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.

Programmability lends computers their power as tools.

It is an egocentric fallacy to assume that others are like us—to attempt to explain other people by reference to one's own experience.

A study of large-scale web-clicking data employed this theory to explain why certain distributions of web page hits emerge on web sites. Huberman et al. [362] proposed a mathematical model that assumes that at any page, users decide to continue clicking as long as its information scent exceeds some threshold. This information scent can be computed using information foraging theory (IFT).

IFT proposes that information-seeking behavior develops to maximize the rate of information gained per unit of time or effort invested. Note that the term information does not refer to the information-theoretic concept but to subjective interest; here, information means anything that users find interesting.

The roots of IFT lie in optimal foraging theory, originally proposed in biology, which describes the hunting and food search behaviors of animals.

A solution strategy—or

Computational rationality is a theory and a modeling approach rooted in bounded rationality and bounded optimality. Recent applications include typing (Figure 21.7), pointing, driving, multitasking, menu selection, and visual search.

The space of all states, actions, and rewards is called the environment.

Costs are negative rewards that the user incurs for transitioning between states or for being in states that are not good for them.

Visual statistical learning is a research topic in perception that studies how the statistical distribution of our environments affects the deployment of gaze.

bounded rationality states that we are only rational to the extent allowed by the involved constraints, or bounds.

An agent is an actor with the ability to choose actions in pursuit of some goal or reward.

It assumes that human long-term memory evolved to help survival by anticipating organismically important events. It is evolutionarily important to remember things that are important for survival. Therefore, the expected value of remembering a thing in the future should affect the probability of recalling it.

According to rational analysis, behavior is sensitive to the statistical distribution of rewards in the environment that a user has experienced. Users learn the way rewards are distributed through continued exposure to an environment and adapt their behavior accordingly. A user's behavior is rational because it is tuned to the distribution of rewards in the environment—the ecology.

The theory assumes that users are 'computationally rational': When picking an action—or deciding how to get from the present state to a state with positive rewards—users are as rational as their cognition allows. Users act based on their often inaccurate and partial beliefs, which they have formed via experience.

Computational rationality is a theory and a modeling approach rooted in bounded rationality and bounded optimality. Recent applications include typing (Figure 21.7), pointing, driving, multitasking, menu selection, and visual search. Its core assumption is that users act in accordance with what they believe is best for them.

Theories of rationality can be used to inform the design of information environments, addressing considerations such as how to distribute and shape information.

These four theories differ in the factors they include and how the agent's decision-making problem is formulated. As such, the theories differ in how easily they help us find a solution to the user's decision-making problem.

Theories of rationality can make quantitative predictions on user behavior in settings where the user's environment and goals (rewards) are well known.

Theories of rationality do not describe what a user has done but ask what that user could have done. Rational behavior refers to behavior that seeks to maximize the expected utility to the user.

Descriptive theories attempt to capture causes behind behaviour that, from a normative perspective, may appear irrational. This view enables predictions of user behaviour in real-world circumstances.

authors sometimes fall back on graphical editors like Google Slides, PowerPoint, Adobe Illustrator, and Mathcha to augment formulas.

Authors of typeset formulas augment those formulas to make them easier to understand.

lab study participants frequently made errors related to incorrectly matched braces when using a LaTeX baseline to augment formulas.

Authors in Head et al. [30] described that "code gets horrible looking" as macros are added to it to specify augmentations.

FreeForm is a projectional editor optimized for notation augmentation. This paper defines the key projections for the text: textual LaTeX, a formula render with tree-aware selections, and a property/hierarchy view.

The key ideas are that authors can interact with different projections of the formula at will, and graphical edits are ultimately connected back to the formula via markup.

Ply allows users to develop, test, and tweak program components, exploring possibilities for how data can be transformed and composed to discover and achieve goals. This style of programming can support many use cases, even those not traditionally considered in the trigger-action programming model.

Through the combination of these features, Ply allows users to develop, test, and tweak program components, exploring possibilities for how data can be transformed and composed to discover and achieve goals.

Frequently, code-generation systems focus on building and then refining a full working application, using visibility of the full underlying code as a fallback when users need to build understanding of the generated program.

the simplicity of links between triggers and actions limits the expressivity of such systems.

Ply provides users with tools to build components incrementally, creating new layers on top of existing components that "wrap" the behavior of underlying layers.

When building a linkage, Ply identifies parameters of the implementation that may be tweaked to customize the behavior of the linkage.

