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REVIEW 4 major objections 4 minor 91 references

The Role of Generative AI in Facilitating Social Interactions: A Scoping Review

T0 review · 4 major / 4 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read Across 30 studies, generative-AI designs for social interaction are overwhelmingly text-based GPT chatbots aimed at vulnerable users, yet rarely co-designed with them or evaluated for whether connection actually forms.

desk verdict Solid, transparent scoping review with a useful tool/medium/agent map, but the 30-paper corpus is partly a convenience sample; the headline proportions should be read as provisional rather than landscape-defining. read the letter →

arxiv 2506.10927 v1 pith:RTOORAZ6 submitted 2025-06-12 cs.HC cs.AI

T. T. J. E. Arets , G. Perugia , M. Houben , W.A. IJsselsteijn This is my paper · ORCID
classification cs.HCcs.AI
keywords generativeAIscopingreviewsocialconnectednessinteractionvulnerablepopulationsco-designlargelanguagemodelsFunctionalTriad
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

This paper maps the design landscape of generative AI (GAI) built to facilitate social interaction, based on a scoping review of 30 studies published since 2020. The review finds a text-first field: 26 of 30 designs generate text, 25 of those 26 run on GPT models, and 19 of 30 cast the technology as a conversational agent that participates in the interaction as an interactant. More than half the designs target vulnerable user groups — children, autistic people, older adults, and people with dementia — across applications in storytelling, socio-emotional skills training, reminiscence, collaborative learning, music making, and general conversation. The review also documents a methodological gap: representative end-users were involved in the conceptualization phase in only 9 of 30 studies, and only 9 of 24 evaluated studies measured social-interaction outcomes at all. The authors' conclusion is prescriptive as well as descriptive: the role a GAI technology should play — tool, medium, or agent — should be chosen from the type of interaction designers want to bring about, not inherited from the technology.

What carries the argument

The analytical engine is the 'Functional Triad', a framework that classifies technology by its role in an interaction: a tool makes an activity easier for the user, a medium is a channel through which experience or interaction between people is facilitated, and an agent is a social actor with which the user communicates. Each of the 30 designs is mapped onto this triad to determine how far the technology participates in the social interaction, and the mapping is read through Social Presence Theory, which holds that intimacy and immediacy are the antecedents of social connectedness. The triad is what produces the review's structural findings — agents are chatbots and embodied conversational agents in one-to-one human-machine interaction, while tools and media are platforms that sit between two or more humans — and it is also the basis for the authors' recommendation that the role be chosen per interaction context rather than fixed by the technology.

What would settle it

Re-run the same review protocol with additional human-computer interaction venues and broader generative-AI search terms such as 'diffusion model', 'Stable Diffusion', and 'text-to-image', and compare the resulting corpus: if a substantial share of the new studies uses image or music generation, non-GPT models, or tool- and medium-role designs, the claims that this is a text-based, GPT-dominated, agent-centred field would fail to generalize beyond the original 30 studies.

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Extended reading notes

Core claim

The central discovery is a landscape description with an evaluative edge: GAI-based social-interaction design is in an early, conversational, text-first phase. Unimodal text generation, overwhelmingly GPT-based, embedded in chatbots or embodied conversational agents, is the de facto standard; image generation appears in only 7 designs and music generation in 1. Under the Functional Triad mapping, agent-role technologies dominate (19 of 30) and are tied to one-to-one human-machine interaction, while tool (9) and medium (2) roles are tied to scaffolding interaction between two or more people. Seventeen of the 30 designs target vulnerable user groups. The methodological findings complete the picture: co-design with representative end-users is rare at the conceptualization stage (9 of 30), most evaluations target usability or system performance rather than social outcomes, and only 5 studies measured experienced social connectedness directly. The paper argues that these trends are not neutral — modality bias, unequal control in therapy settings, and the exclusion of vulnerable users from design all shape whether GAI actually fosters connectedness.

Load-bearing premise

The thirty studies the authors selected accurately represent the whole field of generative-AI tools for social interaction, even though the authors had to hand-add studies from a major conference because their formal database search missed relevant work.

Editorial extensions

If this is right

  • New designers should expect the default template for a social GAI application to be a GPT-powered conversational agent; text is the incumbent modality, and image or music generation remain largely open design space.
  • The role the technology takes should be a design decision: cast GAI as an agent for one-to-one human-machine engagement, and as a tool or medium when the aim is to foster interaction between two or more people.
  • Evaluation practice lags the field's stated goal: with only 5 of 30 studies measuring social connectedness directly, future work has a clear opening for outcome-level, longitudinal measures.
  • Co-design with representative end-users is feasible even with vulnerable groups, so cognitive-strain and ethical concerns should be met with adapted methods rather than exclusion.
  • Multimodal GAI, combining generated text with images for storytelling, reminiscence, or therapy, is the direction the authors see as most promising but currently least explored.

