feedback that is more attuned to the needs and challenges of these
specific groups.

Leveraging this feature not only enriches the educational material but also
fosters an inclusive learning environment. It ensures that content is not
inadvertently biased or neglectful of the diverse experiences and backgrounds
that students bring to the classroom. In essence, by harnessing the diversity
of perspectives that GenAI can offer, educators can craft a more holistic and
inclusive educational experience.

##  5\. Discussion and Future Work

Given the relatively recent release and rise to prominence of easily
accessible GenAI tools, research studying their usage and applications is
still very young. Zhai et al. (Zhai et al., 2020) concluded that “[Machine
Learning] has transformed-—but not yet redefined—-conventional science
assessment practice in terms of fundamental purpose, the nature of the science
assessment, and the relevant assessment challenges.” While not addressing the
purpose and nature of assessments themselves, the presented framework takes
one small step in pushing to redefine how traditional course development is
done - and seeks to change who is qualified to develop high-quality course
content.

Furthermore, this framework provides a novel perspective on what could and
should be done with these tools. Instead of asking, “what should we do about
these tools,” as many of our colleagues and professors around the world appear
to be focusing on (Lau and Guo, 2023), this framework turns the question
around on instructors and facilitates discussion surrounding “how can these
tools help us in all of our activities?”

While our primary focus in this study was to elucidate the potential of GenAI
in assisting educators with content creation, the second research question
posed in our abstract remains an essential area of inquiry: Can the use of
GenAI significantly reduce the workload of instructional staff? This question
is of paramount importance, especially in the context of increasing class
sizes and the constant demand for updated course materials. A specific and
measurable research question could be formulated as: “To what extent can the
integration of GenAI in course content development reduce the time spent by
instructional staff on content creation and revision?” Future studies should
employ both quantitative and qualitative methods to assess the time savings,
if any, and the potential shifts in the nature of the workload. For instance,
while GenAI might reduce the time spent on content creation, it might
introduce new tasks, such as refining GenAI outputs or tailoring generated
content to specific course objectives. Addressing this research question will
provide a more comprehensive understanding of the true impact of GenAI on the
educational landscape.

In an effort to gauge the initial reception and applicability of our
framework, we introduced it to several of our colleagues during workshops.
Although these workshops were conducted recently, and a comprehensive analysis
of the survey results is pending, preliminary feedback suggests a high degree
of satisfaction with the framework and an indication that the workshop
objectives were largely met.

While we sought to be as general and extendable as possible, the applicability
and utility of this framework are inherently bound to some degree to the
selected GenAI model. It works best on a chat-based, highly trained model
which understands nuances and perspectives across academia. Furthermore,
development of this framework was performed in the context of CS undergraduate
courses. More work will be required to validate its utility in other contexts
and potentially adapt the model to one more useful across disciplines.

###### Acknowledgements.

The authors would like to thank Purdue’s Center for Instructional Excellence,
at whose generative AI discussions we met and were encouraged to pursue our
ideas. Furthermore, a special thanks to Emily Bonem and David Nelson for
supporting and encouraging us initially and repeatedly as we execute our
project. Lastly, we would like to thank Andre Beasley for a useful discussion
on general course development using ChatGPT.

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# Spontaneous centralization of control in a network of company ownerships

Sebastian M. Krause<EMAIL_ADDRESS>Tiago P. Peixoto
<EMAIL_ADDRESS>Stefan Bornholdt<EMAIL_ADDRESS>Institut
für Theoretische Physik, Universität Bremen, D-28359 Bremen, Germany

###### Abstract

We introduce a model for the adaptive evolution of a network of company
ownerships. In a recent work it has been shown that the empirical global
network of corporate control is marked by a central, tightly connected “core”
made of a small number of large companies which control a significant part of
the global economy. Here we show how a simple, adaptive “rich get richer”
dynamics can account for this characteristic, which incorporates the increased
buying power of more influential companies, and in turn results in even higher
control. We conclude that this kind of centralized structure can emerge
without it being an explicit goal of these companies, or as a result of a
well-organized strategy.