Each sensor is accompanied by a glanceable visualization of the sensor's output payloads on the Ply canvas. This visualization is specific to the sensor and its output type, showing the most critical information for evaluating whether the sensor is behaving as expected.

Ply uses a server program written in TypeScript to make code generation requests to a large language model and to execute the resulting code, which passes messages to and from sensors and actuators.

Each layer in Ply tracks its dependencies; sensors receive data from their dependencies, actuators push data to their dependencies, and linkages each refer to exactly one sensor and one actuator dependency. Collections of layers and linkages in Ply are isomorphic to node graphs in node-based programming languages.

Code generation offered by large language models can serve to author this glue code for trigger-action programs, allowing for data from triggers to be mapped to input data for actions automatically even when their native data formats or intended functionality do not match exactly.

Ply allows users to develop, test, and tweak program components, exploring possibilities for how data can be transformed and composed to discover and achieve goals. This style of programming can support many use cases, even those not traditionally considered in the trigger-action programming model.

In UTAUT, Venkatesh extended TAM by incorporating two constructs not directly related to a system's perceived properties, but derived from external aspects: social influence and facilitating conditions. Additionally, UTAUT posits four mediating factors that moderate the impact of each key construct on usage intention and behavior, namely gender, age, experience, and voluntariness of use.

While our key focus is to build a theoretical model that explains the process through which older adults accept (or reject) mobile technology, which can provide theoretical guidelines when designing a technology, and which may also be able to generate new investigations and experiments.

We analyzed the second-round interview data using inductive and deductive approaches informed by grounded theory and other qualitative analysis methods [33, 22].

We inductively analyzed the first-round interview data using thematic analysis based on a grounded theory approach [33]. Grounded theory methods build theory iteratively from the data, using rigorous coding practices.

Technology acceptance has been widely studied, and several models have been proposed and tested [10, 37]. However, the HCI literature lacks a comprehensive explanation of technology acceptance among older adults.

Azjen's theory of planned behavior [1, 2] posits that a specific behavior is the result of an intention to carry it out, and that intention is determined by attitudes, norms, and the perception of control over the behavior. Drawing upon this theory of planned behavior, Davis et al. developed the technology acceptance model (TAM) [10].

To summarize, existing models of technology acceptance can provide a partial explanation of older adults' behaviors of mobile technology acceptance. However, we also identified critical elements that are not represented in the existing models. Components in red boldface in Figure 3 provide a preview of the new elements we have identified and their relationship to the components proposed in earlier models.

we found out that the existing models of technology adoption require new theory components to be able to describe technology adoption processes of our participants. In particular, we identified an additional phase that is prominent among the participants, intention to learn, but did not appear in prior models. Then, we identified three new factors that significantly influence their technology acceptance but which are, again, not represented in the existing models: self-efficacy, conversion readiness, and peer support.

Triangulating the empirical findings from our preliminary results with the existing theoretical models, we proposed an extension of the existing theoretical models that explains the technology acceptance behavior of our participants who were aged 60 or over. Our proposed model incorporates key elements of prior models and introduces novel components that significantly influence the participants' technology acceptance, namely one new phase, intention to learn, and three factors, self-efficacy, conversion readiness and peer support.

Consolidating our preliminary findings with the existing models, we propose an extended technology acceptance model for older adults illustrated in Figure 3. Extending to the predecessor theories, our tentative model introduces the perceived effort of learning a new technology as an obstacle for older adults' technology acceptance, which has not been reported in any studies of younger adults' technology acceptance.

Another stream of efforts sought to understand physical and cognitive performance of older adults in interacting with mobile technologies. Studies have shown that typical interaction components and techniques of a smartphone often prevent older adults from smooth and instant interactions with it. For example, the small size and the low contrast of buttons on a mobile display has a significant negative influence on interaction performance such as speed and accuracy [18], and decline in motor skills is correlated with time required to complete a task [30].

Lee and Coughlin reviewed studies of older adults' technology acceptance and identified ten factors that are critical facilitators or determinants of older adults' acceptance of technology: value, usability, affordability, accessibility, technical support, social support, emotion, independence, experience, and confidence [20].

Many studies have empirically investigated technology acceptance practices among older adults. While diverse in detail, most works point out that an individual's personal context [38] and the social context [36] in which the technology is introduced are the primary factors influencing the perception of, experience with, and evaluation of new technological developments among older adults [19].

Seniors have historically been late adopters to the world of technology compared to their younger counterparts [24, 40]. As a result, older adults and their adoption of new technologies have been a topic of active research since the advent of consumer technologies (e.g., automated teller machine [32], scanner-equipped grocery stores [41], electronic funds transfer [15]).

