This week saw the kickoff of the Gloo AI Hackathon’s 30-day hacking window. If you live in the world of AI and coding, this first sentence makes absolute sense. But if you do not, then you have questions. What is a hackathon? What do you mean by a hacking window?

This was the problem we were trying to solve at Biola the past couple of weeks. In our first year in the Biola AI Lab (last year), we were trying to help students, faculty, and staff make sense of AI and its place in academic endeavors. And I think we did a pretty good job. We want to continue these efforts this year, but move to the next level. Enter the hackathon.

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A “hackathon”, simply put, is a competition of ideas expressed in software. Teams form around a specific idea for a new software application and build it, competing against other teams to create solutions that meet the goals of the hackathon. In the case of the Gloo AI Hackathon, the goals are to develop applications that utilize AI in some form that also can be shown to positively align with human flourishing. Winners will be awarded in several categories and monetary awards will be given.

The key to a successful hackathon entry is of course, first, to have a good idea. But equally (and potentially more) important is the makeup of the team. And here is where we ran into trouble. For a small segment of students and alumni (our primary target), the concept of a hackathon is a given. But for the large majority, it sounded intimidating or something only the programmers would be interested in. But a successful team requires much more than a programmer! Here’s how we are putting it in our materials:

A good team should have at least one person who can "create", whether through traditional coding or vibe-coding (or some combination!), as well as other contributors who can help flush out the idea, the use cases, understand the business implications, and tell the story.

So our job is to match those that can “build” with those who know how to tell stories, know business, and those who can provide vision. These are the teams that will win.

Beyond the potential of winning a prize and gaining recognition, a hackathon has another component: networking. And as we all know, networking is the secret sauce to career success. At the Gloo AI hackathon event, the participants will meet a variety of people and companies that share the same interests and values. New friendships will be formed, career trajectories will be improved, and new companies will be launched.

So with the opening of the “hack window” this week (which just means that you can start building your projects), we launched the “AI Lab Hackathon Incubator Kickoff” event this past Monday with much success (highlight video below). As of this writing, we have ten teams and dozens of participants from across campus.

So then, you might be asking, what is the problem? To me, this event should have been ten times as big! I have talked to several faculty on campus that said they would have talked with their students about the event, but were unsure how those students would benefit or even be able to participate. So while we were happy with the turnout, we are already thinking about how to promote more widely (and more clearly) next year.

I would love to hear from any of you about how you understand a hackathon and have been able to promote it to those who are not aware of what they are or the benefits of participating. I would also be happy to answer questions about what we are doing in the AI Lab in general or this hackathon in particular. Leave your questions in the comments.

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In my class last night, we were honored to have a guest with us to talk about AI and human flourishing. Ali Llewellyn from AI is here at Biola this week in support of our AI Lab’s participation in their upcoming AI Hackathon. In fact, I’ve asked my students to participate in the hackathon as an assignment, the details of which I’ll leave for another post.

In my class, we will be looking at developing criteria for evaluating how AI should be used by organizations. We want to go beyond the question “How can we use AI?” and instead answer the question “how should we use AI?” The criteria we want to develop should answer questions for both the use of AI by organizations and the use of AI by humans, since after all, humans are what make up organizations!

As a starting point, then, we looked at the Flourishing AI benchmark developed by Gloo and its partners. The benchmark considers seven components of human flourishing:

1. Character

2. Relationships

3. Happiness

4. Meaning

5. Health

6. Finances

7. Faith

The first six of these are based on dimensions developed as part of the Human Flourishing Program at Harvard; the seventh, faith, was added by Gloo and its partners.

You can read the full study on their site (and even go deeper by reading their white paper), but the bottom line is this: the current generation of frontier models is woefully deficient in matters of faith. Surprised?

As we discussed these results in the class, Ali led us through the Gloo initiative to develop a values-aligned LLM, one that is trained from the beginning to align with human flourishing. We had a great discussion around the need for Christians to be leaders in this space and about how Gloo was approaching this effort.

We spent the last part of class talking about how the work Gloo was doing in human flourishing was a starting point for our work, but that we needed to think through how human flourishing applied to how we use AI, not just how the models would respond. As I’ve stated in earlier posts, technology is not neutral; we must first understand its underlying design and proper implementation before making a decision on our own uses of it.

I encouraged the class to begin thinking of how we might develop a set of criteria for evaluating personal use of AI in light of the human flourishing model. For example, should we use AI to read and respond to our email? There is positive value in reducing the amount of time needed to manage communication; but at the cost of perhaps losing the intimacy of personally engaging with messages from others. I recently came across a post here on Substack that dives deeply into a closely related question.