##  I Introduction

The worldwide network of company ownership provides crucial information for
the systemic analysis of the world economy Schweitzer  _et al._ (2009); Farmer
_et al._ (2012). A complete understanding of its properties and how they are
formed has a wide range of potential applications, including assessment and
evasion of systemic risk Battiston  _et al._ (2012), collusion and antitrust
regulation Gulati  _et al._ (2000); Gilo  _et al._ (2006), market monitoring
Diamond (1984); Chirinko and Elston (2006), and strategic investment Teece
(1992). Recently, Vitali et al Vitali  _et al._ (2011) inferred the network
structure of global corporate control, using the Orbis 2007 marketing database
111http://www.bvdinfo.com/products/company-information/international/orbis.
Analyzing its structure, they found a tightly connected “core” made of a small
number of large companies (mostly financial institutions) which control a
significant part of the global economy. A central question which arises is
what is the dominant mechanism behind this centralization of control. The
answer is not obvious, since the decision of firms to buy other firms can be
driven by diverse goals: Banks act as financial intermediaries doing
monitoring for uninformed investors Diamond (1984); Chirinko and Elston
(2006), managers can improve their power by buying other firms instead of
paying dividends Jensen (1986), speculation on stock prices as well as
dividend earnings can be a significant source of revenue Modigliani and Miller
(1958); La Porta  _et al._ (2000); Jensen (1986), and companies can have
strategic advantages, e.g. due to knowledge sharing Teece (1992); Hamel
(1991); Dyer and Singh (1998). Another possible hypothesis for control
centralization is that managers collude to form influential alliances: Indeed,
agents (e.g. board members) often work for different firms in central
positions Battiston and Catanzaro (2004). Although all these factors are
likely to play a role, we here investigate a different hypothesis, namely that
a centralized structure may arise spontaneously, as a result of a simple
“richt-get-richer” dynamics Simon (1955), without any explicit underlying
strategy from the part of the companies. We consider a simple adaptive
feedback mechanism Gross and Blasius (2008), which incorporates the indirect
control that companies have on other companies they own, which in turn
increases their buying power. The higher buying power can then be used to buy
portions of more important companies, or a larger number of less important
ones, which further increases their relative control, and progressively
marginalizes smaller companies. We show that this simple dynamical ingredient
suffices to reproduce many of the qualitative features observed in the real
data Vitali  _et al._ (2011), including the emergence of a core-periphery
structure and the relative portion of control exerted by the dominating core.
Although this does not preclude the possibility that companies may take
advantage and further consolidate their privileged positions in the network,
it does suggest that deliberate strategizing may not be the dominating factor
which leads to global centralization.

##  II Model description

We consider a network of $N$ companies, where a directed edge between two
nodes $j\to i$ means company $j$ owns a portion of company $i$. The relative
amount of $i$ which $j$ owns is given by the matrix $w_{ij}$ (i.e. the
ownership shares), such that $\sum_{j}w_{ij}=1$. We note that it is possible
for self-loops to exist, i.e. a company can in principle buy its own shares.
In the following, we describe a model with two main mechanisms: 1. The
evolution of the relative control of companies, given a static network; 2\.
The evolution of the network topology via adaptive rewiring of the edges.

###  II.1 Evolution of control

Here we assume that if $j$ owns $i$, it exerts some influence on $i$ in a
manner which is proportional to $w_{ij}$. If we let $v_{j}$ describe the
relative amount of control a company $j$ has on other companies, we can write

$v_{j}=1-\alpha+\alpha\sum_{i}A_{ij}w_{ij}v_{i},$ (1)

where $A_{ij}$ is the adjacency matrix, the parameter $\alpha$ determines the
propagation of control and $1-\alpha$ is an intrinsic amount of independence
between companies 222Eq. 1 can be seen as a weighted version of the Katz
centrality index Katz (1953), which is one of many ways of measuring the
relative centrality of nodes in a directed network, such as PageRank Page  _et
al._ (1999) and HITS Kleinberg (1999). It converges for $0\leq\alpha<1$ and we
enforce normalization with $\sum_{i}v_{i}=N$.. We further assume that the
control value $v_{j}$ directly affects other features such as profit margins,
and overall market influence, such that the buying power of companies with
large $v_{j}$ is also increased. This means that the ownership of a company
$i$ is distributed among the owners $j$, proportionally to their control
$v_{j}$, i.e.

$w_{ij}=\frac{A_{ij}v_{j}}{\sum_{l}A_{il}v_{l}},$ (2)

(see Fig. 1). These equations are assumed to evolve in a faster time scale,
such that equilibrium is reached before the topology changes, as described in
the next section.

