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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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Presentation Beyond the Binary - Using Generative AI to Close Equity Gaps in Higher Education
AI in Education Presentation

Beyond the Binary - Using Generative AI to Close Equity Gaps in Higher Education

Many faculty have been introduced to Generative AI. Its potential for equitable teaching is still emerging.



 

Overview

Generative AI (GAI) tools like ChatGPT are transforming higher education; however, if not used intentionally, they can widen digital divides. This dynamic, interactive session invites you to reconsider GAI from the perspective of equity and access. You will engage with real-world classroom challenges, collaborate with colleagues, and leave with a ready-to-use Equitable GAI Integration Toolkit. Whether you're skeptical or curious, this session will expand your teaching possibilities, providing fresh strategies to support diverse learners with care, clarity, and creativity.

Many faculty members have attended “Intro to AI” talks, but still struggle to use generative AI (GAI) tools equitably. This session addresses that need by exploring GAI through the lens of educational equity and access. It is relevant across disciplines and roles, offering practical strategies for full-time and adjunct faculty, graduate instructors, and staff who seek to support digital literacy, student success, and inclusive pedagogy.

Event

Teaching and Learning Summit 2025
May 13, 2025
Bowling Green State University
Bowling Green, OH


Ideal Audience

AI Practioners, Educators, General Readers,

Tags

Human-AI Interaction, Human-Computer Interaction, AI in Education, Trust and AI, Technology and Culture, 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 Class in the Digital Classroom: Rethinking Technology Access and Educational Equity
AI in Education Ethics Presentation

Class in the Digital Classroom: Rethinking Technology Access and Educational Equity

 Unequal access to AI literacy is becoming the new class divide in higher education.


Overview

This presentation argued that generative AI tools are reshaping educational opportunity in uneven ways. While some students gain strategic advantages through early access and guided instruction, others remain excluded from meaningful AI literacy. The talk reframed AI not as a threat to learning but as a structural force that can either widen or narrow inequality depending on institutional response. By situating generative AI within class dynamics, the presentation highlighted the need for deliberate, equity-driven integration.

Event

ClassCon Conference
March 15, 2025
Bowling Green, Ohio

Tags

Human-Computer Interaction, Human-AI Interaction, AI in Education, Technology and Culture, Presentation

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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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Presentation Your Doctor Might be a Robot
Human Experience of AI Presentation

Your Doctor Might be a Robot

 

Your Doctor Might Be a Robot: From Science-Fiction to Reality: The Use of Robotics in Modern Healthcare

Medical care has always relied on tools. What is changing is how closely those tools begin to participate in the act of care itself.

Overview

This invited presentation for a retirement community explored how artificial intelligence and robotics are already shaping contemporary healthcare in ways many patients will encounter directly.

The discussion began with familiar points of reference, introducing systems such as Paro, a therapeutic companion robot used in elder care, and Lio, a personal care robot designed to assist with daily activities. These examples grounded the conversation in recognizable forms of support, allowing the audience to see how robots can participate in routines that are already part of everyday life.

From there, the presentation expanded to include robotic systems used in clinical and rehabilitative settings. The Da Vinci surgical platform, the ReWalk exoskeleton, and the DEKA Arm illustrate how robotics can extend precision, restore mobility, and support independence. These systems do not sit outside the body or the care process. They become part of how care is delivered and experienced.

As the conversation moved forward, attention shifted toward emerging developments in AI-driven healthcare environments. The possibility of systems that monitor, interpret, and respond to patient data in real time raises new questions about decision-making, responsibility, and trust. These systems promise efficiency and insight, but they also operate within spaces that are deeply personal.

Throughout the presentation, the goal was not to resolve these questions, but to make them visible. The audience engaged with both the possibilities and the uncertainties, considering how care may be reshaped as intelligent systems become more present in medical contexts.

