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.

In other scenarios, the agent may be continuously monitoring and analyzing theuser’s actions and proactively acting to assist the user.

nteraction between usersand agents opens up new ways of thinking about interaction.

Automation technology is capable of doing things on its own. Wecall such technology an agent, also sometimes called a software agent, intelligent agent, conversa-tional agent, personal digital assistant, or intelligent interactive system.

Wecall such technology an agent, also sometimes called a software agent, intelligent agent, conversa-tional agent, personal digital assistant, or intelligent interactive system.

Automation technology is capable of doing things on its own.

An exciting and distinctive aspect of this type of interaction is that users interact with some-thing that may have agency.

They may need to hand over control to a human driver in parts they cannothandle.

Semi-autonomous vehicles can drive parts of aroute autonomously.

They typi-cally do so by using some kind of AI that takes over part of a task from the user.

Our preliminary results indicate that there is an additional phase, the intention to learn, and three relating factors, self-efficacy, conversion readiness, and peer support, that significantly influence the acceptance of mobile technologies among the participants, but are not represented in the existing models. With these findings, we propose a tentative theoretical model that extends the existing theories to explain the ways in which our participants came to accept mobile technologies.

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].

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.

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Today's generations of older adults have not grown up with information and communications technologies that are widely available these days. Thus, there is "a natural confound of age and experience, since today's older adults are exposed to these technologies at a different point in their lives than today's young adults." [17]

Older people are less likely to have peers with sufficient technology experiences compared to their younger counterparts.

Incorporating these human factors and practical design suggestions for older adults, Fisk et al. proposed key recommendations for designing mobile devices for this age group [12].

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].

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].

One exception is the senior technology acceptance model (STAM) [28]. Using TAM, UTAUT, and several other works as theoretical underpinning, Renaud and Biljon proposed a model to explain older adults' mobile phone adoption.

Several studies have attempted to determine older adults' acceptance of technologies in general, and healthcare-related systems in particular, using the UTAUT framework. (e.g., email [14], a telehealth service [7]).

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]).

Seniors have historically been late adopters to the world of technology compared to their younger counterparts [24, 40].

Nowadays, older adults are increasingly adopting and adapting to information and communication technologies [5].

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].

We also identified the factors that are critical to older adults but did not appear in the existing models. Finally, we applied the existing vocabulary to our model to comply with the conventional terms in the field.

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.

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.

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. Initial open codes are primarily descriptive. These may be combined into more sophisticated related sets of descriptors, in which each set is referred to as an axial code. Subsequently, axial codes are combined into more theoretically powerful code complexes, called selective codes. Our approach included a process of open coding, axial coding, and selective coding.

In lightof our preliminary results, we propose a tentative theoreticalmodel that extends the existing theories to explain the waysin which the participants come to accept (or reject) mobiletechnologies.

Lastly, while our findings are based on only 24 participants, the sample size is commensurate with the Ground Theory approach.

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

With these findings, we propose a tentative theoretical model that extends the existing theories to explain the ways in which our participants came to accept mobile technologies.

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.

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.

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.

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.

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

Although many researchers have sought to understand and predict technology acceptance behavior, there has been relatively less effort to build a theoretical model for older adults, with one exception (STAM).

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].

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].

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.

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.

The beauty of the GP-TSM technique lies in its simplicity: at its core, all GP-TSM does is change the visual saliency of words by adjusting their opacity. This preserves the integrity of the original text and minimizes "ergonomic obtrusiveness" [100] while providing readers with a form of "contextual cuing" to arm them with "incidental knowledge about global context", which they can harness to better assign visual attention and memory when reading [40].

Furthermore, according to Stevens's power law, people perceive changes in gray scale not linearly, but rather by a factor of approximately 0.5 [71]. For instance, a threefold increase in opacity might only be perceived as 1.5 times more significant, further complicating the differentiation of levels.

This sequence resonates with efficient content absorption strategies highlighted in speed reading literature, where readers first capture the gist and then delve deeper [1, 63]. The interface, therefore, may inadvertently facilitate this structured, layered reading approach, which might explain the improvement in reading efficiency and comprehension.

We adopt the term "saliency" based on its definition (a "bottom-up, stimulus-driven perceptual quality which makes some items stand out from their neighbors") [42], and its use in augmented reality [85, 88], computer vision [17, 55], and cognitive science [37, 56].

