Together, the vertical and horizontal juxtapositions are designed to help users identify both high-level commonalities and nuanced variations across structurally similar sentences.

Then, we segment sentences within each aspect into grammarpreserving chunks (see prompt used in Appendix D.2). This results in grammatically coherent chunks that are the basis of structure patterns. After identifying chunk boundaries, we again prompt an LLM to generate labels for chunks in a human-in-the-loop approach: starting from an initial set of labels for chunk roles, when a new label is generated, a researcher from the research team examines the new label and merges it with existing labels if appropriate, controlling for the total number of labels.

After obtaining an expanded set of high-level chunk labels, we assign them to each of the sentence chunks by using LLMs in a multiclass classification few-shot learning task, with the initial labels and assignment as examples (see prompt used in Appendix D.3).

We process this data in a three-stage pipeline (Figure 6). In the first stage, Sentence Segmentation and Categorization, abstracts are split into individual sentences using the NLTK package, and each sentence is classified into one of the five pre-defined aspects as listed in Section 4.1.1. Classification is performed by prompting an LLM (see prompt used in Appendix D.1) with the sentence and its full abstract.

We conducted a qualitative analysis of user study transcripts and survey responses using a Grounded Theory approach [8]. First, the lead researcher collected a list of participants' behaviors, approaches, reflections on their experience, and feedback about the interface. The researcher then systematically coded this data, revisiting the data multiples times and refining the codes to ensure consistency and coherence. Through this process, high-level themes were identified and organized using affinity diagramming. Once the thematic structure was finalized, the researcher gathered supporting evidence for each theme and synthesized the findings, which were reviewed by the research team to ensure agreement on the results.

Interviews were video and audio recorded. We transcribed the audio using OpenAI's Whisper automatic speech recognition system and anonymized the transcript before analysis.

Future work could explore more seamless ways of preserving context, such as allowing users to navigate through every sentence of an abstract directly within the Cross-Sentence Relationship pane, fostering a more cohesive understanding of the content.

In this sense, AbstractExplorer enables dialectical activities that users may otherwise have found to be too tedious or difficult to engage with.

Supporting a new corpus would require defining new corpus-appropriate predefined aspects and chunk role labels. Other components would likely be able to remain unchanged.

In this work, we introduce a new paradigm for exploring a large corpus of small documents by identifying roles at the phrasal and sentence levels, then slice on, reify, group, and/or align the text itself on those roles, with sentences left intact.

Our work demonstrates that designs informed by Structure-Mapping Theory can support users in navigating, making use of, and engaging with variation present in information. In this sense, AbstractExplorer enables dialectical activities that users may otherwise have found to be too tedious or difficult to engage with.

Like prior Structural Mapping Theory (SMT)-informed work in text corpora representation, AbstractExplorer's features have enabled some users to see more of both the overview and the details at the same time, facilitating abstraction without losing context.

Dialectical activities cannot be done on a user's behalf by AI; with variation affordances, AI is supporting the user's engagement with the data themselves.

We posit that our approach can generalize to other domains such as journalism, code synthesis, and social media analytics where visual alignment of text can enable meaningful comparisons of underlying patterns to identify relational clarity.

In this work, we introduce a new paradigm for exploring a large corpus of small documents by identifying roles at the phrasal and sentence levels, then slice on, reify, group, and/or align the text itself on those roles, with sentences left intact.

Dialectical activities cannot be done on a user's behalf by AI; with variation affordances, AI is supporting the user's engagement with the data themselves.

We posit that our approach can generalize to other domains such as journalism, code synthesis, and social media analytics where visual alignment of text can enable meaningful comparisons of underlying patterns to identify relational clarity.

We demonstrate how slicing sentences according to roles and visually aligning them can help readers perceive cross-document relationships in a coherent manner.

Our work demonstrates that designs informed by Structure-Mapping Theory can support users in navigating, making use of, and engaging with variation present in information.

pre-computing and reifying cross-document analogous relationships make it psychologically possible for users to engage—if they are willing to be guided by it. (Lower NFC users are more likely to fall into this category.)

These observations underscored the need for an improved UI design for authoring custom aspects, especially for those who are less familiar with the research corpus.

AbstractExplorer’s features have enabled some users to see more of both the overview and the details at the same time, facilitating abstraction without losing context.

Activity log data, which revealed how participants actually used the interface, echoed the above findings. According to the log data, participants spent most of their reading time (66.31%) with vertical alignment on the second element in structure pairs, followed by alignment on the first element (29.19%), and left-justified alignment (5.13%). Highlighting usage showed a similar preference: 91.13% of time with all chunks highlighted, 8.25% with partial highlighting, and minimal time (0.63%) without highlights.

Immediately after the focused reading task, we conducted a short interview asking participants to reflect on their experience with both tasks (Appendix J.2), followed by a post-study survey (Appendix J.3).