Nowadays, older adults are increasingly adopting and adapting to information and communication technologies [5]. For example, smartphone ownership among older adults has significantly risen in recent years [3]. However, its adoption levels among older adults in the US still sit at 27% as of 2015, whereas some 85% of Americans aged 18-29 are smartphone owners [31].

Consolidating our preliminary findings with the existing models, we propose an extended technology acceptance model for older adults illustrated in Figure 3. Extending to the predecessor theories, our tentative model introduces the perceived effort of learning a new technology as an obstacle for older adults' technology acceptance, which has not been reported in any studies of younger adults' technology acceptance.

our key focus is to build a theoretical model that explains the process through which older adults accept (or reject) mobile technology, which can provide theoretical guidelines when designing a technology, and which may also be able to generate new investigations and experiments.

Triangulating the empirical findings from our preliminary results with the existing theoretical models, we proposed an extension of the existing theoretical models that explains the technology acceptance behavior of our participants who were aged 60 or over.

Again following grounded theory practices from [33], we compared the model that emerged from our data with existing theoretical models of technology acceptance to determine differences and similarities between them.

Employing the grounded theory method [33], we allowed recurring themes and concepts in relation to technology acceptance behaviors to arise from the data itself. Then, by triangulating our empirical findings with existing theoretical models from the literature, we found out that the existing models of technology adoption require new theory components to be able to describe technology adoption processes of our participants.

Using TAM, UTAUT, and several other works as theoretical underpinning, Renaud and Biljon proposed a model to explain older adults' mobile phone adoption.

Extending the original TAM and consolidating the constructs of several other existing models, Venkatesh et al. proposed the Unified Theory of Acceptance and Use of Technology (UTAUT) [37].

such systems should be designed to take into account the fact that automated results will inevitably be incorrect on occasion.

users can be trained to understand not only the decision-making tasks but also the underpinning capabilities and limitations of the automation solution.

automated systems that indicate when automation may fail or has failed are more likely to gain an appropriate level of trust from users.

When the system fails, users need to be able to redirect it. To avoid biases and discrimination, some level of transparency and explainability is required.

We propose that cognitive engagement may be a useful construct in conceptualizing human engagement with AI and can help to distinguish between passive engagement, when individuals simply follow AI recommendations, and deeper forms of engagement, when they critically examine these recommendations and compare them with their own knowledge and judgement.

An outcome of deeper cognitive engagement would be an ability to reject information that is inconsistent with individuals' own knowledge and beliefs, and to adjust their own knowledge to incorporate new information.

Given continuous concerns regarding the reliability and trustworthiness of AI, human critical engagement may be a necessary component of successful human-AI interaction, particularly in domains with a high cost of errors, such as health and medicine.

In many areas of human enterprise, individuals increasingly rely on Artificial Intelligence (AI) to inform their decisions and choices.

How do people process the information and advice they receive from AI, and do they engage with it deeply enough to enable learning?

When people receive advice while making difficult decisions, they often make better decisions in the moment and also increase their knowledge in the process.

Incidental learning typically occurs as a byproduct of other activities (e.g., problem solving, advice seeking) rather than as a result of explicit or formal educational activities [47]. However, like formal learning, incidental learning can only occur if people engage deeply with information.

This would suggest that this design did not fully reach the constructive level from the ICAP framework [15, 16].

While prior work has highlighted the critical role of explanations in promoting learning [10, 18], our work additionally demonstrated the value of creating the conditions for learners to engage constructively (as defined in the ICAP framework [15, 16]) with the explanations.

We hypothesize that the observed difference in learning gain was due to the degree of cognitive engagement with AI-generated information. When individuals were provided with a solution to their task (in the form of a decision recommendation), they did not need to engage deeply with the explanations and could simply proceed with action. However, when they needed to arrive at their own decisions, they needed to engage with the provided explanations and synthesize the information to arrive at the conclusions.

Rotgans and Schmidt attributes these differences in cognitive engagement to different degrees of autonomy afforded by different learning tasks [59].

While some authors discuss cognitive engagement as a personal trait of a student that does not depend on context [3], others suggest that cognitive engagement depends on the structure of each task [15, 30, 59].

al. propose Interactive-Constructive-Active-Passive (ICAP) framework to describe a continuum of learning behaviors (from passive, to active, to constructive, to interactive) and argue that each subsequent level leads to an increase in cognitive engagement and learning [15, 16].

