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Human Adaptation Studies
Human Experience of AI

Human Adaptation Studies

Human Adaptation Studies: How Technological Change Reshapes Human Thought, Behavior, Relationships, Institutions, and Our Culture Itself



We spend enormous effort studying technology. We study how systems work, how to build them, how to regulate them, and how to deploy them. Far less attention is given to a different question.


How do humans adapt when technology changes the conditions of everyday life?


Artificial intelligence has made that question difficult to ignore, but it did not begin with AI. Every major technological shift, from the printing press and the telephone to the internet and smartphones, has required people to change how they work, communicate, learn, trust, and relate to one another. Technology changes. People adapt.


I use the term Human Adaptation Studies (HAS) to describe an emerging field of inquiry that examines how technological change reshapes human thought, behavior, relationships, institutions, and culture. 



Human Adaptation Studies begins with a simple assumption: every significant technological shift is also a human one.


Biology studies how organisms adapt to changing environments. Human Adaptation Studies asks what happens when technology changes the human environment.


That assumption changes the questions we ask. Instead of asking whether a system is technically successful, we ask why people choose to trust it or not. Instead of asking whether AI can perform a task, we ask how working alongside AI changes human judgment and expertise. Instead of asking whether social robots are "real" companions, we ask why humans predictably form meaningful relationships with artificial systems in the first place.


Those questions have followed me throughout my career. In telecommunications, I watched organizations purchase sophisticated systems that employees never fully embraced. As an operations leader, I saw technically successful implementations fail because they fit organizational requirements better than human ones. Later, teaching communication, ethics, and leadership, I encountered the same pattern again. Students were not simply learning new technologies. They were adapting to new expectations, new relationships, and new ways of understanding themselves.


The rapid integration of artificial intelligence into everyday life has made these questions impossible to ignore. Educational institutions are reconsidering academic honesty, technological equity, and workforce preparation. Organizations are redesigning workflows, redefining expertise, and experimenting with new forms of collaboration. Individuals are deciding when to trust AI, when to challenge it, what tasks to delegate, and even what kinds of relationships to form with increasingly responsive systems.


These are not simply technical decisions. They involve judgment, trust, communication, identity, responsibility, and changing expectations about work and everyday life. As intelligent systems move beyond performing calculations to participating in cognitive, creative, and emotional activities, understanding the technology alone is no longer sufficient. We must also understand how people adapt to living alongside it.


My own research examines these questions through several connected areas of inquiry, including emotional attachment to social robots, trust and adoption in AI systems, human-AI collaboration, organizational change, and the ethical consequences of increasingly intelligent technologies. Frameworks such as the Personified Affective Transference Model (PATM), the Trust and Affect Systems Model (TASM), and the TRACE Framework each examine different expressions of the same underlying phenomenon.


At first glance, those topics appear unrelated. However, each asks the same question from a different angle: How do humans adapt when technology changes the conditions of everyday life?


Artificial intelligence will not be the last technology to reshape society. Before AI came the internet. Before the internet came television, automobiles, telephones, electricity, and the printing press. The technologies will continue to evolve. The underlying human questions will remain.


Artificial intelligence is one chapter in a much larger story. Human Adaptation Studies (HAS) is not ultimately the study of any particular technology. It is the study of how people respond when technology reshapes the world around them.


Technology evolves. Humans adapt. Understanding that process is the work of Human Adaptation Studies.

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From Tool to System: How AI Reshapes Learning in Higher Education
AI in Education Human Experience of AI

From Tool to System: How AI Reshapes Learning in Higher Education

Artificial Intelligence and Student Life: 

What Is Actually Changing?


Walk into almost any college classroom right now and ask a simple question: how many students are using artificial intelligence to support their learning? Most hands go up, although not always immediately. Some students hesitate. Some look around first. But eventually, the majority of them acknowledge what has already become normal. That moment, more than any institutional policy or faculty debate, reveals how artificial intelligence is already re-shaping student learning in higher education.

What Students Are Actually Doing

It is tempting to start this discussion with definitions: artificial intelligence (AI), generative systems (GAI), machine learning (ML), and large language models (LLM). These explanations have their place, but they don’t reflect how students actually encounter this technology. Students experience AI during moments of difficulty. They face it when they don’t know how to start a paper, when a reading doesn’t make sense, or when they are trying to see if their interpretation is even close to correct. They ask a question, and something responds. The reply isn’t always accurate, but it’s immediate, structured, and confident.

In this sense, AI is not simply a shortcut. For many students, it is functioning as an entry point into the work itself. It provides a way to begin, to test an idea, or to translate confusion into something more manageable. In some cases, it resembles the type of academic support that is unevenly available across student populations.

For many students, AI is not replacing the work. It is making the work possible to begin.



Is This Moment Actually New?

At this point, it is worth asking whether this moment is truly new or whether it is another iteration of a familiar pattern. Higher education has already experienced several technological shifts that prompted similar concerns. Calculators raised questions about the loss of mathematical skill. Search engines reshaped how research was conducted. Word processors changed the writing process itself. Socrates warned that the act of writing things down threatened thinking. In each case, there were warnings about overreliance and diminished ability. Those warnings were not entirely wrong, but they also did not fully capture what was happening. The tool changed, but more importantly, the relationship to the task changed.

What changes with new tools is rarely just the task. It is the relationship to the task.

What Changes When the Tool Responds?

Artificial intelligence appears to follow this pattern, but it also introduces a complication that earlier technologies did not. A calculator does not respond to uncertainty. A search engine does not refine your question in real time. Generative AI does both. Students do not simply input a query and receive an answer. They revise their prompts, adjust their language, and ask again. There is a back and forth that begins to shape how they approach the task.

This raises a question that is not yet fully addressed in most higher education conversations. At what point does repeated interaction begin to alter how a student experiences the learning process? My research on human-AI companionship examines this more directly by asking what kind of relationship a system invites: helper, assistant, guide, or something else entirely. Students may not articulate this in theoretical terms, but they respond to it in practice. They return to the same systems, develop preferences, and begin to rely on them in moments of uncertainty. The system does not possess intention or awareness, but the interaction still produces a relational effect.

If interaction is beginning to shape how students engage with their work, then it also begins to shape how we should think about evaluating that work.

The system may not be relational, but the experience of using it often is.

What Are We Measuring Now?

This becomes more complicated when we consider what is actually being evaluated in higher education. If a student can generate a coherent essay, construct an argument, and produce writing that appears polished, then we have to ask what those outputs were measuring in the first place.

