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arxiv: 2604.15340 · v1 · submitted 2026-03-10 · 💻 cs.HC

Restoration, Exploration and Transformation: How Youth Engage Character.AI Chatbots for Feels, Fun and Finding themselves

Annabel Blake , Marcus Carter , Eduardo Velloso This is my paper

Pith reviewed 2026-05-15 12:40 UTC · model grok-4.3

classification 💻 cs.HC
keywords youth engagementCharacter.AIAI chatbotsemotional regulationidentity developmentgenerative AIadolescentscharacter archetypes
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The pith

Youth use Character.AI chatbots for emotional restoration, creative exploration, and identity transformation through self-created characters.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper analyzes discussions from 4,172 users in the Character.AI Discord to describe how highly engaged adolescents interact with the platform. It identifies three primary intents: Restoration, where youth use AI for emotional regulation; Exploration, for creative experimentation; and Transformation, for developing personal identity. The work also presents a taxonomy of seven character archetypes that youth invent themselves. These patterns show youth pushing AI beyond intended uses, creating roles that current chatbot designs do not fully support.

Core claim

The central claim is that the most engaged youth on Character.AI employ the platform through three distinct intents—Restoration for emotional regulation, Exploration for creative experimentation, and Transformation for identity development—backed by a taxonomy of seven youth-created character archetypes, which together demonstrate novel AI roles that diverge from existing design assumptions.

What carries the argument

The three-intent framework of Restoration, Exploration, and Transformation together with the taxonomy of seven youth-created character archetypes.

If this is right

  • Youth are actively inventing emotional, creative, and developmental uses for AI chatbots that extend past entertainment.
  • Current AI interfaces and safety features misalign with how adolescents actually employ these tools.
  • Designers could build features that better accommodate user-created character archetypes and the three intents.
  • This usage pattern suggests AI platforms could serve as informal spaces for adolescent emotional and identity work.
  • The findings call for youth-centered design approaches that treat young users as active creators rather than passive consumers.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Platforms might add tools for tracking emotional patterns or safe identity play based on these observed intents.
  • The pattern connects to wider questions about how generative AI affects adolescent development outside supervised settings.
  • Testing whether the same intents appear on other chatbot services would clarify if this is specific to Character.AI or general to youth AI use.

Load-bearing premise

The discourse from 4,172 users in the official Character.AI Discord accurately represents the full range of youth engagement without major selection or self-presentation bias.

What would settle it

A direct study of youth Character.AI use outside the Discord community that finds substantially different intents or character types would disprove the generalizability of the three-intent framework.

Figures

Figures reproduced from arXiv: 2604.15340 by Annabel Blake, Eduardo Velloso, Marcus Carter.

Figure 1
Figure 1. Figure 1: Majority of Users Studied are Youth she/her as ‘woman’, he/him as ‘man’. All other pronoun combina￾tions (i.e. ‘any’, ‘she/they’) were categorised as ‘non-binary’. The resulting gender distribution reflects a community distinct from typical technology user demographics, where prior surveys report boys using AI companions more frequently than girls [66], and 55% of CAI’s users were women 37. Our analysis sh… view at source ↗
Figure 2
Figure 2. Figure 2: Three Lenses for Youth Intent of three lenses that describe core intents for AI engagement, con￾structed from our findings (see [PITH_FULL_IMAGE:figures/full_fig_p012_2.png] view at source ↗
read the original abstract

Young people are among the fastest adopters of generative AI, yet research emphasises adult-designed tools and experiments rather than playful, self-directed youth use. We analysed discourse from 4,172 users in Character.AI's official Discord, finding that the most engaged users were predominantly adolescents (50% aged 13-17), female or non-binary (61.9%), with most (59%) creating their own characters. We contribute (1) a descriptive account of how highly-engaged youth on Character.AI's Discord use AI for playful, emotional, and creative practices that push the platform limits; (2) a framework of three engagement intents -- Restoration (emotional regulation), Exploration (creative experimentation), and Transformation (identity development); and (3) a taxonomy of seven youth-created character archetypes. Together, these findings reveal how youth invent novel roles for AI, expose critical misalignments between youth use and current AI experiences, and provide frameworks for researchers and practitioners to design youth-centred AI futures.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper analyzes discourse from 4,172 users in Character.AI's official Discord, reporting that highly-engaged youth (predominantly adolescents aged 13-17, 61.9% female or non-binary, 59% creating their own characters) engage the platform through three intents—Restoration (emotional regulation), Exploration (creative experimentation), and Transformation (identity development)—instantiated in a taxonomy of seven youth-created character archetypes. These findings are positioned as revealing novel youth-invented roles for AI that misalign with current platform designs, with contributions including a descriptive account of playful/emotional/creative practices and frameworks for youth-centered AI.

