Probing How Users Interact with Turn-Level Design Frictions for AI Chatbots

AI chatbots can help people write faster, but they can also encourage overreliance by making it easy to turn minimal input into usable text. We study turn-level design friction: intentional constraints added to each chatbot exchange that slow, limit, or redirect how users request, access, or use model responses. We designed six friction probes, organized around three mechanisms: eliciting user contribution, restricting access to generated content, and reshaping system output. In a within-subject study with 24 participants, all six probes increased workload, task duration, and perceived ownership relative to a conventional AI chatbot, while their effects on recall and recognition were more selective. We further found that participants adapted to friction in different ways, and that the same constraint could support or obstruct involvement depending on users’ goals and workflows.

Additional Key Words and Phrases: LLM, design friction, ownership, writing, responsible AI, memory

Introduction. AI chatbots have become a widely used interface for interacting with large language models (LLMs). Their popularity stems from an interaction model that minimizes user effort: users can provide a brief prompt and immediately receive a Authors’ Contact Information: Helen Weixu Chen, School of Computer Science University of Waterloo, Waterloo, ON, Canada, ; Katy Ilonka Gero, School of Computer Science University of Sydney, Sydney, NSW, Australia, .

Manuscript submitted to ACM Manuscript submitted to ACM 1 arXiv:2608.22427v1 [cs.HC] 23 Aug 2026 2 Chen and Gero fluent, usable response. This workflow makes AI assistance valuable across a wide range of activities, including asking questions, brainstorming ideas, learning new concepts, programming, and writing [9]. However, the same ease of interaction can also position users as passive consumers of AI-generated content [35]. Rather than actively developing, evaluating, or refining ideas, users can often move directly from a minimal prompt to a response that is ready to adopt.

As AI chatbots become increasingly capable, an important design challenge is therefore not only how to improve model outputs, but also how to preserve meaningful user involvement without losing the practical value of AI assistance.

One promising approach is design friction [20] — the intentional introduction of interaction constraints that slow, interrupt, or redirect an otherwise effortless workflow. Friction has been extensively studied in scoial media and digital wellbeing, where it has been used to interrupt habitual use and encourage more deliberate engagement [36, 47]. In AI chatbot interactions, friction might require users to contribute more of their own thinking, make model output less immediately reusable, or perform additional actions before accessing it. Prior work has demonstrated the potential value of such interventions. In writing, Joshi and Vogel [28] found that longer prompts were associated with greater psychological ownership and proposed a hold-to-submit delay to encourage users to write longer prompts. In programming education, Kazemitabaar et al. [30] explored cognitive engagement techniques that require learners to explain, trace, or reason through AI-generated code before using it. However, these studies primarily examine individual techniques within particular tasks. We still lack a comparative account of the interaction mechanisms through which friction operates, the forms of effort that different mechanisms require, the costs they introduce, and how users adapt when familiar chatbot workflows are deliberately disrupted.

In this paper, we investigate turn-level design friction: interaction constraints embedded within individual chatbot exchanges that change how users request, access, interpret, or use AI-generated responses. Drawing on prior HCI work, we identify three interaction mechanisms for introducing friction: eliciting user contribution, restricting access, and reshaping system output. Guided by these mechanisms, we developed six design probes, with two distinct probes representing each mechanism. We evaluated the probes through a within-subject study with 24 participants, using short writing tasks as a context in which participants had to request, interpret, and incorporate AI-generated material into a concrete response. Our mixed-methods evaluation combined measures of workload, task duration, psychological ownership, recall, and recognition with interaction logs and semi-structured interviews.

Our findings show that introducing additional effort was not sufficient to make an interaction productive. All six friction probes increased workload, task duration, and psychological ownership relative to a conventional AI chatbot, but their effects on recall, recognition, and workflow varied. Across these outcomes, the consequences of friction depended on where the added effort was directed and how the constraint fit users’ goals and workflows. Friction could support involvement when it kept users engaged in formulating, interpreting, or assembling the response, but could instead become obstructive when effort shifted toward satisfying the constraint or managing the interface. Participants also adapted their workflows in response to the constraints, changing how they distributed work between themselves and the model.

We contribute a mechanism-based framing of turn-level design friction, instantiated through six interface probes, and an empirical account of the benefits, costs, and workflow consequences of applying friction to AI chatbot interactions.

We provide preliminary evidence for how turn-level frictions can be effective at deepening user engagement, and propose future work to investigate how different mechanisms play out in longer, more naturalistic settings.