Research in cognitive psychology suggested that people process information on different levels. Deep processing occurs when individuals engage in more meaningful analysis of information and link it to existing knowledge structures [2]. In learning sciences, depth of processing is often associated with the degree of cognitive engagement, which is described as a "psychological state in which students put in a lot of effort to truly understand a topic and in which students persist studying over a long period of time." [59].

Researchers in learning sciences use the term "cognitive engagement" to describe learners' engagement with the learning process. When people are cognitively engaged with instructional process and materials, they are more likely to benefit from instruction and are more likely to acquire new skills and knowledge.

Addressing this feedback, by training more abstractive CTR models or performing a post-hoc abstraction of the generated summary, is an interesting future direction we plan to explore.

Additionally, our tool currently helps users in the reviewing step solely with the alignment functionality. Future work should add additional assistance during this step in the form of suggested improvements to selected unsatisfactory content in the summary, in addition to the alignment feature.

Future work should expand the application's capabilities to the multi-document setting, both in terms of the backend models and in terms of accessibility and intuitiveness of the application's frontend design.

Lastly, exploring strategies to scale SUMMHELPER to a multi-document setting presents another promising avenue for future investigation.

Additionally, in light of some user feedback, another interesting extension includes developing more abstractive consolidation and fusion models, which would offer control over the level of abstractness in the outputs.

Future work may include investigating more effective semantic strategies to locate summary-source alignments with acceptable latency.

highlights are incorporated into the input text with special markups, <extra_id_1> and <extra_id_2>, marking the beginning and end of each highlighted span, respectively. In our configuration, we set the maximum input length to 4096 and the maximum target length to 400. A greedy decoding strategy was used in order to optimize the decoding speed.

Our approach locates the longest common subsequence (LCS) between the lemmas of each input sentence and each summary sentence, followed by several heuristics to filter out irrelevant LCSs

For the summarization model, we used a BARTlarge model (Lewis et al., 2019) trained on the CNN/Daily Mail dataset (Hermann et al., 2015), selected for its noticeable popularity.

For the initial auto-consolidation, we deploy an available Controlled Text Reduction model (Slobodkin et al., 2023), which is a Flan-T5large model (Chung et al., 2022), finetuned on the highlights-focused CTR dataset.

we deploy the ExtractiveSummarizer model from the TransformerSum library. The model, a RoBERTabase (Liu et al., 2019) trained on the CNN/DailyMail summarization dataset (Hermann et al., 2015), operates as a binary classifier.

This step coincides with the recently introduced Controlled Text Reduction task (CTR; Slobodkin et al., 2022), which produces a coherent fused version of the content of marked spans ("highlights") in a source document, as interpreted within the context of the full text.

SUMMHELPER is a modular system consisting of separate components, each performing one subtask, allowing user modifications of that sub-task's output. Such decomposition has been studied before in the context of fully automated summarization, with several works separating the process into salience detection and generation components (Barzilay and McKeown, 2005; Li et al., 2018; Ernst et al., 2022). These works focused on optimizing each component as part of a fully-automatic summarization process in order to improve the overall performance of the model. In contrast, our work uses this modularity to not only improve overall system output, but to also give more control to the user over each step in the summarization process.

Our objective in this paper is to promote such a human-involved approach to summarization, allowing to better tailor the eventual output to real-world user needs, and to synergize the efficiency of the computer with the quality of the human (Hoc, 2000; Pacaux-Lemoine et al., 2017; Flemisch et al., 2019).

it is crucial to prioritize and direct human efforts toward more "suspicious" outputs from LLMs

we advocate a collaborative approach where humans and LLMs work together to produce reliable and high-quality labels

LLM annotators and human annotators should not be treated the same, and annotation tools should carefully design their data models and workflows to accommodate both types of annotators

it is advisable to either mask any confidential information or only use in-house LLMs

it is recommended that the format of a prompt be similar to the one used in training as some LLMs have different prompt format than the others

the selection of label options may work better if it is similar to common options for given tasks, such as [positive, neutral, negative] > [super positive, positive, ..., negative] for sentiment classification

designing an annotation task and a prompt similar to more widely used and standardized NLP tasks is beneficial

errors encountered during API calls are handled in two ways: handle within our system or delegate to users. We handle known LLM API errors that can be solved by user-side intervention. This would be in cases such as a Timeout or RateLimitError in OpenAI models

errors such as APIConnectionError in OpenAI models occur because of an issue with the LLM API server itself and requires intervention from OpenAI.

While MEGAnno+ is designed to support any open-source LLM or commercial LLM APIs, in this work, we only demonstrate OpenAI Completion models for clarity and brevity.