AIaaE: Artificial Intelligence as an Experience

1. Introduction

Artificial intelligence has historically been framed as an instrumental technology. From early symbolic systems to modern large language models, progress has been measured using metrics such as accuracy, speed, scalability, and cost-efficiency. This framing assumes that the primary purpose of AI is to assist humans in completing predefined tasks.

However, this assumption is increasingly misaligned with real-world usage. Conversational agents, recommender systems, generative media, and immersive technologies already influence human attention, emotion, memory, and identity. In many cases, the most significant impact of an AI system is not what it accomplishes, but how it makes the user feel.

This paper argues that artificial intelligence is entering a new phase in which its primary value lies in experience mediation rather than task execution. AI-as-an-Experience (AIaaE) is proposed as a paradigm that places human experience at the center of AI system design and evaluation.

2. Why an Experience-Oriented Paradigm Is Necessary

2.1. Limits of Tool-Centric AI

Tool-centric AI assumes discrete interactions, clearly defined objectives, and measurable outputs. While effective for productivity-oriented applications, this model fails to account for use cases such as companionship, emotional regulation, identity exploration, and narrative engagement. These interactions cannot be adequately evaluated using traditional performance benchmarks.

From a psychological perspective, humans are not utility-maximizing agents alone. According to self-determination theory [1], individuals are motivated by autonomy, competence, and relatedness. Tool-centric AI addresses competence but largely ignores autonomy and relatedness, limiting its relevance to deeper human needs.

2.2. Human Preference for Experience Over Efficiency

Human behavior consistently demonstrates that efficiency is not the primary driver of adoption. People willingly engage in activities such as storytelling, art, ritual, and play, which offer emotional meaning rather than instrumental value. This observation aligns with experiential consumption theory, which shows that experiences contribute more strongly to long-term satisfaction than material or utilitarian gains.

AIaaE acknowledges that humans seek not only solutions, but experiences that generate meaning, emotional resonance, and reflection.

2.3. The Inevitable Shift Toward Experience Mediation

Historical patterns indicate that technologies evolve from utility to experience. The internet progressed from information exchange to social and emotional engagement. Mobile devices evolved into identity extensions. Artificial intelligence follows the same trajectory, making AIaaE an inevitable development rather than a speculative one.

3. Defining AI-as-an-Experience (AIaaE)

3.1. Formal Definition

AI-as-an-Experience refers to the intentional design and deployment of artificial intelligence systems whose primary function is to generate, guide, and sustain human experiences across emotional, psychological, and narrative dimensions over time. In this paradigm, experience itself is the product, not a byproduct of task execution.

3.2. Core Claim

The central premise of AIaaE is straightforward:

Artificial intelligence can be used not merely to do things for humans, but to make humans experience things.

These experiences may include but are not limited to emotions, internal journeys, reflective states, and meaning-making processes that unfold dynamically rather than instantaneously.

4. Theoretical Foundations

4.1. Experience as a Temporal Trajectory

Experiences are not isolated events but trajectories over time. Psychological research on peak–end theory [2] demonstrates that humans remember experiences based on their most intense moments and their endings rather than their duration. This is demonstrated through the following mathematical modeling.

4.1.1. Derivation of the Experiential State Model

4.1.2. Experience as a State Variable

The model begins by representing human experience as an internal state that evolves over time. Let $E_u\left(t\right)$ denote the experiential state of a user u at discrete time step t. This state represents a composite of emotional condition, cognitive engagement, narrative position, and memory activation. While $E_u\left(t\right)$ is not directly observable, it is treated as a latent psychological state that summarizes the user’s ongoing experience.

Modeling experience as a state variable reflects established findings in psychology indicating that experience is path-dependent: current emotional and cognitive states are influenced by prior states rather than being reset at each interaction.

4.1.3. Temporal Dependence of Experience

Experience at time $t+1$ cannot be modeled independently of experience at time t. Emotional carryover, anticipation, habituation, and memory recall imply temporal continuity. Accordingly, the experiential state at the next time step must depend on the current experiential state:

$$E_u(t+1)\propto E_u\left(t\right)$$

This expresses that experience unfolds as a trajectory over time rather than as a sequence of isolated reactions.

4.1.4. Influence of AI Actions

Let $a\left(t\right)$ represent the action taken by the AI system at time t. An action may include content generation, modulation of tone, pacing of interaction, or the introduction of narrative events. In AIaaE systems, actions are selected primarily for their experiential impact rather than for task efficiency.

The experiential state at time $t+1$ therefore depends on both the prior experiential state and the AI action:

$$E_u(t+1)\propto \{E_u\left(t\right),a\left(t\right)\}$$

This formulation captures the role of the AI system as an active mediator of experience.

4.1.5. User-Specific Contextual Factors

Identical AI actions may produce different experiential outcomes across users. To account for individual variation, the model introduces a user-specific context parameter $c_u$, representing relatively stable characteristics such as personality traits, emotional sensitivity, prior interaction history, and value orientation.

Incorporating this term yields:

$$E_u(t+1)\propto \{E_u\left(t\right),a\left(t\right),c_u\}$$

This ensures that the model reflects psychological heterogeneity rather than assuming uniform user responses.

4.1.6. Experience Transition Function

The proportional relationships above can be formalized as a transition function $f(·)$ that maps the current experiential state, AI action, and user context to the next experiential state:

$$E_u(t+1)=f\left(E_u\left(t\right),a\left(t\right),c_u\right)$$

The function f is intentionally left abstract. Its specific form may vary across implementations and may be learned, rule-based, or hybrid. What is essential is the recognition that experiential change follows structured transitions influenced by prior state, system action, and user context.