For next week, the class is going to begin looking at business uses of AI. I asked them to read the first three chapters of All in on AI and be prepared to discuss how companies can use AI successfully. Specifically, we will analyze what the authors identify as the three archetypes of AI implementation by organizations: (1) to create a new product or service, (2) to gain operational efficiency, and (3) to influence customer behavior. More on this next week.

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Starting tonight, I will be teaching a course here at Biola University with the title “Business Applications of Artificial Intelligence.” The course description reads:

Students will learn about a variety of topics related to the use of Artificial Intelligence while also gaining skill in using a variety of AI tools. While the primary focus of the course will be AI's implications for business, it will also have a broader focus on concerns related to ethics and human flourishing. All of this will be embedded in a deeper understanding of how these tools and topics should be understood and used to love God and love our neighbors.

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This class is part of a larger set of requirements for students who want to complete the “Business Analytics and Artificial Intelligence” concentration or minor. I also have some MBA students taking it as an elective.

In our first meeting tonight, we will focus on the basics: what is AI and how should we think about using it? I will be introducing them to three foundational documents that we will be using as focal points this semester:

• The AI Explainer

• Biblical Principles for the Understanding and Use of Artificial Intelligence

• Measuring AI Alignment with Human Flourishing

The first two documents primarily focus on how we should think about AI from a personal use level, while the third is the foundational document for Gloo’s research into how Large Language Models are aligned with human flourishing. Starting from these three documents, the class will begin to explore how specific use cases of AI (both personal and business) should be evaluated.

Further reading this semester will include All in on AI, AI Snake Oil, and a variety of online resources including ’s Substack and subscribing to The Rundown AI. And of course I am asking them to subscribe to this newsletter as well!

Beyond this research, the class will be involved in several activities during the semester, including joining a team for the upcoming Gloo AI hackathon and completing a vibe coding project.

I plan on posting at least once a week with updates from the class and the activities that my students are undertaking. In the end, we should all have a better idea of how businesses can effectively use AI in a way that also has positive implications for human flourishing and faith.

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Here are the two questions, worded as closely as I can remember to the actual question stated:

1. If our students are already coming to class knowing how to use AI, and most of them know it even better than we (professors) do, what is the point of us taking the time to teach them how to use it?

2. If AI is dangerous and is a potential threat to human flourishing, why would we want to encourage our students to use it at all?

I appreciated these questions because they force me to face my own assumptions about the place of artificial intelligence in the academic experience, namely that the proper uses of AI need to be taught.

Why teach students something they already know how to do?

The premise of the first question is that most all of our students will come to our classroom already having learned how to use AI and, in many cases, will have more experience and expertise than we do. Of course, the assumption here is that we are talking specifically about generative AI and not other forms of AI. Beyond this initial question, the questioner continued by making the points that we didn’t necessarily feel the need to teach students how to use the Internet, their smartphones, or social media, so what makes this different?

I began my answer by pointing out that, at least in some cases, we did teach students proper uses of the Internet and social media, and we still do. But the question remains, and it is worth answering.

Left to our own devices, we humans will gravitate towards reducing friction in our lives. The companies behind ChatGPT and other generative AI applications know this and they want to make their tools the place to go to “make us more efficient.” But being efficient should not be our goal. There are times we need to struggle: with ideas, with expressing ourselves, with working through problems. There are also times we need to fail so that we will be motivated to improve and mature. Using AI to replace this friction is like going to the gym and watching someone else workout - the muscles will never grow.

And this is why we need to help our students by creating boundaries around the use of AI. Of course, this starts with us, as faculty, doing this in our own lives. But this is a new technology, and we will be learning right along with them in some cases. This process of curating the proper use cases for AI is something I will be focusing on in a class I am teaching at Biola this fall; you can read more about that in my post “When should we use AI?” linked below.

So the real answer then is to identify and prioritize the learning elements in our class with which we want our students to struggle and grow. For example, in a composition class, AI would most likely not be used to write drafts or potentially even outlines. But perhaps it could be used to research sources and help with citation formatting. Alternately, in a business class, AI could be used to compare and contrast data in a particular industry. In both cases, the instructors should first create a clear policy and then demonstrate that policy in class. I discussed some ideas for assignments like this as well as other related ideas in my post earlier this week, “AI in the Classroom,” linked below.

Why teach something potentially dangerous?

This question has bigger implications but is also is easier to answer. Artificial intelligence is now part of the public consciousness. It is installed on every smartphone and every Internet-connect computer has access to it. It is all around us. We must confront it head-on: as with previous technologies, it can create as many problems as it can solve. And it is not neutral: it is designed to entice us and make us dependent upon it. We must not let it.