In this section, we present findings on how AbstractExplorer supports comparative close reading at scale by integrating quantitative survey responses and log data with qualitative analysis of transcripts and open-ended responses. The qualitative analysis process is described in detail in Appendix H.

Throughout the two tasks, we also collected detailed interaction logs including counts of user-defined aspects created, duration of highlighting usage, and time allocation across the three possible alignment options.

Participants with lower NFC more frequently preferred and experienced less cognitive load when skimming with all the features enabled.

Using a two-tailed Mann-Whitney U Test, we found that participants who reported their lowest perceived cognitive load when all three features were enabled had significantly lower NFC than participants who reported their lowest cognitive load level when skimming with no features enabled—in the baseline interface (p=0.03).

Both gaze data and the semi-structured interviews revealed that lower NFC participants were more willing to be guided by the three features and took advantage of them consciously.

To compute a participant's NFC score, we averaged their response to the six questions, each ranging from 1 to 7, after reversing the appropriate questions.

For simplicity of analysis, we denote participants with NFC scores above the overall participants' median NFC of 5.42 (IQR = 0.583) as higher NFC, and lower NFC otherwise.

The study concluded with a 15-minute semi-structured interview. During the interview, participants saw screenshots from the three conditions and were asked which they preferred and disliked, why, what they wished the interface had, what influenced their skimming, and how they normally skimmed texts.

These results suggest that these three features lose their effectiveness when not used together.

Lower NFC participants were generally guided by emergent visual patterns created by the interactions between features, especially blocks of color spanning multiple sentences created when all three features are turned on.

The raw NASA-TLX score is the sum of all 6 NASA-TLX questions after reversing the appropriate questions.

We collected 80 sentences from our abstracts dataset labeled by our system as "Methodology/Contribution." Participants viewed the same 80 sentences in each condition—often with a different subset of sentences initially visible due to ordering changes—but only had two minutes to look at them in each condition.

To contrast participants' gaze patterns in each condition, we used a Tobii Pro Spark eye-tracker placed below the desktop monitor used by all subjects; Tobii Pro Lab software recorded each participant's gaze over time in each condition.

Structural mappings between objects are part of the cognitive process of comparison according to the Structure-Mapping Theory [17], and juxtaposition can facilitate humans in recognizing particular possible structural mappings between objects [75].

Inspired by GP-TSM [24], AbstractExplorer first segments sentences into grammar-preserving chunks—segments that respect grammatical boundaries, i.e., an LLM judges that the sentence can be truncated at that chunk boundary without breaking the grammatical integrity of the preceding text. Each chunk is then classified by an LLM as having one of nine pre-defined roles, each of which has its own assigned color.

This ordering prioritizes dominant structural patterns (largest groups first) while exposing fine-grained variations (via length-sorted triplets), mirroring how humans compare sentences, if SMT is an accurate description in this domain of comparative close reading.

In SMT terminology, rendering and arranging according to corresponding chunks reify "commonalities in structure," while variation within corresponding chunks are "alignable differences" that users are predicted to notice.

AbstractExplorer classifies sentences into five pre-defined aspects common in CHI abstracts: Problem Domain, Gaps in Prior Work, Methodology/Contribution, Results/Findings, and Discussion/Conclusion.

After the interviews, we analyzed the data using the process described in Appendix B

In the first part of the session, we asked participants about their strategies for selecting publication venues for their manuscript submissions, how they identify and synthesize information from venues, their approaches to writing manuscripts, and finally, the technology they have used to help with these processes, current technology shortcomings, and ideas for addressing these challenges.

In order to determine (1) the context in which we might offer novel views of scientific abstracts and (2) the intelligibility of various novel prototype designs for reifying cross-abstract relationships, we conducted a formative interview study with 12 active researchers (see Appendix A for participant information).

We used these mock-ups as design probes [31] to inspire ideation and elicit creative responses. Specifically, we asked participants to compare and contrast alternative mock-ups and reflect on how they could be used or improved to support their known or emerging synthesis and information-foraging goals.

The prior SMT-informed tools in Section 2.3 for both code and natural language corpora suggest that the cognitive process of comparing texts may be no exception to the cognitive processes SMT predicts.

After the interviews, we analyzed the data using the process described in Appendix B

In the second part of the session, we provided participants with mock-ups of possible reifications of cross-document relationships that might help them synthesize information across abstracts.

AbstractExplorer specifically leverages the shared structure in scientific abstracts to facilitate abstract corpus skimming and comparative reading at scale.

SMT provides a framework for understanding how humans compare two or more objects by finding common structural alignments between objects.

Sessions, which were held on Zoom, lasted 55 minutes on average. Participants were compensated with $15 USD.

The interview sessions were divided into two parts: an open-ended semi-structured interview about their backgrounds and practices, followed by feedback on a range of mock-ups, including novel reified relationships between analogous sentences in different abstracts (Figure 2).

These examples of text-centric lossless techniques do not abstract away or summarize; they strategically re-organize and re-render the existing text to help enhance readers' own perceptual cognition, informed by Structural Mapping Theory (SMT) [17].

