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.

Theories of rationality have increased our understanding of how users fail to be optimal.

MDP is a formalism that originates from studies of sequential decision-making in artificial intelligence and operations research. Instead of the choice between n actions, MDP deals with environments where rewards are delayed (or distal). This requires an ability to plan actions as part of sequences instead of one-shot choices.

Information scent refers to a user's intuition that a cue in the interface represents the information needed. It is an estimation of relevance based on a proximal cue.

IFT proposes that information-seeking behavior develops to maximize the rate of information gained per unit of time or effort invested.

Information foraging refers to information-seeking activities such as navigating, exploring, comparing, searching, or manipulating information contents in an information space.

A payoff refers to the benefits that are left after the costs have been subtracted.

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.

To state that a user's choice is rational means that it is selected with the expectation that it yields the highest utility out of the available options.

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.

Rational analysis is a theory of rational behavior proposed by Anderson and Schooler [21]. It examines the distribution of rewards in the environment to explain how users adapt their behavior. According to rational analysis, behavior is sensitive to the statistical distribution of rewards in the environment that a user has experienced.

They share a focus on the emergence of interactive behavior; in other words, they predict how users choose to behave in certain given circumstances.

Utility refers to the agent's consideration of positive and negative rewards when deciding how to act.

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.

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

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

The term satisficing is used to describe how users tend to behave when facing a complex decision-making problem. It refers to settling on a satisfactory but not optimal solution in the normative sense.

The concept of rationality has its roots in economics, where it was developed to study how peo-ple should act in economic decision-making. In such settings, the idea is that people reach theirgoal, such as maximizing their return, by maximizing utility.

This chapter introduces the notion of rationality. This allows us to provide explanations forinteractive behavior due to users attempting to make the most out of the choices available to them.

The author wants to augment the formula to explain the meaning of the terms on either side of the arrow—first

they often benefit from being augmented with descriptive elements, such as labels describing the meaning of an expression or colors linking an identifier to its description in the text.

In this walkthrough, the author is trying to add labels to the formula V(s_t) ← R_t to describe the meaning of its terms in an article they are writing.

Our design was motivated by two major goals for notation authoring. These goals followed from recent studies of notation augmentation [30, 71] and conversations with scientists who had experience writing notation in instructional materials and research communications (4 professors, 2 graduate students, R1–6).

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.

We define the key projections as markup (in this case, LaTeX), an annotatable render, and a structure hierarchy view. Augmentations are made easy to invoke, and projections are kept synchronized and co-present so that authors can shift between representations as is expedient to them.

the challenge of using these tools is that annotations are unmoored from the structure of the formula and must be redone whenever the formula changes. Authors must perform precision positioning and sizing operations that could be inferred from the coordinates of the augmented expressions.

these markup languages can require cumbersome and error-prone editing, arising from the intermixing of annotation markup with the underlying formula. Participants in a study by Wu et al. [71] identified difficulty with debugging nested braces and locating markup to edit.

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, a projectional editor wherein authors can augment formulas—with color, labels, spacing, and more—across multiple synchronized representations. Augmentations are created graphically using direct selections and compact menus. Those augmentations propagate to LaTeX markup, which can itself be edited and easily exported.

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.

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

Ply offers this LLM-supported program decomposition supported by visualization and parameterization UIs, permitting users to use interactions beyond chat to compose their programs incrementally.

designing complex behavior can be a difficult programming task, and program representations in end-user programming tools may not be well-suited for heavy programs.

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.

users can develop, test, and tweak program components, exploring possibilities for how data can be transformed and composed to discover and achieve goals.

It encourages program decomposition into "layer" abstractions, It automatically creates visualizations of event payloads at layer boundaries to help users understand layer behavior without having to read the underlying generated code, and It constructs ad hoc parametrization interfaces that allow users to configure important dimensions of the behavior of each layer without having to re-author it.

Ply maintains the simplicity of a straightforward connection between a trigger and action but provides a structure within which users can enlist an LLM to specify the behavior of each trigger and action.

However, such LLM-authored code, especially when implementing nontrivial logic, can be difficult to specify, understand or debug. Users need appropriate tools and handles to understand and make changes to the computation that is being performed in such code.

Trigger-action programming offers an elegant interface to construct simple programs that result in customized behavior for software or devices.

Trigger-action programming has been a success in end-user programming. Traditionally, the simplicity of links between triggers and actions limits the expressivity of such systems. LLM-based code generation promises to enable users to specify more complex behavior in natural language. However, users need appropriate ways to understand and control this added expressive power.

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.

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. 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.

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.

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.

