











































CV and Résumé Workshop for Mid-Career Professionals


405

POST-CONFERENCE WORKSHOP

Artificial Intelligence and the 
Transformation of Research and 
Learning in Theological Education
Michael Hanegan, Founder, Intersections

ABSTR ACT: The emergence of generative artificial intelligence presents 
an unprecedented disruption and opportunity for higher education, 
and particularly for theological education. The future of learning and 
work will be shaped and reshaped by this technology and its implemen-
tation in our institutions. This workshop is designed to provide some 
foundational frameworks, concepts, and information and make the 
case that theological librarians and libraries must assert themselves at 
the center of these conversations within their institutions. Implications 
for theological libraries and librarians and theological education more 
broadly are explored. 

INTRODUCTION

The emergence of generative artificial intelligence and its impact on 
the present and future of theological education (and theological librar-
ies and librarians in particular) is both complex and wide-ranging. 
This workshop seeks to offer some framework, guidance, example 
policy, and imagination for navigating this uncharted frontier.1 



406    ATL A 2024 PROCEEDINGS

FRAMEWORKS AND GRAMMAR

It is important to recognize that the conversation about AI and the 
future of theological education are part of a much larger conver-
sation about the future of education, the future of work, and the 
future of learning and human formation. This has three immediate 
applications:

1) Navigating the challenges and opportunities that emerge 
around AI and theological education, theological librarians 
will be better informed if they are informed by these larger 
conversations.

2) Theological education has profound contributions to make 
to those larger conversations, particularly as we advance 
around these questions in our field.

3) Understanding the emerging trends and trajectories of the 
future of work can unlock new possibilities for theological 
education and higher education more broadly.

Tool vs. Technology. When we think about artificial intelligence, 
we must make a distinction between a technology and a tool. A 
tool is a specific product/app/website/platform that is powered by 
generative AI (e.g., ChatGPT) or integrates into its existing services 
(e.g., Grammarly). The technology (in this case, generative AI) is 
what powers that ever-expanding suite of tools and integrations. 
This distinction is essential as we begin to think about literacy and 
proficiency. If we work to provide only basic literacy and proficiency 
in tools, not in the underlying technology, then our capabilities are 
very vulnerable to the consequences of tools as they change, evolve, 
and disappear. Doing our best to incorporate a level of literacy and 
proficiency in the technology more broadly enables us to have both a 
more durable and agile set of skills as we navigate a world in which 
the tools evolve and change both arbitrarily and rapidly.

Arrival Technology. Most technological advances require adoption 
to be integrated in a meaningful way in our lives. This has been true 
for nearly every technological advancement that we have experi-
enced in our lifetimes because most of the arrival technologies in 
our world came (at least in some form) before we were born.

As an example, the iPhone was an advancement in technology 
(albeit a substantive one), but we all had a choice whether to adopt 
it when it first came out. Electricity and electrification on the other 



Post-Conference Workshop    407

hand should be understood as an arrival technology. Even if you 
were not interested in incorporating this technology into your life 
specifically, it impacted your experience of the world in ways you 
could neither ignore nor avoid. Generative AI is an arrival technology.

Human-Computer Interaction (HCI). For all our lives the work of 
human-computer interaction has been the conforming of human 
behavior to the functionality and design of the machine. If we want to 
use a computer we must use a keyboard, entering words one letter at 
a time. If we wish to drive a car, we are forced to use the pedals and 
steering wheel in combination to safely commute to our destination. 
The primary way in which most people engage with generative AI 
(at least in non-technical settings) is through natural language. This 
means that the way in which we interact with generative AI requires 
a different, arguably more human, set of skills to both leverage and 
benefit from this technology and the tools that use it.

The Changing Nature of Progress and Advancement. We are histori-
cally used to advances in technology that are iterative, incremental, 
and inconsistent in their overall results. Not all subsequent versions 
of technology, software, or hardware seem better. This is not the 
case with generative artificial intelligence. The current level of the 
technology and the tools that deploy it are so basic that they will 
continually be going forward. Not only will these tools perpetually 
improve (with no current plateau in sight), but they advance more 
than incrementally.

What this means as we work with generative AI tools is that we 
should bear a couple of things in mind:

• If we find that the current capabilities or results of 
a tool either disappoint or frustrate what we are 
trying to accomplish, we must recognize that these 
are temporary limitations. As these tools get increas-
ingly more capable, we should revisit work that 
we have been unsuccessful with in the past. This 
is what some researchers refer to as “the jagged 
frontier.”

• If you aren’t sure whether a tool can do something, 
try it. Some of the most interesting and meaning-
ful use cases for generative AI are discovered not 
designed.