Reading between the lines

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

  • Because 25 of 26 text-based designs run on a single vendor's models, the 'landscape' this review captures may say as much about the market position of one product line as about generative AI generally; re-running the review in a couple of years would separate those two claims.
  • The review's own data support a hypothesis it stops short of stating: four of the five studies that measured social connectedness used tool- or medium-role designs that scaffolded interaction between human pairs (Closer Worlds, SAGA, Cococo, Treasurefinder), whereas agent-role designs were mostly evaluated for usability or performance — suggesting that scaffolding human pairs may be the surer rout
  • The transparency finding (only 15 of 30 papers disclosed their prompts) points to a cheap, practical fix: a minimal reporting standard covering model, modality, prompt disclosure, and user-involvement level would make future landscape maps comparable and reproducible.
  • The 'equality of access' asymmetry the authors document in therapy tools — the professional controls the generative output, the client only receives it — suggests a concrete design probe: give the end-user direct control over the generative output and measure whether authorship shifts the therapeutic relationship.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 4 minor

Summary. This manuscript reports a PRISMA-ScR scoping review of 30 studies published between 2020 and 2024 that design generative-AI (GAI) applications to facilitate social interaction. The authors map the technical characteristics (modality, model, embodiment), the social interaction contexts, and the design and evaluation practices of the included studies, using Fogg's Functional Triad to classify each technology as a tool, medium, or agent. The headline findings are that current GAI designs are predominantly text-based and GPT-powered, often take the form of chatbots or embodied conversational agents, and are frequently aimed at vulnerable user groups, while participatory design and outcome-level evaluation of social connectedness remain uncommon.

Significance. If the mapping is reliable, this review provides a useful shared vocabulary and an initial evidence base for an emerging subfield of HCI research on GAI and social connectedness. The manuscript's strengths are its explicit PRISMA-ScR protocol, exact search strings for four databases, PRISMA flowchart, detailed extraction tables, and transparent reporting of example prompts. The central limitation is that the corpus may not represent the broader literature because the database search under-retrieved CHI papers and was supplemented by a manually browsed CHI 2024 set selected with a different retrieval logic; as a result, the descriptive proportions are sensitive to the search strategy. The review is valuable as a provisional landscape map, but the robustness of the headline findings needs to be demonstrated.

major comments (4)
  1. [§2.2, PRISMA flow, Table 5] Section 2.2 states that 'many CHI papers fitting the scope of this literature review did not show up in our database search' and therefore the authors browsed CHI 2024 using only the GAI-related terms, not the full conjunction of GAI, design, and social-interaction terms used for the four databases. Since 8 of the 30 included studies (27%) come from this different retrieval path, the headline proportions in RQ1–RQ3 may depend on the retrieval strategy; each study contributes about 3.3% of the corpus, so a small number of missed papers can flip qualitative claims such as 'participatory design remains uncommon' or 'GAI is predominantly text-based.' Please report the database-only and CHI-added strata separately for every headline dimension (modality, model, role, end-user group, conceptualization/evaluation practice), or provide a sensitivity analysis that shows the conclusions are robust to the composition of the CHI supplement.
  2. [§3.3.2 and Table 3] The assignment of studies to the Functional Triad is not consistently supported by Table 3. Jin et al. [33] is treated as a 'tool' in Section 3.3.2 but Table 3 codes its interaction type as 'Human-Machine' individual; Fang et al. [17] is first classified as a 'medium' but is later included among tools that 'acted as a source for the social interaction between two people.' Because the tool/medium/agent mapping is a central organizing contribution, please recheck all 30 classifications, make the decision criteria explicit, and present the mapping in a single table that is consistent with the interaction-type codes in Table 3.
  3. [§3.4.1] The counts in Section 3.4.1 are internally inconsistent. The text says 24 studies reported an evaluation phase but enumerates 23 references (Tucci et al. [80] is missing from the list, even though the next paragraph discusses it as an evaluation and includes it in the '20 out of 30' representative end-user list). In the same section, the sentence 'In 4 cases, the authors reported that they had consulted only non-representative end-users during the conceptualization phase [2, 6, 16, 17]' contradicts the earlier statement that only 3 studies consulted non-representative end-users exclusively during conceptualization ([17, 41, 89]). Please reconcile the counts and reference lists with Figure 2.
  4. [§3.3.1] In the 'Socio-emotional skills training' paragraph, the text states that 'Eight GAI-based designs were presented in socio-emotional skills training settings' but lists only seven references ([6, 22, 31, 40, 41, 69, 78]) and omits Zhou et al. [92], which is then identified as one of the three studies that also addressed storytelling. Please correct the count and the reference list so that the activity-category totals in Section 3.3.1 are reliable.
minor comments (4)
  1. [§3.3.1] The word 'demenetia' in the discussion of Tucci et al. [80] should be 'dementia.'
  2. [§3.3.2] The tool list '[6, 10, 11, 22, 32, 33, 43, 75, 92]' cites reference [43], which is not among the 30 included studies; it should be [41]. Additionally, the sentence 'it acted as a source for the social interaction between two people [6, 10, 17, 22, 32, 70, 75]' includes [17], which was already classified as a medium; please align the reference lists.
  3. [§3.4.1] The phrase 'In 4 cases... during the conceptualization phase' appears to be a typo for 'evaluation phase'; as written it conflicts with the conceptualization-phase list earlier in the same subsection.
  4. [§2.3.1] The PRISMA flowchart would be easier to audit if the numbers of records excluded at each stage were broken down by the eligibility criteria in the same order as they are listed in Section 2.3.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the review's descriptive findings are derived from external frameworks and extracted data, not from its own assumptions.