Figure 1: Illustration of the control of firms including indirect control
(left) and the ownership being proportional to the control (right), as
described in the text.

###  II.2 Evolution of the network topology

Companies may decide to buy or sell shares of a given company at a given time.
The actual mechanisms regulating these decisions are in general complicated
and largely unknown, since they may involve speculation, actual market value,
and other factors, which we do not attempt to model in detail here. Instead,
we describe these changes probabilistically, where an edge may be deleted or
inserted randomly in the network, and such moves may be accepted or rejected
depending on how much it changes the control of the nodes involved. For
simplicity, we force the total amount of edges in the network to be kept
constant, such that a random edge deletion is always accompanied by a random
edge insertion. Such “moves” may be rejected or accepted, based on the change
they bring to the $v_{j}$ values of the companies involved. If we let $m$ be
the company which buys new shares of company $l$, and $j$ which sells shares
of company $i$, the probability that the move is accepted is

$p=\min\left(1,e^{\beta(\tilde{w}_{lm}v_{l}-w_{ij}v_{i})}\right),$ (3)

where $w_{ij}$ is computed before the move and $\tilde{w}_{lm}$ afterwards,
and the parameter $\beta$ determines the capacity companies have to foresee
the advantage of the move, such that for $\beta=0$ all random moves are
accepted, and for $\beta\to\infty$ they are only accepted if the net gain is
positive (see Fig. 2). Note that in Eq. 3 it is implied that companies with
larger control will tend to buy more than companies with smaller control,
which is well justified by our assumption that control is correlated with
profit and wealth.

Figure 2: Illustration of the adaptive process, before the rewiring (left) and
afterwards (right), as described in the text.

The overall dynamics is composed by performing many rewiring steps as
described above, until an equilibrium is reached, i.e. the observed network
properties do not change any longer. In order to preserve a separation of time
scales between the control and rewiring dynamics, we performed a sufficiently
large number of iterations of Eqs. 1 and 2 before each attempted edge move.

##  III Centralization of control

A typical outcome of the dynamics can be seen in Fig. 3 for a network with
$N=3\times 10^{4}$ nodes and average degree $\left<k\right>=2$, after an
equilibration time of about $6\times 10^{9}$ steps. In contrast to the case
with $\beta=0$, which results in a fully random graph, for a sufficiently high
value of $\beta$ the distribution of firm ownerships (i.e. the out-degree of
the nodes) becomes very skewed, with a bimodal form. We can divide the most
powerful companies into a broad range which owns shares from $10$ to about
$150$ other companies, and a separate group with $k_{\text{out}}>150$. The
correlation matrix of this network shows that these high-degree nodes are
connected strongly among themselves, and own a large portion of the remaining
companies (see Fig. 3). This corresponds to a highly connected “core” of about
45 nodes with $\left<k_{\text{sub}}\right>\approx 39.8$, which is highlighted
in red in Fig. 3c and can be seen separately in Fig. 3d. The distribution of
in-degree (not shown) is bimodal as well with highest values for the inner
core. With values up to $k_{\rm in}=50$, the highest in-degree (number of
owners) is considerably below the highest out-degree (number of firms owned at
once).

(a)

(b)

(c)

(d)

Figure 3: (a) Degree distribution of the resulting network for
$\left<k\right>=2$, a control propagation value of $\alpha=0.5$, $N=30000$ and
different values of prior knowledge $\beta$; (b) Degree correlation matrix for
$\beta=10$, showing the resulting core-periphery structure; (c) Graph layout
of the whole network, with red nodes representing a chosen fraction of the
most highly connected core, and blue ones the periphery; (d) Subgraph of the
most powerful companies with $v_{i}>20$ (about 100). The node colors and sizes
correspond to the $v_{i}$ values.

Figure 4: Left: Distribution of inherited control $v_{i}-(1-\alpha)$ for
$\alpha=0.5$ and different values of $\beta$; Right: Relative fraction of
control as a function of fraction of most powerful companies.

