"AI Is Turning Too Human": How Teenagers Experience and Negotiate AI in Everyday Life

1.1 AI in Adolescents' Everyday Lives

• Teenagers report using AI for information, schoolwork, entertainment, creative tasks, conversation, and emotional support, including daily use.The cited surveys report 64% of US teenagers aged 13–17 use AI chatbots, while a separate survey found 86% of youth aged 9–17 had interacted with some form of AI.Source: §1.1 · pp. 1–2

1.2 Emerging Concerns and Governance of Adolescent AI Use

• Existing studies examine adolescent AI experiences through predefined questions and categories, leaving spontaneous concerns and connections across domains less understood.Governance frameworks also emphasize age-appropriate use and protections for privacy, agency, safety, development, and well-being.Source: §1.2 · p. 2

1.3 Naturally Occurring Peer Discourse

• Naturally occurring peer discussions can reveal which AI issues adolescents raise themselves and where uncertainty, disagreement, and emerging norms arise.Such discussion may surface new practices and concerns before they are incorporated into established measures.Source: §1.3 · p. 2

1.4 Present Study

• The study examines naturally occurring r/teenagers discourse inductively rather than imposing predefined AI-use domains or benefit-and-risk categories.It asks how adolescents experience, interpret, and negotiate AI’s role in everyday life.Source: §1.4 · p. 2

2.1 Human–AI interaction is rapidly evolving

• Conversational AI supports open-ended informational and creative interaction and can be experienced as a social partner, with self-disclosure and attachment varying across contexts.Adoption research also links use to perceived usefulness, task fit, trust, and habit.Source: §2.1 · p. 3

2.2 Research approaches to technology adoption and experience

• Adoption frameworks and surveys use researcher-defined constructs, while naturally occurring discourse offers a complementary view of experiences expressed outside direct elicitation.This distinction matters when generative-AI practices may develop before established measures represent them.Source: §2.2 · p. 3

2.3 AI use during adolescence

• Adolescence involves developing autonomy, identity, relationships, and higher-order cognition, yet AI experiences have often been studied in separate domains using predefined measures.The paper identifies limited evidence on which concerns adolescents raise spontaneously, how domains intersect, and how experiences change as AI becomes embedded in everyday life.Source: §2.3 · pp. 3–4

3.1 Data Source and AI-Related Post Identification

• The keyword retrieval procedure achieved 91.5% precision, 85.5% recall, and an F1 score of 88.4% against human coding.The refined keyword approach was validated in a June 2025 development sample before application to the full study period.Source: §3.1 · p. 4

3.2 Multi-agent, Human-in-the-Loop Thematic Analysis

• Five LLM agents developed and critically reviewed inductive themes from 600 year-stratified posts, followed by researcher review and refinement into eight final themes.Three agents independently coded posts from complementary perspectives; researchers assessed candidate themes against sampled posts for interpretability, distinction, and grounding.Source: §3.2 · pp. 4–5
• The finalized codebook was applied to 12,324 retrieved posts; 11,083 (89.9%) were assigned themes, while 1,241 (10.1%) lacked sufficient context.Each post received one primary theme and up to two secondary themes when substantively supported.Source: Figure 1 · p. 5

3.4 Quantitative Characterization of Thematic Patterns.

• Theme trends were modeled with logistic regression, overlaps as conditional percentages, and distinctive vocabulary with contrastive TF–IDF.The analysis also characterized community response using comment-count and Reddit-score thresholds set at corpus-wide 75th percentiles; vocabulary was for interpretation and visualization, not coding.Source: §3.4 · p. 6

4.1 AI-related discourse increased over time

• Monthly AI-related posts rose from fewer than 200 at the start of the period to more than 400 by July 2026, with a broader increase especially from early 2025.The three-month moving average revealed the broader trend despite substantial monthly fluctuations; 2026 data cover January through July only.Source: §4.1 · p. 6, Figure 2 · p. 7

