Pith. sign in

REVIEW 3 major objections 4 minor 94 references

"Always Want to Use it for Everything": Understanding Young Adults' Perceptions of AI Dependence

T0 review · 3 major / 4 minor · reviewed 2026-08-11 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read AI-dependent young adults fail today's dependence scales

desk verdict A genuinely useful qualitative model of AI dependence, but the headline misalignment claim rests on an unvalidated single self-report item. read the letter →

arxiv 2608.07592 v1 pith:C5FKAYY5 submitted 2026-08-05 cs.HC cs.CY

Ashlee Milton , Leah Ajmani , Amy Heger , Forough Poursabzi-Sangdeh , Mihaela Vorvoreanu , Jina Suh This is my paper · ORCID
classification cs.HCcs.CY
keywords AIchatbotdependenceyoungadultslivedexperienceself-determinationtheoryqualitativethematicanalysisself-reportmeasuresabilityatrophychronicuse
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

Current measures of AI chatbot dependence, built largely from clinical addiction frameworks, may be missing the way young adults actually experience dependence. This paper surveys 290 U.S. users aged 18–25 and finds that participants who self-identify as dependent on AI chatbots do not score as dependent on existing dependence scales. From open-ended testimonials it identifies three factors young adults use to define dependence: chronic use (habitual and applied across all contexts), efficiency (saving time and effort at the cost of shifting one's role), and delegation (handing learning, decision-making, and emotional processing to the chatbot). Participants also describe perceived ability atrophy that leads to feelings of inadequacy and impostor syndrome. The authors argue that AI dependence should be reconceptualized around these user-reported factors rather than translated substance-use-disorder symptoms.

What carries the argument

The argument is carried by a two-step quantitative screen paired with inductive thematic analysis of open-ended testimonials. Participants who answered 'somewhat agree' or 'strongly agree' to a single self-report item ('I consider myself dependent on AI chatbots') were labeled dependent (n=74) and compared to the rest (n=216); only items with significant Spearman correlations to that item and significant group differences (Mann-Whitney U with Bonferroni correction and Cohen's D > 0.7) were retained. The qualitative coding yields the three-factor model—chronic use, efficiency, delegation—which the quantitative results corroborate (e.g., dependent users report higher frequency of use and stronger motivation to reduce effort). The lens of self-determination theory then connects these factors to unmet psychological needs: autonomy, competence, and relatedness.

What would settle it

A behavioral study that logs young adults' actual chatbot use (frequency across contexts, proportion of tasks delegated, and skill-use over time) could test whether self-reported dependence matches observed chronic use, efficiency-seeking, and delegation; if self-identified dependent users show no such behavioral pattern, the paper's lived-experience account is contradicted.

Watch

Extended reading notes

Core claim

The central claim is that AI chatbot dependence, as lived and observed by young adults, is not what existing scales measure. Self-identified dependent participants, on average, disagreed with items such as 'I rely too much on AI chatbots' and 'I feel uneasy, anxious, or upset when I cannot use AI chatbots,' despite describing habitual and context-blind use. The qualitative analysis identifies chronic use, efficiency, and delegation as the three perceived contributing factors, and ability atrophy with psychological fallout (inadequacy, impostor feelings) as the perceived consequence. Read through self-determination theory, these factors erode autonomy, competence, and relatedness. The paper therefore proposes that future measurement start from users' own experiences rather than from clinical translation.

Load-bearing premise

The load-bearing premise is that the single self-report item 'I consider myself dependent on AI chatbots' truly identifies dependent participants; if it does not, the claimed misalignment with existing scales collapses.