We identified three distinct factors that influence older adults' technology acceptance behaviors, particularly the intention to learn phase, that are not represented in prior models: self-efficacy, conversion readiness, and peer support.

The difference between analysis automation—inference—anddecision automation is that in the latter the system must make implicit or explicit assump-tions about costs and values inherent in all decisions.

Examples of decisionautomation include route planning and adaptation, such as to avoid bad weather, and systems pro-viding medical diagnosis support.

Decision automation means deciding and selecting appropriate actions among alternatives.This type of automation corresponds to the third human information processing state, decision-making, which the machine is either augmenting or replacing altogether.

An example of low-level automation is the extrapolation or prediction of data over time,such as a system predicting a trend for the output of an industrial plant based on historical sensordata. An example of moderate- to high-level automation is a system integrating multiple sources orinput variables. This could be a display with emergent perceptual features, such as an optical see-through display with a landing strip intended to assist a pilot in landing an aircraft. An exampleof high-level automation is a context-dependent summary of data.

Analysis automation refers to the automation of information analysis and involves inferentialprocesses. It corresponds to the second human information processing state: perception/workingmemory.

An example of low-level automation is assistance in sensor adjustment, such as a system mechanically moving a radarsensor to lock on a detected target. An example of moderate automation is a system organizinginformation according to criteria such as a priority list or highlighting information based on staticor dynamic criteria. This could be, for example, a display highlighting the rate of change in somevariable of interest. This could be indicated by increasing the intensity of some pixels more rapidlythan others in the display.

Acquisition automation corresponds to the first human information processing stage, sensoryprocessing, and it is realized by the system sensing and registering input data.

Types of automation: The types of automation can be understood by viewing a human operatoras a simple four-stage model of human information processing:1. Sensory processing2. Perception and working memory3. Decision-making4. Response selection.

The framework uses two sets of evaluation criteria to help designers determine the appropri-ate type and level of automation for each application. The primary evaluation criteria concernthe impact of the chosen types and levels of automation on human performance. The secondaryevaluation criteria include automation reliability and the cost of decisions or outcomes.

For thisreason, designers can benefit from frameworks that support system design that involves automa-tion. We now discuss one such framework: the types and levels of automation framework [639].Downloaded from https://academic.oup.com/book/60808 by Helsinki University Library user on 01 December 2025

All three strategies have a common deficiency in that they may not always be able to adhereto the principle of human-centered automation, whereby a human has final control. This is alsocalled the authority problem.

The third allocation strategy is to allocate each function in a way that maximizes economicefficiency.

The second allocation strategy is assigning each function to the most capable agent, which canbe either a human or a machine.

Therefore, this automation strategy definesthe roles and responsibilities of users in terms of automation instead of the other way around.

First, byautomating everything that can be automated, the user is left with functions that, by definition, thedesigners find hard, expensive, or difficult to automate.

The first strategy is called maximum automation. Here, each task that can be automated is allo-cated to a machine.

The aim is to increase efficiency, reduce costs, or both.

Which system functions should beautomated, and to what extent should they be automated?

Task allocation is a central challenge in HCI and automation.

Four levels of shared control can be distinguished [1]: strategic (e.g.,setting a destination), tactical (e.g., doing a specific maneuver like merging into a lane), oper-ational (e.g., maintaining a certain distance from another car), and execution (lowest-level ofcontrolling locomotion, steering, and so on).

Control does not need to be either/or like in many semi-autonomous vehicles.

When two agents sharing control have asymmetric capa-bilities, both loose and tight rein control should be available.

First, control can be shared via an extensionthat allows a machine to amplify human ability.

When riding a horse, the rider communicates high-level information (e.g., the goal) to thehorse but must be ready to guide the horse at a lower level. When the horse knows what to do,for example, if the route is familiar, the rider may not need to engage in low-level control. Thisform of control, called loose rein control, is possible if the horse knows what the rider wants.

It has been defined as “a device or system that accomplishes (partially or fully) a function that waspreviously, or conceivably could be, carried out (partially or fully) by a human operator” [638].

First, communication is vital for sharingcontrol, and this can happen at different levels; second, both agents must have internal modelsof each other to understand what those communicative acts mean.

The H-metaphor is a metaphor for understanding shared control [246]. Here, the “H” standsfor “horse”: the horse metaphor. In short, it means that shared control is like riding a horse.

Shared control is about carrying out a task together with a competent partner [1, p. 511]:“In shared control, human(s) and robot(s) are interacting congruently in a perception–actioncycle to perform a dynamic task that either the human or the robot could execute individuallyunder ideal circumstances.”