Modulating text saliency is a widely studied aspect of textual information representation. This technique modifies the visual attributes of text to promote words of interest and guide readers' attention, making pertinent information more perceptible and thereby enhancing comprehension and the user experience [12, 42].

compressive summarization aims to select the shortest subsequence of words within a sentence that yields an informative and grammatical sentence [64]. This framework allows for a more concise representation of the original content while retaining the essence of its meaning.

Given the cognitive effort reading requires, readers frequently resort to skimming, which is a rapid, selective, and non-linear form of reading [2]. Eye tracking studies [30, 74] validate that such behavior is extremely common. However, multiple studies have suggested a significant trade-off between reading speed and comprehension [65, 66, 76, 87].

Specifically, automated summarization methods can introduce multiple types of errors: "crimes" of omission, hallucination, and misrepresentation.

Automated text summarization techniques, including but not limited to crowd-powered systems [10], prompting large language models (LLMs) [105], and other AI technologies, can address a subset of these difficulties, i.e., the resulting text may be shorter, with simpler sentence structures and fewer unusual words [62]. However, unless there is information within the original document that is truly redundant, the result is a lossy representation of the original document, regardless of whether the process is abstractive or extractive.

Our goal is to modulate the saliency of words in the original text so that users can easily bypass certain words during skimming while maintaining an uninterrupted reading flow.

Be resilient to AI errors by enabling the reader to (a) notice, (b) have enough context to judge, and (c) easily recover from, automated decisions they disagree with.

Support skimming without interrupting flow. The system should improve skimming of text while minimizing the impact on the user's natural reading flow. In particular, as much as possible, it should avoid presenting users with salient text that is unparsable as a coherent thought, i.e., the system should present a complete sentence rather than a phrase or sentence fragment.

Support reading at multiple levels of detail. The system should help users navigate the full complexity of a text, shifting focus seamlessly between different levels of semantic coverage, or granularity, from the big picture to the fine details.

Integrate seamlessly into existing reading experiences. The system should complement and not interfere with the existing digital reading workflow that people are used to. It should provide all the functionalities in the same view, minimizing the overhead of mode and context switching.

Remain faithful to the original text. The system should not automatically reword or add new words or phrases to the original text. It should preserve the original text, while rendering it in a way that aids reading, skimming, or information retrieval.

We aspired to design a text rendering interface that alleviates some of the cognitive demands of reading, skimming, or performing information retrieval on natural language documents—particularly those with long, complicated sentences—without compromising the integrity of the original content.

Established theories of human cognition describe how exposure to variation and consistency within prescribed structures can help people more robustly form mental models of a phenomenon, e.g., how an LLM behaves. Specifically, in line with Variation Theory [35], the features we instantiate identify patterns of consistency (Figure 1d, "Exact Matches"), variation (Figure 1c, "Unique Words"), or both (Figures 1a, 1b, "Positional Diction Clustering (PDC)"—a novel algorithm we introduce in this paper). In line with Analogical Learning Theory [13], PDC highlights analogous text across LLM responses, i.e., positionally consistent and similar in diction, such that users can see emergent relationships.

users may want to select the best option from among many, compose their own response through bricolage, consider many ideas during ideation, audit a model by looking at the variety of possible responses, or compare the functionality of different models or prompts.

One prior piece of HCI work, ParaLib [51], does explicitly exploit these theories for system feature design, but does this in the domain of code.

There are two hypothesized benefits of this view. One is based on an understanding of human perception: the grid layout should help users compare more LLM responses because the spatial arrangement assists their memory. The other benefit is based on Variation Theory, which posits that discerning the impact of a critical aspect, for example model temperature, is only possible when experiencing variation along that dimension, isolated from variation along other dimensions.

Given that the features implemented in this work are in line with design implications of Variation Theory and Analogical Learning Theory, the results suggest that there may be further utility of these theories for guiding the design of future systems that help users make sense of data and form mental models from examples.

