Socially Interactive Agents for Preserving and Transferring Tacit Knowledge in Organizations

C. Benjamin Nakhosteen; Christian Keller; Martin Benderoth; Patrick Gebhard; Stefan Schaffer; Tanja Schneeberger

REVIEW 4 major objections 4 minor 4 references

Socially Interactive Agents for Preserving and Transferring Tacit Knowledge in Organizations

T0 review · 4 major / 4 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read A position paper argues that Socially Interactive Agents—AI avatars that converse through natural language and social cues—can act as scalable knowledge-transfer facilitators, using LLM dialogue, retrieval-augmented generation, and chain-of

desk verdict A clearly written, honest position paper proposing SIAs as tacit-knowledge transfer facilitators—but the core mechanism is in tension with its own definition of tacit knowledge, and the CoT citation is conceptually off. read the letter →

arxiv 2508.19942 v1 pith:VUW7ZOF5 submitted 2025-08-27 cs.HC

Martin Benderoth , Patrick Gebhard , Christian Keller , C. Benjamin Nakhosteen , Stefan Schaffer , Tanja Schneeberger This is my paper · ORCID

classification cs.HC

keywords tacitknowledgetransfersociallyinteractiveagentsLLMdialogueretrieval-augmentedgenerationchain-of-thoughtpromptingonboardingretention

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

The paper argues that the hardest part of organizational knowledge retention—capturing the unwritten, experience-based know-how of employees—can be tackled by Socially Interactive Agents acting as AI-driven knowledge-transfer facilitators. It claims these agents can replace or scale the human facilitator role by combining a socially embodied presence with large language model dialogue, retrieval-augmented generation, and chain-of-thought prompting, so that employees externalize insights they would not normally put into words. If this works, organizations could systematically interview departing experts, preserve the heuristics behind their decisions, and shorten onboarding for new hires. The paper frames this as a new direction because existing methods such as documentation, mentoring, and human-facilitated interviews are effective but do not scale, while current AI systems mostly capture explicit knowledge rather than deep experiential knowledge. It does not report an empirical test; it lays out the requirement that employees trust the agent enough to disclose, and sketches the technical architecture and application scenarios that would realize the idea.

What carries the argument

The central object is the Socially Interactive Agent as a knowledge-transfer facilitator: a virtually or physically embodied agent that communicates through natural multimodal behavior—verbal, paraverbal, and nonverbal—just as a human conversation partner would. The mechanism that carries the argument is the combination of three technologies inside that social frame: large language models generate contextually relevant dialogue; retrieval-augmented generation connects the conversation in real time to organizational databases, guidelines, and documented experiences; and chain-of-thought prompting gives the agent an interview strategy, letting it ask probing questions in a structured order tha

What would settle it

A controlled comparison in which senior employees approaching retirement are interviewed by the proposed SIA and by a human facilitator, with recorded answers rated by successors for undocumented, actionable heuristics. If the SIA elicits no more or worse usable know-how than a simple written questionnaire, or if employees refuse to discuss the reasoning behind real past decisions, the central claim is contradicted.

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Extended reading notes

Core claim

The paper's central claim is that established findings about Socially Interactive Agents—people treat them as social partners, build rapport with them, and sometimes disclose more sensitive information to them than to humans—can be redirected to knowledge management. It proposes a system in which an SIA conducts long-term, empathic dialogues with employees, using large language models to generate natural conversation, retrieval-augmented generation to pull in relevant company documents and prior recorded experiences, and chain-of-thought prompting to steer the conversation toward the reflective 'how' of expert decision-making. The intended result is a recorded, context-linked account of heur

Load-bearing premise

The approach works only if employees actually tell the agent their deep, experience-based knowledge—and if what they say can be turned into guidance a successor can use; the paper asserts that trust-building design will achieve this, but does not test it.