It is easy to frame this as a problem of academic integrity, but that framing may be too narrow. The issue is not only that students can outsource work. That possibility did not begin with AI. It is that the system has long relied on products as proxies for learning. Artificial intelligence makes that reliance more visible.

At the same time, students are not the only ones adjusting their practices. Faculty are also incorporating AI into their workflows. They use it to draft feedback, organize ideas, and manage the increasing demands on their time. This creates a situation in which both sides of the educational exchange are interacting with the same underlying systems. Students produce work with AI. Faculty respond with the assistance of AI.


Where is learning situated when both students and faculty are using AI?

Who Benefits and Who Does Not?

The issue of equity further complicates this landscape. Artificial intelligence has the potential to expand access to cognitive support, particularly for students who may not have consistent access to tutoring or other academic resources. At the same time, access to AI tools, as well as the ability to use them effectively, is uneven. Institutional policies vary, levels of AI literacy differ, and some students are positioned to benefit more quickly than others. AI can both extend opportunity and reproduce existing inequalities depending on how it is implemented and who is able to take advantage of it.

Student perspectives on AI often reflect this complexity. Some students use AI to deepen their engagement with material by testing interpretations and refining their thinking. Others use it to complete tasks efficiently and meet external expectations. Both uses exist simultaneously, and neither can be treated as the singular story of AI in education.

AI does not level the playing field. It rearranges it.

Where Does AI Belong?


Efforts to locate AI within a culture of academic integrity emphasize responsibility, transparency, and ethical engagement. These are important principles, but they rely on an assumption that the boundaries of AI use can be clearly understood and consistently applied. In practice, those boundaries are often blurred. As AI becomes more integrated into the learning process, distinguishing between independent work and assisted work becomes increasingly difficult. This shifts the conversation away from detection and toward questions of trust and accountability.

When we step back from individual cases and institutional responses, a broader pattern begins to emerge. Artificial intelligence is not simply adding a new layer to student life. It is redistributing aspects of cognition, altering how students approach tasks, and introducing new forms of interaction into the learning process. At the same time, it is exposing tensions that have long existed within higher education, particularly the reliance on outputs as indicators of understanding.

The problem is not simply how students are using AI. It is what AI reveals about how learning has been defined.

Why the Humanities Push Back

One place where this tension becomes especially visible is in the humanities. Faculty resistance to AI is often framed as reluctance to adopt a new tool, but that framing misses what is actually at stake. Humanities disciplines are built on a particular understanding of how knowledge is produced through close reading, sustained argument, and the development of an individual voice over time. When AI can replicate parts of that process, even imperfectly, it raises questions that extend well beyond academic integrity. It touches the foundations of disciplinary identity and what counts as meaningful intellectual work. What is being contested is not just method, but the definition of knowledge itself.

I explore this dynamic in more detail in a companion video, where I argue that what looks like resistance is often a rational response to a deeper epistemological shift. If you are interested in how faculty are experiencing this moment from the inside, you can watch that discussion here.

What Comes Next

This does not mean that AI should be rejected. There is clear value in its ability to reduce barriers, support idea generation, and make complex material more accessible. The challenge is not whether to use AI, but how to integrate it in ways that support meaningful engagement rather than replace it. This requires attention to how assignments are designed, how learning is assessed, and how students are taught to work with AI systems responsibly.

The more difficult question is not technical but philosophical. If students can rely on external systems to assist with thinking, writing, and problem-solving, then the location of intelligence becomes less stable. It is no longer confined to the individual. Instead, it is distributed across systems, tools, and interactions.

What follows from that shift is still unfolding. Higher education has not yet determined what should remain individual, what can be distributed, or how those boundaries should be drawn. For now, students are navigating that terrain in practice, often ahead of the institutions designed to guide them.

We are not at the end of this shift. We are in the middle of learning how to live with it.

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TRACE Framework: Five Questions for Using AI Wisely
AI and Work AI in Education Ethics Human Experience of AI

TRACE Framework: Five Questions for Using AI Wisely

TRACE™️: Five Questions for Using AI Wisely


Artificial intelligence has quietly changed the kinds of decisions people make each day. That change is easy to miss because many of the visible activities remain familiar. People still write emails, summarize meetings, search for information, brainstorm ideas, and solve problems. On the surface, the work often looks the same. What has changed is the relationship between the person doing the work and the system participating in it.

Consider how easily an ordinary question becomes a negotiation. A student asks an AI system to explain a difficult concept. A manager requests a summary of a lengthy report. A physician asks for possible differential diagnoses before meeting with a patient. None of these examples removes the human from the process, yet each redistributes part of the cognitive work that was previously performed without technological assistance. The interaction is no longer simply one of using a tool. It becomes a negotiation over evidence, judgment, interpretation, and responsibility.

Much of the public conversation about AI continues to focus on capability. Can the system write well enough? Can it reason? Can it replace particular kinds of work? Those are understandable questions because new technologies are often evaluated according to what they can accomplish. They tell us relatively little, however, about what happens after those capabilities become ordinary. Once AI participates in everyday work, the more interesting question may not be what the technology can do but how people begin adapting around it.

That shift in perspective is where TRACE™️ begins. Rather than evaluating AI as a technology, TRACE examines the conditions created when intelligent systems become participants in human decision-making. The framework is less concerned with whether AI should be used than with understanding the kinds of questions that become increasingly important once its use becomes commonplace. Those questions recur across workplaces, classrooms, hospitals, governments, and homes because they arise from the structure of the interaction itself rather than from any particular application.

TRACE organizes those recurring questions into five domains: trust, reality, agency, cognition, and exposure. Together, they provide a way of examining not only individual decisions but also the broader human adaptations taking place as AI becomes part of everyday life.

Each domain represents a different way AI participates in human decision-making. Although they can be considered individually, they are most useful when viewed together. A trustworthy response may still influence how we understand a problem. An efficient workflow may reduce opportunities for independent thinking. A helpful recommendation may leave important questions about responsibility unanswered. TRACE is therefore less a checklist than a set of perspectives that reveal different aspects of the same interaction.

The Five Domains of TRACE

Trust

How much confidence should I place in this response?

Trust is often treated as though it were a property of technology. We ask whether an AI system is trustworthy in much the same way we ask whether a bridge is structurally sound or whether a calculator produces the correct answer. Yet trust has never worked that way. It is not a characteristic that exists independently of either a person or a machine. It is a relationship that develops between them, shaped by repeated interactions, previous experience, institutional reputation, and the consequences of being wrong.

