Messages in a Digital Bottle: A Youth-Coauthored Perspective on LLM Chatbots and Adolescent Loneliness

Di Fu; Jinyao Liu

arxiv: 2604.03470 · v1 · submitted 2026-04-03 · 💻 cs.HC

Messages in a Digital Bottle: A Youth-Coauthored Perspective on LLM Chatbots and Adolescent Loneliness

Jinyao Liu , Di Fu This is my paper

Pith reviewed 2026-05-13 18:13 UTC · model grok-4.3

classification 💻 cs.HC

keywords LLM chatbotsadolescent lonelinessyouth authorshiphuman-computer interactiondesign implicationsneurodivergent youthimmigrant adolescentsanxiety and depression

0 comments

The pith

LLM chatbots can temporarily ease loneliness for some adolescents but risk deepening isolation for others when they replace real connections.

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

This paper offers a critical synthesis of how large language model chatbots interact with adolescent loneliness, written primarily from the lived experience of its 16-year-old first author who recently immigrated. It examines differences across subgroups such as those with anxiety or depression, neurodivergent youth, and immigrant adolescents, mapping both the short-term conditions that may lessen isolation and the breakdowns that can intensify it. The work derives three population-sensitive design implications from this analysis and positions the youth perspective as the central interpretive frame rather than one data point among many. A sympathetic reader would care because rising loneliness among teenagers coincides with widespread chatbot use, making it urgent to understand when these tools help versus harm. The paper frames itself as work-in-progress that will next expand to a broader panel of adolescent coauthors for empirical validation.

Core claim

Adolescent loneliness takes distinct forms in chatbot conversations depending on the user's subgroup, with temporary reductions possible under specific conditions such as low-stakes emotional support yet breakdowns that deepen isolation when chatbots become substitutes for human relationships, all derived through a youth-authored lens that foregrounds the primary author's migration and daily experience to generate three targeted design implications.

What carries the argument

The youth-authored critical synthesis, with the 16-year-old immigrant author's lived experience serving as the primary interpretive lens applied to interdisciplinary literature on social computing, developmental psychology, and HCI.

If this is right

Chatbot designs should account for subgroup differences so that features like always-available listening reduce isolation for anxious or depressed teens without creating dependency.
For neurodivergent youth, interfaces need adjustments that avoid overwhelming sensory or social simulation elements that could increase loneliness.
Immigrant adolescents may benefit from chatbots that support cultural transition talk but require safeguards against replacing local peer networks.
Three explicit population-sensitive design implications follow directly from the subgroup analysis.

Where Pith is reading between the lines

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

The youth-coauthorship model could be tested in other domains such as educational AI tools or social media design where adolescent input is currently limited.
Longitudinal tracking of chatbot conversation patterns across these subgroups would reveal whether short-term relief persists or shifts into isolation over months.
Policymakers might consider guidelines for platforms offering teen chatbots that require clear indicators when the interaction is shifting from support to substitution.

Load-bearing premise

A single youth author's personal experience can serve as the main basis for generalizing chatbot effects across multiple distinct adolescent subgroups without immediate broader empirical checks.

What would settle it

Interviews or usage diaries collected from a panel of adolescents spanning the anxiety, neurodivergent, and immigrant subgroups that either match or contradict the identified conditions for temporary relief versus deepened isolation.

read the original abstract

Adolescent loneliness is a growing concern in digitally mediated social environments. This work-in-progress presents a youth-authored critical synthesis on chatbots powered by Large Language Model (LLM) and adolescent loneliness. The first author is a 16-year-old Chinese student who recently migrated to the UK. She wrote the first draft of this paper from her lived experience, supervised by the second author. Rather than treating the youth perspective as one data point among many, we foreground it as the primary interpretive lens, grounded in interdisciplinary literature from social computing, developmental psychology, and Human-Computer Interaction (HCI). We examine how chatbots shape experiences of loneliness differently across adolescent subgroups, including those with anxiety or depression, neurodivergent youth, and immigrant adolescents, and identify both conditions under which they may temporarily reduce isolation and breakdowns that risk deepening it. We derive three population-sensitive design implications. The next phase of this work will expand the youth authorship model to a panel of adolescents across these subgroups, empirically validating the framework presented here.