The human perceptual, comparative mental machinery that SMT describes is part of what enables humans to form more abstract structured mental models from concrete examples, among other critical knowledge tasks.

Researchers have done rich historical investigations of individual notations (e.g., [3, 70, 74, 76, 115]), but the more general mechanisms and patterns through which new notations are created and formalized are less understood.

We began with a diverse set of notations across five disciplines—music, dance, chemistry, physics, and computer programming—that had prior historical literature to draw upon in our initial analysis.

Since histories of specific notations tends to miss detailed, direct observations around the initial creation process, we complement this "macro" analysis with occasional references to experiment-based literature from experimental semiotics, communication theory, and cognitive science into how people use notations to ground communication, largely in lab studies.

we conducted a comparative historical analysis of the development of different notations which individually have been documented in prior literature. Specifically, we conduct a parallel comparative history which "seek[s] above all to demonstrate that a theory similarly holds good from case to case... [and where] differences among the cases are primarily contextual particularities against which to highlight the generality of the [theorized] processes"

Studying software teams, Cherubini et al. [34] found a "tendency to adopt informal, ad-hoc notations" and a "limited adherence to standards of any sort."

Studies are also conducted on various existing notating practices, usually in specific domains (e.g., how programmers draw diagrams to communicate ideas [34, 62, 63]).

Seemingly 'obvious' notations to academics are also not obvious to everyone: e.g., about one-third of the U.S. and German populations have low literacy in reading data visualizations [51].

As current AI technologies rely upon, reproduce, and amplify established, dominant, already-formalized abstractions and notations in order to function

Our work contributes to a longstanding dream of dynamic abstractions in HCI, where users can dynamically communicate and express themselves through notations (interfaces) that they are most comfortable with at the moment of expression, beyond ones predefined by developers [96, 143, 144, 148, 149].

Here we present suggestions for system designers, with concrete examples inspired by our patterns. These are just some interesting ideas that came to mind, rather than an exhaustive list.

Alongside the social stages of notation development above are three functional stages that emerge from reflection upon our analysis—descriptive, generative, and evaluative stages (borrowing terminology from Generative Theories of Interaction [11])

Our historical analysis suggests that, cognitively and socially, a notation proceeds by: (1) Enumerating dimensions of meaningful variation in the target domain, which proliferate as more situations are encountered or considered (whether by inventors or users) (2) Mapping dimensions of meaningful variation to perceptual channels of representation (3) Designing the notation to leverage perceptual affordances by visual analogy to embodied transformations like pouring cups or rotating shapes, and ensuring these "natural" manipulations hold meaning in the target domain

These stages form a spectrum and are not rigid boundaries. We clustered patterns into the most relevant stage for ease of presentation; however, patterns can be applicable across stages.

Our review identified many empirical patterns in the notation development process. We state each pattern, briefly describe it, and provide examples.

SDT broadly differentiates three types of motivation [157]: Intrinsic motivation denotes activity pursued for its inherently interesting or enjoyable qualities. Extrinsic motivation refers to activity pursued for a separable outcome. Amotivation denotes the absence of intentional motivation, where a person may no longer be aware why they pursue an activity.

Basic psychological needs theory (BPNT) posits three basic psychological needs that energise organismic processes: competence, the feeling of having an effect; autonomy, a sense that actions are self-endorsed and performed willingly; and relatedness, a sense of reciprocal care, value, and belonging in relation to other social figures and collectives [158].

SDT is broadly organised into six mini-theories, whose underlying concepts are continuously developed, critiqued, and revised (e.g., [186, 190, 191]).

At its core, SDT is a scientific theory [163], in that it contains a number of empirically-testable propositions [199] that generalise across varied contexts, which serve to explain and predict the impact of certain events on motivation and wellbeing.

SDT is a psychological macro-theory of human motivation, growth, and wellbeing [47, 48, 163] that characterises humans as fundamentally active organisms.

Self-Determination Theory (SDT), a major psychological theory of human motivation, has become increasingly popular in Human-Computer Interaction (HCI) research on games and play.

To our knowledge, the first SDT research involving videogames [18] was conducted shortly after Deci's original formulation of CET [129] and investigated whether extrinsic rewards would reduce intrinsic motivation even for 'highly intrinsically motivating' activities such as videogame play. Videogames' intrinsically motivating qualities were also examined in early research on learning [e.g., 351]; however, focused examination of other core SDT concepts such as need satisfaction largely began much later [365].

Research on games and play in HCI (henceforth HCI games research), however, has continued to employ broad psychological theories as foundational work [417, 556]. One prominent example can be seen in self-determination theory (SDT) [481, 483], an influential theory of human motivation, which has provided HCI games research with propositions and concepts that can help explain motivational and experiential qualities of games and game-adjacent systems (e.g., gamification).

