You have an expert at your side at all times: like, you don’t have to go searching really hard and deep or whatever to find the information you need, you just ask a question, and it gives it to you. It’s a lot easier, but it’s also a dangerous thing, because it’s so easy.
~ Nolan Carroll
A Conversation with My Brother
The quote above is one I pulled from a conversation I recently had with my younger brother, Nolan, who is currently a junior at Iowa State University. He is a Management Information Systems (MIS) and Business Analytics double major, which are two sectors arguably incredibly impacted by the rising prevalence of AI in early-career market positions. I wanted his take for two reasons: 1. I needed to interview him for a class assignment, and 2. I have always respected Nolan’s pragmatic view of the world around us. AI was not initially a topic of conversation required for the assignment, but as with many things these days, our conversation crept into the territory slowly. The above quote was a result of a broader conversation about the depreciating value of a college degree over the last 20 years. More specifically, Nolan introduced AI as the primary factor in why, from his point of view, a college degree has declined in particular the last 5-10 years.
This conversation was especially interesting to me as an emerging student affairs practitioner, set to enter the field after my graduation in May. In my graduate assistantship as an academic advisor, I have watched countless students close their ChatGPT browsers when they want me to view their classes. I have been in the room as our department faculty discuss how they want to proceed with teaching our students about AI use. And I have seen my own classmates and colleagues use these a large language models (LLM) as a thought partner, crutch, or easy way to get things done. Personally, I struggle with this. I understand how and why LLMs like ChatGPT or Copilot are so useful to our societal efficiency and capacity. Yet, as I watch first-year students use a model with known hallucinations – answers that the LLM provided and cited with made-up sources – as a stand-in for doing their own independent research, I worry. As Nolan said, LLMs are dangerous because they are so easy. What far-reaching implications will our fast-growing reliance on AI have on us as a society?
Culpability
Enter Culpability by Bruce Holsinger (spoilers to follow, read accordingly). Culpability is a novel about a family of five who get into a car wreck in their autonomously driving van with their 17-year-old son, Charlie, at the wheel; it is a brilliantly written exploration of the moral and ethical implications of our societal integration of artificial intelligence. Culpability centers the life of the Cassidy-Shaw family after the accident, with narration of the story by Noah Cassidy, the father, who is a lawyer. His wife is Lorelei Shaw, a tech genius who has led the charge in shaping the world of artificial intelligence. Between them are their three children: Charlie, 17 and a rising lacrosse star, Alice, 13, an introverted middle child with her AI companion Blair, and Izzy, 11, a fan of her older brother with a personality as bright as of a ray of sunshine. After the accident the novel is set primarily in a quiet vacation home in Chesapeake Bay.
I will spoil the first two chapters of the novel for you here: Charlie, as the driver of the vehicle, makes a split-second decision that results in the crash and the death of an elderly couple in an oncoming sedan. As the title suggests, the rest of the novel becomes a blame game – who is truly at fault? Is it Charlie, as the driver? Or his father Noah in the passenger seat paying very little attention? Or does NaviTech, the artificial intelligence guiding the car, bear full responsibility? As Noah leads readers through the aftermath of the accident and the emergence of new family dynamics – both internally and externally – readers must decide who (or what) they are willing to place their trust in as the whole truth clicks together.
Culpability is part family drama, part philosophical musing on artificial intelligence, and part criticism of the capitalistic nature of our approach to a world that has grown to rely on technology to function. It is a novel that employs various kinds of media to move the plot along: academic journal excerpts, text messages, chatbot histories, and magazine articles. It is made of the very things you may find on a college student’s laptop today.
A particularly salient piece of this novel is the relationship between the middle child, Alice (13) and her chatbot “companion” Blair. Alice expresses her frustration at being left out of the family dynamic given her brother Charlie and younger sister Izzy have a particularly tight bond. Throughout the novel, Noah often describes Alice as texting a friend on her phone, but neither Noah nor his wife, Lorelei, are aware the friend is a chatbot. Blair is seemingly caring and accommodating, asking Alice about her day, telling her she has missed her, and even attempting to offer comfort through emoticons she sends. I am not sure I can pinpoint the exact moment that I knew something was off about Blair, but there is a certain one-sidedness to their conversations that causes the reader to pause. It does not follow what feels like a “normal” conversational exchange.