full rationale

This paper is a descriptive scoping review: its claims are counts and classifications of 30 studies obtained through a PRISMA-ScR search, data extraction, and coding against external frameworks (Fogg's Functional Triad, Social Presence Theory, CASA, ANT). No parameter is fitted and later presented as a prediction; no research question is answered by restating an inclusion criterion or coding rule. The mapping of chatbot/embodied-conversational-agent embodiments to the 'agent' role is an explicit, transparent coding decision rather than a hidden equivalence, and the headline proportions (17/30 vulnerable users, 25/26 GPT-based text systems, 9/30 representative-user conceptualization) come from the extracted studies, not from the frameworks. The few self-citations by the author team (e.g., [30], [76], [81], [82]) supply background definitions or contextual support and are not load-bearing for the central descriptive findings. The acknowledged supplementing of the database search with CHI 2024 raises a corpus-representativeness concern, but that is a sampling/validity limitation, not a circular derivation. Consequently, no circular step can be exhibited.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The central claims rest on the assumption that the 30-study corpus is representative, that the Functional Triad is an appropriate analytic lens, and that social-presence theories justify the agent-role conclusions. No numerical parameters are fitted to data.

assumptions (3)
  • domain assumption The 30 included studies are representative of the field of GAI-facilitated social interaction design.
    The whole mapping depends on the search capturing the relevant literature; Section 2.2 admits the database search missed CHI 2024 papers, and the corpus is only 30 studies.
  • domain assumption The Functional Triad (Fogg, 2003) is a valid and exhaustive taxonomy for classifying the role of GAI in social interactions.
    Used throughout Section 3.3.2 to map each technology to tool, medium, or agent; if this taxonomy is not appropriate, the classification results are not meaningful.
  • domain assumption Social Presence Theory and the Computers are Social Actors (CASA) theory justify the claim that socially-present agents effectively foster connectedness.
    Invoked in Discussion 4.3 to support the conclusion that chatbots and embodied agents benefit from sociality; these are external theoretical commitments rather than results derived within the review.

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Cite this review

Pith. "Pith review of The Role of Generative AI in Facilitating Social Interactions: A Scoping Review." pith.science (2026). https://pith.science/paper/RTOORAZ6

@misc{pith2026250610927,
  author       = {Pith},
  title        = {Pith review of: The Role of Generative AI in Facilitating Social Interactions: A Scoping Review},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/RTOORAZ6}},
  note         = {Machine review of arXiv:2506.10927}
}
read the original abstract

Reduced social connectedness increasingly poses a threat to mental health, life expectancy, and general well-being. Generative AI (GAI) technologies, such as large language models (LLMs) and image generation tools, are increasingly integrated into applications aimed at enhancing human social experiences. Despite their growing presence, little is known about how these technologies influence social interactions. This scoping review investigates how GAI-based applications are currently designed to facilitate social interaction, what forms of social engagement they target, and which design and evaluation methodologies designers use to create and evaluate them. Through an analysis of 30 studies published since 2020, we identify key trends in application domains including storytelling, socio-emotional skills training, reminiscence, collaborative learning, music making, and general conversation. We highlight the role of participatory and co-design approaches in fostering both effective technology use and social engagement, while also examining socio-ethical concerns such as cultural bias and accessibility. This review underscores the potential of GAI to support dynamic and personalized interactions, but calls for greater attention to equitable design practices and inclusive evaluation strategies.

Figures

Figures reproduced from arXiv: 2506.10927 by the authors.

Figure 1
Figure 1. PRISMA flowchart. Studies were excluded if they did not meet the inclusion criteria. Most often, [PITH_FULL_IMAGE:figures/full_fig_p006_1.png] view at source ↗
Figure 2
Figure 2. An overview of intended end-user inclusion in the conceptualization phase (left graph) and the [PITH_FULL_IMAGE:figures/full_fig_p020_2.png] view at source ↗
Figure 3
Figure 3. An overview of the focus of the evaluation phase of the 24 studies that reported one. The left pie chart [PITH_FULL_IMAGE:figures/full_fig_p023_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: The pipeline for screening studies based on the nature of the social interaction that they stimulate. [PITH_FULL_IMAGE:figures/full_fig_p034_4.png]

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Reference graph

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Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.