Psychological concepts and models have long been employed in human–computer interaction (HCI) to theorise the human user [88]. However, early applications of cognitive psychological theory did not develop into a coherent foundation of knowledge about human factors [89, 109, 455]—circumstances that Rogers [456, p. 22] attribute to "the stark differences between a controlled lab setting and the messy real world setting" for which interactive artefacts and systems are designed. The deployment of broad theory in HCI has subsequently declined in the intervening years [455, 456], and this sporadic progress in theory development in domains such as usability and user experience (UX) has been identified as a cause for concern [249, 314].

Alignment is a bilateral process; it refers not only to AI acting according to human intentions but also to humans better leveraging AI by understanding the mechanisms behind it [54].

Data labeling as a cognitive task—including defining a concept or determining how two similar objects may have different labels—requires both comparison and integration [62].

However, relying exclusively on existing examples is not ideal for tasks requiring nuanced understanding of user intentions, as these examples often fail to represent diverse and edge-case scenarios [31].

When training samples are scarce, model performance heavily depends on the quality of available training examples [15].

An important challenge in interactive machine learning, particularly in subjective or ambiguous domains, is fostering bi-directional alignment between humans and models.

In the context of co-adaptive learning, supporting the intertwined evolution of both the user's understanding and the model's learning is crucial [16].

Machine teaching, a part of the human-in-the-loop approach, has been used as a process in which a human expert (the "teacher") provides guidance to a machine learning model to help it learn important and robust features for decision making [57].

A targeted approach in IML is machine teaching (MT) [60], an interactive framework that allows users to devise and select useful data for labeling, with the goal of teaching the model relevant features during training [7, 18].

Interactive ML (IML) methods, like active learning [3], continuously apply human feedback during model training to iteratively build and refine the model [35, 42, 43].

The goal of this meet-up is to create a space for CHI attendees to discuss and practise reflection in HCI research and design.

Reflection is not limited to one subfield or methodology: it is a concern that cuts across the entire discipline.

We understand reflection as a multifaceted concept [6, 24, 25] with implications and relevance at different levels for researchers [2, 3, 8, 28]:

Reflection has been a recurring theme in HCI – from Schön's reflective practitioner [24] to Sengers et al.'s reflective design [25].

Importantly, reflection happens on multiple levels: as individuals questioning assumptions and choices, as groups working together in projects or labs, and as a community negotiating shared values, norms, and directions.

Reflection, as we envision it here, is not limited to standardised methods and codified practices, but also includes the hidden, vulnerable conversations about academic life.

Structures — reflection on the structures that condition HCI and our own standings within them: societal constructs (positions, values, power) shaping what problems are visible and whose knowledge is legitimised.

Practice — reflection on practice (processes): the ways we design, study, and collaborate [32].

Self — reflection on the self: a deliberate, structured evaluation of one's thoughts, feelings, and actions [7, 13].

We understand reflection as a multifaceted concept [6, 24, 25] with implications and relevance at different levels for researchers [2, 3, 8, 28].

While reflection is acknowledged as crucial for rigorous and responsible research, it often remains tacit and under-discussed.

Reflection has been a recurring theme in HCI – from Schön's reflective practitioner [24] to Sengers et al.'s reflective design [25]. However, it is seldom centred in our collective conversations [2].

As the community around augmented reading broadens and as possibilities continue to unfold, it is the purpose of this workshop to set up our community to drive innovation in a productive, desirable, and responsible way.

The landscape of technology for consuming information is changing rapidly. One mode of information consumption, reading, stands to see profound changes due to its ubiquity and frequency as a cognitive task.

Recent changes in the technological landscape are significantly changing the reading experience. AI has introduced many new possibilities for interfaces to augment or transform text to be more rapidly scanned, navigated, understood, and compared to other texts.

Reading is one of the most ubiquitous modes for consuming information.

Gebreegziabher et al. [24] argued that counterfactual generation that follows the principles of VT allowed the introduction of discriminatory variance for the model to learn on.

Building on methods proposed in PaTAT [24], Mocha first generates human-readable neuro-symbolic pattern rules from partially labeled text data for classification.

These theories have proven insightful for understanding how humans grasp and compare concepts, shaping the development of human-AI collaboration systems for sensemaking [29], hypothesis testing [2], as well as model training [24].

Both systems enabled users to quickly identify variations and patterns within the data and support exploration and hypothesis testing.

The last two prior works also combine Variation Theory (VT) and SAT together, as we did (i.e., a corollary of SAT referred to as Analogical Transfer/Learning Theory).

In line with previous work, Mocha aims to support a user's efforts in the disambiguation of concepts through structural comparisons of counterfactual data in the context of machine teaching.

Results show that participants successfully customized interfaces using natural language. Users found the system intuitive and achieved good performance regardless of technical background, we report analysis of optimal prompt length, challenges in separating functional and visual instructions in structured templates, correlation between LLM experience and success, and learning effects.

By allowing users to express desired changes using their own words and harnessing the generative capabilities of LLMs, MorphGUI mitigates the limitations of predefined options and reduces the need for technical expertise. The framework translates functional and stylistic requests into either modifications of existing application components or generation of new ones.

