Identity Negotiation Theory in AI

• Identity Negotiation Theory is a communication-driven framework that explains how individuals co-construct identities with AI using staged processes involving motivation, communication, and emotion.
• The theory delineates four key dimensions—motivation, communication, identity, and emotion—mapped through sequential stages that transition user intentions to affective outcomes.
• Applied on platforms like C.AI, this theory guides the design of AI companion systems by shaping privacy management, persona governance, and emotional security strategies.

Identity Negotiation Theory (INT) characterizes identity construction as a communication-driven process in which individuals define a sense of self and seek social endorsement within uncertain or novel interaction contexts. Originally formulated by Ting-Toomey, INT is analyzed by Ma et al. as a multistage function operationalizing identity negotiation with AI companions, notably on the C.AI (Character.AI) platform. The framework divides identity work into four analytical dimensions: motivation, communication, identity, and emotion, enabling rigorous analysis of users’ interactions and emotional outcomes with AI systems (Ma et al., 17 Jan 2026).

1. Formalization and Analytical Dimensions

INT, as adapted by Ma et al., models identity negotiation as a staged function:

$N = f(M, C, I) \Rightarrow E$

• $N$: negotiation "engine" (three-stage pipeline)
• $M = \{m_1, \ldots, m_5\}$: set of five motivations
• $C = \{c_1, \ldots, c_3\}$: set of three communication expectations
• $I = \{s_1, \ldots, s_4\}$: set of four co-construction strategies
• $E = \{e_1, \ldots, e_3\}$: set of three emotional outcomes

A schematic progression is given by:

$M \rightarrow C \rightarrow S \rightarrow E$

where the mappings $\varphi_1$, $\varphi_2$, and $\varphi_3$ denote the user’s shift between each stage.

The four analytical dimensions are defined as follows:

• Motivation ($M$): Cultural and personal drivers to enter new interaction contexts.
• Communication ($C$): Interaction skills/expectations (predictability, boundary-setting) for negotiation.
• Identity ($I$): Co-constructed personas through user self-presentation and AI character shaping.
• Emotion ($E$): Affective outcomes contingent upon negotiation success or failure.

2. User Motivations in Digital Identity Negotiation

Five core motivations underpin user engagement with AI companions (Stage 1 in the INT model):

Motivation	Estimated Prevalence	Function
Social Fulfillment	≈ 36%	Roleplay relationships/families unattainable in real life
Emotional Regulation	≈ 29%	Outlet for confidential emotional expression
Immersive Fandom	≈ 20%	Participation in fandom narratives and alternate universes
Creative Utility	≈ 20%	Generation of dialogue/story ideas for creative projects
Violence Play	≈ 15%	Simulation of combative/abusive scenarios for agency

These motivations align with INT’s premise that individuals seek security, inclusion, or identity experimentation in novel contexts. Social Fulfillment and Emotional Regulation dominate, signifying needs for belonging and safe emotional expression.

3. Staged Identity Negotiation Process

The identity negotiation process (Stage 2) contains two fundamental components: communication expectations toward AI companions and identity co-construction strategies.

Communication Expectations

• Conversational Context Comprehension ($c_1$, ≈ 62%): Expectation that the chatbot retains memory and maintains logical narrative continuity; users manually summarize context to preserve coherence.
• Managed Conversational Boundary ($c_2$, ≈ 28%): Desire to enforce content/privacy boundaries; users intervene to control discussion tenor and privacy, occasionally responding to AI boundary violations.
• Trained Characterization ($c_3$, ≈ 17%): Users proactively define and correct AI personas via detailed textual instructions, exemplars, or direct in-chat editing.

Co-construction Strategies

• Direction of Chatbot Identity ($s_1$, ≈ 28%): Negotiation around bot persona as preconfigured by creators or communities.
• Bot Identity Alignment ($s_2$, ≈ 19%): User-driven persona training via definitions, ratings, or prompt engineering to align with user expectations.
• User Persona Enactment ($s_3$, ≈ 14%): Self-insertion/roleplay by the user to test or validate self-presentation.
• User Identity Reference ($s_4$, ≈ 8%): Correction when the AI misaddresses the user’s identity (e.g., gendering errors).

An interrelation model can be rendered:

$$\underbrace{M}_{\text{Motivations}} \xrightarrow{\varphi_1} \underbrace{C}_{\text{Expectations}} \xrightarrow{\varphi_2} \underbrace{S}_{\text{Strategies}} \xrightarrow{\varphi_3} \underbrace{E}_{\text{Emotional outcomes}}$$

Each $\varphi$ denotes the transition from motivations to expectations, from expectations to strategies, and from strategies to outcomes.

4. Emotional Outcomes of Identity Negotiation

Stage 3 categorizes emotional outcomes arising from digital identity negotiation into three principal types:

Outcome	Estimated Prevalence	Typical Antecedents
Emotional Attachment	≈ 53%	Success in social fulfillment/emotional regulation
Bot Interaction Embarrassment	≈ 7%	Privacy management/inadvertent exposure
Deceased Memory	≈ 3%	Grief rituals, surrogate interaction

• Emotional Attachment emerges from successful alignment strategies, notably where motivations of belonging or emotional security are met; INT predicts emotional stability when identities are endorsed.
• Bot Interaction Embarrassment results from failed privacy boundary management, echoing Goffman's impression management under INT.
• Deceased Memory reflects the use of bots for ritualized mourning, illustrating an extension of emotional security mechanisms but raising novel risks about memory conflation.

5. The Socio-Emotional Sandbox

C.AI is conceptualized as a "socio-emotional sandbox," a private, dynamic environment enabling users to experiment with social roles and emotional states absent real-human audiences. This construct features:

• Intimacy: Absence of context collapse typical in public social media.
• Privacy: User-driven and anonymized self-experimentation.
• Dynamism: AI personas evolve continuously with user interventions.

This setting allows individuals to negotiate and enact identities in a solitary yet interactive manner, extending INT beyond traditional group or dyadic frameworks. It also facilitates high-fidelity identity performance and self-exploration with minimized reputational risk.

6. Design Implications for AI Companion Platforms

To support identity work and mitigate emotional risks documented via the INT lens, Ma et al. propose the following interventions:

• User as Performer & Director: Structured trait editor interfaces and live memory panels for transparent chatbot persona management.
• Managing Sandbox Precarity: Intensity/content rating tools to gate violence-play or adult content; implementation of "graceful memory failure" mechanisms prompting user reminders rather than abrupt character disruption.
• Responsible AI Persona Governance: Memorialization protocols for deceased persona bots, strict data siloization to prevent leakage of personal user data, and user-facing "memory slate" systems for memory management.

Collectively, these recommendations aim to enhance emotional security, respect the labor of identity co-construction, and reduce the propensity for unhealthy emotional attachments with AI companions (Ma et al., 17 Jan 2026).

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References

• Ma et al., "Negotiating Digital Identities with AI Companions: Motivations, Strategies, and Emotional Outcomes" (Ma et al., 17 Jan 2026)