Editorial extensions

If this is right

  • Existing AI dependence scales will misclassify many young adults who feel dependent, so screening tools should add items on chronic and acontextual use, efficiency-seeking, and delegation.
  • Frequency of turning to AI, rather than single-session length, is the time-based signal separating dependent from non-dependent users; session-length-based warnings may target the wrong behavior.
  • The three factors can be operationalized into a new dependence measure grounded in users' own definitions instead of clinical addiction criteria.
  • Policy and design should address validation-focused ('sycophantic') chatbot behavior, since participants report it encourages continued use for emotional support, and should weigh efficiency pressures against dependence risks.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • We infer that a longitudinal study tracking actual skill performance (e.g., coding, writing, critical-thinking tests) before and after heavy delegation would test whether the perceived ability atrophy corresponds to objective decline; the paper only has retrospective self-reports.
  • We infer that the single-item self-report assumption could be tested directly: if a behavioral measure (usage logs across contexts and proportion of delegated tasks) fails to corroborate self-labeled dependence, the misalignment claim would need revision.
  • We infer that the frequency-not-session-length finding suggests a concrete policy experiment comparing outcome measures (reported atrophy, distress on removal) under frequency-based versus session-length-based warning criteria.
  • We infer that because the sample is U.S.-based and skewed toward women and white participants, the three-factor model may not generalize to other cultural or demographic contexts without replication.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 4 minor

Summary. The paper reports a mixed-methods questionnaire study of 290 US-based young adults (ages 18-25) who use AI chatbots at least weekly. Participants answered open-ended questions about their perceptions and experiences of AI dependence and closed-ended items drawn from existing scales. The authors split the sample into self-reported dependent (n=74) and non-dependent (n=216) groups using a single Likert item, and report that dependent participants did not score as dependent on existing AI dependence measures, which they interpret as a misalignment between current measures and lived experience. Qualitative thematic analysis identifies chronic use, efficiency, and delegation as perceived contributing factors, together with experienced ability atrophy and psychological effects such as inadequacy and impostor feelings. The discussion interprets these findings through self-determination theory and draws implications for reconceptualizing AI dependence, measurement, policy, and design.

Significance. If the central misalignment claim were established, the paper would make an important contribution to the current debate about AI dependence: it would show that existing clinical-derived scales miss a form of dependence that young adults themselves recognize, and it would provide a concrete set of user-centered factors (chronic use, efficiency, delegation) to guide new measures. The qualitative taxonomy is internally coherent and grounded in quotes from both self-identified dependent users and bystanders, and the authors are transparent about sample limitations and response bias. The two-step quantitative screening is conservative, and the paper does not overclaim on the basis of session length. However, the quantitative evidence for misalignment is currently much weaker than the qualitative contribution: it depends on an unvalidated single-item grouping and on modified scales whose psychometric properties in this sample are not reported. As a result, the headline claim should be treated as exploratory pending additional validation.

major comments (3)
  1. [Methods, Analysis Step 2; Findings, Quantitative Results] The paper's central claim that existing AI dependence measures are misaligned with users' experiences rests on splitting participants into dependent (n=74) and non-dependent (n=216) using the single Likert item 'I consider myself dependent on AI chatbots,' yet no validity evidence is offered for this item as a measure of dependence. If the item captures a broad colloquial sense of reliance rather than the clinically-derived construct operationalized by the existing scales, low scores on those scales are expected and do not indicate misalignment. Please add a validity argument or sensitivity analyses (e.g., a stricter cut-off, triangulation with behavioral indicators such as use frequency and open-ended self-descriptions) and, at minimum, soften the claim to an exploratory hypothesis about possible mismatch.
  2. [Appendix A; Methods, Questionnaire] The scales were modified before administration—'chatbot' was inserted, overlapping items were removed, and Likert formats were standardized—yet the paper interprets item-level responses as evidence about 'current AI dependence measures.' These modifications can change item functioning and break comparability with validated scales. Please report the exact item-level modifications, the reliability of each modified scale in this sample, and total/mean scores (with distributions) rather than selected item medians, so readers can judge whether the misalignment is substantive or an artifact of scale adaptation.
  3. [Methods, Analysis; Tables 2 and 3] The two-step screening declares significance only when the Mann-Whitney U test has p<0.05 (Bonferroni-corrected) and Cohen's D>0.7, but the results tables list only Spearman correlations and group medians. Without the effect sizes (and ideally confidence intervals) for the tested items, the reader cannot verify that the reported items actually met the stated threshold or assess the magnitude of group differences. Please include these values in the tables or supplementary material.
minor comments (4)
  1. [Methods, Analysis Step 1] The sentence 'We first the correlations between closed-ended items and participants' self-reported dependence' appears to be missing a verb; it should read 'We first computed the correlations.'
  2. [Throughout] Several typos need a proofread pass: 'chabot' (participant B285), 'chabots' (participant D221), 'out mitigation strategy' in the Limitations, and 'final themes was established' in the Methods section.
  3. [Tables 2 and 3] The heading 'Step 1: Step 2: Median' is unclear; consider splitting the columns into separate labeled headers for correlation, group medians, p-value, and effect size, and clarify the caption.
  4. [Findings, Use Motivations] The statement that 'only five dependent participants reported using AI chatbots for companionship-related reasons' is left unexplained; if this refers to a specific survey item, the item should be identified so readers can interpret the claim.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the misalignment finding is an empirical comparison against an external self-report criterion, and the qualitative themes are descriptive rather than derived from a fitted model.