Similarly to the out-degree, the distribution of control values $v_{i}$ is
also bimodal for larger values of $\beta$, as can be seen in Fig. 4, and is
strongly correlated with the out-degree values. The total fraction of
companies controlled by the most powerful ones is very large, as shown on the
right panel of Fig. 4. For instance, we see that a fraction of around $0.15\%$
of the central core controls about $57\%$ of all companies. The companies with
intermediary values of control (and out-degree) also possess a significant
part of the global control, e.g. around $.85\%$ of the most powerful control
an additional $25\%$ of the network. It is important to emphasize the
difference between these two classes of companies for two reasons: Firstly the
inner core inherits control from intermediate companies without the need to
gather up all the minor companies. In fact the ownership links going out from
the inner core (about $10^{4}$) is enough to cover the direct control of only
a third of all companies, while the effective control is more than a half.
Secondly, the fraction of intermediary companies increases for larger
networks. For a network with $N=3\times 10^{5}$, the inner core includes a
fraction of only $0.04\%$, controlling an effective $41\%$ of the total
companies. Nonetheless, all the most powerful companies together account for
around $1\%$ of the network and $82\%$ of the total control; values which do
not change considerably with system size.

Let us compare the results presented so far with empirical data presented in
Vitali  _et al._ (2011). For different reasons, this comparison can only be
qualitative. First of all, the empirical data includes economic agents with
different functions (shareholders, transnational companies and participated
companies) out of different sectors (eg. financial and real economy), while we
consider identical agents. Secondly, we force every company to be owned 100%,
while the empirical data neglects restrained shares and diversified holdings.
Thirdly, the control analysis in Vitali  _et al._ (2011) is done somewhat
differently: All the $600,508$ economic agents were considered for the
topological characterization, while many companies (80% of all agents there)
were neglected for the control analysis. In the empirical data, a strongly
connected component of $1,318$ companies controls more than a half of all
companies arranged in the out component. This concentration is compatible with
the core-periphery structure presented in Fig. 3, however the empirical data
does not show a distinct bimodal structure. Nonetheless, there are highly
connected substructures in the core, e.g. a structure with 22 highly connected
financial companies ($\left<k_{\rm sub}\right>\approx 12$) was highlighted in
Battiston  _et al._ (2012). The control concentration in the empirical data
was reported as a fraction of $0.5\%$ which controls $80\%$ of the network.
This is similar to the results of our model (see Fig. 4 on the right). There
are, however, features that our model does not reproduce, the most important
of which being the out-degree distribution of the network, which in Vitali
_et al._ (2011) is very broad, and displays no discernible scales, where in
our case it is either bimodal or Poisson-like. One possible explanation for
this discrepancy is that we have focused on equilibrium steady-state
configurations of the dynamics, whereas the real economy is surely far away
from such an equilibrium. A more precise model would need to incorporate such
transient dynamics in a more realistic way. Nevertheless, the general tendency
of the control to be concentrated on relatively few companies is evident in
such equilibrium states, and features very prominently in the empirical data
as well.

###  III.1 Transition to centralization

To investigate the transition from homogeneous no centralized networks with
increasing $\beta$, we measured the inverse participation ratio
$I=\left[\frac{1}{TN}\sum_{ti}v_{i}(t)^{2}\right]^{-1}$ with the time $t$
summing over a sufficiently long time window of length $T$ after
equilibration. Since $\frac{1}{N}\leq I\leq 1$, we expect $I=1$ in the
perfectly homogeneous case where $v_{i}=1$ for all nodes, and $I=\frac{1}{N}$
if only one node has $v_{i}>0$, and the control is maximally concentrated. As
can be seen in Fig. 5, we observe a smooth transition from very homogeneous
companies connected in fully random manner for $\beta=0$, to a pronounced
concentration of control for increased $\beta$, for which the aforementioned
core-periphery is observed. The transition becomes more abrupt when either the
average degree $\left<k\right>$ is increased or the parameter $\alpha$ (which
determines the fraction of inherited control) is decreased.

Figure 5: Inverse participation ratio
$I=\left[\frac{1}{TN}\sum_{ti}v_{i}(t)^{2}\right]^{-1}$ as a function of
$\beta$, for a network with $N=10^{4}$, and for (left) $\left<k\right>=2$ and
different values of $\alpha$ and (right) $\alpha=0.5$ and different values of
$\left<k\right>$.

Figure 6: Distribution of out degrees (left) and inherited control
$v_{i}-(1-\alpha)$ (right) for $\beta=10$, $\left<k\right>=2$ and $N=30000$ as
in Fig. 3 and 4, but for different values of $\alpha$.