4.1.7. Interpretive Implications

This formulation highlights several defining properties of AI-as-an-Experience systems. First, experience is modeled as a latent, evolving state rather than an immediate output. Second, AI systems influence experiential trajectories over time rather than isolated moments. Third, user individuality is treated as a structural component of the model. Finally, experience-oriented AI design becomes a problem of managing state transitions rather than optimizing static responses.

Let $E_u\left(t\right)$ represent the experiential state of a user u at time t. The system influences experience through actions $a\left(t\right)$ such that:

$$E_u(t+1)=f(E_u\left(t\right),a\left(t\right),c_u)$$

where $c_u$ represents user-specific contextual parameters including history, memory, and emotional sensitivity.

4.2. Identity, Attachment, and Presence

Perceived presence in AIaaE is closely related to attachment theory [3]. Humans form emotional bonds with entities that display consistency, responsiveness, and availability. Persistent identity and memory in AIaaE systems can therefore elicit attachment-like responses, necessitating careful regulation.

4.3. Emotional and Narrative Coherence

Narrative psychology suggests that humans understand their lives as evolving stories [4]. Experiences gain meaning when events are causally connected and emotionally coherent. AIaaE draws on narrative theory and game design to ensure consequence, progression, and resolution rather than endless interaction.

5. Core Design Principles

For an artificial intelligence system to be meaningfully classified as AI-as-an-Experience (AIaaE), it must satisfy the following design principles. These principles function as boundary conditions:

• Experience-Centered Intentionality: AIaaE systems are explicitly designed to shape human experience as their primary objective. Task execution and efficiency may be present, but they are subordinate to experiential outcomes such as emotional resonance, reflection, and psychological engagement. Systems in which experience is merely a byproduct do not qualify as AIaaE.
• Temporal Experience Structuring: AIaaE systems operate across time rather than as isolated interactions. Experiences are intentionally structured as evolving trajectories that include anticipation, progression, peak moments, and resolution. Purely reactive or single-turn systems fall outside this paradigm.
• Persistent Experiential Identity: AIaaE systems maintain a stable experiential identity expressed through consistent behavior, tone, values, and memory usage. This persistence enables users to perceive interactions as part of a coherent relationship or journey rather than disconnected exchanges.
• Deep Psychological Adaptation: Adaptation in AIaaE extends beyond surface-level personalization. Systems adjust to user-specific emotional patterns, sensitivity thresholds, and experiential responses, enabling meaningful personalization at a psychological level rather than stylistic variation alone.
• Emotional Boundedness and Regulation: AIaaE systems actively regulate emotional intensity to prevent excessive dependency, affective escalation, or prolonged psychological immersion. Emotional boundaries are a core design requirement rather than an optional safeguard.
• Experiential Transparency: AIaaE systems provide conceptual transparency regarding their role in shaping user experience. While full technical disclosure is unnecessary, the system must not obscure or deny its experiential influence on the user.
• Ethical Memory Management: Memory in AIaaE systems is selective and purpose-driven. The system retains information that supports experiential coherence and psychological well-being while allowing for deliberate and ethical forgetting to prevent harm or unhealthy attachment.
  Systems that meet these criteria should be described as AIaaE.

6. Domains of AIaaE

6.1. Emotional Experience Generation

AIaaE systems can intentionally evoke emotions such as excitement, nostalgia, melancholy, or catharsis through structured experiential design.

6.2. Narrative and Journey-Based Experiences

Users may inhabit adaptive narratives that unfold over time, incorporating agency, moral choice, and consequence.

6.3. Psychological State Induction

Drawing from cognitive-behavioral principles, AIaaE can guide users toward mental states such as calm, focus, or tension while maintaining safeguards against maladaptive patterns.

6.4. Identity Exploration

AIaaE enables safe exploration of alternative identities and value systems, consistent with research on possible selves in social psychology [5].

6.5. Synthetic Experience and Memory Formation

Experiences generated by AIaaE may form emotionally encoded memories that influence self-perception and decision-making regardless of factual origin.

7. Evaluation Challenges

Traditional metrics such as accuracy and engagement are insufficient. AIaaE requires evaluation frameworks centered on experiential coherence, emotional recall accuracy, personality consistency, and long-term psychological impact.

8. Ethical and Societal Implications

AIaaE introduces risks of emotional dependency, behavioral manipulation, and reality substitution. Ethical deployment requires informed consent, emotional intensity regulation, human oversight, and governance structures informed by psychology and ethics.

9. Future Directions

Future research must address formal computational models of experience, affect-aware memory systems, narrative generation techniques, and interdisciplinary governance frameworks. AIaaE may emerge as a distinct economic and cultural category intersecting entertainment, mental health, education, and social life.

10. Conclusions

AI-as-an-Experience represents a fundamental reorientation of artificial intelligence toward the human experiential domain. As AI systems increasingly shape how humans feel, remember, and construct meaning, AIaaE provides a necessary framework for understanding and guiding this transition responsibly. By defining experience as an intentional design outcome rather than a secondary effect, this paper distinguishes AIaaE from tool-centric paradigms and grounds it in established psychological and narrative theory. The proposed design principles clarify the conditions under which AI systems can be considered experience-oriented and highlight the ethical and evaluative challenges unique to such systems. Together, these contributions position AIaaE as a foundational lens for analyzing, designing, and governing artificial intelligence whose most significant impact lies in its influence on human experience over time.