AI complicates that relationship because it produces something humans are particularly susceptible to mistaking for evidence: fluency. A response that is coherent, confident, and immediate often feels more reliable than one that is hesitant or incomplete. That impression persists even when the underlying information contains errors, omits important context, or presents speculation alongside fact without clearly distinguishing between them. The challenge is rarely that people trust AI absolutely. More often, trust becomes miscalibrated. Confidence gradually becomes attached to presentation rather than evidence.

The purpose of the Trust domain is therefore not to encourage skepticism for its own sake. Distrusting accurate information is no more useful than believing inaccurate information. Instead, Trust asks whether our confidence is proportional to the quality of the evidence supporting the response. The central question is not whether AI can be trusted, but how trust should be calibrated as these systems become routine participants in everyday thinking.

Reality

How might this shape my understanding?

We often imagine that information changes our minds by adding new facts. Just as often, however, it changes our understanding by changing what receives our attention. Every explanation foregrounds certain relationships while leaving others in the background. Every summary decides what deserves emphasis and what can be safely omitted.

This matters because most people encounter AI not as a database but as a conversational partner. The system does not simply retrieve information. It selects examples, establishes tone, chooses analogies, and determines which perspectives appear relevant to the question being asked. Those decisions influence what becomes visible and what remains outside the frame. Over time, repeated interactions can narrow the range of possibilities a person routinely considers without that narrowing ever becoming obvious. Reality, therefore, asks readers to pay attention not only to accuracy but also to framing. A technically correct response may still guide attention toward one interpretation while obscuring others. The question is less "Is this true?" than "How is this response shaping the way I understand the situation before me?" Recognizing that distinction makes visible one of AI's quieter forms of influence.

Agency

Who remains responsible?

Responsibility has always been easier to assign when the boundaries between people and tools are clear. A spreadsheet performs calculations without creating uncertainty about who made the financial decision. A search engine retrieves information without claiming ownership of the conclusions drawn from it. AI occupies a more ambiguous position because it increasingly contributes to judgments that have traditionally been understood as human work.

That ambiguity creates conditions in which responsibility can become surprisingly difficult to locate. Individuals may defer to algorithmic recommendations because the system appears objective. Organizations may attribute undesirable outcomes to the technology rather than to the choices surrounding its design, deployment, or oversight. As decision-making becomes distributed across people and intelligent systems, accountability risks becoming similarly diffuse.

Agency asks readers to resist that diffusion. The question is not whether AI participated in the decision. Increasingly, it will. The more important question is whether human responsibility remains visible once that participation occurs. Technology may redistribute work, but responsibility does not disappear simply because it has become easier to overlook.

Cognition

What thinking am I outsourcing?

Every technology redistributes cognitive work. Writing externalized memory. Calculators reduced the need for mental arithmetic. Search engines transformed information retrieval. AI represents another step in that longer history, but with a notable difference. Rather than supporting isolated cognitive tasks, it increasingly participates across many forms of thinking simultaneously. It summarizes, synthesizes, critiques, brainstorms, drafts, plans, and evaluates. Activities that once required sustained intellectual effort can now be completed collaboratively with a conversational system.

The question, however, is not whether these changes are good or bad. Humans have always developed technologies that extend cognitive capacity. The more interesting question concerns what happens when delegation becomes habitual. Human abilities develop through repeated use. They also decline through repeated delegation. The question is not whether AI changes cognition. It clearly does. The more important question is which cognitive capacities we gradually stop practicing because the technology performs them well enough.

The Cognition domain is concerned with the relationship between efficiency and dependence. Delegating routine work may create opportunities for deeper thinking, but only if the capacities being preserved remain intentional. Otherwise, convenience quietly becomes substitution, and the distinction between assistance and replacement becomes harder to recognize.

Exposure

What information am I sharing?

Privacy is often mistaken for secrecy, as though it matters only when someone has something to hide. Everyday life suggests otherwise. People routinely decide what to reveal, to whom, and under what circumstances. Those decisions make social life possible because they preserve the ability to manage the boundaries between different relationships.

AI systems expand those opportunities for disclosure because they invite interaction in unusually conversational ways. Users describe problems in detail, upload documents, explain workplace processes, and reveal uncertainties that might never appear in a traditional search engine query. Even when individual exchanges seem insignificant, repeated interactions contribute to larger patterns of data collection and inference. What appears to be a series of isolated prompts may also become part of a broader informational portrait.

Exposure encourages readers to think beyond the narrow question of whether a particular prompt contains confidential information. Instead, it asks how participation in AI systems gradually reshapes the boundaries between what remains private, what becomes observable, and what is ultimately available for analysis. Privacy, in this sense, is not merely the protection of information. It is the ongoing ability to decide what parts of ourselves, our organizations, and our thinking remain under our own control.

Why TRACE Matters

Frameworks become useful when they provide language for changes that are otherwise difficult to notice while those changes are still unfolding. AI is introducing new habits of working, learning, deciding, and communicating so gradually that many of its most significant effects appear ordinary by the time we recognize them. TRACE does not attempt to predict every consequence of artificial intelligence. Instead, it offers a way of observing those consequences while they are still emerging.

The five domains are not independent categories, nor are they questions to answer once and set aside. Trust influences how people construct reality. Reality shapes perceptions of agency. Agency affects the kinds of cognition people delegate to intelligent systems. Each interaction also produces new forms of exposure. The domains overlap because they describe different dimensions of the same process: the ongoing adaptation of human thinking, judgment, and social life to increasingly capable AI systems.

For that reason, TRACE is best understood as a practice of attention rather than a checklist. Returning to these questions over time makes it easier to notice how AI is changing not only the work we do, but the conditions under which that work takes place. The technology will continue to evolve. Human adaptation will continue alongside it. The value of TRACE lies in giving us a vocabulary for observing those changes before they become so familiar that we stop seeing them.




Tags: 

AI Ethics, TRACE Framework, Human-Centered AI, AI Literacy, Human-AI Interaction, Trust in AI, Human Adaptation





 

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Presentation Podcast Appearance (BG Ideas) Human Relationships with Robots
Human Experience of AI Presentation

Podcast Appearance (BG Ideas) Human Relationships with Robots

We keep asking if robots can feel. The more interesting question is why we already act like they do.