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.

Desk Editor's Note private letter to a colleague

Youth-led synthesis on chatbots and adolescent loneliness foregrounds lived experience effectively but generalizes from limited validation.

read the letter

The paper's main point is that a 16-year-old Chinese immigrant's lived experience can serve as the central lens for understanding how LLM chatbots influence loneliness among different groups of adolescents. From that starting point it reviews relevant literature and arrives at three design implications meant to be sensitive to subgroups like those dealing with anxiety or depression, neurodivergent teens, and other immigrants. What works here is the clear commitment to youth authorship and the way it connects personal migration stories to broader patterns in chatbot use. The conditions it identifies for temporary relief from isolation versus risks of increased loneliness draw reasonably from the cited interdisciplinary sources. This framing avoids treating the youth perspective as just another data point and instead builds the argument around it, which could encourage more of this kind of input in HCI work on mental health tools. The weaker part is the jump from one individual's experience to claims about multiple distinct subgroups. The manuscript acknowledges this by calling the work in-progress and planning a larger panel, but the implications presented now lack the empirical steps or verification that would make them more convincing. Since there is no new data collection or detailed method for how the synthesis was done, the piece functions more as an informed opinion than a fully tested set of recommendations. This kind of paper fits readers in HCI and developmental psychology who are thinking about ethical design of AI for young people. It offers a starting point for discussion rather than definitive answers, so it would appeal to those open to perspective pieces that highlight underrepresented voices. I think it deserves peer review. The youth co-authorship model is worth refining through referee feedback, and the topic of chatbots and teen loneliness is timely enough that a developed version could contribute usefully.

Referee Report

1 major / 0 minor

Summary. This manuscript offers a youth-coauthored perspective piece on LLM chatbots and adolescent loneliness. The first author, a 16-year-old Chinese migrant to the UK, drafted the paper based on her lived experience, which is positioned as the primary interpretive lens rather than one data point. Grounded in literature from social computing, developmental psychology, and HCI, the work explores differential effects of chatbots on loneliness for subgroups including those with anxiety or depression, neurodivergent adolescents, and immigrant youth. It identifies conditions where chatbots may reduce isolation temporarily and breakdowns that could deepen loneliness, leading to three population-sensitive design implications. The paper is explicitly a work-in-progress, with future plans to involve a panel of adolescents for validation.

Significance. Should the synthesis prove robust upon validation, this paper could significantly influence the HCI community by modeling direct youth involvement in research on digital mental health tools. The approach of foregrounding lived experience provides a unique counterpoint to traditional empirical studies, potentially leading to design implications that are more attuned to diverse adolescent experiences with chatbots. This is particularly valuable given the growing role of LLMs in social interactions. The innovative authorship structure is a notable strength that merits recognition if the claims are substantiated.

major comments (1)

[Abstract] The examination of how chatbots shape loneliness differently across subgroups (anxiety/depression, neurodivergent, immigrant adolescents) and the derivation of three population-sensitive design implications depend on the single 16-year-old author's lived experience serving as the primary interpretive lens (Abstract). The manuscript itself flags this as work-in-progress and defers broader validation to a future panel, but the current synthesis and implications rest on the representativeness of this one perspective without shown cross-validation steps, which is load-bearing for the central claim of population-sensitive effects.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the positive assessment of the manuscript's potential significance and for recognizing the value of the innovative youth-coauthored structure. We address the single major comment below, maintaining the work-in-progress framing while defending the intentional design of foregrounding lived experience.

read point-by-point responses

Referee: [Abstract] The examination of how chatbots shape loneliness differently across subgroups (anxiety/depression, neurodivergent, immigrant adolescents) and the derivation of three population-sensitive design implications depend on the single 16-year-old author's lived experience serving as the primary interpretive lens (Abstract). The manuscript itself flags this as work-in-progress and defers broader validation to a future panel, but the current synthesis and implications rest on the representativeness of this one perspective without shown cross-validation steps, which is load-bearing for the central claim of population-sensitive effects.