So, as with the answer to the first question, we must become wise and learn when it is appropriate to use and when it is not. And further, when we do use it, we need to understand how to use it well so we are using it to our benefit, not just to get things done more quickly. And then transfer that knowledge to our students.

Here at Biola University, this is one of the primary reasons we started the Artificial Intelligence Lab. We want to provide our students, faculty, and staff to have a place to get answers about AI, a place where we can apply our values to its use.

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Start the conversation!

Let me know how you would add to these answers or if you have a different take. I’m listening.

These days, when someone mentions using AI in an educational setting, they are almost always referring to ChatGPT and its less popular siblings, enchanted with the prompt-and-response conversations that feel miraculous. But this is distracting us from the real breakthroughs that AI (of all types) will bring us. From medical breakthroughs to self-driving vehicles, we will all benefit from where this science is taking us. So when I start my presentation, I will first remind the audience that figuring out how to use the chat interfaces is not the end game of proper AI use.

But we’ve been here before: new technologies of the past have caused a stir in academic settings: calculators, the Internet, and smartphones have all presented challenges of one type or another, but we have figured out how to deal with them. Even Plato warned against the new technology of writing, worried that it would weaken our ability to memorize (he was correct). In all of these cases, and many others, we have adapted and adopted with varied measures of success. Now, with the current challenge of generative AI, we have to adapt and adopt yet again, which is where the practical part of my presentation takes us.

Generative AI brings several challenges to the classroom environment, which I lay out as follows:

• Learning process circumvention. AI tools create illusions of productivity while undermining essential cognitive development. Students are tempted to bypass cognitive struggle, leading to a loss of critical thinking skills.

• Assessment validity crisis. There's a growing difficulty in evaluating genuine student learning and understanding. Traditional assessment methods are losing effectiveness, making it challenging to accurately measure authentic comprehension and knowledge acquisition.

• Professional readiness gap. There are significant long-term implications for workforce preparation and resilience. Graduates may be unprepared for workplace realities, exhibiting over-reliance on AI tools (or the opposite) and a lack of essential resilience.

So then what is an instructor to do? How do we address these issues? There are many, many solutions to this problem. But again, we must adapt and adopt. I start by explaining what I consider the basic strategies: a clear syllabus statement, demonstrating proper use in the classroom, creating assignments that are “AI-resistant” and “AI-enhanced” and, finally, developing new forms of assessment.

I go on to provide several examples of the assignments and assessments which they may find useful.

AI-Resistant Assignments

These assignments are designed to require skills that AI cannot easily replicate, such as real-world interaction, personal reflection, and multimodal creativity.

• In-Class Timed Writing: Use personalized prompts based on class discussions. This requires real-time, original thought that is difficult for AI to pre-generate.

• Multimodal Projects: Ask students to combine writing with presentations or artwork. AI struggles to seamlessly integrate diverse creative outputs and understand human aesthetic choices.

• Process Documentation: Require students to submit drafts, annotations, and reflections on their work. This demands insight into the human learning journey and self-assessment, which AI cannot genuinely perform.

• Fieldwork with Observation: Have students engage in direct observation and data collection in the real world. This necessitates sensory input and the interpretation of nuanced, contextual data that AI cannot access.

AI-Enhanced Assignments

These assignments treat AI as a tool to be used critically and strategically, helping students develop essential skills for the modern workplace.

• AI-Assisted Research: Allow students to use AI for tasks like summarization or brainstorming, but require them to critically evaluate the output for bias and accuracy, document their process, and integrate the findings with their own original analysis.

• Critical Evaluation of AI Content: Have students analyze AI-generated text, images, or code against specific standards to identify strengths, weaknesses, and ethical implications, thereby developing their critical thinking and media literacy.

• Prompt Engineering Exercises: Teach students to design and refine prompts to get high-quality outputs from AI models. This cultivates problem-solving skills and strategic thinking for effective human-AI collaboration.

• Comparative Analysis: Ask students to compare AI-generated work with human-created examples to analyze differences in style, depth, and nuance. This deepens their understanding of both human and artificial capabilities.

AI-Resistant Assessments

To ensure academic integrity in the age of AI, assessments must evolve. These strategies focus on evaluating a student's genuine understanding and learning process.

• Design Authentic Assessments: Focus on tasks that require direct application of knowledge or skills, such as presentations, oral exams, or practical demonstrations. This makes AI generation of the final output impossible and ensures genuine learning outcomes.