Editorial extensions

If this is right

Knowledge retention could scale beyond one-on-one human-facilitated interviews: every retiring expert, not just a select few, could receive a structured exit interview that captures decision heuristics.
Captured knowledge would be context-linked rather than stored as isolated text: RAG could tie an employee's statements to the actual process documents and prior examples, making the knowledge usable by successors.
Onboarding could become personalized and always available, with an agent that guides new hires through processes, personnel, and training, potentially reducing time to productivity.
Chain-of-thought-guided interviewing could shift knowledge capture from facts and figures to the 'how'—judgments, assumptions, and unwritten rules—addressing a known gap in documentation and training.
Trust, transparency, and personalization would become acceptance criteria for any real deployment, since the paper's own analysis concludes that without them the agent will not be used.

Reading between the lines

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

A natural test of the proposal is to compare the quantity and quality of heuristics elicited by a social agent versus a human facilitator under blind review; if disclosure is comparable, the scalability argument holds, and if it is not, the social presence claim is weakened.
The approach could be extended beyond retrospective exit interviews to in-situ capture: if the agent is present during daily work, it might record tacit knowledge as it is exercised rather than relying on later recall, reducing the distortion that memory introduces.
A testable design question the paper leaves open is how much embodiment is needed: measuring willingness to disclose with a voice-only interface versus an embodied agent would reveal whether the social machine or the conversational strategy is the true driver of elicitation.
Because the paper treats trust as the load-bearing variable, an honorable falsification path is to measure whether employees with privacy concerns withhold the reasoning behind actual past decisions even when they state a preference for the agent over a human interviewer.

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Signed reviews

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

This is a conceptual proposal, not a study, and it reads like one. What is actually new is the application of socially interactive agents (not just chatbots) to organizational tacit knowledge transfer, framed around the facilitator role and grounded in the SIA literature on rapport and reduced disclosure barriers. The paper does that well: it spells out why traditional methods are resource-heavy, identifies trust as the decisive variable, and honestly says no system exists yet. The survey of relevant prior SIA work is legitimate, including the self-citations, which are all on-topic. Credit where due: this is a usable roadmap for a plausible research direction.

The soft spots are real, and one is load-bearing. Section 2.1 defines tacit knowledge as 'subjective, experience-based, and difficult to articulate' and cites Polanyi, whose whole point is that much knowledge resists articulation. The solution approach, however, is entirely language-based: empathic dialogue, LLM generation, RAG, CoT. The paper never squares that circle. At best, dialogue can elicit explicit-izable fragments—stories, rules, judgments—but the embodied, intuitive component may not survive the transfer. That does not kill the idea, but it needs to be acknowledged and argued, not skipped. The paper currently treats externalization through conversation as unproblematic.

Second, the CoT citation is wrong. The authors cite Wei et al. (2022) to support the claim that CoT lets the agent 'ask the right questions in the correct order,' but chain-of-thought prompting is a method for eliciting an LLM's own reasoning over a prompt—not a dialogue-management strategy. That is a technical misreading, and it matters because it is the sole justification for how the agent steers the conversation. Easy to fix, but it should be fixed.

There is no empirical data, no implementation, no worked example. For a position paper that is acceptable, but the authors occasionally drift into 'the potential is enormous' territory, and the application scenarios are speculative. The paper also concedes the ethical and privacy risks, which is good, but these are listed rather than analyzed.

The central argument does not collapse; it just is not established. The paper is a proposal with a promising premise and correctable flaws. Who gets value: researchers working on SIAs, knowledge management, or AI-mediated organizational learning—especially anyone designing studies around knowledge preservation. I would not cite it as evidence, but I might cite it as a formulation of a research gap.

Engage with it. Send it to peer review; the concept is relevant and the issues are discussable rather than fatal. The authors should be pushed on the tacit-externalization tension and the CoT citation before publication.

Referee Report

4 major / 4 minor

Summary. This paper proposes that Socially Interactive Agents (SIAs) can serve as scalable facilitators for preserving and transferring tacit knowledge in organizations. It reviews tacit knowledge management, summarizes SIA research on rapport, trust, and self-disclosure, and outlines a system combining LLMs, RAG, and chain-of-thought prompting to elicit employees' experiential knowledge through empathic dialogue. Application scenarios include onboarding, knowledge retention for retiring experts, and career-biographical development. The paper also discusses ethical and operational risks. It is a vision/position paper: no implementation, empirical data, or formal proof is presented, and the conclusion explicitly acknowledges that the development is at an early stage.