Authors: We agree that the synthesis is grounded in the first author's lived experience as the primary interpretive lens, as explicitly stated in the abstract and full text. This is not an oversight but a deliberate methodological choice for this work-in-progress: to model direct youth involvement and offer a counterpoint to traditional empirical studies, rather than claiming statistical representativeness. The manuscript already discloses the WIP status and defers panel validation to future work, so the population-sensitive implications are presented as insights derived from this grounded perspective, not as validated general claims. No cross-validation steps are shown because none have been conducted yet; this limitation is transparent. We therefore see no need to alter the current manuscript, as the framing already positions the work appropriately. revision: no

Circularity Check

0 steps flagged

No significant circularity; qualitative synthesis is self-contained

full rationale

The paper is a qualitative work-in-progress synthesis that foregrounds one youth author's lived experience as the primary interpretive lens while grounding claims in external interdisciplinary literature from social computing, developmental psychology, and HCI. No equations, fitted parameters, quantitative predictions, or self-referential derivations exist. The manuscript explicitly defers broader empirical validation to a future multi-youth panel and does not reduce any central claim to a self-citation chain, ansatz, or input-by-construction tautology. All load-bearing steps rely on interpretive synthesis of cited external sources rather than author-defined quantities.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the domain assumption that a single youth perspective can serve as primary interpretive lens for subgroup differences in loneliness; no free parameters or invented entities are introduced.

axioms (1)

domain assumption Lived experience of a single youth author provides a valid primary interpretive lens for analyzing chatbot effects on loneliness across subgroups
Explicitly stated in the abstract: 'Rather than treating the youth perspective as one data point among many, we foreground it as the primary interpretive lens'

pith-pipeline@v0.9.0 · 5478 in / 1096 out tokens · 40768 ms · 2026-05-13T18:13:35.449135+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

30 extracted references · 30 canonical work pages

[1]

Sanni Aalto, Raija-Leena Punamäki, Mervi Vänskä, Reeta Kankaanpää, Tiina Turunen, Oskari Lahtinen, Ilse Derluyn, Caroline Spaas, Lucia De Haene, Signe Smith Jervelund, et al. 2025. Patterns of mental health problems and resilience among immigrant and refugee adolescents: a latent profile analysis.European Journal of Psychotraumatology16, 1 (2025), 2479924...

work page doi:10.1080/20008066.2025.2479924 2025
[2]

2024.Youth statistics: Internet and social media

Act For Youth. 2024.Youth statistics: Internet and social media. https://actforyouth.org/adolescence/demographics/internet.cfm

work page 2024
[3]

Alershi, M

S. Alershi, M. Vorvoreanu, and E. Horvitz. 2019.Guidelines for human-AI interaction design. https://www.microsoft.com/en-us/research/blog/ guidelines-for-human-ai-interaction-design/ Microsoft Research

work page 2019
[4]

2024.Mental health statistics: prevalence, services and funding in England

Carl Baker and Esme Kirk-Wade. 2024.Mental health statistics: prevalence, services and funding in England. Technical Report. House of Commons Library. https://commonslibrary.parliament.uk/research-briefings/sn06988/

work page 2024
[5]

2018.If

Taina Bucher. 2018.If... Then: Algorithmic Power and Politics. Oxford University Press

work page 2018
[6]

Clark and Susan E

Herbert H. Clark and Susan E. Brennan. 1991. Grounding in Communication. InPerspectives on Socially Shared Cognition. American Psychological Association, 127–149

work page 1991
[7]

Julian De Freitas, Zeliha Oğuz-Uğuralp, and Ahmet Kaan-Uğuralp. 2025. Emotional manipulation by AI companions.arXiv preprint(2025). arXiv:2508.19258. Available online August 2025; accessed January 2026

work page arXiv 2025
[8]

Rianne Dekker and Godfried Engbersen. 2014. How social media transform migrant networks and facilitate migration.Global Networks14, 4 (2014), 401–418

work page 2014
[9]

