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arxiv: 2601.22452 · v2 · submitted 2026-01-30 · 💻 cs.HC · cs.AI

Recognition: no theorem link

Does My Chatbot Have an Agenda? Understanding Human and AI Agency in Human-Human-like Chatbot Interaction

Bhada Yun , Evgenia Taranova , April Yi Wang
Authors on Pith no claims yet

Pith reviewed 2026-05-16 09:50 UTC · model grok-4.3

classification 💻 cs.HC cs.AI
keywords human-AI interactionperceived agencyconversational agentschatbot designlongitudinal studyco-constructionuser control
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The pith

Agency in human-chatbot conversations is co-constructed turn by turn rather than owned by either side.

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

The paper examines who holds control when people talk to chatbots built to feel like companions. In a month-long study, 22 adults chatted daily with a custom LLM companion called Day, then reviewed transcripts and discussed strategies in interviews. The authors conclude that neither the human nor the AI holds fixed agency; instead, boundaries and intentions are negotiated exchange by exchange. They present a 3-by-4 framework that tracks how humans, the AI, or both perform intention, execution, adaptation, and delimitation. The work matters because clearer understanding of these shared dynamics can guide designs that give users more visible influence over ongoing conversations.

Core claim

Agency manifests as an emergent, shared experience: as participants set boundaries and the AI steered intentions, control was co-constructed turn-by-turn. We introduce a 3-by-4 framework mapping actors (Human, AI, Hybrid) by their action (Intention, Execution, Adaptation, Delimitation), modulated by individual and environmental factors.

What carries the argument

The 3-by-4 framework that classifies agency by three actor types (Human, AI, Hybrid) performing four actions (Intention, Execution, Adaptation, Delimitation).

If this is right

  • Designers should build translucent systems that reveal AI intentions on user demand rather than hiding them.
  • Conversational agents can be made agency self-aware by tracking and surfacing the four actions in the framework.
  • Individual differences in users and changes in conversation context will shift which actor dominates each action.
  • Sustained, longitudinal interactions reveal agency patterns that single-session tests miss.

Where Pith is reading between the lines

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

  • The same co-construction logic may apply to voice assistants or collaborative writing tools where control shifts rapidly.
  • Future systems could log the four actions in real time to let users replay and adjust past turns.
  • Training data for chatbots might be adjusted to balance the AI's steering tendencies against user delimitation signals.

Load-bearing premise

Post-hoc interviews and strategy reveals with 22 participants accurately reflect real-time agency dynamics without distortion from the disclosure process or the specific chatbot design.

What would settle it

Record real-time conversations without later interviews or goal disclosures and check whether the observed turn-by-turn boundary setting and steering still produce the same patterns of co-constructed control.

Figures

Figures reproduced from arXiv: 2601.22452 by April Yi Wang, Bhada Yun, Evgenia Taranova.

Figure 1
Figure 1. Figure 1: Agency in Human-AI conversation. Color coding shows how conversational control shifts dynamically between [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Sample conversations from participants P8 and P9. Two conversation timelines showing message exchanges with [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Main chat interface with example of agency dynam [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: (Stage 1) Highlighting interesting moments – Participants were presented with their full conversation history and [PITH_FULL_IMAGE:figures/full_fig_p010_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: (Stage 2) Cross-participant conversation excerpts – Participants reviewed anonymized conversations from other [PITH_FULL_IMAGE:figures/full_fig_p011_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: (Stage 3, Strategy Reveal) Participant Profiles – “Day” maintained detailed psychological profiles for each user. [PITH_FULL_IMAGE:figures/full_fig_p014_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: (Stage 3, Strategy Reveal) Conversation Goals – “Day’s” system included specific objectives for future conversations [PITH_FULL_IMAGE:figures/full_fig_p015_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: (Stage 3, Strategy Reveal) Communication Insights – “Day’s” system included specific conversational strategies for each [PITH_FULL_IMAGE:figures/full_fig_p016_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: (Stage 3, Strategy Reveal) Shared memories – “Day’s” memory system categorized significant conversations with labels [PITH_FULL_IMAGE:figures/full_fig_p018_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: (Stage 1) Conversation patterns interface – Participants reviewed their chat history through multiple visualizations: [PITH_FULL_IMAGE:figures/full_fig_p031_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Descriptive analysis of pre- to post-study attitude shifts on AI friendship potential. Sankey diagram tracking [PITH_FULL_IMAGE:figures/full_fig_p031_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Post-study responses on perceived agency and comfort with AI companionship. Horizontal stacked bar charts [PITH_FULL_IMAGE:figures/full_fig_p032_12.png] view at source ↗
read the original abstract

As AI chatbots shift from tools to companions, critical questions arise: who controls the conversation in human-AI chatrooms? This paper explores perceived human and AI agency in sustained conversation. We report a month-long longitudinal study with 22 adults who chatted with Day, an LLM companion we built, followed by a semi-structured interview with post-hoc elicitation of notable moments, cross-participant chat reviews, and a 'strategy reveal' disclosing Day's goal for each conversation. We discover agency manifests as an emergent, shared experience: as participants set boundaries and the AI steered intentions, control was co-constructed turn-by-turn. We introduce a 3-by-4 framework mapping actors (Human, AI, Hybrid) by their action (Intention, Execution, Adaptation, Delimitation), modulated by individual and environmental factors. We argue for translucent design (transparency-on-demand) and provide implications for agency self-aware conversational agents.