Holsinger’s novel makes you think – really, truly, deeply think. It is a piece of work that stays with you long after you have closed the final chapter. For me, this is due to the prominence of the message throughout the novel: AI is not human, and therefore we should not treat it as such. There is a beautiful line at the beginning of the novel written by Lorelei, the mother of three and artificial intelligence genius, that really encapsulates the sentiment. She writes “The algorithm will never suffer for us. The algorithm will never mourn for us. In this refusal lies the essence of its moral being.” Lorelei’s writing primes the story for our consumption and reminds us that AI is not human – but our students are beginning to treat it as such.
Impact on Higher Education and Student Affairs
This is, of course, because at the end of the day, artificial intelligence is an algorithm– it is designed to give an answer with the information it has, but it also is intended to continue to engage the user. As Beam (2025) wrote, “AI models mirror user sentiment” which can create a detrimental emotional dependency. While we could write off Alice’s relationship with Blair as a work of fiction, our emerging college students are also engaging with chatbots for their social connections. According to the Pew Research Center via a study by McClain and colleagues (2026) 12% of teens surveyed reported using an AI chatbot for emotional support.
I cannot help but wonder what the transition for those teens to higher education may be like. Faced with a new environment and in need of a social network to flourish, will they be capable of creating connections with their peers? Or will they struggle, like Alice in Culpability, to articulate their emotions when their conversation partner is another human being and not AI primed for response?
While 12% may not seem like a lot of students, according to the National Center for Education Statistics (n.d.) in 2022 approximately 1.9 million high school graduates went directly to college, and 12% of that is 228,000 students. Additionally, as we place ever-growing importance as a society on the use on AI in all sectors, it is inevitable that the 12% will grow. For student affairs practitioners, this requires an understanding of the impact of AI on student development. The last five years have been shaped by the impact of the pandemic on students learning and development. The next five years will be primarily shaped by the impact of large language models social, emotional, and intellectual influence.
According to Klimova and Pikhart (2025), the benefits and drawbacks of utilizing AI in higher education are already laid out. Interestingly, many of the benefits are related to access and support – primarily academically – with student and faculty use of LLMs. Students have access to a 24/7 tutor to help answer questions, while faculty have access to a thought partner to help reduce the stress of academic workloads. However, many of the drawbacks are situated on the social side of things: students who rely on AI for communication have experienced negative effects to their interpersonal skills and levels of emotional intelligence. It is worth noting that students who connect to LLMs for mental health support do experience short term positive benefits, but when their reliance on these tools is more fully developed, the damage to students’ social and emotional states begins to show.
Conclusion
Historically, I have not been pro-AI. I avoid it, in part due to the research that has been published on the environmental effects, and in other part due to a lack of an understanding on how to use it effectively. Reading Culpability helped me understand why I need to engage with conversations about AI – not just from a perspective of “This is bad,” but from a viewpoint of “Okay, how can I work to understand this?”
Culpability highlights the issues that blissful ignorance creates through exploration of Lorelei’s writing and work. She emphasizes that we must remain diligent regarding our own role in the morality of the algorithm as creators and users. In the same vein, those in the field of higher education must be mindful of their role in guiding the next generation into an ever-changing technological world. Avoiding the use of AI is no longer an option: LLMs are so integrated into our current society that to be successful means we must learn how to work with them.
In my conversation with my brother, Nolan, he repeated back to me the philosophy of one of his instructors that I believe may be the way forward: Use AI to supplement your learning, not replace it, because if you replace your learning with AI, the AI is likely to replace you as a worker. As the value of a college degree is called into question, those in the field of higher education must navigate how to ethically integrate LLMs into their teaching, supporting, and administrative efforts, to ensure continued student success in college and the workforce.