L. D. Hawke, G. Strudwick, B. Ritchie, J. Gibson, T. Phi, J. Hou, and L. Gallagher. 2025. Recommendations for the Development of Digital Conversational Agents for Youth with Mental Health Conditions: A Position Paper.Digital Health11 (2025), 20552076251393355. doi:10.1177/20552076251393355

work page doi:10.1177/20552076251393355 2025
[10]

Arthur Bran Herbener and Malene Flensborg Damholdt. 2024. Are lonely youngsters turning to chatbots for companionship? The relationship between chatbot usage and social connectedness in Danish high-school students.International Journal of Human-Computer Studies196 (2024), 103409. doi:10.1016/j.ijhcs.2024.103409

work page doi:10.1016/j.ijhcs.2024.103409 2024
[11]

Lyndsey Hipgrave, Jessie Goldie, Simon Dennis, and Amanda Coleman. 2025. Balancing risks and benefits: clinicians’ perspectives on the use of generative AI chatbots in mental healthcare.Frontiers in Digital Health7 (2025), 1606291

work page 2025
[12]

Hosseini

Z. Hosseini. 2026.Immigrants’ perceptions of AI-driven chatbots in public services. https://helda.helsinki.fi/items/3e48b389-68f0-4093-85e8- bd90fc59e50f HELDA – Helsinki University Digital Repository

work page 2026
[13]

Yu Jin, Jiayi Liu, Pan Li, Baosen Wang, Yangxinyu Yan, Huilin Zhang, Chenhao Ni, Jing Wang, Yi Li, Yajun Bu, and Yinghui Wang. 2024. The Applications of Large Language Models in Mental Health: Scoping Review.Journal of Medical Internet Research27 (2024), e69284. doi:10.2196/69284

work page doi:10.2196/69284 2024
[14]

Florian Onur Kuhlmeier, Luise Bauch, Ulrich Gnewuch, and Stefan Lüttke. 2024. Designing Chatbots to Treat Depression in Youth: Qualitative Study.JMIR Human Factors12 (2024), e66632. doi:10.2196/66632

work page doi:10.2196/66632 2024
[15]

Kim Malfacini. 2025. The impacts of companion AI on human relationships: risks, benefits, and design considerations.AI & SOCIETY(2025), 1–14. doi:10.1007/s00146-025-02318-6

work page doi:10.1007/s00146-025-02318-6 2025
[16]

Filip Meckovsky, Lukas Novak, Zdenek Meier, Peter Tavel, and Klara Malinakova. 2025. Highly sensitive persons feel more emotionally lonely than the general population.Scientific Reports15, 1 (2025), 2707

work page 2025
[17]

Ong, and Nick Haber

Jared Moore, Declan Grabb, William Agnew, Kevin Klyman, Stevie Chancellor, Desmond C. Ong, and Nick Haber. 2025. Expressing stigma and inappropriate responses prevents LLMs from safely replacing mental health providers. InProceedings of the ACM Conference on Fairness, Accountability, and Transparency (FAccT). 599–627

work page 2025
[18]

2021.Why don’t teenagers get help for anxiety and depression?https://evidence.nihr.ac.uk/alert/why- dont-teenagers-seek-help-anxiety-depression/

National Institute for Health and Care Research. 2021.Why don’t teenagers get help for anxiety and depression?https://evidence.nihr.ac.uk/alert/why- dont-teenagers-seek-help-anxiety-depression/

work page 2021
[19]

Humza Naveed, Asad Ullah Khan, Shi Qiu, Muhammad Saqib, Saeed Anwar, Muhammad Usman, Naveed Akhtar, Nick Barnes, and Ajmal Mian. 2025. A comprehensive overview of large language models.ACM Transactions on Intelligent Systems and Technology16, 5 (2025), 1–72. doi:10.1145/3744746

work page doi:10.1145/3744746 2025
[20]

Daniel Perlman and Letitia Anne Peplau. 1981. Toward a Social Psychology of Loneliness.Personal Relationships(1981), 31–56

work page 1981
[21]

1996.The Media Equation: How People Treat Computers, Television, and New Media Like Real People and Places

Byron Reeves and Clifford Nass. 1996.The Media Equation: How People Treat Computers, Television, and New Media Like Real People and Places. Cambridge University Press

work page 1996
[22]