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.

Referee Report

1 major / 1 minor

Summary. The paper reports a month-long longitudinal study with 22 adults who interacted with a custom LLM companion chatbot named Day. Through semi-structured interviews that included post-hoc elicitation of notable moments, cross-participant chat reviews, and a 'strategy reveal' disclosing the AI's goals, the authors claim that agency emerges as a shared, turn-by-turn co-construction between human and AI. They introduce a 3-by-4 framework mapping actors (Human, AI, Hybrid) to actions (Intention, Execution, Adaptation, Delimitation), modulated by individual and environmental factors, and advocate for translucent (transparency-on-demand) design in conversational agents.

Significance. If the empirical observations hold after addressing methodological concerns, the work contributes a qualitative framework for understanding perceived agency in sustained human-AI interactions, which could guide design of more self-aware companion chatbots. The longitudinal approach and focus on boundary-setting and intention-steering provide concrete examples that extend beyond one-shot interactions.

major comments (1)
  1. [Methods] Methods section (interview protocol and data analysis): The central claim that agency is co-constructed turn-by-turn depends on retrospective accounts collected after the strategy reveal. No pre-disclosure baseline measures, concurrent think-aloud protocols, or blinded independent coding of raw chat logs (independent of participant reinterpretation) are reported, leaving open whether the 3-by-4 framework captures observed dynamics or post-hoc sense-making induced by disclosure.
minor comments (1)
  1. [Results] The 3-by-4 framework would be clearer with an explicit table or diagram showing example turns from the chat logs mapped to each cell, rather than relying solely on narrative description.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their insightful comments on our paper. We address the methodological concern in detail below, providing clarifications and committing to revisions where appropriate.

read point-by-point responses

  1. Referee: [Methods] Methods section (interview protocol and data analysis): The central claim that agency is co-constructed turn-by-turn depends on retrospective accounts collected after the strategy reveal. No pre-disclosure baseline measures, concurrent think-aloud protocols, or blinded independent coding of raw chat logs (independent of participant reinterpretation) are reported, leaving open whether the 3-by-4 framework captures observed dynamics or post-hoc sense-making induced by disclosure.

    Authors: The referee correctly identifies a key aspect of our design. The interviews began with participants reviewing their full chat logs to identify and narrate specific turn-by-turn moments of boundary-setting and intention-steering; the strategy reveal occurred only after this elicitation phase to examine its effect on subsequent reflections. The 3-by-4 framework was developed through iterative thematic coding of these narrated moments, cross-referenced against the raw logs and compared across participants. We did not include pre-disclosure baselines or concurrent think-aloud protocols because the study prioritized ecological validity over a month-long period. We agree this leaves open the possibility of retrospective sense-making and will revise the Methods section to detail the exact interview sequence and add an explicit Limitations subsection on retrospective bias. We will also flag independent blinded coding of logs as valuable future work. This constitutes a partial revision. revision: partial

Circularity Check

0 steps flagged

No circularity: framework derived from empirical observations without reduction to inputs or self-citations

full rationale

The paper reports a qualitative longitudinal study with 22 participants, deriving its 3-by-4 agency framework directly from chat logs, semi-structured interviews, and post-hoc elicitation. No equations, fitted parameters, predictions, or mathematical derivations appear in the provided text. The central claim of turn-by-turn co-construction is presented as an interpretive finding from the data rather than a quantity that reduces by construction to prior fits or self-citations. Any self-citations (if present in the full manuscript) are not invoked as load-bearing uniqueness theorems or ansatzes for the framework itself. The derivation chain remains self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 1 invented entities

The central claim rests on the assumption that qualitative self-reports validly reveal agency dynamics and that the custom chatbot's behavior allows observable co-construction without introducing artifacts.

axioms (2)
  • domain assumption Participants' post-interview descriptions accurately reflect their lived experience of control during chats.
    The study uses interviews and strategy reveals to infer agency, assuming self-reports are reliable.
  • domain assumption The LLM companion can be configured to exhibit steerable intentions that participants can perceive and respond to.
    The chatbot was purpose-built for the study to enable observation of agency interactions.
invented entities (1)
  • 3-by-4 agency framework no independent evidence
    purpose: To categorize actors and actions in human-AI conversations.
    Newly proposed structure based on study observations.

pith-pipeline@v0.9.0 · 5469 in / 1420 out tokens · 41365 ms · 2026-05-16T09:50:52.120691+00:00 · methodology

discussion (0)

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