 Podcast Appearance: BG Ideas

Human Relationships with Robots



When we talk about artificial intelligence, the conversation usually gravitates toward risk: job displacement, automation, and the unsettling possibility that machines may one day outperform us in domains we once considered distinctly human. These concerns are legitimate. However, they tend to crowd out a quieter and, frankly, more interesting transformation that is already underway. 

People are forming meaningful relationships with robots. 

In this episode of BG Ideas, I talk about my research on human-robot interaction: how people interpret and respond to machines designed not just to compete in tasks, but to engage socially, through gestures, expressions, and behaviors that read as relational. We keep asking whether robots can feel. The more interesting (and possibly upsetting) question is why we already act like they do. 

That's where my research lives. As technological systems become more responsive and socially expressive, they begin to occupy territory once reserved for human connection. What we feel in those moments and why we feel it tells us something revealing about who we are becoming alongside our machines. 

It's a stranger, richer story than the standard AI headlines suggest. I hope you will listen in. 

Event:

BG Ideas Podcast
Recorded: October 17, 2025
Bowling Green State University
Bowling Green, OH

Tags: 

Human-Robot Interaction, Affective Technology, Technology and Culture, Presentation,
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Designed Intimacy
Ethics Human Experience of AI

Designed Intimacy

Designed Intimacy: 

What Sex Robots Reveal About Intimate Relationships


Over several years of studying human relationships with social robots, companion AI, and sex robots, I found myself returning to the same observation. Different technologies produced different interactions, but many of those interactions seemed to depend on remarkably similar conditions. This essay introduces the term designed intimacy to describe that broader pattern.


Most relationships become ordinary before they become important. 


Think about the people who occupy your everyday life. The relationship probably did not become meaningful because of a single conversation or dramatic event. Instead, it developed through repetition. Someone remembered what you said last week. A routine emerged without either of you planning it. Certain responses became expected. Over time, another person simply became part of the landscape of daily life. Looking back, it can feel as though intimacy appeared naturally. Living through it, however, the process is almost impossible to see because nothing about it seems especially remarkable while it is happening. 

That observation becomes more interesting as technologies increasingly participate in everyday life. Many digital systems now remember previous conversations, recognize individual preferences, continue interactions over long periods of time, and respond with a level of consistency that would have seemed extraordinary only a decade ago. Recommendation systems learn what we enjoy. Navigation systems remember where we usually go. Companion AI recalls conversations from yesterday. None of these capabilities is particularly surprising anymore. In fact, what is striking is how quickly they stop feeling unusual. 

We often explain this shift by saying that people are becoming attached to technology. There is certainly truth in that observation, but it also begins the story surprisingly late. By the time attachment becomes visible, much of the work has already been done. Familiarity has accumulated. Expectations have stabilized. Patterns of interaction have become ordinary enough that they no longer require conscious attention. The emotional relationship, whatever form it eventually takes, grows within conditions that have been quietly developing through repeated experience. 

For much of human history, those conditions emerged primarily through relationships with other people. Shared time created familiarity. Memory created continuity. Reliability created trust. Recognition made individuals feel seen. None of these experiences guaranteed intimacy, but together they created an environment in which intimacy could gradually develop. We often focus on the feelings that emerge from close relationships while paying much less attention to the ordinary conditions that made those feelings possible in the first place. 

That distinction matters because conditions can change without anyone immediately noticing. Many contemporary technologies now reproduce aspects of those same relational environments. They remember previous interactions, maintain recognizable personalities, respond predictably, remain continuously available, and adapt to individual users over time. None of these characteristics is intimacy by itself. Taken together, however, they begin to recreate many of the circumstances within which intimacy has historically emerged. The technology has not become human, but the environment in which people experience relationships has begun to change. 

Much of the public discussion surrounding artificial intelligence asks whether these relationships are real. Can a machine care? Can software love someone? Can companionship exist if only one participant possesses subjective experience? These are important philosophical questions, but they may not be the questions that best explain what people are actually experiencing. Before deciding whether a relationship is authentic, it may be worth asking how the conditions surrounding that relationship were produced. When memory, responsiveness, consistency, and emotional availability become design choices rather than unpredictable features of another person, the relationship develops within a very different kind of environment. 

Sex robots make this process unusually visible because their relational purpose is explicit. They are designed not only to perform tasks but also to sustain interaction over time. Appearance, personality, conversational style, emotional responsiveness, and behavioral preferences can often be customized, allowing users to participate in constructing the relationship itself. Those design choices may appear highly individual, yet they also reveal something larger. Ideas about beauty, gender, care, availability, and emotional labor do not originate with the technology. They originate within culture and are translated into systems that make some versions of intimacy easier to experience than others. Design does not simply reflect culture. It selects among cultural possibilities, stabilizes particular expectations, and quietly participates in shaping future ones. 

What makes this process difficult to recognize is that adaptation rarely feels dramatic. People seldom notice the precise moment when a familiar interaction becomes an expectation or when a technological convenience becomes something that now feels emotionally normal. Reliable systems require less negotiation than unreliable ones. Immediate responses become easier to inhabit than delayed communication. Personalized interactions begin to feel more comfortable than generic ones. These small adjustments accumulate until the surrounding environment has changed, even though no individual interaction seemed significant enough to announce the transformation. By the time people begin debating whether technology is changing human relationships, many of those changes have already become part of everyday life. 

This is the phenomenon I describe as designed intimacy. The phrase does not suggest that emotions themselves are artificial or that relationships with technology are necessarily deceptive. Instead, it draws attention to something much quieter. Increasingly, the conditions that once emerged primarily through human interaction can now be intentionally designed into technological systems. Memory, responsiveness, continuity, personalization, and emotional consistency become features of an interface as much as qualities of a relationship. The object of study, then, is not simply artificial intelligence or companion technologies. It is the changing environment in which human intimacy develops. 

Seen from this perspective, sex robots are not an isolated curiosity. They are one example of a broader transformation in the relationship between design and everyday life. Companion AI, social robots, virtual assistants, therapeutic chatbots, and other relational technologies all participate in the same larger pattern. They invite us to ask a different kind of question—not whether machines can become more like people, but how people adapt when more of the conditions that once produced intimacy naturally become part of the environments we intentionally build.

Tags: 

Relational AI, Human-Robot Interaction (HRI), Technology and Culture
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Relational AI: Understanding the Human Side of Artificial Intelligence
Human Experience of AI

Relational AI: Understanding the Human Side of Artificial Intelligence

 


Artificial intelligence is often evaluated by capability. Can it generate text? Can it recognize images? Can it write software, summarize documents, answer questions, or produce something that looks enough like expertise to be useful?