408    ATL A 2024 PROCEEDINGS

• Don’t get too committed to individual tools for the 
long term. This is true for three reasons: (1) The 
tools themselves will change over time; (2) There 
are other tools that will come along that are perhaps 
more aligned to your work; and (3) We simply don’t 
really know what is coming next. Stay open-minded, 
be willing to learn, and build the skills necessary to 
pivot when it’s the right move.

POLICY AND VALUES

There are several important and urgent questions that are raised 
about how we think about policy and practice at the institutional, 
instructional, and individual levels.

Expectations Should Relate to Current Practice. As it relates to our 
concerns, critiques, and critical engagement with generative AI, it 
is important that as we raise questions, issues, and challenges, we 
are conscious of those same things in our current practice. This 
does not serve to discount what we might raise, but it can provide 
a constructive reframing of the actual concern at hand. This is also 
important because oftentimes, particularly as it relates to a technology 
that is this new, this disruptive, and improving this fast, we resort 
to objections that give us the feeling of certainty and predictability. 
This is an instance of a particular kind of cognitive bias known as 
the illusion of explanatory depth.

Rules and Restrictions are Ineffective and Counterproductive. 
Traditional approaches of creating policy filled with specific prohi-
bitions and instructions are both ineffective and counterproductive 
as it relates to generative artificial intelligence for three reasons:

1) Any kind of policy about AI use that includes specific 
use case instructions or prohibitions, or endorsement of 
specific tools are vulnerable to rapid (if not immediate) 
obsolescence.

2) AI detection tools have been shown to be inaccurate, to be 
discriminatory, and to be the object of lawsuits against insti-
tutions who use them to prove academic misconduct.

3) This approach exacerbates inequity, diminishes transpar-
ency on behalf of the institution or faculty member, and 
does not serve students.



Post-Conference Workshop    409

It is for these reasons that some institutions are explicitly enacting 
policies that prohibit the use of AI Detection Tools (and plagiarism 
detection tools more broadly).

Recommendations for an Academic Misconduct Policy. As detec-
tion tools lose legitimacy and effectiveness, increase inequitable 
and punitive measures for many of our most vulnerable learners, 
and do not contribute to the constructive work of students, we are 
faced with one other issue: the nature of writing and exams are 
being forced to evolve because of generative AI. Having served on 
academic misconduct committees as a graduate student, university 
administrator, and teacher, my explicit academic misconduct policy 
is as follows:

If it is determined that the student has committed a form of academic 
misconduct in their work (without utilizing AI and plagiarism detection 
tools), the formal institutional response should involve three steps: (1) 
Inquire in a human-centered way about the extenuating circumstances 
around the student’s decision to cheat, (2) inform the student that such 
conduct is unacceptable and harms not only the integrity of their work 
but also the quality of their learning, and (3) require the student to com-
plete the assignment again in a timeline that considers any extenuating 
circumstances disclosed in the first step.

As these generative artificial intelligence tools continue to evolve 
and improve, the very nature of our learning and work—especially 
the work of writing—will dramatically change. At this early stage of 
this disruption, it is difficult to ascertain what that might begin to 
look like. This is all the more reason that we should not then con-
tinue to engage in using time, energy, and money in the traditional 
processes and proceedings of academic misconduct policy, commit-
tees, and processes.

Constructive Policy Values. When we move beyond the traditional 
approach of prohibition and punitive results, we are left with the 
need for an alternative framework for how we guide access, adop-
tion, and use of this technology and its tools in academic work. I 
propose five values as the formative posture by which we evaluate 
both our practice (institutional, instructional, and individual) and 
our assessment of tools that will be adopted, allowed, or enabled 
by the institution.

• Transparency. We openly and honestly represent the 



410    ATL A 2024 PROCEEDINGS

power and importance of our tools, workflows, and 
learning for all. 

• Rigor. We engage with these technologies to 
enhance the quality, complexity, and impact of our 
work for us and for the world. 

• Curiosity. We expand the realm and potential of our 
inquiry in ways that make room for ongoing learn-
ing and growth. 

• Inclusion. We recognize that access alone is not equi-
table and actively work toward literacy and profi-
ciency for all. 

• Play. We cultivate both joy and excitement in learn-
ing as a driver of exploration, increased engage-
ment, and learning. 

These five policy values are more extensively explored in our 
whitepaper, “Artificial Intelligence and the Future of Theological 
Education” (Hanegan and Rosser 2023).