4.2 AI discourse spans eight areas of adolescent experience

• 36.8% of coded posts concerned everyday/social use, the largest theme; authenticity had the strongest increase in odds per year (OR = 1.46, 95% CI 1.36–1.57).Control and safety (OR = 1.24, 95% CI 1.16–1.33) and future opportunities and human roles (OR = 1.29, 95% CI 1.20–1.39) also increased, while companionship, creativity, personification, and everyday/social use declined in relative odds; academic use showed no evidence of linear change.Source: §4.2 · pp. 7–8, Figure 3 · p. 8

4.3.1 AI in everyday practice

• Adolescents distinguished AI support from substitution in schoolwork and described using it as an adviser, editor, or practical resource in everyday peer activity.They questioned how much human effort and thinking remained in academic work, while everyday/social use was the most prevalent theme.Source: §4.2 · pp. 7, 9, Figure 3 · p. 8

4.3.2 AI as a Social and Relational Actor

• AI companionship offered connection and disclosure when human support felt difficult, while adolescents also described it as artificial; conversational responses could be perceived as signs of emotion or agency.The accounts of personification did not necessarily indicate attachment or literal belief in AI sentience.Source: §4.3.2 · p. 9

4.3.3 Negotiating What is Human and Authentic

• Authenticity concerns included both uncertainty about synthetic content and the risk that human work would be dismissed as AI-generated.For creative work, adolescents emphasized meaningful human effort, expression, authorship, and ownership rather than treating AI involvement as simply acceptable or unacceptable.Source: §4.3.3 · pp. 9–10

4.3.4 Negotiating Control and the Human Future

• Adolescents questioned who could use their personal information and images, and what opportunities and roles should remain human as AI capabilities expand.Some saw AI as extending possibilities in learning and other fields, while others worried about displacement of jobs and creative work.Source: §4.3.4 · pp. 10–11

4.4 Connections across thematic domains

• Everyday/social use appeared as a secondary theme in 29.9% of personification posts, 22.3% of control-and-safety posts, and 19.9% of companionship posts.Authenticity also intersected with control-and-safety and academic-use accounts, while companionship and personification sometimes co-occurred.Source: Figure 4 · p. 11, §4.4 · p. 12

4.5 Themes elicit distinct patterns of community response

• Personal control and safety had the strongest combined community response: 30.9% of posts received at least eight comments and 49.6% reached the score threshold.Companionship posts elicited more high-comment engagement than the corpus overall (34.2% versus 26.5%) but were less likely to reach the score threshold (37.2% versus 40.2%).Source: §4.5 · p. 12, Figure 5 · p. 12

5.1 AI is becoming ordinary, but reliance remains contested

• Everyday and social use was the dominant theme, but adolescents distinguished support from substitution in schoolwork and creative work [38].They questioned uses that replaced thinking or authorship and valued meaningful human effort and expression.Source: §5.1 · p. 13

5.2 AI is also entering social and relational spaces

• Conversational AI served as a space for disclosure, affection and support, yet adolescents recognized its artificiality and its potential to occupy human relationship roles [39].Some found talking to AI easier than talking to other people.Source: §5.2 · p. 13

5.3 Authenticity and control are emerging concerns

• Authenticity-related discourse increased most strongly over time, alongside concerns about personal control and safety [40].Adolescents worried both about mistaking AI-generated material for real content and about genuine human work being mislabeled as AI-generated.Source: §5.3 · p. 13
• Concerns about representation and judgments online connected authenticity with control over photographs, identity and personal information.The discussion supports child-centred approaches emphasizing privacy, data agency, transparency and meaningful consent.Source: §5.3 · pp. 13–14

5.4 Adolescents are negotiating the boundaries of AI

• Across domains, adolescents negotiated how much thinking to delegate, what counts as human expression, when AI occupies social roles, and what opportunities should remain human.They also raised whether human and synthetic content can be distinguished and who controls identity and personal information.Source: §5.4 · p. 14

5.5 Limitations and practical implications

• The study reflects discourse from r/teenagers, not a representative adolescent sample, and its themes are an interpretive framework rather than an exhaustive taxonomy.Historical data availability and retrieval may have affected corpus coverage over time; LLM-assisted coding may reflect model-specific interpretive tendencies despite human review and validation.Source: §5.5 · p. 14
• The authors call for AI literacy addressing appropriate reliance, authorship, privacy, authenticity and social use, alongside developmentally appropriate systems and adaptive governance.They caution against both blanket prohibition and unrestricted adoption, and say intensive use should not automatically be labeled “AI addiction.”Source: §5.5 · pp. 14–15