Graphical user interface (GUI) customization relies on predefined configuration options and settings, constraining diverse individual needs and preferences within predetermined boundaries and often requiring technical expertise. To address these limitations, this work introduces MorphGUI, a framework leveraging Large Language Models (LLMs) to enable interface customization through natural language.

MorphGUI: Real-time GUIs Customization with Large Language Models

implications for society focus on a technology's societal impact. The purpose of these implications is to raise awareness, stimulate reflection, and prompt action in relation to the impact of emerging technologies on our lives.

Policy implications seek to inform or persuade regulators, politicians, and others in governing positions.

While the term practitioner in HCI research often refers to those in design-related roles (e.g., a UX designer), the design and evaluation of sociotechnical systems also lead to implications for other domains. The target audience for implications for practice can be specific professionals, such as teachers or healthcare staff, or those in leadership positions.

The prototypical implications of HCI work are implications for design. These implications seek to inform the design of technology, bridging the gap between research findings and real-world design challenges.

Implications for the HCI community may follow from studies or reflections on how we operate as an academic community, for example, through bibliographical analysis or a critique of ethical shortcomings.

The purpose of creating implications for theory is to improve our ability to understand and predict phenomena in interactive computing.

Theoretical implications concern the basic constructs of HCI and our understanding of how they affect each other.

Methodology implications aim to inform the way we design and analyze studies within HCI. These implications focus on aspects such as the selection and recruitment of participants or the analysis of data or reporting thereof.

Many participants thought that it was important to consider how closely the final product aligned with their initial conceptions (P7, novelist; P8, web developer; P11, filmmaker), "almost like a success-type question" (P3, dancer). This idea can be thought of as an aspect of intentionality — as P11 (filmmaker) stated, "Did your intentions translate into the final work?"

Intentionality can be supported through periodic intent check-ins and visual diffs that surface drift from initial goals.

Levene and Friedman [20] examined the effects of creation and intent on ownership judged and found that the effects of creation hold even when controlling for other factors. They also showed that successful and intentional creations are ascribed more ownership than unsuccessful or unintentional creations, and that creation is ascribed more ownership than the equivalent labor.

Even though the majority of participants stated that intentionality doesn't play a role in their conceptions of ownership as it is "a given" (P5, architect) and that "everything is intentional" (P17, illustrator, graphic designer), these cases showcase that intentionality can indeed play a role in ownership sentiments, especially when the ability to be intentional is taken away.

there seem to be times when material constraints can indeed shift ownership feelings, especially when control, intentionality, and creative vision all lie at an intersection: "I lose ownership points there, because I'm limited by this specific tool even if I have a specific vision" (P4, nonfiction writer)

The one participant who did directly reference intentionality did so more in terms of the medium they work with: "We're still digging up shards of pottery from hundreds and thousands of years ago; once you fire something, it doesn't go away. It's hard as rock. So you really want to be sure and confident and intentional when you make something out of clay and fire it, because it can't be undone" (P20, ceramicist).

While continuity is distinct from control or intentionality, it can still shape one's capacity to make intentional creative decisions, particularly when involvement is limited to a part rather than the whole project.

Only one participant directly mentioned the term intentionality, but a few participants reported that whether or not they were able to work on the project from start to finish (a sense of continuity perhaps) was important to their sense of ownership.

Intentionality – How intentional were you about the creative decisions that you made?

Intentionality can be supported through periodic intent check-ins and visual diffs that surface drift from initial goals.

Levene and Friedman [20] examined the effects of creation and intent on ownership judged and found that the effects of creation hold even when controlling for other factors. They also showed that successful and intentional creations are ascribed more ownership than unsuccessful or unintentional creations, and that creation is ascribed more ownership than the equivalent labor.

Even though the majority of participants stated that intentionality doesn't play a role in their conceptions of ownership as it is "a given" (P5, architect) and that "everything is intentional" (P17, illustrator, graphic designer), these cases showcase that intentionality can indeed play a role in ownership sentiments, especially when the ability to be intentional is taken away.

However, there seem to be times when material constraints can indeed shift ownership feelings, especially when control, intentionality, and creative vision all lie at an intersection: "I lose ownership points there, because I'm limited by this specific tool even if I have a specific vision" (P4, nonfiction writer); "I wrote everything that I wanted to, I planned everything the way that I wanted it to be. But when I went to shoot, and I started facing challenges, I realized I don't have enough time, enough budget, and the crew is not experienced enough. So then, your idea of making the film itself changes" (P11, filmmaker).

The one participant who did directly reference intentionality did so more in terms of the medium they work with: "We're still digging up shards of pottery from hundreds and thousands of years ago; once you fire something, it doesn't go away. It's hard as rock. So you really want to be sure and confident and intentional when you make something out of clay and fire it, because it can't be undone" (P20, ceramicist).