Related work. 2.1 Impacts of LLM Assistance on User Involvement LLMs can substantially reduce the effort required to complete complex tasks, but this convenience can also change how much cognitive work users perform themselves. Prior work has linked over-reliance on AI, such as accepting model recommendations without sufficient scrutiny, to weaker critical thinking, analytical reasoning, and decision-making [55].

Cognitive offloading offers one explanation for these effects: by shifting cognitive processes to external tools, users can reduce the mental work they perform themselves [46]. Consistent with this account, Gerlich [22] found that heavier AI use was associated with lower critical-thinking performance, with cognitive offloading mediating this relationship.

Beyond reducing the amount of cognitive work users perform, LLM assistance can also shape how they reason about a task. Prior work on AI-assisted decision making has documented automation bias, in which people follow AI recommendations, including incorrect ones, rather than independently evaluating them [7]. Similar effects appear with generative AI. Choi et al. [14] found that LLM suggestions increased expert annotation speed by 133.5%, but also shifted annotators’ decisions toward the model and left several low-prevalence but important concepts overlooked.

Even assistance used primarily for navigating information can influence subsequent deliberation: Zhi et al. [58] found that early LLM access could anchor which information and positions users subsequently considered, with its effects on critical-thinking performance varying with the time available for the task.

LLM assistance can also change users’ involvement in shaping and internalizing the resulting work. In AI-assisted writing, Kreminski [34] argues that when users can produce substantial text while making comparatively few creative decisions, for instance using a short AI prompt to produce a long story, the resulting work may not reflect the author’s expressive intent. In knowledge work, Kobiella et al. [32] similarly found that users’ sense of ownership and accomplishment was closely tied to their perceived contribution and control: participants described post-processing ChatGPT output as helping them retain ownership, whereas a more dominant role for ChatGPT could distance them from the resulting work. Neurophysiological evidence points to related differences in how AI-assisted writing is internalized. Kosmyna et al. [33] observed weaker neural connectivity during LLM-assisted essay writing than during unassisted writing and poorer immediate recall of participants’ own essays.

Method. Most of these designs could be organized around three recurring interaction mechanisms: eliciting user contribution, restricting access, and reshaping system output. Guided by these mechanisms, we developed and iteratively refined a broader set of friction interfaces, ultimately retaining six design probes for evaluation.

3.1 Deriving Friction Mechanisms from Prior HCI Work We used Google Scholar to assemble an exploratory corpus of 24 HCI papers published within the past five years, searching with combinations of terms related to friction and interaction regulation, including design friction, selfcontrol, self-regulation, constraint, restriction, and lockout. Rather than limiting the corpus to AI chatbots, we considered designs across technology contexts, including digital wellbeing, social media, decision support, AI-assisted writing, and programming education. We retained papers containing concrete interaction interventions that introduced additional effort, delay, restriction, or other disruption into an otherwise familiar or smooth interaction. Because a single paper could contain multiple substantially different interventions, we treated each design as the unit of analysis. This resulted in a corpus of 73 unique designs from 24 papers. The full corpus is reported in Appendix A.

The corpus served as a design resource for identifying recurring ways in which prior interfaces introduced friction.

For each design, we examined what the intervention required users to do differently from the familiar interaction, Manuscript submitted to ACM Probing How Users Interact with Turn-Level Design Frictions for AI Chatbots 5 for example, whether they had to contribute information, wait, inspect, interpret, verify, or transform content before proceeding. We iteratively compared these interaction patterns and grouped designs according to the primary way in which they altered the interaction. Most clustered around three recurring mechanisms: eliciting user contribution, restricting access, and reshaping system output. Four designs instantiated two mechanisms and were therefore coded under both, resulting in 77 design–mechanism entries in Appendix A. Designs that did not clearly instantiate any of the three were retained under Other. Because these designs did not share a sufficiently coherent interaction pattern, we did not treat Other as a fourth mechanism. We define the three mechanisms below.

M1. Eliciting user contribution requires users to provide additional information, reasoning, judgment, or action beyond what the default interaction would ordinarily require.

M2. Restricting access delays, gates, or conditionally withholds content or functionality that would otherwise be immediately available.

M3. Reshaping system output changes the amount, form, organization, or communicative role of what the system presents, thereby changing how users must interpret or use it.