• Adolescents questioned how much thinking and creative work to delegate to AI and who should control personal information and images.They also asked what is human-made versus AI-generated and whether conversational AI is a tool or a social actor.Source: §6 · p. 15
• Their discussions concerned what opportunities and roles should remain human.Source: §6 · p. 15
• The conclusion frames adolescent engagement as negotiating what AI is, how people should relate to it and where its boundaries should lie.Source: §6 · p. 15

• The illustrated keyword-dictionary examples span general attitudes, academic uses, generated content, conversational AI and AI-companion platforms.Titles appear as cleaned; where a keyword occurred in a post body, the appendix provides a short contextual excerpt.Source: Appendix Table A2. Final AI keyword… · p. 22
• The discovery sample comprised 600 AI-related posts, with 200 sampled from each of 2023, 2024 and 2025, and was used to develop themes rather than estimate prevalence.Sampling was independent within each year, used a fixed random seed and removed duplicate post IDs.Source: Appendix Table A2. Final AI keyword… · p. 22
• Posts were coded inductively from their full text without a predefined taxonomy, with prompts emphasizing meaningful AI-related content and exact supporting quotations.The prompts directed coders to preserve ambiguity, disagreement and minority perspectives and avoid unsupported psychological diagnoses.Source: Appendix Table A2. Final AI keyword… · pp. 23–24
• The coding prompts assigned attention to semantic, experiential and relational perspectives, including how writers position themselves, AI and other people.The relational prompt asked for inductive codes, exact quotations and brief interpretations of relational or normative meaning.Source: Appendix Table A2. Final AI keyword… · pp. 23–25

• Three agents independently coded the same discovery posts from semantic, experiential and relational perspectives, then their outputs were consolidated with post IDs, agent, code and verbatim evidence retained.Source: A2.3 Code consolidation · p. 25
• The higher-order synthesis compared codes across posts and analysts, merging similar codes while preserving meaningful differences and tensions.It aimed for a manageable set of recurring thematic findings supported by multiple posts.Source: A2.3 Code consolidation · pp. 25–26
• The synthesis instructions ruled out prevalence and causal claims, diagnosis, and forcing every code into a finding.Source: A2.3 Code consolidation · p. 26

AI companionship

• Reviewers retained companionship as a distinct theme but removed the unsupported causal implication that emotional meaning arose precisely where human connection was difficult.The final interpretation emphasized connection, disclosure, attachment, and potential substitution for human relationships.Source: A2.5 Critical review · p. 27

Personification and authenticity

• Reviewers split human-like agency attribution from uncertainty about whether content was AI-generated, preserving personification and authenticity as distinct themes.The audit trail describes separate patterns of playful personification or unease about agency, and uncertainty about authenticity.Source: A2.5 Critical review · pp. 27–28

Academic AI use

• The academic theme retained the distinction between AI as learning support and delegation, while reviewers rejected the stronger claim that AI destabilizes learning, authorship, and assessment.The final theme also covered authorship, assessment, and contested school use.Source: A2.5 Critical review · p. 27

AI creativity

• The creativity theme was retained because evidence supported competing judgments about human expression, effort, originality, accessibility, labor, and attribution.Its final interpretation centered on human contribution in judgments of creative legitimacy.Source: A2.5 Critical review · p. 28

Everyday and social AI use

• Reviewers broadened peer negotiation into everyday and social AI use, covering experimentation, entertainment, advice, sharing, and peer interaction.The review cautioned that some evidence reflected ordinary social interaction rather than explicit normative judgment.Source: A2.5 Critical review · p. 28

Personal control and safety

• Reviewers separated privacy, consent, identity, and representation from verification and authenticity concerns, which were assigned to a separate theme.The personal-control theme retained privacy, consent, identity, and representation.Source: A2.5 Critical review · p. 28