Ben Shneiderman. 2020. Human-centered artificial intelligence: Reliable, safe and trustworthy.International Journal of Human–Computer Interaction 36, 6 (2020), 495–504

work page 2020
[23]

Brandtzaeg

Marita Skjuve, Asbjørn Følstad, Knut Inge Fostervold, and Petter B. Brandtzaeg. 2022. A Longitudinal Study of Human–Chatbot Relationships. International Journal of Human-Computer Studies168 (2022), 102903. doi:10.1016/j.ijhcs.2022.102903

work page doi:10.1016/j.ijhcs.2022.102903 2022
[24]

Teachers’ vocal expressions and student engagement in asynchronous video learning,

D. Tozadore. 2026. Technologies for the Integration of Children with Migration Background in Early Education: A Systematic Review.International Journal of Human–Computer Interaction(2026), 1–27. doi:10.1080/10447318.2025.2592778 Manuscript submitted to ACM 8 Liu et al

work page doi:10.1080/10447318.2025.2592778 2026
[25]

2011.Alone Together: Why We Expect More from Technology and Less from Each Other

Sherry Turkle. 2011.Alone Together: Why We Expect More from Technology and Less from Each Other. Basic Books

work page 2011
[26]

Lily Verity, Suzanne Stewart, Stephen Houghton, Pamela Qualter, Leslie Macqueen, and Simon Hunter. 2025. Loneliness from the Perspective of Young People with Autism and/or ADHD: A Thematic Analysis of Adolescents’ Experiences and Understanding.Children12, 10 (2025), 1285

work page 2025
[27]

Lisboa White, E

L. Lisboa White, E. C. Tecwyn, and S. Petty. 2024. Experiences of Loneliness and Connection for Autistic Young People: A Systematic Review.Review Journal of Autism and Developmental Disorders(2024). doi:10.1007/s40489-024-00487-6

work page doi:10.1007/s40489-024-00487-6 2024
[28]

Tianling Xie and Iryna Pentina. 2022. Attachment theory as a framework to understand relationships with social chatbots: A case study of Replika. InProceedings of the 55th Hawaii International Conference on System Sciences (HICSS). doi:10.24251/hicss.2022.258

work page doi:10.24251/hicss.2022.258 2022
[29]

2025.Parents of teenager who took his own life sue OpenAI

Naomi Yousif. 2025.Parents of teenager who took his own life sue OpenAI. https://www.bbc.co.uk/news/articles/cgerwp7rdlvo BBC News

work page 2025
[30]

Yaman Yu, Yiren Liu, Jacky Zhang, Yun Huang, and Yang Wang. 2025. Youth-Centered GAI Risks (YAIR): A Taxonomy of Generative AI Risks from Empirical Data. InTwenty-First Symposium on Usable Privacy and Security (SOUPS 2025). 149–165. Manuscript submitted to ACM

work page 2025

[1] [1]

Sanni Aalto, Raija-Leena Punamäki, Mervi Vänskä, Reeta Kankaanpää, Tiina Turunen, Oskari Lahtinen, Ilse Derluyn, Caroline Spaas, Lucia De Haene, Signe Smith Jervelund, et al. 2025. Patterns of mental health problems and resilience among immigrant and refugee adolescents: a latent profile analysis.European Journal of Psychotraumatology16, 1 (2025), 2479924...

work page doi:10.1080/20008066.2025.2479924 2025

[2] [2]

2024.Youth statistics: Internet and social media

Act For Youth. 2024.Youth statistics: Internet and social media. https://actforyouth.org/adolescence/demographics/internet.cfm

work page 2024

[3] [3]

Alershi, M

S. Alershi, M. Vorvoreanu, and E. Horvitz. 2019.Guidelines for human-AI interaction design. https://www.microsoft.com/en-us/research/blog/ guidelines-for-human-ai-interaction-design/ Microsoft Research

work page 2019

[4] [4]

2024.Mental health statistics: prevalence, services and funding in England

Carl Baker and Esme Kirk-Wade. 2024.Mental health statistics: prevalence, services and funding in England. Technical Report. House of Commons Library. https://commonslibrary.parliament.uk/research-briefings/sn06988/

work page 2024

[5] [5]