What we must ask whenever we consider specific actions, pro-
hibitions, or practices is the extent to which those choices serve to 
maintain or enhance these five values. If we discover that any of 
these policy values are diminished, we should understand this as 
an indication that this decision will fail to be both human-centered 
and effective.

Human-Centered; Pedagogy, Design, and Formation; and AI. I con-
tend that the progression of these three elements is essential to all 
the work that we do going forward at the intersections of artificial 
intelligence, theological education, and theological libraries. They 
must always be considered in this order; moreover, each subsequent 
element should be shaped by what precedes it.

• Human-Centered. What kind of human beings do we 
wish to be? To what ends do we pursue our work, 
and how do these things lead not only to the flour-
ishing of the individual, but contribute to the larger 
human family?

• Pedagogy, Design, and Formation. Informed by our 
understanding of and commitment to our human-
centered work, how does our pedagogy, design, and 
formational work sustain or enhance the goals of 



Post-Conference Workshop    411

our work? How does it address our concerns not 
only for the work of our students, but their forma-
tion beyond their learning?

• Artificial Intelligence. How does the application 
and leveraging of artificial intelligence serve our 
human-centered pedagogy, design, and formation (if 
at all)?

TRENDS AND TRAJECTORIES

Multi-Modal to Omni-Modal. These tools currently accept and gen-
erate a wide variety of modalities. While each tool currently has 
limitations of both the modalities that it receives and generates, 
there will be a time in the future (with some speculating that we 
might be there before the end of 2024), when any modality can be 
leveraged for input and requested for output. When this happens, 
the speed, complexity, and creativity of what we can do with these 
tools will increase by an order of magnitude.

Differentiated to Integrated. Thus far, when we have wanted to 
meaningfully engage generative AI tools, we have had to go outside 
much of the software and infrastructure that already exists towards 
specific tools. We might open a new window and use ChatGPT or 
engage with a plug-in like Grammarly. What is coming very soon 
and very broadly is the kind of seamless integration that will not 
only accelerate the adoption of this technology and these tools but 
will dramatically affect the way we do our work with those tools.

Iterative to Exponential. Traditionally we are used to tools and 
technology that get better incrementally. Our experience with genera-
tive artificial intelligence will not follow this trajectory. The CTO of 
OpenAI (makers of ChatGPT) recently said that the levels of capability 
between GPT-3, GPT-4, and GPT-5 would be the difference between 
a kindergartener, a smart high schooler, and a person with a PhD. 
We do not have personal experience of that scale of learning and 
capacity-building in such a short period of time.



412    AT L A 2024 PROCEEDINGS

THE NEXT SERIES OF ADVANCEMENTS IN GENERATIVE AI

New Flagship Models. The introduction of the newest state-of-the-art 
(SOTA) model, Claude 3.5 – Sonnet, occurred recently. In the coming 
months (though the timeline is not public), we expect new flagship 
models from Google (Gemini), OpenAI (ChatGPT), Meta (Llama), 
Anthropic (Claude), and several other open-source models. This 
does not even include the models which are being built in stealth 
and can be released at any time.

New Integrations. Between now and the beginning of the 2024-
2025 school year, we will see some significant integrations into major 
platforms and tools used in research and learning. This includes 
Google, Microsoft, and Apple. With a dramatic increase in ease of 
access and use that will inevitably increase the speed of adoption, 
any kind of policy that treats generative AI as an external tool will 
be rendered obsolete.

Major Enhancements. As these tools continue to advance in size 
and capabilities, we should expect an increase not only in the qual-
ity of their current capabilities, but in the number of their capabili-
ties and applications as well. In other words, current use is not an 
indication of how these tools will be used in the very near future. 
This is also an invitation to begin thinking about how these rapidly 
advancing capabilities can unlock both new projects and learning.

THEOLOGICAL EDUCATION

Theological education is uniquely suited to meet this current moment 
for a few reasons:

Unapologetically Interdisciplinary. Theological education, more so 
than many other disciplines, holds within its very design and nature 
a cross-disciplinary or interdisciplinary posture. Students regularly 
take coursework in languages, philosophy, ethics, history, litera-
ture, and other areas within this interdisciplinary work. The focus 
of theological education on service to the world and the academy 
inescapably involves a wide range of content, design, and practice.

The utility and application of formal theological training to 
meaningful and impactful work in other fields and disciplines is 



Post-Conference Workshop    413

well-documented. The breadth of inquiry that constitutes theological 
education lends itself to the kinds of shifts that are coming to higher 
education and human learning more broadly with the advent and 
acceleration of the technologies and tools that leverage artificial 
intelligence.