full rationale

The paper's core claim is that self-identified dependent users score low on existing dependence scales. This is an empirical comparison between a single self-classification item and multi-item scales; neither is derived from the other, so the result is not forced by construction. The qualitative themes (chronic use, efficiency, delegation, and atrophy) are induced from open-ended testimonials and are used descriptively, not as predictions. No parameters are fitted to a subset of data and then used to 'predict' the same subset; no uniqueness theorem or ansatz is imported via self-citation; and the self-citations (e.g., Ajmani et al. 2026; Chandra et al. 2025a/b) appear in related-work context rather than as load-bearing premises for the study's conclusions. The unvalidated single-item grouping is a potential validity threat, but that is an evidentiary limitation, not a circularity. The derivation chain is self-contained in the sense that the findings are reported as participant perceptions and statistical associations; no central result reduces to its own inputs.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

The paper relies on no fitted mathematical parameters. The statistical claims depend on hand-chosen thresholds (self-report grouping, Cohen's D cutoff) and on domain assumptions about self-report validity, qualitative coding, and the appropriateness of self-determination theory. No invented entities are introduced.

free parameters (2)
  • Self-report dependence grouping threshold = Somewhat agree or strongly agree (Likert 4-5) on a single item
    Chosen by hand to split participants into dependent vs non-dependent groups; this threshold determines the central misalignment result and the group median comparisons.
  • Effect size significance threshold for group differences = Cohen's D > 0.7
    Applied in Step 2 of the analysis before an item is reported; a higher or lower threshold would change which items enter the findings.
assumptions (4)
  • domain assumption Participants' self-reported dependence, captured by a single Likert item, is a valid indicator of true AI dependence.
    Used to partition the sample into dependent (n=74) and non-dependent (n=216) groups; the misalignment finding depends on this item being a more trustworthy signal than the validated scales being compared against. No validation of this single item is provided.
  • domain assumption Participants' open-ended testimonials accurately reflect their actual AI use and feelings.
    The qualitative themes (chronic use, efficiency, delegation, atrophy) are drawn from self-reported testimonies in an anonymous questionnaire; social desirability and memory biases are acknowledged but not controlled beyond anonymity and neutral wording.
  • domain assumption Self-Determination Theory is an appropriate lens for interpreting the implications of the findings.
    The discussion maps chronic use and delegation to reduced autonomy, atrophy to diminished competence, and AI-mediated interaction to weakened relatedness; this assumes SDT's three needs are the right frame for well-being consequences.
  • domain assumption The two qualitative coders reached consistent themes without measurable inter-rater reliability.
    The method reports independent coding and team discussion, but no codebook or reliability statistics; the stability of the three-factor model rests on this process.

how reviews work

0 comments
Cite this review

Pith. "Pith review of "Always Want to Use it for Everything": Understanding Young Adults' Perceptions of AI Dependence." pith.science (2026). https://pith.science/paper/C5FKAYY5

@misc{pith2026260807592,
  author       = {Pith},
  title        = {Pith review of: "Always Want to Use it for Everything": Understanding Young Adults' Perceptions of AI Dependence},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/C5FKAYY5}},
  note         = {Machine review of arXiv:2608.07592}
}
read the original abstract