• Implement Process-Focused Grading: Emphasize evaluating the developmental journey of an assignment by looking at drafts, research notes, and reflections, rather than only the final product. This approach promotes original thought and makes it more difficult for AI to bypass the learning process.

• Allow AI-Assisted Submissions: Provide clear frameworks for students to use AI tools transparently. This can be done by requiring documentation of their AI interactions, such as prompt logs or usage summaries, which fosters responsible AI literacy while maintaining academic integrity.

• Develop Clear Rubrics: Create detailed rubrics that explicitly outline acceptable and unacceptable uses of AI. This clarifies expectations for students and provides a structured way to assess how responsibly they have integrated AI into their academic work.

I want to be clear that I did not come up with all of these. Many of these were provided from sources I’ve found online, most prominently University of Waterloo Centre for Teaching Excellence: Guide to Assessment in the Generative AI Era and Cornell Center for Teaching Innovation: AI in Assignment Design. I highly recommend these two resources!

The presentation will conclude with some demonstrations and a faculty discussion. I expect some lively discussion, some good questions, and ideas for the direction of our AI Lab for the coming year.

What about you? I would love to get feedback on these ideas, your own ideas, and even links to more resources. I encourage you to share this post and start a discussion in the comments.

Of course, the first problem to tackle is to determine exactly what the use cases should be evaluated against. Here at the AI Lab at Biola University, we published our Biblical Principles for the Understanding and Use of AI last summer, so that was a starting point. Earlier this summer, Gloo and partners published a benchmark for Flourishing AI, which is a great addition. I also admire the work of both Nicholas Carr and Jonathan Haidt, which also can be applied here.

Using these as a starting point gives us some good ideas for criteria to consider when using AI. Here are just a few ideas that could be developed into part of the evaluation rubric:

• Does this use of AI encourage isolation or more meaningful human interaction?

• Does this use of AI short-circuit deeper thought and reflection?

• Does this use of AI demonstrate responsible stewardship of time and money?

• Does this use of AI still leave room for true creativity and excellence?

I would love to get feedback on these and ideas for more, so feel free to drop a comment. If you know of others writing about this, please do share! And as I begin to work through these ideas with my students, I’ll be sharing more, so take a moment to subscribe if you’d like to stay in the loop.

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In reviewing this document a year after its creation, I feel that it stands up pretty well. While you can read the full document at the link above, let’s walk through a few of the key points.

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Start at the beginning: Defining AI

In all my classes, I now ask students to learn this simple definition of AI:

Artificial Intelligence (AI) is a computer-based application designed to mimic human intelligence and behavior.

I like this definition because it forces us to confront the fact that AI is not just one thing. While the hype and focus over the past two years has been on generative AI such as ChatGPT, this technology we call artificial intelligence can take several different forms, only one of which is a chatbot. Beyond that, it also should give us pause and motivate us to better understand AI and its role in our lives.

Beyond this simple definition, the explainer document goes on to break AI down into two functional categories (discriminative and generative) and two levels (narrow and general). As it was when the explainer document was first published, the arrival of “Artificial General Intelligence” (AGI) is still a subject of debate: is AGI truly possible and, if so, when will we have it? There is now a move to rename AGI to “Superintelligence” or something equally magnificent. While some believe it will immediately usher in a new era of progress (or holocaust!), I am more aligned with the idea that AI will be slowly implemented as we adapt to its capabilities.

How does AI work?

The next section in the explainer tackles the underlying technology of AI and how it works. In short: we don’t really know. From the explainer document:

Traditional computer applications are built using programming languages that specify exactly how the system should respond to inputs and any results obtained can be traced back through the computer program. With AI, the responses are instead “learned” through the process of training and feedback. These learned responses are stored within the model and cannot be easily interpreted (this is commonly described as the “black box”). This embedded logic produces a large range of outputs, some of which may not have been foreseen.

So in a sense, AI is “grown” more than “built,” with data being the primary nutrient. An important implication of this fact is that the AI applications we use are only as good as the data they are trained on. And never better.

I will say that, since the publication of this document, some of the leading AI chatbot models have indeed tried to solve this problem by releasing “reasoning” models that provide some insight into the steps they are taking in coming up with their response. But we still cannot easily see inside the black box.

AI does not think

The last part of the explainer document focuses on a few important questions, the first of which is about the illusion of AI “thinking.” Because AI is designed to mimic humans, we tend to think of it as having human qualities. This is especially true when we are using the chat interfaces. Let’s be clear: AI is not human, it does not think, consider, reflect, and it has never experienced anything. I would also argue that it cannot truly create, but I’ll leave that for another post.