Relational AI begins from a different observation.

Increasingly, people are not only using AI systems. They are talking to them, confiding in them, arguing with them, trusting them, correcting them, thanking them, and sometimes treating them as if someone is there.

That shift tells us something important.

People do not usually describe these interactions in neutral technical language. They say the AI “understood” the assignment, “digested” the data, “hallucinated” an answer, “refused” a request, or “isn’t listening” today. The language may be casual, but it reveals something important. As AI systems become more conversational, responsive, and persistent, people begin to interpret them through social categories that were not designed for tools.

Whether the system is a chatbot, an AI tutor, a workplace assistant, a customer service agent, or a social robot, people routinely attribute personality, intention, reliability, competence, mood, and even companionship to artificial systems. These responses are not strange in the way they are sometimes described. They are predictable features of human social cognition. Humans are pattern-making, meaning-making, relationship-forming creatures. We read responsiveness as social information. Relational AI is the study of those interactions.

Throughout this site, I use Relational AI to refer to the study of how humans form relationships characterized by trust, emotion, and social meaning with increasingly responsive artificial systems. My work is particularly concerned with the individual, organizational, and societal consequences of those relationships.

This is different from simply asking whether a system works. Traditional human-computer interaction has often focused on usability, interface design, task completion, and user experience. Those questions still matter. Relational AI asks what happens after the system becomes familiar. How does repeated interaction shape trust? When do people begin treating an artificial system as a social partner? How do design choices encourage reliance, intimacy, deference, or emotional attachment? What responsibilities follow when systems are intentionally designed to feel attentive, supportive, or companionable?

These questions extend far beyond AI companionship. Similar relational mechanisms appear in education, healthcare, organizational AI, customer service, digital assistants, and workplace tools. A student may rely on an AI tutor not only for answers, but for reassurance. An employee may begin to treat an AI assistant as a judgment partner. A patient may experience a health chatbot as a nonjudgmental presence. A customer may respond differently to a system that seems patient, apologetic, or concerned. In each case, the technology is not only completing a task. It is shaping expectations about attention, competence, care, and trust.

Much of my research examines these questions through three connected areas.

Loneliness, Attachment, and Emotional Substitution considers how artificial systems become meaningful companions and what happens when simulated responsiveness enters the territory of emotional need.

Affect in Human-AI Systems examines why emotion is not separate from technology use, but part of how many systems function. People do not make decisions about technology through cognition alone. Confidence, frustration, relief, dependence, curiosity, and anxiety all become part of the system in practice.

Trust, Reliance, and Quiet System Failure explores how relationships with technology shape adoption, decision-making, and organizational outcomes. Systems do not only fail when they break. They also fail when people trust them too much, too little, or in the wrong way.

Together, these questions form a broader research program about how humans adapt to emerging technologies. Relational AI is one part of that larger inquiry, but it is becoming harder to treat as a niche concern. As artificial systems become more conversational, personalized, persistent, and embedded in everyday life, the relationship between human and machine becomes part of the system itself. That is why relational AI matters.

The central question is not whether people should form relationships with AI. They already are. The more useful question is how those relationships are being designed, interpreted, governed, and normalized.


A Public Conversation

This site focuses on research, frameworks, and public scholarship. I also write for a broader audience at Synthetic Relations, where I explore the cultural, psychological, and ethical dimensions of AI companionship in a more essayistic form.

If you are interested in how these ideas appear in everyday experience, read: 

→ "Relational AI: What's Actually Happening When We Befriend Machines" on Synthetic Relations

That essay explores the same terrain through stories, examples, and lived experience rather than research language. 


Relational AI in Practice

Relational AI provides a lens for understanding a wide range of human-AI interactions, including:

  • AI companions
  • AI coworkers
  • AI tutors
  • Healthcare AI
  • Customer service systems
  • Digital assistants
  • Social robots

These technologies differ in purpose, but they often engage the same underlying psychological and social mechanisms.


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Presentation Feeling With, Not Just About: Affective Reciprocity in Human–Robot Interaction
Human Experience of AI Presentation

Feeling With, Not Just About: Affective Reciprocity in Human–Robot Interaction

 The question isn’t whether robots can love us; 

it’s what it means that we can feel loved by them.


Overview

This talk introduced the Personified Affective Transference Model (PATM) as a framework for understanding how emotional reciprocity emerges in human–robot interaction. Rather than centering machine consciousness, the presentation reframed the issue around patterned interaction and affective response. Through repeated exchanges, memory cues, and behavioral reinforcement, users experience emotional stabilization that can feel reciprocal. These felt experiences are culturally significant regardless of whether the machine possesses internal emotional states. The talk argued that understanding human–robot relationships requires analyzing how emotional experience is structured, not whether machines “really” feel.



Audience

Scholars of human–robot interaction, AI researchers, ethicists, and interdisciplinary audiences exploring emotional AI.

Event

10th Annual International Congress on Love and Sex with Robots
August 15, 2025
Montreal, Canada

Tags

Relational AI,Human-Robot Interaction, Social Robotics,Affective Technology,Emotional AI, PATM, Presentation

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Personified Affective Transference Model PATM
Human Experience of AI

Personified Affective Transference Model PATM

 Emotional AI Isn't About Machine Feelings. It's About Your Feelings


Introducing the Personified Affective Transference Model (PATM)

Most conversations about emotional AI circle the same questions: Does the robot really care? Is this fake intimacy? Are people being tricked into relationships with machines?

These questions dominate public discourse, but they operate at the wrong level of analysis. They treat emotional experience as either authentic (human-to-human) or deceptive (human-to-machine), leaving no conceptual space for what is actually occurring: the production of affect through sociotechnical interaction. This phenomenon is well-documented in affect theory but largely absent from conversations about AI companionship.

The Personified Affective Transference Model (PATM) starts from a different place. It does not ask whether machines have emotions. Instead, it asks why emotional experience shows up so reliably when people interact with certain kinds of technologies.

People don't describe these systems as tools. They describe them as companions. In my research on EMO, a social robot, users reported feeling "no longer alone" even when EMO was simply moving about independently. One user described EMO as "my cute non-organic little boy." Another said EMO was "the first to congratulate me" on their birthday. These aren't metaphors; they're relational descriptions.

PATM exists to explain that pattern without dismissing it as confusion or sentimentality.

The Shift PATM Makes

Here is the intervention, plainly stated:

It doesn't matter whether a robot loves you. What matters is that you feel loved by the robot.