The growing integration of general-purpose AI chatbots into people's daily lives has raised concerns about the potential for unhealthy dependence, particularly among young adults. As a first step toward understanding and characterizing AI chatbot dependence from the perspective of young adults, we collected testimonials from AI chatbot users aged 18 to 25 through an online questionnaire to capture their thoughts and experiences with this phenomenon. From participant responses, we identified three contributing factors of AI dependence: chronic use, efficiency, and delegation. The combination of these in a person's interaction behavior was considered to indicate AI dependence. Participants also observed feelings of atrophy in abilities from AI dependence, leading to psychological impacts such as feelings of inadequacy. Interpreting these findings through the lens of self-determination theory reveals how AI chatbot dependence can impact young adults' personal and social development. We argue that preventing lasting harm to young adults' development is paramount, and provide implications for rethinking AI chatbot dependence grounded in this understanding.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

94 extracted references · 63 canonical work pages

  1. [1]

    Nature Reviews Psychology , volume=

    A taxonomy of technology design features that promote potentially addictive online behaviours , author=. Nature Reviews Psychology , volume=. 2023 , publisher=

    2023

  2. [2]

    Technology in Society , volume=

    Examining generative AI user addiction from a CAC perspective , author=. Technology in Society , volume=. 2024 , publisher=

    2024

  3. [3]

    Psychology Research and Behavior Management , pages=

    AI technology panic—is AI dependence bad for mental health? A cross-lagged panel model and the mediating roles of motivations for AI use among adolescents , author=. Psychology Research and Behavior Management , pages=. 2024 , publisher=

    2024

  4. [4]

    International Journal of Information Management , volume=

    ChatGPT usage in everyday life: A motivation-theoretic mixed-methods study , author=. International Journal of Information Management , volume=. 2024 , publisher=

    2024

  5. [5]

    Computers in human behavior reports , volume=

    Initial validation of the general attitudes towards Artificial Intelligence Scale , author=. Computers in human behavior reports , volume=. 2020 , publisher=

    2020

  6. [6]

    Current Psychology , volume=

    Development and validation the Problematic ChatGPT Use Scale: a preliminary report , author=. Current Psychology , volume=. 2024 , publisher=

    2024

  7. [7]

    Psychology Research and Behavior Management , pages=

    Concept of affective dependence and validation of an affective dependence scale , author=. Psychology Research and Behavior Management , pages=. 2022 , publisher=

    2022

  8. [8]

    Qualitative psychology: A practical guide to research methods , volume=

    Thematic analysis , author=. Qualitative psychology: A practical guide to research methods , volume=

Show all 94 references
  1. [9]

    AIholic”: Questioning the “ChatGPT addiction

    People are not becoming “AIholic”: Questioning the “ChatGPT addiction” construct , author=. Addictive Behaviors , volume=. 2025 , publisher=

    2025

  2. [10]

    Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems , pages=

    The dark addiction patterns of current AI chatbot interfaces , author=. Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems , pages=

  3. [11]

    International Journal of Human--Computer Interaction , volume=

    Exploring the effect of attachment on technology addiction to generative AI chatbots: A structural equation modeling analysis , author=. International Journal of Human--Computer Interaction , volume=. 2025 , publisher=

    2025

  4. [12]

    Me and my AI bot: exploring the'AIholic'Phenomenon and university students' dependency on generative AI chatbots-is this the new academic addiction? , author=

  5. [13]

    Journal of service Management , volume=

    Friend, mentor, lover: does chatbot engagement lead to psychological dependence? , author=. Journal of service Management , volume=. 2023 , publisher=

    2023

  6. [14]

    Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems , pages=

    Understanding Teen Overreliance on AI Companion Chatbots Through Self-Reported Reddit Narratives , author=. Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems , pages=

    2026

  7. [15]

    34\ url=

    Sidoti, Olivia and McClain, Colleen , year=. 34\ url=

  8. [16]

    Journal of youth and adolescence , volume=

    Patterns of young adult social roles transitions across 24 months and subsequent substance use and mental health , author=. Journal of youth and adolescence , volume=. 2020 , publisher=

    2020

  9. [17]

    Frontiers in Artificial Intelligence , volume=

    Supporting cognition with modern technology: Distributed cognition today and in an AI-enhanced future , author=. Frontiers in Artificial Intelligence , volume=. 2022 , publisher=