3.2 From Friction Mechanisms to Six Design Probes Guided by the three mechanisms, we developed and iteratively refined a broader set of design candidates before selecting six as probes. In adapting friction to AI chatbots, we distinguished our goal from that of many digital wellbeing and social media interventions, for which discouraging, interrupting, or terminating continued use may itself be an intended outcome. Our goal was not to discourage chatbot use as an end in itself, but to reshape how users engaged with AI assistance — potentially reducing unreflective reliance while keeping users involved in the task and in decisions about how model responses were requested, interpreted, and used. We therefore considered friction productive when it redirected users’ effort or attention toward the task, their own reasoning, or the model-generated content, rather than merely making the interaction more difficult or unpleasant.

Discussion. Our findings paint a mixed picture of design friction in AI chatbot interactions. Regarding user involvement (RQ1), all six friction probes increased perceived ownership relative to ChatGPT, while their effects on memory were more selective: most improved recall, but only Elenchus and Less is More significantly improved recognition of participants’ own wording. Regarding interaction costs (RQ2), these benefits came at a consistent cost, as every friction increased workload and task duration. Finally, participants did not simply comply with the constraints (RQ3); they adapted by decomposing tasks, bringing their own ideas earlier into the interaction, compressing model responses, or working around the friction to restore familiar chatbot workflows. These results shift the question from whether friction can increase involvement to when and how it can do so productively.

First, there was no universally productive friction: the same constraint could support or obstruct users depending on when it appeared and what they wanted from the AI. This was particularly visible with Elenchus. P6, for example, had already decided the direction of their argument and wanted the model to provide “information or things to back up my thoughts”; continued Socratic questioning therefore interfered with rather than supported the assistance they were seeking. Other participants described workflows in which they preferred to develop their writing themselves and turn to AI later for editing or grammatical support. In these cases, a friction designed to elicit further ideation may reproduce work the user has already done. Conversely, when users have not yet formed a position, the same questioning interaction may prevent them from immediately outsourcing that reasoning to the model. This tension echoes prior work on cognitive forcing functions, which found that interventions that reduce overreliance can nevertheless be less preferred by users [7]. Our Manuscript submitted to ACM Probing How Users Interact with Turn-Level Design Frictions for AI Chatbots 15 findings suggest that one reason for this tension is fit: friction preserves useful human work only when that work is still relevant to the user’s current goal. Rather than asking which friction should be applied universally, designers may therefore need to consider when friction is appropriate, for example, whether users are forming an argument, seeking supporting information, drafting, or revising.

This also raises a question about our decision to scope friction at the turn level. Turn-level interventions offer a lightweight and modular design unit, but applying the same friction across turns can overlook how users’ goals change over the course of a task. This tension was also visible when participants were asked to design their own model. Rather than converging on a single interaction style, many described systems that could switch between reflective questions and direct assistance, or allow particular forms of friction to be turned on and off depending on the task. P6, for example, wanted a model that could guide with questions during ideation but provide specific examples when directly requested, while P24 considered Socratic questioning useful for studying but wanted it to be optional for writing. This tension connects to mixed-initiative interaction, which emphasizes that system intervention should account for users’ goals and the timing and costs of assistance rather than treating initiative as fixed [3]. Our findings therefore suggest that turn-level friction may be more useful as a repertoire of interventions that users can invoke as needed than as a uniform rule applied throughout an interaction.

Conclusion. We presented six turn-level friction probes for AI chatbot interactions, using writing as a task context to study how friction affects user involvement, interaction costs, and workflow adaptation. A within-subject study with 24 participants showed that friction consistently increased perceived ownership, workload, and task duration, while its effects on memory varied across designs. Participants also adapted their workflows in response to different forms of friction.

Broadly, our work suggests that productive friction depends not on making AI interaction harder, but on preserving meaningful human work when it matters.

Limitations. Writing tasks were short and bounded. Our goal was to study turn-level friction in AI chatbot interactions. We used short writing tasks because they provided a bounded setting in which participants could request, interpret, and incorporate model output under each friction condition, allowing us to evaluate multiple friction probes. However, our findings also show why these results should not be interpreted as identifying universally effective or ineffective frictions: the value of a friction depended on what users were trying to accomplish and where it intersected with their workflow. Even within these short writing tasks, we saw participants note that in different stages they used the AI chatbot differently and had different expectations for support. A friction that is productive during ideation, for example, may be obstructive during information seeking. Future work should evaluate turn-level friction in longer tasks contexts, where users are more likely to use chatbots in a more diverse set of ways.