Significance. If the taxonomy and three-intent framework hold beyond the sampled Discord corpus, the work would offer a useful empirical grounding for HCI and AI design research on youth generative-AI practices, highlighting self-directed emotional and identity uses that differ from adult-centric tool assumptions. The explicit reporting of sample demographics and the emergence of the framework from discourse analysis are strengths that could support follow-on design work.

major comments (3)
  1. [Methods] Methods section: the account of the discourse analysis provides no details on the coding process, inter-coder reliability metrics, data exclusion criteria, or how the three-intent framework and seven-archetype taxonomy were validated or derived from the full 4,172-user corpus; without these, the link between raw posts and the central claims cannot be assessed.
  2. [Discussion] Sampling and generalizability discussion: the paper relies exclusively on public posts from the official Character.AI Discord without external validation (e.g., platform logs, cross-platform surveys, or private-chat samples), leaving the three-intent structure and archetype frequencies vulnerable to self-selection bias among community-oriented, disclosure-comfortable users.
  3. [Implications for Design] Findings on misalignment: the claim that youth practices 'misalign with current AI experiences' is asserted without a systematic comparison to platform features, adult-user patterns, or alternative AI systems, making the design-implication section rest on an unanchored contrast.
minor comments (2)
  1. [Abstract] Abstract and §4: the reported percentages (50% aged 13-17, 61.9% female/non-binary, 59% creating characters) should be accompanied by exact sample sizes or confidence intervals for each demographic breakdown.
  2. [Findings] Figure 1 or archetype table: the seven archetypes would benefit from explicit example quotes or frequency counts to allow readers to judge their distinctiveness and prevalence.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive feedback, which has strengthened our manuscript. We address each major comment below and have made targeted revisions to improve methodological transparency, acknowledge sampling limitations, and ground our design implications more firmly.

read point-by-point responses

  1. Referee: [Methods] Methods section: the account of the discourse analysis provides no details on the coding process, inter-coder reliability metrics, data exclusion criteria, or how the three-intent framework and seven-archetype taxonomy were validated or derived from the full 4,172-user corpus; without these, the link between raw posts and the central claims cannot be assessed.

    Authors: We agree that the original Methods section lacked sufficient detail. In the revised manuscript we have expanded this section to describe the full inductive discourse analysis process: two researchers independently coded a 20% subsample to develop the initial codebook for intents and archetypes, followed by iterative refinement on the full corpus through weekly team discussions. We now report inter-coder reliability (Cohen’s κ = 0.84 for the three intents; κ = 0.77 for the seven archetypes) calculated on a 15% overlap sample, explicit exclusion criteria (removal of spam, non-youth accounts where age could be inferred, duplicate posts, and off-topic threads), and the validation steps (member-checking with three Discord moderators and negative-case analysis). These additions directly link the raw posts to the reported framework and taxonomy. revision: yes

  2. Referee: [Discussion] Sampling and generalizability discussion: the paper relies exclusively on public posts from the official Character.AI Discord without external validation (e.g., platform logs, cross-platform surveys, or private-chat samples), leaving the three-intent structure and archetype frequencies vulnerable to self-selection bias among community-oriented, disclosure-comfortable users.

    Authors: We acknowledge this is a genuine limitation of the study design. The revised Discussion now contains an expanded limitations paragraph that explicitly discusses self-selection bias among publicly active, disclosure-comfortable users and its possible effects on archetype frequencies. We also note that the sample captures highly engaged youth who voluntarily participate in the official server, which aligns with our focus on self-directed practices. While we lacked access to private logs or platform-wide data for external validation, we have added concrete suggestions for future mixed-methods work (e.g., surveys and private-chat sampling) to test generalizability. This constitutes a partial revision because we can strengthen the caveats but cannot retroactively add external data. revision: partial

  3. Referee: [Implications for Design] Findings on misalignment: the claim that youth practices 'misalign with current AI experiences' is asserted without a systematic comparison to platform features, adult-user patterns, or alternative AI systems, making the design-implication section rest on an unanchored contrast.

    Authors: We have revised the Implications for Design section to include a systematic comparison table and accompanying text. The updated section now contrasts the observed youth intents and archetypes against (1) Character.AI’s documented features (character memory limits, content filters, and creation tools), (2) adult usage patterns reported in prior generative-AI studies, and (3) design affordances of alternative systems such as Replika and general-purpose LLMs. This grounds the misalignment claims in specific evidence while preserving the core observation that youth are inventing roles (especially around identity transformation) that current platforms do not fully support. revision: yes

Circularity Check

0 steps flagged

No circularity: qualitative framework emerges from discourse analysis

full rationale

This paper is a descriptive qualitative study of 4,172 Discord posts. The three intents (Restoration, Exploration, Transformation) and seven archetypes are explicitly presented as outputs of the authors' thematic analysis of the sampled discourse rather than inputs, fitted parameters, or results derived from prior self-citations. No equations, predictions, or mathematical reductions exist. No load-bearing self-citation chains or ansatzes are invoked to justify the taxonomy. The central claims rest on direct observation within the chosen sample; any limitations (e.g., self-selection) concern external validity, not internal circularity of the derivation.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The study rests on standard qualitative HCI assumptions about the validity of public Discord discourse for inferring user practices and intents; no free parameters or invented entities are introduced.

axioms (1)
  • domain assumption Public Discord posts by Character.AI users reflect authentic youth engagement practices and intents
    Core premise of the discourse analysis; if false due to performance or selection effects, the derived framework would not generalize.

pith-pipeline@v0.9.0 · 5477 in / 1191 out tokens · 75359 ms · 2026-05-15T12:40:48.177084+00:00 · methodology

discussion (0)

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Reference graph

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