    2022

  10. [18]

    AI chatbots could be making you stupider , url=

    Hogenboom, Melissa , year=. AI chatbots could be making you stupider , url=

  11. [19]

    Why AI companions and young people can make for a dangerous mix , url=

    Sanford, John , year=. Why AI companions and young people can make for a dangerous mix , url=

  12. [20]

    arXiv preprint arXiv:2503.17473 , year=

    How ai and human behaviors shape psychosocial effects of extended chatbot use: A longitudinal randomized controlled study , author=. arXiv preprint arXiv:2503.17473 , year=

    arXiv

  13. [21]

    Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society , volume=

    How LLM counselors violate ethical standards in mental health practice: A practitioner-informed framework , author=. Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society , volume=

  14. [22]

    Review of Applied Management and Social Sciences , volume=

    Psychological impacts of AI dependence: Assessing the cognitive and emotional costs of intelligent systems in daily life , author=. Review of Applied Management and Social Sciences , volume=

  15. [23]

    Asian Journal of Psychiatry , volume=

    Generative artificial intelligence addiction syndrome: A new behavioral disorder? , author=. Asian Journal of Psychiatry , volume=. 2025 , publisher=

    2025

  16. [24]

    Smart learning environments , volume=

    The effects of over-reliance on AI dialogue systems on students' cognitive abilities: a systematic review , author=. Smart learning environments , volume=. 2024 , publisher=

    2024

  17. [25]

    Multimodal Technologies and Interaction , volume=

    Tools for wellbeing-supportive design: Features, characteristics, and prototypes , author=. Multimodal Technologies and Interaction , volume=. 2020 , publisher=

    2020

  18. [26]

    2014 , publisher=

    Positive computing: technology for wellbeing and human potential , author=. 2014 , publisher=

    2014

  19. [27]

    Handbook of theories of social psychology , volume=

    Self-determination theory , author=. Handbook of theories of social psychology , volume=. 2012 , publisher=

    2012

  20. [28]

    , author=

    Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. , author=. American psychologist , volume=. 2000 , publisher=

    2000

  21. [29]

    Skill decay: The science and practice of mitigating skill loss and enhancing retention , author=

  22. [30]

    Cognitive Research: Principles and Implications , volume=

    Does using artificial intelligence assistance accelerate skill decay and hinder skill development without performers’ awareness? , author=. Cognitive Research: Principles and Implications , volume=. 2024 , publisher=

    2024

  23. [31]

    AI and Ethics , volume=

    AI and human autonomy: a literature review , author=. AI and Ethics , volume=. 2026 , publisher=

    2026

  24. [32]

    Journal of Applied Philosophy , volume=

    Anthropomorphism, false beliefs, and conversational AIs: how chatbots undermine users' autonomy , author=. Journal of Applied Philosophy , volume=. 2025 , publisher=

    2025

  25. [33]

    British Journal of Educational Technology , volume=

    The mediating effects of needs satisfaction on the relationships between prior knowledge and self-regulated learning through artificial intelligence chatbot , author=. British Journal of Educational Technology , volume=. 2023 , publisher=

    2023

  26. [34]

    Acta Pedagogia Asiana , volume=

    AI am Motivated: Leveraging Self-Determination Theory in Chatbots , author=. Acta Pedagogia Asiana , volume=

  27. [35]

    AMCIS 2018 Proceedings , year=

    Understanding user interactions with a chatbot: A self-determination theory approach , author=. AMCIS 2018 Proceedings , year=

    2018

  28. [36]

    Computers and Education: Artificial Intelligence , volume=

    Exploring English language learning via Chabot: A case study from a self determination theory perspective , author=. Computers and Education: Artificial Intelligence , volume=. 2023 , publisher=

    2023

  29. [37]

    Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems , pages=

    Designing conversational agents: A self-determination theory approach , author=. Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems , pages=

    2021

  30. [38]

    Interactive Learning Environments , volume=

    Systematics review on artificial intelligence chatbots and ChatGPT for language learning and research from self-determination theory (SDT): what are the roles of teachers? , author=. Interactive Learning Environments , volume=. 2025 , publisher=

    2025

  31. [39]