Grounded in the Deep Traditions of Learning and Formation. 
Theological education draws not only from its deep roots in learn-
ing and higher education as one of the most interdisciplinary and 
high-utility fields in the world, but it also moves among the col-
lective history, wisdom, and practice of the world’s spiritual and 
religious traditions. This rich foundation positions us with not only 
the experience but the resources necessary to constructively shape 
and enhance the future of learning and human formation, both in 
higher education and more broadly.

THEOLOGICAL LIBRARIES AND LIBRARIANS

Historical Limitations for Libraries and Librarians. Libraries from 
the earliest times have been limited by the contents of their collec-
tions and the wisdom and expertise of those who led them as librar-
ians and guides. As technology has advanced, we have included in 
our arsenal digital databases and resources. While our collections 
have undoubtedly expanded, these long-standing limitations have 
remained in place.

The emergence of generative artificial intelligence introduces an 
increasing suite of capabilities and applications that enhance the 
capacity of libraries and librarians to overcome or even transcend 
some of these limitations. What has previously been inconceivable 
because it was either cost-prohibitive or required immense amounts 
of additional time or expertise is increasingly within reach.

The Strong One Under the Floor. There is a Japanese proverb, 
縁の下の力持ち (en no shita no chikara mochi) which means, “The 
Strong One Under the Floor.” This is a wonderful metaphor for 
understanding the power and impact of libraries and librarians. 
Not only have libraries and librarians served as the underlying 
foundation of learning within our institutions, but the deep skills 
and practices of research and learning that come from those who 



414    ATL A 2024 PROCEEDINGS

have committed themselves to the work of librarianship are more 
essential than ever in our emerging AI-powered future.

What is important now is that libraries and librarians begin to 
assert their rightful position at the center of their institutions as 
those who both hold up the floor (and thus the house as a whole), 
but who also possess the strength to lead into the future.

Fundamental Literacies and Generative Artificial Intelligence. The 
work of learning requires so many additional skills and literacies, 
particularly in our current world in which disinformation, bias, and 
other challenges are ever present. In our new reality, increasingly 
shaped by the presence and integration of generative AI in the work 
of research and learning, these underlying skills and literacies become 
simultaneously more important and in need of their own kind of 
enhancement. A few examples can help us to understand what I 
mean here by saying that AI alters the skills and literacies we need:

• Our skills of knowledge management become more 
important when the amount of information we can 
both access and engage is increased exponentially 
in both quantity and speed.

• Our skills in information literacy are more impor-
tant as we work with new tools that are predicated 
upon certain forms of ranking, aggregating, and col-
lecting information.

• Our need for critical AI literacy and proficiency will 
be essential in navigating this future.

TOOLS: IDENTIFY, DEPLOY, AND STACK

Identifying and Deploying Generative AI in Research and Learning. 
There is an innumerable array of tools that can be used in research 
and learning. The questions that are most important revolve around 
which tools and how they should be deployed. While these questions 
and the quantity and capabilities of our tools are perpetually evolv-
ing, there are several considerations that can help to inform our 
decisions and processes in using these powerful tools for research 
and learning.

Aligning with Values. Earlier in this workshop I shared our five 
policy values, which are appropriate here as a measure of the utility 



Post-Conference Workshop    415

and appropriateness of using generative AI tools in research and 
learning.

• Transparency. Is my work clearly and honestly 
represented, including the ways in which I leverage 
tools and technology to enhance my research and 
learning? 

• Rigor. Have I used these tools as a shortcut in a way 
that diminishes the quality and character of my 
work or as a tool to enhance it? 

• Curiosity. Have I engaged in work that explores my 
line of inquiry and is interested not in curtailing it, 
but in its increase in clarity and depth? 

• Inclusion. Has this been used in any way that dis-
criminates, diminishes, or elevates a person or 
people over others, or to expand our concern and 
care of others? 

• Play. Did I engage in my work in such a way that 
brought joy, curiosity, and energy to my work?

Experimental and Iterative Approaches. Due to the rapidly evolv-
ing landscape of tools and overall capabilities of generative AI it is 
important to recognize that our processes and what we are able to 
accomplish are perpetually evolving. While it has been tempting in 
higher education (and theological education is by no means immune 
to this) to create relatively “stable” and “standard” research practices 
and habits, such an approach no longer makes sense, particularly 
as it relates to the adoption, deployment, and integration of AI tools 
in research and learning.

This means that there are three essential features of our work of 
research and learning that should always be under consideration:

• We should always seek to understand and document 
our research and learning processes to better under-
stand where gains with generative AI may be found. It 
is essential that we very clearly identify how we do 
our work with as much effort as we seek to accom-
plish our work. This enables us to maximize our 
agility and to improve our research and learning.