Only one participant directly mentioned the term intentionality, but a few participants reported that whether or not they were able to work on the project from start to finish (a sense of continuity perhaps) was important to their sense of ownership.

The study protocol was approved by our institutional ethics review board (IRB). All participants provided informed consent prior to participation. Each received $25 in compensation, either as cash or a gift card.

Our methodological design was guided by the goal of comparing how participants described ownership before and after being introduced to the framework, with a focus on understanding the coverage and utility of the framework's dimensions. To capture this contrast, we asked them to reflect on both a high-ownership and a low-ownership creative project, enabling comparison across contexts as well as within individual experience. We refer to these phases as the pre-webtool and post-webtool sections of the study.

We analyzed interview transcripts using thematic analysis. Each transcript was segmented into meaningful units (quotes or lines), which were then coded based on the core theme or idea expressed. Codes were iteratively refined and collapsed, with similar codes grouped together into broader categories that reflected shared orientations toward ownership. Through repeated reduction, these categories were distilled into a set of central themes that captured the most salient patterns across the dataset.

In the post-webtool phase, participants were introduced to the Creative Ownership Webtool, which asked them to evaluate each product across the nine subdimensions of the Person, Process, and System framework, resulting in a numerical value for each project. Finally, participants reflected on the framework outputs, discussing whether the results aligned with their intuitions, which dimensions resonated or felt less relevant, and what aspects of ownership they felt might be missing.

Interviews were structured into two phases. In the pre-webtool phase, participants first provided background information on their creative trajectory, education, and domain of practice. They then reflected on two creative products selected in advance—one associated with high ownership and one with low ownership—explaining the reasoning behind their classifications and the factors that influenced them.

We conducted semi-structured interviews lasting 45–60 minutes, guided by a shared set of questions and thematic prompts while allowing flexibility for participants to reflect on their individual experiences. This approach encouraged rich, situated accounts of ownership while maintaining comparability across interviews.

Potential participants were identified through a combination of referrals from the researchers' professional networks, publicly available sources, and local art communities in the Greater Boston area. To be eligible, participants were required to: (1) work or participate significantly in a creative field, (2) have at least two finished creative products—one associated with high feelings of ownership and one with low feelings of ownership, (3) be fluent in English, and (4) be over 18 years of age. We recruited 20 participants via word of mouth, email, and snowball sampling.

We conducted semi-structured interviews with 21 creative professionals across a diverse range of fields. We used a two-phase, within-participant protocol. Participants first described one high-ownership and one low-ownership project without the framework, then used our instrument to rate both works and reflect on the output.

Building on these efforts, our aim is to develop a framework for ownership that is specifically tailored to creative practice and designed for use in HCI research.

Efforts have been made in HCI to establish more unified frameworks, though these remain limited in scope.

Building upon literature across psychology, philosophy, the humanities and social sciences more broadly, and within human-computer interaction, we introduce a nine-subdimension framework of creative ownership organized across Person, Process, and System.

We introduce a framework of creative ownership comprising three dimensions - Person, Process, and System - each with three subdimensions, offering a shared language for both system design and HCI research.

A Paradigm for Creative Ownership

Pre-framework interviews concentrated on Embodiment, Control, and Abstraction. With the framework in view, attention distributed across all nine dimensions.

Pre-framework talk concentrated on a limited subset of subdimensions (embodiment, control, abstraction).

Hegel's ideas of ownership stem from the notion that the "will" can be embodied in external entities, and that this embodiment is necessary for one's actualization as a person cannot come to exist without both relation to and differentiation from the external environment.

There almost appears to be a divide between "process-focused" (P18, painter, sculptor) and "person-focused" (P3, dancer) creatives.

The sentiments highlighting the importance of embodiment largely paralleled those expressed prior to the participants viewing the framework. Participants stated that it was important to them that their work reflected their "value system" (P5, architect), "emotional experience in [their] lived feelings" (P2, ukulelist, singer), and that it was a "labor of love" (P16, cartoonist).

Participants felt that when the work reflected their "signature style" (P4, nonfiction writer) or "distinctive mark" (P8, web developer), they had a stronger sense of creative ownership.

Participants used a variety of words to get this message across: self-indulgence, passion, obsession, vulnerability. Being able to engage in their own explorations, share their backgrounds and experiences, and, in the words of one participant, "imbue more of [themselves]" (P9, dancer), was key across the study.

P19 (painter, glass artist) chose a piece that was an exploration of body and memory: "It was a lot of looking through and reflecting what I was thinking."

P4 (nonfiction writer) cited that they chose the work because it was both crafted in their signature style, and was an emotional piece written about their mother.

Embodiment of values, personality, and identity was repeatedly cited by participants as a strong reason why they feel creative ownership over their work.

Embodiment – How much do you feel that the finished product embodies your values, personality, and identity?

Qualitatively, pre-framework talk concentrated on a limited subset of subdimensions (embodiment, control, abstraction). Once introduced, participants articulated and prioritized all nine subdimensions, enabling finer distinctions (e.g., conceptual authorship vs. physical production) and revealing medium-dependent nuances.