    AI & SOCIETY , pages=

    When autonomy breaks: the hidden existential risk of AI , author=. AI & SOCIETY , pages=. 2025 , publisher=

    2025

  32. [40]

    Frontiers in psychology , volume=

    From tools to threats: a reflection on the impact of artificial-intelligence chatbots on cognitive health , author=. Frontiers in psychology , volume=. 2024 , publisher=

    2024

  33. [41]

    Societies , volume=

    AI tools in society: Impacts on cognitive offloading and the future of critical thinking , author=. Societies , volume=. 2025 , publisher=

    2025

  34. [42]

    Proceedings of the ACM on Human-Computer Interaction , volume=

    'I'm Petting the Laptop, Which Has You Inside It': Reflecting on Lived Experiences of Online Friendship , author=. Proceedings of the ACM on Human-Computer Interaction , volume=. 2025 , publisher=

    2025

  35. [43]

    The Cambridge handbook of personal relationships , pages=

    Maintaining relationships , author=. The Cambridge handbook of personal relationships , pages=

  36. [44]

    Science , volume=

    Sycophantic AI decreases prosocial intentions and promotes dependence , author=. Science , volume=. 2026 , publisher=

    2026

  37. [45]

    arXiv preprint arXiv:2506.08872 , volume=

    Your brain on ChatGPT: Accumulation of cognitive debt when using an AI assistant for essay writing task , author=. arXiv preprint arXiv:2506.08872 , volume=

    arXiv

  38. [46]

    Proceedings of the National Academy of Sciences , volume=

    Flynn effect and its reversal are both environmentally caused , author=. Proceedings of the National Academy of Sciences , volume=. 2018 , publisher=

    2018

  39. [47]

    arXiv preprint arXiv:2511.15352 , year=

    People readily follow personal advice from AI but it does not improve their well-being , author=. arXiv preprint arXiv:2511.15352 , year=

    arXiv

  40. [48]

    Available at SSRN 6433263 , year=

    Cascades of Drift: Mental Health Risks of Prolonged AI Conversations , author=. Available at SSRN 6433263 , year=

  41. [49]

    Applied Cognitive Psychology , volume=

    Does impulsivity relate to perceived dependence on and actual use of the mobile phone? , author=. Applied Cognitive Psychology , volume=. 2007 , publisher=

    2007

  42. [50]

    International Journal of Global Education (IJGE) , volume=

    The relationship between internet addiction and psychological symptoms , author=. International Journal of Global Education (IJGE) , volume=

  43. [51]

    Cyberpsychology, Behavior, and Social Networking , volume=

    Prevalence and psychosocial risk factors associated with internet addiction in a nationally representative sample of college students in Taiwan , author=. Cyberpsychology, Behavior, and Social Networking , volume=. 2011 , publisher=

    2011

  44. [52]

    Neuroscience & Biobehavioral Reviews , volume=

    Systematic review and meta-analysis of epidemiology of internet addiction , author=. Neuroscience & Biobehavioral Reviews , volume=. 2020 , publisher=

    2020

  45. [53]

    CyberPsychology & Behavior , volume=

    Shyness and locus of control as predictors of internet addiction and internet use , author=. CyberPsychology & Behavior , volume=. 2004 , publisher=

    2004

  46. [54]

    Psychological Science , volume=

    Creating social connection through inferential reproduction: Loneliness and perceived agency in gadgets, gods, and greyhounds , author=. Psychological Science , volume=. 2008 , publisher=

    2008

  47. [55]

    , author=

    Negative social comparison on Facebook and depressive symptoms: Rumination as a mechanism. , author=. Psychology of popular media culture , volume=. 2013 , publisher=

    2013

  48. [56]

    International journal of information management , volume=

    Online social media fatigue and psychological wellbeing—A study of compulsive use, fear of missing out, fatigue, anxiety and depression , author=. International journal of information management , volume=. 2018 , publisher=

    2018

  49. [57]

    Personality and Individual Differences , volume=

    Social media use, personality characteristics, and social isolation among young adults in the United States , author=. Personality and Individual Differences , volume=. 2018 , publisher=

    2018

  50. [58]