Centralization of control can emerge in different ways depending on the
parameters $\alpha$ and $\beta$. In Fig. 6, it is shown that different values
of $\alpha$ for a high value of $\beta=10$ can lead to a detached controlling
core ($\alpha=0.2$) or to broadly distributed control values ($\alpha=0.8$).
With smaller values of $\alpha$, indirect control is suppressed and companies
can gain power only by owning large numbers of marginal companies. E.g.: for
$\alpha=0.2$, this leads to a highly connected core of $41$ companies having
$\left<k_{\rm sub}\right>\approx 18.2$, the rest of the companies have very
little influence. For larger values of $\alpha$, indirect control has a larger
effect, which leads to a hierarchical network where companies with small
numbers of owned firms $k_{\rm out}$ may nevertheless inherit large control
values $v_{i}$. The case with $\alpha=0.5$ and $\beta=10$ shown in Figs. 3 and
4 exhibits a mixture of these two scenarios. The transition to a centralized
core also occurs when increasing $\beta$ and keeping $\alpha$ constant (see
right panel in Fig. 5).

Figure 7: Left: Graph layout of a $10\times 10$ lattice with $\alpha=0.9$. The
vertex sizes and colors correspond to the $v_{i}$ values, and the edge
thickness to the $w_{ij}$ values. Right: Distribution of inherited control
$v_{i}-(1-\alpha)$ for static poisson graphs having $\left<k\right>=2$ and
$N=30\,000$, with different values of $\alpha$ (for $\alpha=0.5$ and
$\alpha=0.8$ shifted). The dashed line is a power law with exponent $-1$.

One interesting aspect of the centralization of control as we have formulated
is that it is not entirely dependent on the adaptive dynamics, and occurs also
to some extent on graphs which are static. Simply solving Eqs. 1 and 2 will
lead to a non-trivial distribution of control values $v_{i}$ which depend on
the (in this case fixed) network topology and the control inheritance
parameter $\alpha$. In Fig. 7 is shown on the left the control values obtained
for a square $2D$ lattice with periodic boundary conditions, and bidirectional
edges. What is observed is a spontaneous symmetry breaking, where despite the
topological equivalence shared between all nodes, a hierarchy of control is
formed, which is not unique and will vary between each realization of the
dynamics. A similar behavior is also observed for fully random graphs, as
shown on the right of Fig. 7, where the distribution of control values becomes
increasingly broader for larger values of $\alpha$, asymptotically approaching
a power-law $\rho(v)\sim v^{-1}$ for $\alpha\to 1$. This behavior is similar
to a phase transition at $\alpha=1$, where at this point Eq. 1 no longer
converges to a solution.

##  IV Conclusion

We have tested the hypothesis that a rich-get-richer process using a simple,
adaptive dynamics is capable of explaining the phenomenon of concentration of
control observed in the empirical network of company ownership Vitali  _et
al._ (2011). The process we proposed incorporates the indirect control that
companies have on other companies they own, which increases their buying power
in a feedback fashion, and allows them to gain even more control. In our
model, the system spontaneously organizes into a steady-state comprised of a
well-defined core-periphery structure, which reproduces many qualitative
observations in the real data presented in Vitali  _et al._ (2011), such as
the relative portion of control exerted by the dominating companies. Our model
shows that this kind of centralized structure can emerge without it being an
explicit goal of the companies involved. Instead, it can emerge simply as a
result of individual decisions based on local knowledge only, with the effect
that powerful companies can increase their relative advantage even further.

It is interesting to compare our model to other agent based models featuring
agents competing for centrality. The emergence of hierarchical, centralized
states with interesting patterns of global order was reported for agents
creating links according to game theory Holme and Ghoshal (2006); Lee  _et
al._ (2011); Do  _et al._ (2010) as well as for very simple effective rules of
rewiring according to measured centrality König and Tessone (2011); Bardoscia
_et al._ (2013). The latter is combined with phase transitions according to
the noise in the rewiring process. The stylized model of a society studied in
Bardoscia  _et al._ (2013) shows a hierarchical structure, if the individuals
have a preference for social status. The intuitive emergence of hierarchy is
associated with shrinking mobility of single agents within the hierarchy. This
effect is present in our model as well and deserves further investigation.

Our results may shed light on certain antitrust regulation strategies. As we
found that a simple mechanism without collusion suffices for control
centralization, any regulation which is targeted to diminish such activities
may prove fruitless. Instead, targeting the self-organizing features which
lead to such concentration, such as e.g. limitations on the indirect control
of shareholders representing other companies, may appear more promising.

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