Participants also found the categories legible, and a recurrent split emerged between person-focused and process-focused practices. Employment context further moderated ownership: low-ownership projects were often job-driven, whereas high-ownership projects skewed toward self-initiated work. These findings support modeling ownership as a multi-dimensional profile with moderators rather than a single latent factor.

Pre-framework interviews concentrated on Embodiment, Control, and Abstraction. With the framework in view, attention distributed across all nine dimensions. Quantitatively, high-ownership cases exhibited higher overall scores, whereas low-ownership cases showed greater dispersion. Taken together, these patterns indicate that the framework broadens the analytic space of ownership and supports the capture of heterogeneous routes to ownership, particularly in low-ownership contexts.

Overall, these results demonstrate both the coverage and diagnostic power of the framework: all nine sub-dimensions shifted between conditions, and the variance patterns in the low ownership condition surfaced the diverse ways participants experience reduced ownership.

For HCI, the immediate use is practical: report ownership as a profile rather than a single score, state construct boundaries, and use the dimensions as design levers (e.g., decision rights for Control, intent alignment for Intentionality, attribution for Recognition, modality-aware workflows for Production/Abstraction, and role clarity for Interdependence).

Responses for low-ownership projects showed substantially greater variance, with wider inter-quartile ranges and more outliers than in the high-ownership condition. Whereas ratings for high-ownership projects clustered tightly at the upper end of the scale, low-ownership responses spanned nearly the full range, from near zero to moderately high values. This indicates that while participants converge on what constitutes high ownership, experiences of low ownership are more heterogeneous, reflecting different ways ownership may be diminished (e.g., limited control, lack of recognition, or minimal effort).

Methodologically, we recommend reporting an ownership profile rather than a single score and explicitly stating construct boundaries. A brief "ownership design card" in Methods—specifying manipulated versus measured dimensions, expected moderators (e.g., medium tangibility, employment context), and anticipated trade-offs—would improve interpretability and comparability.

Across all nine sub-dimensions of the framework—Embodiment, Occupancy, Recognition, Control, Intentionality, Effort, Production, Abstraction, and Interdependence—participants gave consistently higher ratings for projects they associated with high ownership compared to low ownership (Figure 2). This pattern held across the board, suggesting that the framework reliably distinguishes between ownership conditions rather than capturing isolated dimensions.

A potential risk is profile drift under sustained high-automation use (e.g., declines in perceived Effort or Control). Because the framework is lightweight, it can function as a periodic check-in to track such changes and recommend countermeasures (e.g., adding decision checkpoints or narrowing automation scope).

The framework yields actionable implications for system design. Treating ownership as a first-class experience goal positions each dimension as a design lever. Control can be protected by making decision rights explicit, keeping suggestions reversible, and attaching rationales to consequential edits. Intentionality can be supported through periodic intent check-ins and visual diffs that surface drift from initial goals. Recognition benefits from attribution by default. Production and Abstraction suggest modality-aware workflows (concept-first versus material-first), and Interdependence calls for role visibility and decision traceability in collaborative tools. The aim is not to prescribe features but to make ownership designable: systems can be tuned to the ownership profile a context demands.

In study of AI-driven scriptwriting by Weber et al. [42], participants associated ownership with ease, expression, collaboration, uniqueness, and enjoyment.

Weber et al. [43], for example, use the term "artistic ownership" in studying support for creative goals, yet operationalize it through adjacent concepts such as creative vision, intentions, collaboration, pride, control, and emotional response [43]. Even when researchers begin with a focused definition, as in Wasi et al.'s work [41] on content ownership, related ideas often surface—embodiment, identity, originality, and effort among them.

Some studies conflate ownership with adjacent ideas (e.g., control, vision, identity); others elicit participants' views without a common scaffold, making results hard to compare across settings and media.

As one participant put simply, "Did I love it?" (P3, dancer).

P4 (nonfiction writer) reported a similar sentiment but used the term pride instead — "That sense of proudness doesn't really have anything to do with how much I feel ownership about it, at least not directly."

P2 (ukulelist, singer) reported feeling a "creative attachment" to a piece, even though they didn't feel any ownership over it — "A little bit of my heart and the soul is in this thing, even though it doesn't have anything to do with me otherwise."

In their 2003 paper, Pierce et al. [32] define psychological ownership as "that state where an individual feels as though the target of ownership or a piece of that target is 'theirs'."

In the field of psychology, there have been numerous theoretical propositions and empirical studies attempting to explain the formation of psychological ownership. Several scholars have created frameworks based on decades of psychological research that capture key themes that have emerged time and again such as effectance and control of possessions [10, 25, 44], positive affect [10], and symbolic meaning and personhood [35].

Hegel's ideas of ownership stem from the notion that the "will" can be embodied in external entities, and that this embodiment is necessary for one's actualization as a person cannot come to exist without both relation to and differentiation from the external environment [34].