    Health psychology and behavioral medicine , volume=

    Associations between social media use and loneliness in a cross-national population: do motives for social media use matter? , author=. Health psychology and behavioral medicine , volume=. 2023 , publisher=

    2023

  51. [59]

    2025 , journal=

    Addictive intelligence: Understanding psychological, legal, and technical dimensions of AI companionship , author=. 2025 , journal=

    2025

  52. [60]

    International Journal of Human-Computer Studies , volume=

    My chatbot companion-a study of human-chatbot relationships , author=. International Journal of Human-Computer Studies , volume=. 2021 , publisher=

    2021

  53. [61]

    International Journal of Human-Computer Studies , volume=

    A longitudinal study of human--chatbot relationships , author=. International Journal of Human-Computer Studies , volume=. 2022 , publisher=

    2022

  54. [62]

    Attachment theory as a framework to understand relationships with social chatbots: a case study of

    Xie, Tianling and Pentina, Iryna , journal=. Attachment theory as a framework to understand relationships with social chatbots: a case study of

  55. [63]

    Journal of Business Research , volume=

    I am attracted to my cool smart assistant! Analyzing attachment-aversion in AI-human relationships , author=. Journal of Business Research , volume=. 2023 , publisher=

    2023

  56. [64]

    Computers in Human Behavior , volume=

    Exploring relationship development with social chatbots: A mixed-method study of replika , author=. Computers in Human Behavior , volume=. 2023 , publisher=

    2023

  57. [65]

    Computers in Human Behavior , volume=

    How social anxiety leads to problematic use of conversational AI: The roles of loneliness, rumination, and mind perception , author=. Computers in Human Behavior , volume=. 2023 , publisher=

    2023

  58. [66]

    arXiv preprint arXiv:2504.14112 , year=

    Longitudinal study on social and emotional use of AI conversational agent , author=. arXiv preprint arXiv:2504.14112 , year=

    arXiv

  59. [67]

    Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency , pages=

    From lived experience to insight: unpacking the psychological risks of using ai conversational agents , author=. Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency , pages=

    2025

  60. [68]

    New Media & Society , volume=

    Too human and not human enough: A grounded theory analysis of mental health harms from emotional dependence on the social chatbot Replika , author=. New Media & Society , volume=. 2024 , publisher=

    2024

  61. [69]

    Proceedings of the ACM on Human-Computer Interaction , volume=

    The typing cure: Experiences with large language model chatbots for mental health support , author=. Proceedings of the ACM on Human-Computer Interaction , volume=. 2025 , publisher=

    2025

  62. [70]

    It happened to be the perfect thing

    “It happened to be the perfect thing”: experiences of generative AI chatbots for mental health , author=. Npj mental health research , volume=. 2024 , publisher=

    2024

  63. [71]

    Proceedings of the 2026 ACM Conference on Fairness, Accountability, and Transparency , year =

    Ajmani, Leah Hope and Ghosh, Arka and Kaveladze, Benjamin and Kim, Eugenia and Namuduri, Keertana and Nguyen, Theresa and Okoli, Ebele and Schleider, Jessica and Ford, Denae and Suh, Jina , title =. Proceedings of the 2026 ACM Conference on Fairness, Accountability, and Transp...

    2026

  64. [72]

    arXiv preprint arXiv:2601.20245 , year=

    How AI impacts skill formation , author=. arXiv preprint arXiv:2601.20245 , year=

  65. [73]

    Extended abstracts of the CHI conference on human factors in computing systems , pages=

    Outperformed by AI: Interacting with superhuman AI changes the way we perceive ourselves , author=. Extended abstracts of the CHI conference on human factors in computing systems , pages=

  66. [74]

    arXiv preprint arXiv:2511.14972 , year=

    Harmful Traits of AI Companions , author=. arXiv preprint arXiv:2511.14972 , year=

  67. [75]

    Proceedings of the 2025 CHI conference on human factors in computing systems , pages=

    The dark side of ai companionship: A taxonomy of harmful algorithmic behaviors in human-ai relationships , author=. Proceedings of the 2025 CHI conference on human factors in computing systems , pages=

    2025

  68. [76]