2018.If

Taina Bucher. 2018.If... Then: Algorithmic Power and Politics. Oxford University Press

work page 2018

[6] [6]

Clark and Susan E

Herbert H. Clark and Susan E. Brennan. 1991. Grounding in Communication. InPerspectives on Socially Shared Cognition. American Psychological Association, 127–149

work page 1991

[7] [7]

Julian De Freitas, Zeliha Oğuz-Uğuralp, and Ahmet Kaan-Uğuralp. 2025. Emotional manipulation by AI companions.arXiv preprint(2025). arXiv:2508.19258. Available online August 2025; accessed January 2026

work page arXiv 2025

[8] [8]

Rianne Dekker and Godfried Engbersen. 2014. How social media transform migrant networks and facilitate migration.Global Networks14, 4 (2014), 401–418

work page 2014

[9] [9]

L. D. Hawke, G. Strudwick, B. Ritchie, J. Gibson, T. Phi, J. Hou, and L. Gallagher. 2025. Recommendations for the Development of Digital Conversational Agents for Youth with Mental Health Conditions: A Position Paper.Digital Health11 (2025), 20552076251393355. doi:10.1177/20552076251393355

work page doi:10.1177/20552076251393355 2025

[10] [10]

Arthur Bran Herbener and Malene Flensborg Damholdt. 2024. Are lonely youngsters turning to chatbots for companionship? The relationship between chatbot usage and social connectedness in Danish high-school students.International Journal of Human-Computer Studies196 (2024), 103409. doi:10.1016/j.ijhcs.2024.103409

work page doi:10.1016/j.ijhcs.2024.103409 2024

[11] [11]

Lyndsey Hipgrave, Jessie Goldie, Simon Dennis, and Amanda Coleman. 2025. Balancing risks and benefits: clinicians’ perspectives on the use of generative AI chatbots in mental healthcare.Frontiers in Digital Health7 (2025), 1606291

work page 2025

[12] [12]

Hosseini

Z. Hosseini. 2026.Immigrants’ perceptions of AI-driven chatbots in public services. https://helda.helsinki.fi/items/3e48b389-68f0-4093-85e8- bd90fc59e50f HELDA – Helsinki University Digital Repository

work page 2026

[13] [13]

Yu Jin, Jiayi Liu, Pan Li, Baosen Wang, Yangxinyu Yan, Huilin Zhang, Chenhao Ni, Jing Wang, Yi Li, Yajun Bu, and Yinghui Wang. 2024. The Applications of Large Language Models in Mental Health: Scoping Review.Journal of Medical Internet Research27 (2024), e69284. doi:10.2196/69284

work page doi:10.2196/69284 2024

[14] [14]

Florian Onur Kuhlmeier, Luise Bauch, Ulrich Gnewuch, and Stefan Lüttke. 2024. Designing Chatbots to Treat Depression in Youth: Qualitative Study.JMIR Human Factors12 (2024), e66632. doi:10.2196/66632

work page doi:10.2196/66632 2024

[15] [15]

Kim Malfacini. 2025. The impacts of companion AI on human relationships: risks, benefits, and design considerations.AI & SOCIETY(2025), 1–14. doi:10.1007/s00146-025-02318-6

work page doi:10.1007/s00146-025-02318-6 2025

[16] [16]

Filip Meckovsky, Lukas Novak, Zdenek Meier, Peter Tavel, and Klara Malinakova. 2025. Highly sensitive persons feel more emotionally lonely than the general population.Scientific Reports15, 1 (2025), 2707

work page 2025

[17] [17]

Ong, and Nick Haber

Jared Moore, Declan Grabb, William Agnew, Kevin Klyman, Stevie Chancellor, Desmond C. Ong, and Nick Haber. 2025. Expressing stigma and inappropriate responses prevents LLMs from safely replacing mental health providers. InProceedings of the ACM Conference on Fairness, Accountability, and Transparency (FAccT). 599–627

work page 2025

[18] [18]

2021.Why don’t teenagers get help for anxiety and depression?https://evidence.nihr.ac.uk/alert/why- dont-teenagers-seek-help-anxiety-depression/