Theories of human concept learning suggest that a key step in forming accurate, robust mental models of a phenomenon is to be able to discern the underlying dimensions of variation (Variation Theory) and any latent structures beneath superficial details (Analogical Learning Theory). By detecting and communicating which sentences are both structurally analogous (by virtue of their position within the response) and semantically related (by virtue of highly overlapping content), users should be able to more easily identify emergent structures, as well as compare and contrast particular compositions of structural elements across responses and syntactic elements that may vary in meaningful ways across analogous sentences within those responses. These theories assert that these subtasks are key ingredients in forming those robust accurate mental models, i.e., learning from the LLM responses in order to better perform their overarching task.

In this work, in line with Variation Theory, the existing and novel features instantiated and described in the next subsection collectively identify patterns of consistency, variation, or both; they are explicitly designed to make emergent dimensions of consistency and variation easier for the user to perceive.

Variation Theory describes how helping people perceive the different dimensions of consistency and variation across examples (here, LLM responses) of the object of learning helps them more quickly and robustly leap to more accurate mental models. Analogical Learning Theory describes how people can form mental models or schema from perceiving structural analogical relationships across superficially varying examples (again, here LLM responses).

Variation Theory [35] and Analogical Learning Theory [13, 14] each propose mechanisms for how people may conceive and update their mental models based on concrete examples, or use their mental model in new situations.

participants seemed to prefer engaging with the text directly without having to articulate a lens with which to look at the corpus, since their analysis goal may be initially under-defined.

we want to decorate text to show pre-computed relationships, such as string matches or analogous sentences, across responses. In this way, we help users shift cognitive bandwidth away from identifying overlapping or \

In our formative study, we found that automated analysis rarely captured what the participants were looking for when inspecting LLM responses.

We want to support a wide range of tasks that involve sensemaking. For example, we want to support the detection of similarities and differences between individual responses as well as groups of responses, and support the detection of

We aim to make 10s to 100s of LLM responses cognitively comfortable to peruse, as this was the scale we found to be most relavent in our formative study.

Our preliminary evaluation shows that users pay attention toitems with kinetic scrolling feedback during search, recogni-tion, and skimming tasks.

Tomodel degrees of interest (DOI) for a variety of existing webpages, we introduce social wear, a method for capturing DOIbased on social signals that indicate collective user interest.

This allows users to quickly find interesting content while ex-ploring without further cluttering the limited visual space.

To support nav-igation of long documents on touchscreen devices, we intro-duce content-aware kinetic scrolling, a novel scrolling tech-nique that dynamically applies pseudo-haptic feedback in theform of friction around points of high interest within the page.

Navigating long documentswith small screens can be challenging both physically andcognitively because they compel the user to scroll a great dealand to mentally filter for important content.

Long documents are abundant on the web today, and are ac-cessed in increasing numbers from touchscreen devices suchas mobile phones and tablets.

For example, in control-theoretical analysis, we see the interaction as dynamically changing states.

For example, in interaction-as-rationality, courses of action emerge as a joint function of the user's goals and capabilities and the properties of the environment.

For example, statistical models, such as Fitts' law, describe a relationship that is considered a statistical determination.

For example, interaction-as-tool-use focuses on this idea (Chapter 19). Tools change people and their activities; in turn, this changes the tools.

An interactive AI system can possess an objective function it pursues when acting, for example, when correcting a character it flags as a typo.

In the case of particle physics, the propositions that make up a theory may concern the nature and behavior of particles.

HCI theories contain statements that link humans and technology and possibly some outcomes (e.g., poor usability, high user experience).

Suchtechniques provide different levels of automation.

tool use allows us to assess text entry in terms of its utility—how well it supports users in thatpursuit—and its usability—how easy it is to enter text and learn how to enter text.

The basic assumption under-lying theories of rationality is that people choose how to act according to their benefit; in otherwords, they are rational.

For example, word predictionusage may not be beneficial if the word suggestions are poorly suited to the individual’s language orif the user can type words faster than the time required to scan and select among word suggestions.

The effect of such automation critically dependson how well its design appreciates the actor’s unique capabilities.

Text entry can also be seen as a task where different subtasks are shared between the human and the computer (Chapter 20).

One example is autocorrect, which automatically corrects typing errors while the user is typing. Another example is the use of word predictions, which allow the user to select a word from a set of word suggestions instead of typing out the word in full.

For example, text entry methods such as eye typing are designed to allow nonspeaking users with motor disabilities to enter text using their eye movements only.