Significance. The paper addresses a practically important problem—the loss of expert knowledge in aging organizations—and proposes a genuinely new role for SIAs as knowledge transfer facilitators. Its strengths are a coherent synthesis of prior SIA findings on trust and disclosure, a clear mapping of organizational pain points, and an explicit treatment of privacy, bias, and transparency concerns. If the proposed mechanism worked, it could complement human facilitators and reduce costs. However, the central value claim is not demonstrated. The step from conversational output to usable tacit knowledge is assumed rather than argued, and the single cited technical mechanism (chain-of-thought prompting) is misapplied. The contribution is therefore a plausible research agenda rather than a validated solution.

major comments (4)

[§2.1 and §3] The paper defines tacit knowledge as 'difficult to formalize' and 'deeply embedded in the actions, experiences, ideals, values, and feelings of employees,' yet the entire solution is based on natural-language dialogue. No argument is given that conversational elicitation can preserve the utility of the tacit dimension rather than only its explicit-izable fragments. This is load-bearing because the paper's promise is transfer of tacit knowledge, not documentation of recollections. A worked example showing what artifact is produced and how a newcomer would use it to act differently would at least make the claim concrete.
[§3] The statement that 'SIAs could use CoT prompting to ask the right questions in the correct order, just as an experienced interviewer would (Wei et al., 2022)' is a technical misreading. Chain-of-thought prompting elicits intermediate reasoning from a large language model for a given prompt; it is not a dialog-management strategy for selecting questions in a conversation. Since this is the paper's only concrete mechanism for steering knowledge elicitation, the technical foundation is not sound. The authors should either correct this claim and cite appropriate dialogue-policy or active-learning methods, or specify an architecture that makes question selection explicit.
[§3–4] The trust-and-disclosure evidence comes from health screening and job-interview training, where the incentives, stakes, and power relations differ from an employee interacting with an organization-provided AI. The paper asserts that employees are reluctant to share tacit knowledge and that trust-building will overcome this, but it offers no study, pilot, or even a scenario transcript to support transferability to this new context. Because the system's entire value depends on employees disclosing substantive experiential knowledge, this is a load-bearing assumption that needs at least a formative user study or a clearly testable design hypothesis.
[§4] The application scenarios claim that the approach 'could significantly shorten the period of onboarding' and provide a 'digital legacy' for retiring experts, but no comparison with human-facilitated knowledge transfer is provided. The scalability argument assumes that SIA-mediated elicitation is at least comparable in effectiveness to human facilitation; without a baseline, the central value proposition is unsubstantiated. A task analysis, cost model, or small-scale evaluation would make the proposal more than an analogy.

minor comments (4)

[Figures] Figure 1 and Figure 2 captions appear, but the figures themselves are not present in the manuscript; the Figure 1 caption also appears truncated ('Vision of an AI preservation'). Please provide the figures and complete captions.
[References] The reference list is not consistently formatted: for example, the Beyrodt et al. entry is not a standard citation, some entries lack page ranges, and one URL uses an unsecured 'http'. Please normalize to the journal's reference style.
[§6] The claim that 'there are no known systems' integrating SIA-based and immersive technologies into knowledge transfer is overly strong without a systematic literature search. A softer wording such as 'to our knowledge, no published systems...' would be more appropriate, together with a brief search scope.
[§2.1] The example of tacit knowledge in 'manual labor' is unnecessarily narrow; the subsequent examples of decision-making and creativity already make the point. Replacing 'manual labor' with 'sensorimotor skills' would better align with the knowledge-society framing.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper is a proposal/position piece with no derived predictions; its self-citations are empirical and non-load-bearing.