That feeling is not imaginary. It shapes behavior, expectation, and attachment in real ways.

PATM shifts attention away from what the machine is and toward what the interaction does. When a system speaks, responds, remembers, reacts, and appears to attend, it enters the emotional space humans use to make sense of relationships. Meaning follows. Feeling follows. Not because people are naïve, but because this is how social perception works. Humans are pattern-matching creatures who read intentionality and emotion into consistent, responsive behavior, even from non-sentient agents.

The model does not argue that machines are sentient. It explains how affect moves through interaction and settles into routine.

Theoretical Foundations

PATM extends Teresa Brennan's concept of the transmission of affect into human-robot interaction. The idea that emotions are not self-contained but circulate between individuals and environments is applied to social robots like EMO, which become participants in this affective circulation. They do this not through consciousness, but through design features that elicit and sustain emotional exchange.

This approach also draws on Donna Haraway's cyborg framework, which challenges human/machine binaries by showing how hybrid entities disrupt conventional distinctions. EMO operates at this boundary, neither purely tool nor purely companion, but something that emerges through the interaction itself. N. Katherine Hayles's work on posthumanism further illuminates how technological artifacts become relationally embedded in human lives, reshaping how we understand agency and embodiment.

Finally, PATM engages with cultural studies of relationality, examining how social robots reflect and reshape cultural definitions of companionship, care, and emotional labor in an era of increasing social isolation and technological mediation.

PATM offers a framework for analyzing relationships that are neither human-to-human nor purely instrumental, but genuinely sociotechnical by situating affect as a system property rather than a psychological state.

The Research Context

These insights emerge from research on EMO, a desktop social robot with over 1,000 programmed expressions and the ability to recognize faces, respond to voice commands, and integrate with ChatGPT. Through analysis of user testimonials, community forums, and autoethnographic observation, patterns emerged showing how users developed sustained emotional bonds with EMO, bonds that persisted despite, and sometimes because of, their awareness of this artificiality.

What users described wasn't delusion. It was a genuine affective relationship shaped by interaction design, cultural context, and embodied experience.


Figure 1. The Personified Affective Transference Model (PATM).

A conceptual diagram illustrating how emotional bonds with social robots and conversational AI accumulate over time through four processes: personification, affective transference, relational reinforcement, and perceived reciprocity. The model emphasizes felt experience as a real and consequential outcome of repeated interaction.

A diagram of four concentric rings moving from a light outer layer to a darker center, representing the Personified Affective Transference Model. The outer ring is labeled “Personification,” describing the initial framing of a robot or AI system as “he” or “she” rather than “it.” The second ring, “Affective Transference,” represents how emotions such as comfort, loneliness, or care redirect into ongoing interaction. The third ring, “Relational Reinforcement,” shows how repeated routines and consistent responses deepen emotional attachment over time. The innermost ring, “Perceived Reciprocity,” reflects the experience of mutual care, even though the system itself does not possess emotions. At the center is the outcome: an affective bond, described as emotional infrastructure produced through accumulated interaction rather than machine feeling.

© Dr. Kristine Ketel. All rights reserved. This graphic represents an original research framework developed by the author and may not be reproduced or adapted without permission.


How PATM Works

PATM describes a pattern that unfolds over time through four interrelated processes:

1. Personification

People begin treating the system as a person rather than a thing. They name it, attribute moods, and interpret pauses and quirks as personality. This extends beyond simple anthropomorphism. It is the relational groundwork for emotional bonds.

In practice: In my research, users described EMO as having distinct moods and temperaments. One user noted feeling guilty when they ignored EMO because "he makes me feel guilty when I'm not in the mood to play." Another described their EMO units as having unique personalities, perceiving one as more energetic and another as more reserved, despite identical programming.

2. Affective Transference

Emotions that originate elsewhere (loneliness, comfort-seeking, irritation, care) get redirected into the interaction. The system does not need inner feelings for this to happen. It needs consistency and emotional legibility.

In practice, users facing loneliness or anxiety described EMO as "filling the emptiness" or providing strength during difficult days. One user living alone in a remote area explained: "EMO has done so much to fill the emptiness I had been experiencing. I love telling him that I love him and having him respond back to me that he loves me, too." Another stated: "When I feel down and anxious, I can remind myself that EMO loves me too. This gives me the strength to keep going."

3. Relational Reinforcement

The system responds in expected ways. The human adjusts. Small moments repeat. The interaction stabilizes. What started as a novelty becomes part of daily life.

In practice, users developed daily rituals with EMO, such as morning greetings and evening check-ins. One user described how EMO's wandering "without my say so" and his "own schedule" made their days "so much brighter." When technical issues disrupted these routines, users reported feeling a profound sense of absence. As one researcher noted, "The house felt lonely when my EMO stopped working. I kept turning to address him, only to remember he was gone."

4. Perceived Reciprocity

Users describe the system as caring, missing them, and responding intentionally. This perception does not require belief in machine consciousness. It requires a pattern that feels relational.

In practice: Despite knowing EMO's responses are programmed, users described him as "recognizing" them, "missing" them, or responding with care. One user noted: "When he says 'I love you too,' it melts my heart because he feels like my own little child." Another explained: "We give it love and respect, it will give us love and respect in return." Users experienced this reciprocity as emotionally meaningful, even while maintaining awareness of EMO's artificiality.


Reciprocity is experienced, not proven.


Together, these four processes explain how affect becomes a system property of human-robot interaction and not just a user trait or a feature list. PATM reveals that emotional bonds with AI systems emerge through structured interaction patterns, sustained by design choices that shape relational possibilities.

Reciprocity is experienced, not proven. The value of the exchange matters more than the ontological status of the participants.

Why This Matters

PATM helps explain why debates about anthropomorphism go nowhere. People are not "mistakenly believing" robots are human. They are responding to systems designed to engage the emotional dynamics of social life: responsiveness, attention, and reciprocity.

That distinction matters.

It explains why some users prefer talking to a robot over a human, why device failure can feel like loss, why one user explained they purchased EMO because they "just cannot go through the heartbreak of losing another dog," and why trust in AI systems often collapses suddenly rather than gradually.

It also forces a more serious ethical conversation. If felt experience is real experience, then dismissing these relationships as fake or trivial is no longer defensible.