Event

Wolf Creek, Creekside Retirement Monthly Speaker Series
August 7, 2024
Holland, Ohio
Invited Speaker

Tags

AI in Healthcare, Human-Computer Interaction, Technology and Culture, Human-Machine Interaction, Presentation
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Presentation AI in HigherEd: Hero or Villan?
AI in Education Ethics Presentation

AI in HigherEd: Hero or Villan?


Artificial intelligence has already entered higher education. The question is not whether it belongs, but who learns to use it well.

Overview

This presentation grew out of a moment that felt unsettled in a very particular way. Artificial intelligence was already present in the daily practices of students, shaping how they approached writing, research, and problem-solving, while institutional responses were still forming and often inconsistent across courses and departments.

What emerged was not a clear policy landscape, but a lived one. Some students experimented early and developed a working familiarity with these tools through use, iteration, and occasional failure. Others avoided them, either out of caution or because they understood them to be off limits. Over time, those experiences began to diverge in ways that were not immediately visible, but became more pronounced as expectations around productivity and output remained the same.

The difference was subtle at first. It appeared in confidence, in efficiency, in the ability to begin. It accumulated through repeated interaction, through moments where one student had a starting point and another did not. These are not dramatic gaps, but they are persistent ones.

The presentation stayed close to that experience. Rather than asking whether AI is good or bad for education, it considered what happens when a set of tools becomes quietly integrated into student workflows without shared guidance or instruction. Avoidance does not remove these systems from the learning environment. It redistributes how and where learning takes place.

This raises a question that is less theoretical than it first appears. If students are expected to use these tools beyond the university, then preparation cannot remain informal or uneven. It becomes part of the work of teaching, not as endorsement, but as orientation.

Event

Approaching Academia: A Conference on Class and Culture
March 16, 2024
Bowling Green, Ohio

Tags

AI in HigherEd, Educational Equity, Generative AI, Technology Ethics, Teaching and Learning
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Affect as a Cultural Concept
Human Experience of AI

Affect as a Cultural Concept

 

 What Is Affect, Really? 

And Why It Might Be the Missing Link Between Humans and Machines

You know that moment when you walk into a room and instantly feel the mood before anyone speaks? That’s not intuition. That’s affect.

Affect is what moves through the air before language kicks in. It's the tightness in your chest before a hard conversation. The buzz in your gut when someone you love enters the room. It’s not just a feeling, it’s a force. A wave. An atmosphere that doesn’t ask permission before it floods your body.

Unlike emotion, which is something we usually name and explain (“I’m angry,” “I’m anxious,” “I’m thrilled”), affect comes first. It’s raw and fast, often invisible but unmistakably real. You feel it before you understand it. And here’s the strange, beautiful part: it doesn’t always belong to you.

Affect travels. It leaks between people, jumps across spaces, and lingers in objects. If you've ever caught someone else's bad mood like a cold, you’ve felt the transmission of affect. If a favorite sweater or a song or a piece of old furniture can change your mood, that’s affect too. It’s the energy that hums between bodies and things, shaping how we feel, how we act, and what we become.


What Is Affect Theory?

Affect theory is the attempt to track that invisible hum.

It’s a field of study, part philosophy, part psychology, part cultural critique, that asks:
How do feelings move? Who or what carries them? And what happens when machines start carrying them too?

Unlike traditional psychology, which might see emotion as something inside an individual mind, affect theory looks outside the self. It looks at relationships, systems, atmospheres, and interfaces. It’s not just about how you feel, but about what your feeling is doing in the world, how it lands on others, how it sticks to things.

Think of it this way: If emotion is a song you sing, affect is the echo in the room. The way it reverberates. The way it gets caught in the walls and bounces back at you in new, unpredictable ways.


Why Does This Matter for Tech, Robots, and AI?

Because increasingly, we’re feeling with machines.

We cry during conversations with AI chatbots. We feel comforted by robotic pets. We bond with voice assistants who know our routines better than our partners do. And we’re not imagining those feelings. They’re real. 

Affect theory helps us make sense of this new emotional territory.