full rationale

The paper does not contain a mathematical derivation or fitted model; its central claim is a design proposal that SIAs can act as knowledge-transfer facilitators. The argument relies on cited empirical findings that SIAs can build rapport, increase trust, and lower self-disclosure barriers (e.g., Bickmore et al. 2020; Lucas et al. 2014, 2017; Gratch et al. 2007). Several of these citations are to the authors' own prior work (Schneeberger et al. 2019a, 2019b, 2023; Gebhard et al. 2018, 2019), but those works are independent empirical studies of affective response and agent interaction, not restatements of the paper's target conclusion that tacit knowledge can be preserved by SIAs. No parameter is fitted and then renamed a prediction, and no uniqueness theorem or ansatz is imported from the authors' prior work to force the proposed design. The conceptual tension between calling tacit knowledge 'difficult to articulate' (Sec. 2.1) and proposing language-based externalization (Sec. 3) is a substantive evidentiary gap, but it is a correctness/feasibility concern, not a circularity: the paper does not define tacit knowledge in terms of what SIA dialogue can capture, nor does it infer the efficacy of the approach from the definition. The Chain-of-Thought reference (Wei et al., 2022) is arguably a misapplication, but it is external support rather than a self-citation chain, and it does not make the proposal circular. Accordingly, no circular step meets the quoting-and-reduction standard, and the appropriate score is 0.

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

The paper contributes a conceptual architecture grounded in prior SIA literature; it introduces no measured quantities, but depends on several untested psychological and technical premises.

assumptions (3)

domain assumption Tacit knowledge is largely implicit, experience-based, and can be externalized through guided dialogue.
Section 2.1 defines tacit knowledge and assumes it can be tapped via dialogue; this is a central premise not empirically validated in the paper.
domain assumption SIAs reliably elicit trust, rapport, and self-disclosure from users, sufficient for knowledge sharing.
Section 2.2 cites studies on SIA-induced disclosure and trust, but no study on tacit knowledge sharing; the transfer to organizational knowledge is assumed.
domain assumption LLM-based dialogue with RAG and CoT prompting can produce context-relevant, reflective interviews that uncover implicit assumptions.
Section 3 describes this as the technical basis; no prototype or evaluation is provided.

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Cite this review

Pith. "Pith review of Socially Interactive Agents for Preserving and Transferring Tacit Knowledge in Organizations." pith.science (2026). https://pith.science/paper/VUW7ZOF5

@misc{pith2026250819942,
  author       = {Pith},
  title        = {Pith review of: Socially Interactive Agents for Preserving and Transferring Tacit Knowledge in Organizations},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/VUW7ZOF5}},
  note         = {Machine review of arXiv:2508.19942}
}

read the original abstract

This paper introduces a novel approach to tackle the challenges of preserving and transferring tacit knowledge--deep, experience-based insights that are hard to articulate but vital for decision-making, innovation, and problem-solving. Traditional methods rely heavily on human facilitators, which, while effective, are resource-intensive and lack scalability. A promising alternative is the use of Socially Interactive Agents (SIAs) as AI-driven knowledge transfer facilitators. These agents interact autonomously and socially intelligently with users through multimodal behaviors (verbal, paraverbal, nonverbal), simulating expert roles in various organizational contexts. SIAs engage employees in empathic, natural-language dialogues, helping them externalize insights that might otherwise remain unspoken. Their success hinges on building trust, as employees are often hesitant to share tacit knowledge without assurance of confidentiality and appreciation. Key technologies include Large Language Models (LLMs) for generating context-relevant dialogue, Retrieval-Augmented Generation (RAG) to integrate organizational knowledge, and Chain-of-Thought (CoT) prompting to guide structured reflection. These enable SIAs to actively elicit knowledge, uncover implicit assumptions, and connect insights to broader organizational contexts. Potential applications span onboarding, where SIAs support personalized guidance and introductions, and knowledge retention, where they conduct structured interviews with retiring experts to capture heuristics behind decisions. Success depends on addressing ethical and operational challenges such as data privacy, algorithmic bias, and resistance to AI. Transparency, robust validation, and a culture of trust are essential to mitigate these risks.

Figures

Figures reproduced from arXiv: 2508.19942 by the authors.