One of the most fundamental materialist theories is Locke's labor theory, which posits that "every man has a property in his own person," and thereby goes on to argue that when one mixes their labor with natural resources, the resulting good becomes their property - evoking the embodiment theory of personhood [22, 34].

Materialist theories stem from notions of property as control over material entities, going as far as to stipulate that physical, material states are the ultimate determinants of reality, taking precedence over thought, consciousness, and abstract entities [27, 38]. On the contrary, idealism posits that something mental is the ultimate foundation of reality, and idealist theories of property and personhood are concerned with symbolic and mental conceptions of ownership [12].

Building upon literature across psychology, philosophy, the humanities and social sciences more broadly, and within human-computer interaction, we introduce a nine-subdimension framework of creative ownership organized across Person, Process, and System. Person captures how the artifact relates to the self; Process characterizes the decisions, intentionality, and effort by which it is created; System situates creation within its material, collaborative, and contextual conditions.

Research on the self-creation effect illustrates how creating something oneself can lead to stronger object valuation and a more profound sense of ownership - aspects that are often overlooked by traditional frameworks of ownership. Therefore, we draw upon existing frameworks and approaches to produce a framework that is more streamlined for creative contexts.

In their 2003 paper, Pierce et al. define psychological ownership as "that state where an individual feels as though the target of ownership or a piece of that target is 'theirs'." In this paper, we will focus on a narrower definition revolving around creative ownership in which the target of ownership is a creative product or artifact that the individual in question had a role in creating — no matter how small or large.

In the field of psychology, there have been numerous theoretical propositions and empirical studies attempting to explain the formation of psychological ownership. Several scholars have created frameworks based on decades of psychological research that capture key themes that have emerged time and again such as effectance and control of possessions, positive affect, and symbolic meaning and personhood. These frameworks span a range of formulations ranging from Targets-Antecedents-Consequences-Interventions to corrective dual-process models, among others. Some of the major themes found across frameworks include responsibility, accountability, identity, self-efficacy, belongingness, control, self-congruity, psychological closeness, object-knowledge, self-investment, and rights over the object.

Hegel's ideas of ownership stem from the notion that the "will" can be embodied in external entities, and that this embodiment is necessary for one's actualization as a person cannot come to exist without both relation to and differentiation from the external environment. While the specifics of theories vary, the investment of one's self, values, and identity as a means of developing feelings of ownership is a common theme that arises.

One of the most fundamental materialist theories is Locke's labor theory, which posits that "every man has a property in his own person," and thereby goes on to argue that when one mixes their labor with natural resources, the resulting good becomes their property - evoking the embodiment theory of personhood. "Bundle of Rights" views hold ownership as a set of contractual obligations between people in relation to property.

While there are many schools of philosophical thought that could be used to frame a discussion of ownership, two juxtaposing ones that encompass the duality of ownership related values are materialism and idealism. Materialist theories stem from notions of property as control over material entities, going as far as to stipulate that physical, material states are the ultimate determinants of reality, taking precedence over thought, consciousness, and abstract entities. On the contrary, idealism posits that something mental is the ultimate foundation of reality, and idealist theories of property and personhood are concerned with symbolic and mental conceptions of ownership. This dualistic framing captures both the tangible and intangible elements of ownership.

Engineering refers to the use of technical principles, such as mathematics, science, and technical know-how, to realize a design that best meets a given set of expectations, which are typically captured in a requirements specification.

Designing is the process of arriving at a plan, specification, prototype, system, or service—a design. In HCI, this often means designing a user interface and relevant parts of the underlying interactive system.

HCI focuses on people who use an interactive system or are affected by its use. This focus is often called being user-centered or human-centered to contrast it with a focus on the technology itself [423, 604].

Finally, interaction often involves co-adaptation between people and computers [646], meaning that both the user and the system learn and adapt to each other during interactions.

Interaction is, in other words, not a property of the system design or the user but something that emerges when they influence each other.

The development of technology for interactive computing systems has been an important driver behind the widespread adoption of computing we have witnessed in the last 50 years.

In HCI, evaluation refers to the application of some systematic methodology to attribute human-related values to an artifact, prototype, system, or process. Examples of such attributes include performance, experience, safety, and ethical aspects, such as the avoidance of bias or harm.

Programmability lends computers their power as tools. Computer programs can decompose complex activities into sequences of much simpler operations.

A special part of a computing system is the user interface. It is the part that the user can see and utilize to control the computer. Through the user interface, users can provide input and instructions to a computer and receive feedback from it. In short, the user interface enables interaction with a computer.

A mental model captures how people understand something. For instance, people have vastly different beliefs about how calculators work [598]. These beliefs can explain the errors and the issues they face when using calculators.

Interactive systems are tools that help users achieve their goals.

The remarkable efficiency, flexibility, and scalability of computers as tools boil down to the concept of a programmable machine capable of interpreting computer programs.