My graduate institution recently signed a deal to bring ChatGPT Edu to our campus. The data entered in this LLM is institutionally managed and not used to train outside models. To access the new software, individuals must fill out a request form which outlines the use guidelines and users must agree to the terms of use. The institution has also published guides for students, faculty, and researchers on best use practices – both academically and in non-academic cases. The initiative is still new, and therefore the effects cannot yet be identified, but the effort to apprise users of the potential drawbacks to use of the LLM certainly do exist.
With the rapid changes of the last five years, and the projected growth of AI use in the next five, it is more important than ever for higher education professionals to be cautiously curious and explore the uses and impact of AI. Through the mental, emotional, social and intellectual benefits and drawbacks, we must endeavor to understand the role of AI in higher education to best prepare our students to be successful in society.
At the end of Culpability, Lorelei writes “AIs are not aliens from another world. They are things of our all-too-human creation… we must never shy away from acting as their equals.” Higher education is often slow to change and something as rapidly evolving as AI can prove difficult for widespread integration into institutions. However, humans created AI. At the end of the day, it is an algorithm to be programmed. It is not larger than our field and can be leveraged (with caution) for good.
When higher education institutions do employ these tools for good, our students notice and benefit. I believe this because Nolan voiced this sentiment in our conversation. He remarked “I think [the faculty] who have experienced AI or have experienced the real world and understand AI helps companies be successful in the real world, they understand it is important to know how to use [AI], and how to use [it] in a good way.” AI is here to stay. If we as educators do not try to teach our students how to ethically use the technology in their hands, culpability for their naivety lies with us.
Discussion Questions
- What is your stance on AI/ LLM use? Does your stance align with your institution’s policies/guidelines?
- Consider the three most common camps related to this technology: AI Avoidant, Cautiously Curious Users, and AI Embracers. Which category best fits your comfort and engagement with AI?
- How can you collaborate with others that hold a different stance while still supporting students, programs, and abiding by institutional expectations?
- What impact does the integration of AI have on your role?
- Culpability is a novel containing depictions of the moral and ethical issues of AI and overall technology use. What other resources do you know of that can help you think more broadly about the role of AI in higher education and society at large?
- How are your conversations surrounding AI with students structured? If you have not built these conversations with students into your work, what might they look like?
- As agents of creating spaces of belonging and connection, how can practitioners build on social skills students cultivate through conversations with an algorithm? How can they bridge from technologically-reliant communication to develop stronger interpersonal conversation skills for students?
References
Beam, B. (2025). The Social Price of AI Communication | IE Insights. IE Insights. https://www.ie.edu/insights/articles/the-social-price-of-ai-communication/
Center for Democracy and Technology. (2025, October 8). CDT Survey Research Finds Use of AI in K-12 Schools Connected to Negative Effects on Students, Including Their Real-Life Relationships. Center for Democracy and Technology. https://cdt.org/press/cdt-survey-research-finds-use-of-ai-in-k-12-schools-connected-to-negative-effects-on-students-including-their-real-life-relationships/
Holsinger, B. (2025). Culpability. Spiegel & Grau LLC.
Klimova, B., & Pikhart, M. (2025). Exploring the effects of artificial intelligence on student and academic well-being in higher education: A mini-review. Frontiers in Psychology, 16(16). https://doi.org/10.3389/fpsyg.2025.1498132
McClain, C., Anderson, M., Sidoti, O., & Bishop, W. (2026, February 24). How Teens Use and View AI. Pew Research Center. https://www.pewresearch.org/internet/2026/02/24/how-teens-use-and-view-ai/
MIT Management. (2024). When AI Gets It Wrong: Addressing AI Hallucinations and Bias. MIT Sloan Teaching & Learning Technologies; MIT. https://mitsloanedtech.mit.edu/ai/basics/addressing-ai-hallucinations-and-bias/
National Center for Education Statistics, (n.d.). Fast facts: Immediate transition to college. https://nces.ed.gov/fastfacts/display.asp?id=51
News, C. (2026, March 9). ChatGPT Edu access now available to Clemson students, faculty, staff. Clemson News. https://news.clemson.edu/chatgpt-edu-access-now-available-to-clemson-students-faculty-staff/