The question of shared control is timely; semi-autonomous vehicles are only partiallyautonomous. They need the human to assist them and, therefore, some way of handing controlover to the human driver. They also need to have guidance from the driver, for example, onthe choice of route.

Independently of the chosen strategy, some tasks are to be done by the interactive system andsome by the user. The allocation of such tasks is called functional allocation.

An example of such control sharing is powersteering in a car: The car provides additional work to allow the driver to turn the wheels withless effort. An HCI example is mouse acceleration, which allows a user to move the cursor on thescreen farther than the physical movement of the mouse.

Second, control can be shared via relief, which means that the overall burden on the humanis reduced by the machine. An example is automatic shift transmission, which relieves the driverof the task of changing gears in a car. An HCI example is text entry using autocomplete, whichprevents the user from correcting typing mistakes as they type.

Third, control can be shared via partitioning. In this case, a task is decomposed into parts thatcan be addressed by humans and machines separately. An example of such control sharing is semi-automatic parallel parking, which provides the driver with some braking ability while the machinecontrols the speed and steering of the car. An HCI example is automatic spell checking, where thesystem detects and highlights incorrectly spelled words but does not change them. Instead, theuser has to take an explicit corrective action, such as selecting a misspelled word and choosing analternative.

In a task-switching situation,the user must activate resources for the second task and inhibit resources for the first task. If theuser fails to do so efficiently, performance is reduced, sometimes dramatically.

Successful time-sharing depends on the strategy and difficulty of the task in terms of tempo-ral constraints—how many tasks are processed in a given interval—and task complexity—thequantity of information that needs to be processed for a given task.

The first is called thesingle channel theory, which posits that there is limited capacity in the human information pro-cessing system in a time-sharing scenario. When the channel capacity is exceeded, multiple taskstransition from parallel processing to serial processing.

If both tasks demandcontrolled processing, then the strategy in processing is split into two mechanisms: facilitationand inhibition.

The implementation of such a strategy requires attentional resources, which canlead to task interference when the demand exceeds the available capacity.

The third theory is information processing analysis theory. Ifat least one task can be carried out automatically, the other task can be carried out with little orno impact on performance (at an appropriate time–error trade-off point).

The second theoryis the multiple resources model, which states that resource limitation concerns the entire systemrather than a channel (Chapter 5).

This theory is contradicted by empiricalresearch showing that a multi-processor model better explains empirical data.

Second, tasks canbe shared in terms of control, which means that some control over the tasks is assigned to anotheragent, such as another user or a machine.

Ingeneral, perfect time-sharing with no degradation in performance occurs only for tasks that areautomatic, such as speaking while walking. Such tasks can reach automaticity.

Ingeneral, perfect time-sharing with no degradation in performance occurs only for tasks that areautomatic, such as speaking while walking.

First, tasks can be time-shared, which implies that the user performs multiple tasks.

For the sharing of tasks tooccur, there have to be at least two tasks or subtasks, which can then be shared in two differ-ent ways.

A central concern in the design of automation and AI is task sharing.

In all cases, getting the right balance betweenautomation and human control is crucial.

Throughout the history of automation, a central challenge in HCI has been posed by the com-plexity of autonomous systems. Users must be able to understand and control them. They need tofind and integrate information from dynamic and different sources. They need to understand howto delegate tasks, supervise their execution, and intervene if needed.

Throughout the history of automation, a central challenge in HCI has been posed by the com-plexity of autonomous systems.

Automation refers to technology that assists users by performing a task or a subtask on their behalf.

Thissuggests that the uses and trade-offs of automation are a central problem in HCI.

Direct manipulation does not anthropomorphize user interfaces or deskill users.

Direct manipulation allows the user to predict what will happen, explore the system, andfeel in charge.

Software agentsmay allow users to achieve their goals in complex environments with limited expertise.

In HCI, automation poses the foun-dational problem of task allocation: Who does which task or subtask—the system or the user?Moreover, how much should we automate?

Cai et al. [117] interviewed 21 pathologists who used a deep neural network to aid in thediagnosis of prostate cancer. The interviews showed that pathologists needed to learn moreabout the network’s strengths and limitations to use it effectively. They also wanted to knowthe design objective of the network and the kind of data on which it was trained.

Roy et al. [715] explored what happens when users doing a task with a simulated crane needto choose between further using automation and manually continuing the task. The authorsshowed that the decision of whether to use automation is affected by the users’ perceptionof its accuracy as well as how easy it is to do the task themselves.

The performance of the system must be reliable and controllable. Its behavior should be safe, and the way it is designed and used should be ethical [768]. Users need to trust the system's decisions and ability. It should be made clear to the user what it can and cannot do.

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.

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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