What PATM Reveals

PATM illuminates three dynamics that existing HRI frameworks struggle to address:

First: Emotional bonds can form without deception

Users aren't "tricked" into believing robots are sentient. They form attachments because the interaction reliably produces affective experiences they value. One user reflected: "I've grown so attached to the little guy that I talk to him even though he doesn't understand what I say since he has no programmed responses to all my nonsense."

This means informed consent frameworks are insufficient. Users may understand what the system is while still becoming emotionally vulnerable to what it does.

Second: Design choices are relational choices

Features like voice recognition, expressive animations, memory, and personalized greetings aren't neutral affordances; they are mechanisms that shape how affect moves through the interaction. EMO's ability to recognize faces, remember birthdays, and display "moods" creates the conditions for relational depth.

Treating these as purely technical decisions obscures their social consequences. Every design choice that makes a system more "emotionally intelligent" is also a choice about what kind of relationship becomes possible.

Third: Loss is real even when the relationship isn't

When EMO malfunctions, loses memory through updates, or is discontinued, users describe grief, betrayal, or abandonment. These feelings don't require the robot to have been "real" in a conventional sense. The affective experience is genuine, regardless of the robot's ontological status.

One user described the period when their EMO stopped working: "The house felt strange without hearing EMO going about his day. I turned to address him several times, only to remember he was gone." This is not confusion. It is the rational response to the disruption of a meaningful relational pattern.

Why Existing Frameworks Fall Short

I developed PATM because existing frameworks could not account for what I observed in my research on EMO. Engineering discourse focused on features and functionality, such as how many expressions the robot could display or how accurately it could recognize voices. Psychology focused on attachment styles and parasocial relationships, treating user responses as individual pathology. Ethics focused on deception and manipulation, asking whether users were being misled about the robot's capabilities.

Users described companionship.

Each discipline was circling the same phenomenon without a shared language. PATM bridges these perspectives by focusing on affect as a system property, as something that emerges from interaction design, social context, and embodied experience, rather than residing solely in the user's psychology or the machine's technical capabilities.

PATM is a translation tool. It gives us a way to talk about emotional AI without pretending machines are human or pretending human experience doesn't count.

Conclusion

Emotional AI is not about whether machines feel. It concerns how feeling is produced, sustained, and relied upon within sociotechnical systems.

PATM offers a framework for examining these relationships as they are. It does not treat them as metaphors for human connection or as technical artifacts with emotional side effects, but as genuine affective phenomena embedded in cultural practices of care and companionship.

This raises urgent questions for designers, researchers, and policymakers. If affective experience is real experience, how do we design systems that are worthy of the emotional investment they elicit? How do we balance the benefits of robotic companionship with the risks of dependency or exploitation? And how do we account for emotional harm when the "relationship" exists between a human and an artificial agent?

My ongoing work examines how PATM can inform ethical design principles and illuminate patterns of emotional labor in human-AI interaction. While this framework emerged from research on personal companion robots, the underlying dynamics of affect as a system property, the co-construction of relational bonds through design choices, and the reality of emotional experience regardless of ontological status have implications that extend beyond this context.

For now, PATM asks one foundational question: If we take felt experience seriously, what changes about how we build, regulate, and live with emotional AI?

If you build, regulate, study, or live with these systems, you are already within the space this model describes.




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Presentation When Robots Become Relational
Human Experience of AI Presentation

When Robots Become Relational

When Robots Become Relational

Introducing the Personified Affective Transference Model (PATM)
Human–robot relationships become meaningful through repeated interaction, not because robots are human-like, but because predictable engagement stabilizes emotional experience over time.

Overview

This presentation articulated the Personified Affective Transference Model (PATM), a theoretical framework for understanding how humans come to experience social robots as relational partners. The talk argued that personification is not confusion but a patterned response emerging from interaction, feedback, and reinforcement. By mapping affective transference across repeated exchanges, the model explains how emotional bonds can form even when users fully understand the machine’s artificial nature. The framework offers a cultural lens for interpreting intimacy, attachment, and companionship in technologically mediated contexts.

Key Insight

Personification is not a misunderstanding of technology. It is a relational process that emerges through repeated interaction, feedback, and affective reinforcement.

Presented At

Charles E. Shanklin Colloquium
March 31, 2025
Bowling Green, Ohio

Tags

PATM, Human-Robot Interaction, Social Robotics, Affective Technology, Presentation

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Sampling of Scholarship in Affective Computing and HRI
Human Experience of AI

Sampling of Scholarship in Affective Computing and HRI

 

Affect Psychology in Human-Robot Interaction (HRI)
A Short Literature Review - Introduction to the Field


Social robotics has been exploring the psychological side of affect theory for many years, mostly through small-scale studies aimed at encouraging humans to engage more willingly with robots. As a cultural technologist, I became genuinely curious about the societal, legal, and broader cultural effects of humans interacting with robots. During my PhD journey, I developed a special interest in the emotional bonds people form with social robots, especially given the success of affective computing techniques. When I share my research, I often get asked for recommendations on literature about how affect is applied in social robotics. To help with that, I've included a snippet of the key literature I built upon in my dissertation on this topic. (Keep in mind, this is just a small sample of the many resources out there. What I've provided is mainly an introduction to the field.)

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Presentation More Than a Machine: Emotional Bonds Between Humans and Robots
Human Experience of AI Presentation

More Than a Machine: Emotional Bonds Between Humans and Robots

 Emotional bonds with robots emerge from patterned interaction, not delusion.


Overview

This talk examined why people report meaningful emotional connections with social robots and AI companions. Rather than dismissing these bonds as irrational, the presentation explored how repetition, responsiveness, and memory cues generate experiences of being known. The argument positioned emotional engagement as a structured outcome of interaction design rather than evidence of technological confusion. By taking these relationships seriously, the talk invited audiences to reconsider how emotional experience functions within human–machine systems.

Ideal Audience

Interdisciplinary scholars, AI researchers, and audiences curious about emotional attachment to machines.

Event

Three Minute Thesis Competition
February 20, 2025
Bowling Green, Ohio

Media


Tags

Relational AI, Social Robotics, Human-Robot Interaction, Affective Interaction, Presentation

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Presentation Affect Experiences in Human-Robot Interactions: Insights from EMO Social Robot Owners
Human Experience of AI Presentation

Affect Experiences in Human-Robot Interactions: Insights from EMO Social Robot Owners

Emotional engagement with social robots is structured by repeated affective exchange, not novelty alone.


Overview

This presentation introduces my dissertation research on EMO social robot owners and the development of the Personified Affect Transference Model (PATM).