National Institute for Health and Care Research. 2021.Why don’t teenagers get help for anxiety and depression?https://evidence.nihr.ac.uk/alert/why- dont-teenagers-seek-help-anxiety-depression/

work page 2021

[19] [19]

Humza Naveed, Asad Ullah Khan, Shi Qiu, Muhammad Saqib, Saeed Anwar, Muhammad Usman, Naveed Akhtar, Nick Barnes, and Ajmal Mian. 2025. A comprehensive overview of large language models.ACM Transactions on Intelligent Systems and Technology16, 5 (2025), 1–72. doi:10.1145/3744746

work page doi:10.1145/3744746 2025

[20] [20]

Daniel Perlman and Letitia Anne Peplau. 1981. Toward a Social Psychology of Loneliness.Personal Relationships(1981), 31–56

work page 1981

[21] [21]

1996.The Media Equation: How People Treat Computers, Television, and New Media Like Real People and Places

Byron Reeves and Clifford Nass. 1996.The Media Equation: How People Treat Computers, Television, and New Media Like Real People and Places. Cambridge University Press

work page 1996

[22] [22]

Ben Shneiderman. 2020. Human-centered artificial intelligence: Reliable, safe and trustworthy.International Journal of Human–Computer Interaction 36, 6 (2020), 495–504

work page 2020

[23] [23]

Brandtzaeg

Marita Skjuve, Asbjørn Følstad, Knut Inge Fostervold, and Petter B. Brandtzaeg. 2022. A Longitudinal Study of Human–Chatbot Relationships. International Journal of Human-Computer Studies168 (2022), 102903. doi:10.1016/j.ijhcs.2022.102903

work page doi:10.1016/j.ijhcs.2022.102903 2022

[24] [24]

Teachers’ vocal expressions and student engagement in asynchronous video learning,

D. Tozadore. 2026. Technologies for the Integration of Children with Migration Background in Early Education: A Systematic Review.International Journal of Human–Computer Interaction(2026), 1–27. doi:10.1080/10447318.2025.2592778 Manuscript submitted to ACM 8 Liu et al

work page doi:10.1080/10447318.2025.2592778 2026

[25] [25]

2011.Alone Together: Why We Expect More from Technology and Less from Each Other

Sherry Turkle. 2011.Alone Together: Why We Expect More from Technology and Less from Each Other. Basic Books

work page 2011

[26] [26]

Lily Verity, Suzanne Stewart, Stephen Houghton, Pamela Qualter, Leslie Macqueen, and Simon Hunter. 2025. Loneliness from the Perspective of Young People with Autism and/or ADHD: A Thematic Analysis of Adolescents’ Experiences and Understanding.Children12, 10 (2025), 1285

work page 2025

[27] [27]

Lisboa White, E

L. Lisboa White, E. C. Tecwyn, and S. Petty. 2024. Experiences of Loneliness and Connection for Autistic Young People: A Systematic Review.Review Journal of Autism and Developmental Disorders(2024). doi:10.1007/s40489-024-00487-6

work page doi:10.1007/s40489-024-00487-6 2024

[28] [28]

Tianling Xie and Iryna Pentina. 2022. Attachment theory as a framework to understand relationships with social chatbots: A case study of Replika. InProceedings of the 55th Hawaii International Conference on System Sciences (HICSS). doi:10.24251/hicss.2022.258

work page doi:10.24251/hicss.2022.258 2022

[29] [29]

2025.Parents of teenager who took his own life sue OpenAI

Naomi Yousif. 2025.Parents of teenager who took his own life sue OpenAI. https://www.bbc.co.uk/news/articles/cgerwp7rdlvo BBC News

work page 2025

[30] [30]

Yaman Yu, Yiren Liu, Jacky Zhang, Yun Huang, and Yang Wang. 2025. Youth-Centered GAI Risks (YAIR): A Taxonomy of Generative AI Risks from Empirical Data. InTwenty-First Symposium on Usable Privacy and Security (SOUPS 2025). 149–165. Manuscript submitted to ACM

work page 2025