When you interact with a chatbot, it processes your input to determine the most likely meaning. Based on this, it generates a response (text, image, or video) that it calculates has the highest probability of being a successful answer. The AI does not truly understand either your input or its own output. Its rapid processing speed can make it seem human-like or even superhuman, which may lead us to mistakenly believe it possesses intelligence or wisdom.

Why should we care?

We are seeing new uses and capabilities of artificial intelligence every day. Whether or not you believe superintelligence is coming soon, we should all agree that understanding and effectively using AI are essential for anyone who wants to lead and shape the future of organizations and culture. We need people of faith in those places.

But while we are leaning in to understanding and using AI, we should also recognize that technology is not neutral: the use of technology subjects us to its designs, incentives, and biases. In other words, it is not just how we use it, but what we use it for or even that we use it at all. While this will be a subject of some of my future posts, you can get a preview of the principles behind this concern by reviewing the other document published by the AI Lab last summer: Biblical Principles for the Understanding and Use of Artificial Intelligence.

What’s missing?

As I stated earlier, much of the AI Explainer document still stands up a year after its release. If I were to write it again today, I would probably want to add a section on AI agents and their implications. I’ll leave that for an upcoming post as well.

What do you think?

I would encourage you to take a look at the explainer document and then give me your feedback. Do you like this definition of AI? Is there something that can be explained better? Leave your thoughts in the comments.

As we move into this “Age of AI,” it is more important than ever to take a critical look at our relationship with our digital tools and how they impact us both individually and as a culture. If you believe, as I do, that we are created in the image of God (Imago Dei), then you believe that human beings thrive when we are at our fullest capacity for loving God and loving each other. And that means taking a critical look at how we interact with the world, including through the use of technology. In this newsletter, we will look at the use of technology (with a focus on AI) through the lens of faith, with a special focus on its use in academic and faith environments.

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Who I am.

My name is David Bourgeois and I have been working with digital technologies for my whole life. As a kid, I loved science fiction, from Star Trek reruns to Isaac Asimov’s Robots and Foundation series. These stories stoked my imagination about how technology would create an amazing future with space exploration and robot companions. I learned programming on a Commodore 64 in my living room one summer and went on to get degrees in Mathematics (BS), Management Science (MS), and Information Systems (PhD). Today, my love for technology (and reading science fiction) remains, but it is tempered with the reality of the impact that these technologies have had on me, my family, and our culture.

I am currently a Professor of Business Analytics and Artificial Intelligence in the Crowell School of Business at Biola University, where I have served since 2003. In 2024, I was appointed as the Director of the AI Lab at Biola. Before working at Biola, I served in a variety of roles as an information technology professional. You can find out more about me on my LinkedIn.

Thinking about unintended consequences.

This is not a newsletter that is anti-technology. To quote the apostle Paul: “May it never be!” I love learning about new technologies, exploring new ways that they can solve problems, and teaching about them to students. But we have lost our way; we have let the promise of an easier digital life replace the hard work and struggles that it takes to mature and grow as humans.

If we look to the past, we can see how new technologies can have unintended consequences. In 1985, Neil Postman published the seminal Amusing Ourselves to Death, which is just as relevant today as it was then. One of the book’s key points is that in the move from primarily a print-based society to a television-based society, we have begun to expect to be entertained, controlled by our love of amusement and distraction, sacrificing our capacity for rational thought and civic engagement. Sound familiar?

And the change has just intensified ever since. In 2010, Nicholas Carr wrote The Shallows: What the Internet Is Doing to Our Brains, highlighting how our attention spans were decreasing and our ability to think deeply was being curtailed. And in 2024, Jonathan Haidt gave a devastating report on the impact of smartphones and social media on the mental health of our children in The Anxious Generation.

When television, the Internet, social media, and the smartphone were first introduced, they were seen as signs of progress and a social good. I would argue that they still do bring good, but perhaps in some cases, the bad outweighs the good. And now we are face-to-face with an onslaught of new capabilities made possible through artificial intelligence. If we are to be at our fullest capacity for loving God and loving others, then we need to learn from these past experiences and figure out how to apply them so that we can use technology well.

Why should you subscribe to Imago Digerati?

Imago Digerati is a Christian community exploring how to be image-bearers of God in a technological age by championing our unique human dignity, creativity, and relational nature. The name represents the intersection of two ideas: “imago” is the first part of “Imago Dei”, the concept of human beings being created in the image of God; the term “digerati” means people with expertise or professional involvement in information technology.

With this as the context, I will present ideas, pose questions, and share my own research and experiences with you. I invite you to subscribe to this newsletter and join me as I ask questions and explore how faith intersects with our use of technology.