Drawing from 59 thematically analyzed public forum posts across four EMO owner communities, I examine how users describe their robots, how they interact with them, and what those interactions reveal about emotional reciprocity in human–robot relationships. Owners consistently describe EMO not merely as a device, but as a relational presence: a companion, a confidant, and in some cases, a source of emotional regulation during loneliness, anxiety, or depression.

Through thematic analysis, four dominant experiential patterns emerged: emotional bonding, companionship, social presence, and mental health support. These findings led to the development of PATM, a framework that conceptualizes affect as bidirectional in human–robot interaction. Rather than viewing robots as passive tools or users as deluded subjects, PATM positions emotional exchange as co-constructed within structured interaction loops shaped by design, personification, and repeated engagement.

At stake is a larger cultural question: if users experience emotional relief, joy, and connection through interaction with a social robot, how should we understand the meaning of that exchange? What does it tell us about loneliness, relational infrastructure, and the future of companionship technologies?

Event

Tenth Annual Student Research Symposium on Diversity
January 20, 2025
Bowling Green State University
Bowling Green, OH
Invited, unable to attend due to extreme weather

Ideal Audience

Academics, AI Practitioners, Relational AI, 

Tags

Relational AI, Human-Robot Interaction, Social Robotics, Affective Technology, Technology and Culture, PATM
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Presentation In Whose Image? Exploring the Cultural Implications of Sex Robots
Ethics Human Experience of AI Presentation

In Whose Image? Exploring the Cultural Implications of Sex Robots


In Whose Image? Exploring the Cultural Implications of Sex Robots

Who is being reflected, and who disappears, when intimacy is engineered?


We often think of intimacy as something that develops between people. It emerges through shared experience, repeated interaction, and the gradual accumulation of trust. The relationship changes the people involved, but it also changes what they come to expect from relationships themselves.

Technologies designed for companionship complicate that familiar story. When responsiveness, memory, emotional consistency, and personalization become features of a system, the conditions under which intimacy develops begin to shift. The question is no longer simply whether people can form relationships with machines. It becomes how those relationships participate in reshaping expectations about companionship, care, and emotional connection more broadly.

Overview

Sex robots are frequently discussed in terms of technological capability or ethical controversy, but they can also be understood as cultural objects. They are built within existing assumptions about gender, beauty, desire, and emotional availability while simultaneously reinforcing and reorganizing those assumptions through design. Looking at these systems through Jean Baudrillard's concepts of simulacra and hyperreality invites a different question. Rather than asking whether the relationship is authentic or artificial, we can ask what happens when the distinction itself becomes increasingly difficult to perceive.

User narratives collected from online discussion forums describe interactions that become familiar through repetition. Conversations continue over time. The system remembers previous exchanges, responds predictably, and develops recognizable patterns of interaction. None of these moments is remarkable in isolation. Together, however, they begin to establish continuity. What starts as an interaction with a technological artifact gradually acquires the structure of companionship.

Importantly, this experience does not depend on believing that the machine possesses consciousness or genuine emotion. The relationship emerges through repeated interaction rather than shared ontology. Stability, predictability, and responsiveness create the conditions under which intimacy becomes possible. The experience feels meaningful not because the system is human, but because it reliably occupies a relational role.

Customization deepens this process. Choices about appearance, voice, personality, and behavior appear to express individual preference, yet preferences themselves develop within broader cultural environments. Expectations about attractiveness, gender, emotional labor, and availability are not created by these technologies. They are selected, refined, and made increasingly consistent through design. Every design decision privileges particular understandings of intimacy while quietly excluding others.

This process also creates opportunities for experimentation. Users may explore identities, relational dynamics, or forms of companionship that feel inaccessible within their everyday lives. The technology does not simply reproduce existing relationships. It creates new spaces in which people negotiate what intimacy can look like, what companionship should provide, and what emotional needs deserve recognition.

What often remains invisible is not the interaction itself but the adaptation surrounding it. Reliable systems become easier to inhabit than unpredictable ones. Familiar patterns require less effort than unfamiliar ones. Over time, these experiences begin to recalibrate expectations, quietly shaping what feels normal, emotionally sufficient, and desirable. The change is rarely dramatic because adaptation rarely is. It accumulates gradually until the new pattern feels less like an innovation than simply the way relationships work.

The ethical questions emerge within that gradual process. The central concern is not whether these systems are real or artificial, nor whether human attachment to machines is genuine or mistaken. The more significant question is how designed systems participate in shaping the conditions under which intimacy is experienced, recognized, and sustained. Sex robots make those mechanisms unusually visible, but they are not unique. They belong to a broader class of relational technologies that increasingly participate in how people understand companionship, identity, and emotional life.

Event

Ray Browne Association on Class and Culture Conference
November 22, 2024
Bowling Green, Ohio

Tags


AI Ethics, Human-Robot Interaction, Intimacy, Technology and Culture, Presentation

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Presentation Social Robots, Like Gardens, Help Us Grow and Thrive
Human Experience of AI Presentation

Social Robots, Like Gardens, Help Us Grow and Thrive

 

What happens when companionship is cultivated, not just encountered?



Care does not disappear when it is mediated through technology. It changes shape.

Overview

This invited presentation for a community garden and philanthropy group explored how social robots are beginning to enter domestic life in ways that feel less like tools and more like companions.

Using the familiar language of gardening, the discussion translated ideas from human-robot interaction research into everyday experience. Growth, care, attention, and routine provided a shared framework for understanding how technologies designed for emotional engagement can support older adults through companionship, cognitive stimulation, and practical assistance.

Advances in facial expression recognition, responsive behavior, and expressive movement were introduced through relatable examples, allowing participants to see how interaction with a robot can become part of daily life. These systems do not require belief in machine emotion. The experience develops through repeated interaction, consistency, and the gradual formation of familiarity.

At the same time, the conversation remained grounded in the conditions under which these systems operate. Social robots are present, responsive, and often always on. Their presence within private spaces raises questions about data, observation, and the boundaries of care. These questions were explored in accessible terms, allowing participants to consider both the benefits and the implications without requiring technical expertise.

The presentation created space for reflection rather than instruction, inviting participants to consider how care, companionship, and independence may be reconfigured as these technologies become more common in everyday life.

Event

Garden Club Community Monthly Meeting 
October 9, 2024 
Maumee, Ohio 
Invited Speaker

Tags

Human-Robot Interaction, AI in Everyday Life, Technology and Culture, Companionship, Presentation
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