Persons with varying capabilities and backgrounds need toeffectively enter text, or these systems fail as tools.

Tool use alsotriggers the concept of accessibility.

Thinking about text entry as

Text entry is also a good example of tool use (Chapter 19). A text entry method is a tool that allows the user to communicate with someone or something, typically other people or a service, using asynchronous text messages and longer documents.

Text entry can also be carried out via spoken dialogue.

When you enter a letter (a turn), a small pop-up appearsfor you to confirm your press (a turn). A word prediction list may show possible completions of theword. At every press, these are updated, over time forming a graphical dialogue between the userand the system.

The essence of dialogue is that communi-cation is organized in sequences of turns.

Text entry can also be seen as dialogue (Chapter 18).

For example, we may choose a song to play to regulate our emotions or influence co-present others.

Many things we do with computers, such as listening to music or postingmessages, are about control.

Moreover, control theory offers a broad view of interaction; itis not limited to input.

Viewing text entry as a control problem can providea more in-depth understanding of the human perceptual and motor control aspects of text entry,such as a user’s ability to touch individual letter keys on a touchscreen keyboard or a user’s abilityto perform a touchscreen gesture.

Interaction is understood as actions and reactions that minimize the discrepancybetween the present state and the goal state.

In text entry, the theory provides us with concepts andmetrics that aid our understanding of typing performance.

For example, the information rate ofa text entry method can be quantified using the concept of throughput from information theory.

As another example, text entry often relies on language models, and such modeling is based oninformation theory.

A fundamental goal of interaction is to provide users with tools that allow them to achieve goals that they would otherwise not have been able to achieve.

One goal of interaction is communicating intentions, for example, issuing commands to a computer, selecting graphical elements in image processing software, or entering text.

First, text entry can be seen as the transmission of typed information to a computer.

Informationtheory provides a rigorous formalism to understand and quantify interaction via the concept ofpassing messages through a noisy channel.

This con-cept, communication of information, is rooted in information theory (Chapter 17).

Accessibility is an extension of usability to ensure that as many people as possible find a tool easy to operate, regardless of individual capabilities.

Usability refers to the ease with which a tool is operated.

Utility captures how well a tool supports users in achieving their goals.

A model is a formally expressed set of propositions that follows some axiomatic system, such as algebra, logic, or a programming language.

The explanatory power of a theory refers to the empirical accuracy and coverage of the explanations offered by the theory.

A latent factor is something that affects observations about interaction without being directly observable.

Finally, in quantitative determination, interaction is described as a continuous unfolding of states.

In structural determination, the end results of the interaction are jointly determined by multiple causes that make up the whole.

In statistical determination, there is a stochastic relationship between the two entities.

In mechanical determination, an antecedent determines a consequent.

Propositions characterize entities and link them to other entities, some of which are conceptual.

To conclude, Bunge’stypology is useful in understanding the landscape of HCI theories.

An interactive AI system can possess an objective function it pur-sues when acting, for example, when correcting a character it flags as a typo.

However, computers can also have goals or at least algorithmic objectives. This adds another levelto teleological determination.

Intentionality is a litmus test for HCI theories.

All theories of interaction discussed in this part assume intentionality: People have goalsthat have a (mutually) causal role in interaction.

In teleological determination, goals or purposes determine interaction in some way.

One type of determination is shared by all theories of interaction in HCI: teleological determina-tion.

For example, in control-theoretical analysis, we see the interaction asdynamically changing states.

Each state dynamically leads to a new state depend-ing on the forces involved.

Finally, in quantitative determination, interaction isdescribed as a continuous unfolding of states.

For example, ininteraction-as-rationality, courses of action emerge as a joint function of the user’s goals and capa-bilities and the properties of the environment.

In structural determination, the end results of theinteraction are jointly determined by multiple causes that make up the whole.

Here, the statistical model (regression) links the time it takes for a user toelicit a response to the design of the task environment (distance and width of buttons).

For example, statistical models, such as Fitts’ law, describe a relationship that is considered a sta-tistical determination.

In statistical determination, there is a stochastic relationship between the two entities.

The pressing of a button would count as a mechanistic determination.

In mechanical determination, an antecedentdetermines a consequent.