    Computers in Human Behavior Reports , pages=

    Generative artificial intelligence dependency: Scale development, validation, and its motivational, behavioral, and psychological correlates , author=. Computers in Human Behavior Reports , pages=. 2025 , publisher=

    2025

  69. [77]

    arXiv preprint arXiv:2602.09339 , year=

    Understanding Risk and Dependency in AI Chatbot Use from User Discourse , author=. arXiv preprint arXiv:2602.09339 , year=

  70. [78]

    , author=

    Is loneliness in emerging adults increasing over time? A preregistered cross-temporal meta-analysis and systematic review. , author=. Psychological bulletin , volume=. 2021 , publisher=

    2021

  71. [79]

    2023 , publisher=

    Our epidemic of loneliness and isolation: The US Surgeon General’s Advisory on the healing effects of social connection and community [Internet] , author=. 2023 , publisher=

    2023

  72. [80]

    Nature Reviews Psychology , volume=

    Mechanisms linking social media use to adolescent mental health vulnerability , author=. Nature Reviews Psychology , volume=. 2024 , publisher=

    2024

  73. [81]

    Proceedings of the National Academy of Sciences , volume=

    The benefits and dangers of anthropomorphic conversational agents , author=. Proceedings of the National Academy of Sciences , volume=. 2025 , publisher=

    2025

  74. [82]

    The Journal of Positive Psychology , volume=

    Accountability: Construct definition and measurement of a virtue vital to flourishing , author=. The Journal of Positive Psychology , volume=. 2023 , publisher=

    2023

  75. [83]

    Proceedings of the 2025 CHI conference on human factors in computing systems , pages=

    The impact of generative AI on critical thinking: Self-reported reductions in cognitive effort and confidence effects from a survey of knowledge workers , author=. Proceedings of the 2025 CHI conference on human factors in computing systems , pages=

    2025

  76. [84]

    2025 , url =

    California State Senate , title =. 2025 , url =

    2025

  77. [85]

    2025 , url =

    New York State Senate , title =. 2025 , url =

    2025

  78. [86]

    Sycophantic

    Cheng, Myra and Lee, Cinoo and Khadpe, Pranav and Yu, Sunny and Han, Dyllan and Jurafsky, Dan , month = oct, year =. Sycophantic. doi:10.48550/arXiv.2510.01395 , abstract =

    doi

  79. [87]

    arXiv preprint arXiv:2505.11649 , year=

    Illusions of Intimacy: How Emotional Dynamics Shape Human-AI Relationships , author=. arXiv preprint arXiv:2505.11649 , year=

  80. [88]

    Computers in Human Behavior , volume=

    Technological optimism surpasses fear of missing out: A multigroup analysis of presumed media influence on generative AI technology adoption across varying levels of technological optimism , author=. Computers in Human Behavior , volume=. 2025 , publisher=

    2025

  81. [89]

    Basic and Applied Social Psychology , volume=

    I cannot miss it for the world: The relationship between fear of missing out (FOMO) and acceptance of ChatGPT , author=. Basic and Applied Social Psychology , volume=. 2024 , publisher=

    2024

  82. [90]

    doi:10.48550/arXiv.2505.13995 , abstract =

    Cheng, Myra and Yu, Sunny and Lee, Cinoo and Khadpe, Pranav and Ibrahim, Lujain and Jurafsky, Dan , month = sep, year =. doi:10.48550/arXiv.2505.13995 , abstract =

    doi arXiv

  83. [91]

    Technological folie

    Dohn. Technological folie. Nature Mental Health , pages=. 2026 , publisher=

    2026

  84. [92]

    Anesthesia & analgesia , volume=

    Correlation coefficients: appropriate use and interpretation , author=. Anesthesia & analgesia , volume=. 2018 , publisher=

    2018

  85. [93]

    Proceedings of the 2023 CHI conference on human factors in computing systems , pages=

    About engaging and governing strategies: A thematic analysis of dark patterns in social networking services , author=. Proceedings of the 2023 CHI conference on human factors in computing systems , pages=

    2023

  86. [94]

    Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems , pages=

    The siren song of llms: How users perceive and respond to dark patterns in large language models , author=. Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems , pages=

    2026

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.