• We should maintain a running list of capabilities 
and processes that would aid us in our work as we 



416    ATL A 2024 PROCEEDINGS

regularly examine whether new tools or advance-
ments can meet those needs or desires. The full capa-
bilities and applications of these tools is unknown, 
which is why any single point-in-time measurement 
of their abilities is limited in its explanatory power 
and relevance.

• We should recognize that oftentimes the limitations 
of these tools lie not necessarily in their capability, 
but in our use of them. This highlights the impor-
tance of ongoing learning and training (at places 
like libraries), and the understanding that some-
times the outputs of these tools are arbitrary and 
not a reflection of the tool or its user, and therefore 
we must keep trying.

STACKS (Strategic Tool Assembly for Curated Knowledge Solutions). 
One of the first things you will learn in using generative AI tools 
for research and learning is that there is no kind of omni-tool (at 
least not yet). To do your work in a way that brings the full benefits 
of these tools you will have to not only combine them but do so in 
the proper order. While there is no out-of-the-box process that will 
work for all learning and research, there are a couple of guidelines 
that one can use to become more proficient at knowing when tool 
switching and tool stacking are most strategic.

• Only Use the Features You Need. It can be tempting 
to try to take advantage of every possible feature, 
modality, and use case. This is often a drain on time 
and doesn’t contribute to the overall work. Choose 
the right tool for the work that you want to accom-
plish, not the most powerful or feature-heavy option 
available. Biggest is not always the best.

• Organize Your Work in AI Tools. Create for yourself 
a system for the knowledge management necessary 
to collect and organize the work you generate with 
these tools. This is especially true when doing data 
analysis, research in databases, and strategizing on 
ideation, structure, and research plans. Results are 
only as good as your ability to find them later.

• Spend money sparingly and strategically. There 
are a lot of tools that offer premium plans. These 



Post-Conference Workshop    417

costs can add up quickly. You should be able to do 
just about everything you will need for research 
and learning with free, publicly available tools. 
The main reason that a paid subscription would 
be useful is to remove rate limits or unlock certain 
features.

• Always use multiple iterations and attempts. Because 
of the ways that these tools are built you may find 
that the quality of your results can be entirely arbi-
trary. This is why you should always try more than 
one way and more than one time for your work. 
Also, you can often ask the tool itself for strategy, or 
look to YouTube or X for use cases.

THIS IS OUR ONLY CHANCE

There are three reasons that librarians, particularly theological 
librarians, must assert themselves at the center of conversations 
in our institutions about generative artificial intelligence and the 
future of learning:

• Librarians Bring Unique Expertise to Learning. More 
than ever, the hard-earned wisdom, experience, 
and expertise of librarians are needed as we navi-
gate our way towards the next iteration of human 
learning. If we outsource this to other groups within 
higher ed institutions (e.g., instructional design or 
faculty development) then both the institution and 
those they serve will be profoundly disadvantaged 
from the absence of the additional literacies and 
competencies that are a natural part of the work of 
libraries and librarians. These include things like 
knowledge management, information literacy, best 
practices in research, and information science.

• If Not Librarians, Then Who? The landscape of edu-
cation and generative artificial intelligence is mov-
ing too fast, with too many players, for institutions 
to simply take a “wait and see” approach. If libraries 
and librarians do not actively assert themselves at 
the center of this conversation as its rightful guides, 
then the money, momentum, and energy will be 



418    ATL A 2024 PROCEEDINGS

redirected elsewhere, and the library will either be 
left behind or become one of the sources of diverted 
funds and institutional commitment.

• Librarians Bring a Human-Centered Approach to the 
Future. As we move into this next step in the work 
of human learning and formation, it is important 
that we cultivate postures and practices that are 
explicitly human-centered. There are many compet-
ing forces that are entering into this space that are 
either indifferent to such an orientation or more 
concerned with other objectives. Leadership in this 
space that holds this fundamental ethical commit-
ment is essential to the cultivation and sustaining of 
a world that leads further towards flourishing for 
the whole human family. In this moment, there is no 
more important call.

REFERENCES

Hanegan, Michael and Chris Rosser. 2023. “Artificial Intelligence 
and the Future of Theological Education.” https://bit.ly/
theological-education-and-ai.

https://bit.ly/theological-education-and-ai
https://bit.ly/theological-education-and-ai


Post-Conference Workshop    419

ENDNOTES

1  For a more comprehensive overview of this workshop with additional informa-
tion, links, and resources please visit: https://bit.ly/atla-and-ai.

https://bit.ly/atla-and-ai



