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Artificial Intelligence Isn’t God (or Even Human)

Artificial Intelligence Isn’t God (or Even Human)

Last updated on: October 6, 2025 at 10:23 am by James F. McGrath

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Artificial Intelligence Isn’t God (or Even Human) 2025-10-06T10:23:47-04:00 James F. McGrath

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I will be giving a talk soon with the title “AI Isn’t God (or Even Human).” If you’d like to hear the talk, it will be the annual Carr Lecture at Mount Union University in Ohio. For those who aren’t in that area, but are interested in the new book that I have out about artificial intelligence, there will be an online conversation that you can join in on this Thursday. The Carr Lecture won’t be about teaching, although it will touch on that. Rather, the focus will be on exploring what science fiction has imagined with what we have now, including highlighting how the influence of sci-fi has led many to think current technology is something it is not (or indeed, many things that it is not).

I will also be leading a workshop prior to AAR/SBL in November that provides an opportunity for professors to explore the crafting of assignments that meet their needs, whether the focus is the prevention of inappropriate use of generative AI, or integration of AI to harness its strengths in service of student learning that also instills an understanding of the technology and its weaknesses. Here is the description:

AI is in the news everywhere today. ChatGPT has become a household name. But faculty are often less enthusiastic, viewing AI as the “magic cheating machine.” We are often engaged in a rear-guard effort to try to shore up our assignments against the AI onslaught. At stake is preserving the integrity of what we teach while dealing with a world where AI is becoming ubiquitous. In light of this ThatCamp is offering a workshop on Teaching and Learning in the age of AI. Whether your concern is how to craft activities and assignments that ensure students master material while eliminating the need to police AI usage, or your aim is to find ways to integrate AI into class activities and assignments in ways that enhance student learning and prepare them to not only master religious studies content but this technology, you will leave this workshop with practical suggestions and hands-on experience of how to implement them. This pre-conference workshop at AAR features James McGrath (Butler University Professor and co-author of Real Intelligence: Teaching in the Era of Generative AI) and Randall Reed (Appalachian State University and co-author of Understanding AI and Religion: Meaning Making in the Digital Age).

The workshop is presented as part of ThatCamp, a pre-conference workshop held on Friday (the day before the start of AAR/SBL). Registration is $50 and it can be added to your AAR/SBL registration here: (https://na.eventscloud.com/ereg/modifyreg.php?eventid=831898&t=a88447020f07e02af279b5c1ecef7b00)

We worked on this quite a bit since it is difficult to present it in ways that will welcome the AI-detractors and the AI-enthusiasts. Randall Reed and I both worked on getting AI to generate images to illustrate the point and the image below was the result.

On the left is an image of humanities professors fighting a robot. On the right is an image of AI as a tool helping humanities research.

It is instructive that there is an AI Hallucination Leaderboard. On the one hand, they were able to get one LLM down below a 1% hallucination rate. That is impressive, when you understand (as Ankur Gupta and I explain in our book Real Intelligence) that LLMs are speech-imitators. They have no built-in mechanism to recognize and evaluate information or facts. They provide information essentially accidentally, by virtue of how it is woven into patterns of human speech. What is not clear from such a statistic is what kinds of questions were asked. If you ask about the greatest hits of the Beatles any AI chatbot will probably do well. If you ask it to outline an introductory class on a particular topic and provide recommended readings, depending on the topic, you may have a fair amount of fabrication.

My favorite way of helping people think about this is by analogy with Star Trek fandom. Imagine if in 0.6 of episodes, and so probably 5 episodes of the Star Trek franchise, the episode revolved around the crew going on a wild goose chase because the computer had confidently told them something that had no basis in reality.

Now, you might say that the crew faced issues with the computer malfunctioning, being taken over by alien entities, and otherwise causing them problems. (Indeed, just the holodeck computer alone surely caused issues in at least 5 episodes.) However, those are instances of glitches, interference, and other such things. In the case of LLMs, they will fabricate no less than .5% of the time. That is a feature, as they say, and not a bug. Imagine if, in addition to those instances when a “Level 5 Diagnostic” might indicate something was amiss, the computer when functioning within specified parameters would fabricate simply by virtue of what it is and how it functions. Would you be content? Would you want to serve aboard a starship under such circumstances?

Again, you might say “AI is improving.” There are limits to improvement when it comes to a particular technology. The terminology of “artificial intelligence” is misleading since it was coined as an aspirational aim of computer scientists, and yet has been prematurely or at least imprecisely applied to technology that is certainly artificial but does not come close to matching what we normally mean by intelligence.

Nevertheless, for those who know a field well enough to know whether what a chatbot produces is mundane, nonsense, or helpful and new, this technology can be a helpful tool in a number of ways. When I asked a question related to linguistics to an AI chatbot and then sent it to a friend who is a linguist, he said that some of it was correct and obvious, some of it was nonsense, and one thing was something he might not have thought of and that was genuinely interesting. There is no capacity in current AI technology of any sort to generate exciting new information and perspectives and recognize the significance of those.

Of related interest:

Time Magazine had an article on AI in healthcare. Here’s a taste:

[T]he promise of AI in medicine will only be realized if it is built and used responsibly.

Today’s AI algorithms are powerful tools that recognize patterns, predict, and even make decisions. But they are not infallible, all-knowing oracles. Nor are they on the verge of matching human intelligence, despite what some evangelists of so-called artificial general intelligence suggest. A handful of recent studies reflect the possibilities but also the pitfalls, pointing out how medical AI tools can misdiagnose patients and how doctors’ own skills can weaken with AI.

A perspective from MIT suggests that past education technology failures have lessons for the future of AI in education

Paul Levinson on ChatGPT’s inability to index

Liza Long had a student tell her that AI is God

Why Large Language Models Hallucinate

Chatbots and Psychosis

AI is bad at summarizing scientific papers accurately

Mike Bird has started a series on how he uses AI

Librarians are dealing with people looking for books and articles based on references fabricated by AI

OpenAI on what generative AI can and cannot do

Using AI to write corporate press releases

Series with Tripp Fuller and many guests

Dictionary companies sue Anthropic

Training DeepSeek to avoid controversial topics

Companies fire competent coders, opting to rely on AI…and then have to hire competent coders to fix the code

Albania decides government by AI can be no worse that government by humans

How DeepSeek was trained more cheaply

OpenAI Former Research Head Bob McGrew: The Breakthroughs Needed for AGI Have Already Been Made

Where AI thrives, religion may struggle

Trump posts AI video that shows he knows about and supports Project 2025 (which should come as no surprise)

How many people are interacting romantically with AI?

AI LibGuide for Instructors

Autonomous vehicles and safety

AI and transfer credit in higher education (also here)

Professors teaching AI with print books

Dr. Jeanne Beatrix Law writes, “academic integrity in the AI era isn’t just about citation or compliance. It’s about responsible genealogy, tracing how our thinking formed, naming who helped shape it, and building scholarly ecosystems that reflect accountability, transparency, and generosity.” She also posted about a recent survey in Canada about generative AI use, and an OpenAI usage study.

On educational leadership in the era of generative AI

I’m sure that I’ll also be at Starbase Indy in November and will talk about AI there. I’ll also be present in my capacity as one of the contributors to Beyond the Stars, a Starbase Indy anthology! It is already available for pre-order on Amazon. Let me share the first line of my story, in the hope that it will make you want to read the whole thing:

If anyone asks you why the second coming hasn’t happened yet, direct them to classified file 739-SX-27.

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