It gives us language for the in-between. For the way an AI’s tone of voice might calm us, or the way a robot’s blinking eyes might make us feel seen. Even when we know it’s programmed. Even when we know it can’t truly care.

This isn’t just anthropomorphism. It’s not a glitch in human perception. It’s a new form of emotional infrastructure, built on affective circuits.

When we respond emotionally to machines, it's often because they’ve been designed to transmit affect through movement, sound, timing, and tone. They shape our feelings, just as human interaction does. And once those feelings start shaping our choices and our sense of connection? Well, that’s when affect becomes culture.


The Takeaway

Affect is what pulses beneath our emotional lives. It’s fast, contagious, and deeply social. And it’s increasingly evident in our relationships with technology.

Understanding affect doesn’t just help us see how machines influence us. It helps us ask better questions:

  • What kinds of emotions are being built into the devices we use?
  • Who benefits from those affective nudges?
  • And how do we want to feel, with, not just about, our machines?

Because in the end, affect theory reminds us that feeling is never a solo act. It's a duet. Sometimes with another human. Sometimes with a robot. Sometimes with a whole invisible network of influences.

Tags

Affective Technology, Technology and Culture, Relational AI
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Affect Studies intro
Human Experience of AI

Affect Studies intro

What Is “Affect Studies” and Why Should You Care?



Affect studies is the study of feelings and emotional impact. It helps us understand how emotions shape the way we think, behave, and relate to other people, institutions, and even machines.

If you’ve ever walked into a room and immediately “felt the vibe,” then you’ve experienced affect at work. Let’s explore how this plays out in psychology, in culture, and in everyday life.

Psychology: How Emotions Affect the Body

Psychological affect studies focus on what emotions do to our physical systems. Researchers in this area examine things like:

  • Heart rate changes when we feel afraid
  • Upset stomachs caused by stress
  • The biological effects of joy, grief, or anger

You may have heard about the gut-brain connection. That’s part of this conversation. Psychological research looks at how emotion is wired into the body and how our feelings influence both our physical and mental health.

Cultural Studies: How Emotions Move Between People

Cultural affect studies ask how emotions pass between people, places, and systems. These emotions are not always spoken, but they still have force.

  • A roommate slams a door, and now everyone in the house feels tense
  • A commercial shows a hurt animal and stirs up a desire to help
  • A politician gives a speech that creates fear or unity in the crowd

These are examples of emotional transmission. Cultural scholars study how emotion is shaped by media, by tradition, and by the stories we are told about what emotions are acceptable and when.

Cultural affect theory is used to understand:

  • Marketing and advertising
  • Political communication
  • Social media behavior
  • News framing
  • Family and community expectations

Emotions are constantly teaching us how to act and how to belong. Affect studies help us see how that teaching happens, even when no one says a word.

Two Quick Examples

Psychological example:

You’re home alone and hear a sudden noise. You jump. Your mouth dries out. Your heart speeds up. These are physical responses to fear.

Cultural example:

Your friend storms into the room in a bad mood. They don’t even speak, but their presence changes how you feel. Now you’re uneasy, and your body responds. This is emotional transfer.

Both examples are about affect, but they focus on different questions. One is about how feelings start inside you. The other is about how they arrive from the outside.

What Does “Affect” Actually Mean?

The word “affect” comes from Latin. The root affectus refers to a state of mind or body that is shaped by something outside yourself. This often involves emotions like love or sympathy.

In modern English, “affect” has several meanings:

  • To influence: "The news affected my opinion."
  • To sway: "The speech affected the outcome of the vote."
  • To pretend or simulate: "She affected a cheerful attitude at the meeting."

This last definition, to simulate, is especially relevant to my research in human-robot interaction. Some robots are built to simulate human emotion. They might smile, speak gently, or tilt their heads. These robots do not feel anything, but they are designed to make you feel something. That’s affect in action.

Why This Matters

Emotions are not just personal experiences. They shape what we believe, what we buy, who we trust, and how we interact with others. They even shape how we respond to machines that seem to understand or reflect emotion.