**Figure 1.** Vision of an AI preservation In times of demographic change, a shortage of professionals and the increasing importance of knowledge as a key value creation factor, it is essential for companies to secure and pass on the valuable experiential knowledge of their employees (Tachkov & Mertens, 2016). There is a broad consensus that organizations must take measures to promote the transfer of their employees’ knowledge. … view at source ↗

**Figure 2.** Approach for introducing SIAs as knowledge transfer fa [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗

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Reference graph

Works this paper leans on

4 extracted references · 2 canonical work pages

[1]

L., Nunnari, F., Chehayeb, L., Prajod, P., Schneeberger, T., André, E., Malosio, M., Gebhard, P

Beyrodt, S., Nicora, M. L., Nunnari, F., Chehayeb, L., Prajod, P., Schneeberger, T., André, E., Malosio, M., Gebhard, P. & Tsovaltzi, D. (2023). Socially interactive agents as cobot avatars: Developing a model to support flow experiences and well-being in the workplace. In Proceedings of the 23rd ACM International Conference on Intelligent Virtual Agents ...

arXiv 2023
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Let me ex- plain!

https://doi.org/10.3389/frobt.2017.00051 Lugrin, B. (2021). Introduction to socially interactive agents. In B. Lugrin, C. Pelachaud & D. Traum (Hrsg.), The handbook on socially interactive agents: 20 years of research on embodied conversational agents, intelligent virtual agents, and social robotics. Vol- ume 1: Methods, behavior, cognition (pp. 77–104). ...

work page arXiv 2017
[100]

M., Rizzo, A., Gratch, J., Scherer, S., Stratou, G., Boberg, J., & Morency, L.- P

https://doi.org/10.1016/j.chb.2014.04.043 Lucas, G. M., Rizzo, A., Gratch, J., Scherer, S., Stratou, G., Boberg, J., & Morency, L.- P. (2017). Reporting mental health symptoms: Breaking down barriers to care with vir- tual human interviewers. Frontiers in Robotics and AI , 4, Article

work page doi:10.1016/j.chb.2014.04.043 2014
[2025]

CC BY-NC-ND 4.0 -4- Cassell, J., Sullivan, J., Churchill, E., & Prevost, S. (2000). Embodied conversational agents. MIT Press. Fiske, S. T., Cuddy, A. J., & Glick, P. (2007). Universal dimensions of social cognition: Warmth and competence. Trends in cognitive sciences, 11(2), 77–83. Gao, Y., Xiong, Y, Gao, X., Jia, K., Pan, J., Bi, Y., Dai, Y., Sun, J., W...

arXiv 2000

Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.

[1] [1]

L., Nunnari, F., Chehayeb, L., Prajod, P., Schneeberger, T., André, E., Malosio, M., Gebhard, P

Beyrodt, S., Nicora, M. L., Nunnari, F., Chehayeb, L., Prajod, P., Schneeberger, T., André, E., Malosio, M., Gebhard, P. & Tsovaltzi, D. (2023). Socially interactive agents as cobot avatars: Developing a model to support flow experiences and well-being in the workplace. In Proceedings of the 23rd ACM International Conference on Intelligent Virtual Agents ...

arXiv 2023

[2] [51]

Let me ex- plain!

https://doi.org/10.3389/frobt.2017.00051 Lugrin, B. (2021). Introduction to socially interactive agents. In B. Lugrin, C. Pelachaud & D. Traum (Hrsg.), The handbook on socially interactive agents: 20 years of research on embodied conversational agents, intelligent virtual agents, and social robotics. Vol- ume 1: Methods, behavior, cognition (pp. 77–104). ...

work page arXiv 2017

[3] [100]

M., Rizzo, A., Gratch, J., Scherer, S., Stratou, G., Boberg, J., & Morency, L.- P

https://doi.org/10.1016/j.chb.2014.04.043 Lucas, G. M., Rizzo, A., Gratch, J., Scherer, S., Stratou, G., Boberg, J., & Morency, L.- P. (2017). Reporting mental health symptoms: Breaking down barriers to care with vir- tual human interviewers. Frontiers in Robotics and AI , 4, Article

work page doi:10.1016/j.chb.2014.04.043 2014

[4] [2025]

CC BY-NC-ND 4.0 -4- Cassell, J., Sullivan, J., Churchill, E., & Prevost, S. (2000). Embodied conversational agents. MIT Press. Fiske, S. T., Cuddy, A. J., & Glick, P. (2007). Universal dimensions of social cognition: Warmth and competence. Trends in cognitive sciences, 11(2), 77–83. Gao, Y., Xiong, Y, Gao, X., Jia, K., Pan, J., Bi, Y., Dai, Y., Sun, J., W...

arXiv 2000