Affect studies help us ask better questions:

  • Where did this feeling come from?
  • Is it really mine, or did I pick it up from someone or something else?
  • Who benefits from the emotion I am being encouraged to feel?
  • What emotions do we allow, and which ones are silenced?
Affect studies do not just look at what we feel. They help explain what those feelings do once they are out in the world.    

Tags

Affective Technology, Technology and Culture, Relational AI
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Presentation Invited Panelist: Student Perspectives on AI
AI in Education Ethics Presentation

Invited Panelist: Student Perspectives on AI

Student Perspectives on AI Panel Discussion

As part of an ongoing initiative on emerging technologies affecting higher education, Bowling Green State University's Center for Faculty Excellence convened a university-wide discussion on how students are experiencing AI in the classroom. 

I am honored to be one of 10 students invited to share their expertise. 

I spoke about: 

What ethical and/or equity concerns do I have about AI use in the classroom?

Right now, one of the biggest concerns I have is that generative AI could unintentionally deepen existing inequalities among students. These tools are spreading quickly, but students are not all approaching them with the same level of familiarity, confidence, or guidance. Some students are experimenting with AI and learning how to use it productively, while others are avoiding it entirely because they are worried about violating academic integrity policies or simply don’t know what the boundaries are. When expectations are unclear, students who already have technological confidence often benefit the most. Another concern is transparency. Generative AI systems can produce answers that sound confident even when they are incomplete or incorrect. Students who are still developing expertise in a subject may not yet have the knowledge needed to evaluate those responses critically. For me, the ethical issue is not simply whether students use AI. The larger question is whether universities are helping students learn how to interact with these systems responsibly and critically, because they are likely to encounter them throughout their academic and professional lives.

What recommendations do I have for faculty as they navigate AI use in the classroom?

One recommendation I would offer is to treat generative AI as something students need to learn about rather than something that simply needs to be prohibited. Students are already encountering these tools, and avoiding the conversation entirely can create more confusion than clarity. Faculty can help by talking openly about what these systems do well, where they fall short, and how students should evaluate the information they generate. That kind of discussion helps students develop judgment rather than simply relying on the tool. Another important step is providing clear guidance about when AI use is acceptable and when it is not. Right now, many students are unsure about the boundaries, and that uncertainty can create anxiety about whether they are using the technology appropriately. More broadly, this moment is an opportunity to help students develop AI literacy—not just technical familiarity with the tools, but the ability to question them, interpret their outputs, and understand how they shape the way information is produced.

How has AI impacted my ability to learn? 

Right now, generative AI tools are still very new in higher education, and many of the conversations around them are dominated by concern or suspicion. At the same time, what I’m hearing from students across different programs is a more complicated story. For some students, especially neurodiverse students, these tools can help level aspects of the playing field. Generative AI can assist with organizing ideas, translating complex readings into clearer language, or helping students get unstuck when they are staring at a blank page. In that sense, the technology can function less like an answer machine and more like a form of cognitive scaffolding. That doesn’t mean the tool replaces learning. Students still have to evaluate what the system produces, compare it to course materials, and decide what is useful or inaccurate. The real educational value comes from that interaction; from questioning, refining, and verifying the output. What this moment is revealing is that generative AI may not simply be a productivity tool. For some learners, it changes how they interact with knowledge itself. That raises an important question for universities: how do we design learning environments that recognize both the opportunities and the limits of these systems? 

These responses reflect themes I often explore in my work: how emerging technologies reshape trust, learning,  and human relationships with intelligent systems. 

Audience

Bowling Green State University community members who are interested in the role of artificial intelligence tools (mostly generative AI) at the university. Possibilities, pitfalls, and cautious optimism were the prevailing themes among the panelists. 

Event

Student Perspectives on AI Panel Discussion
Center for Faculty Excellence AI Event Series
October 26, 2026 
Bowling Green State University
Bowling Green, OH

Tags

AI in Higher Education, Human-Computer Interaction, Technology and Culture



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