Classiﬁcation of Augmented Reality Design Recommendations on User

Experience Dimensions: Preliminary Study Results

Stefan Graser

1 a

, Jessica Kollmorgen

2 b

, Martin Schrepp

3 c

Mar

ıa Jos

e Escalona

4 d

and Stephan B

ohm

1 e

CAEBUS Center for Advanced E-Business Studies, RheinMain University of Applied Sciences, Wiesbaden, Germany

University of Applied Sciences Emden/Leer, Emden, Germany

SAP SE, Walldorf, Germany

University of Seville, Seville, Spain

Keywords:

Augmented Reality (AR), Corporate Training (CT), AR Design Recommendations, AR Guidelines, User

Experience (UX), UXARcis.

Abstract:

Augmented Reality (AR) in Corporate Training (CT) enables immersive and interactive learning scenarios,

resulting in a new user experience (UX). Within software development, UX is a crucial success factor. While

numerous AR-speciﬁc design recommendations exist, it remains unclear how these contribute to the actual user

experience perceived by learners. This misalignment between intended and actual UX highlights the challenge

for AR authors. Concerning UX evaluation, questionnaires can be used to collect data from target groups and

produce reliable quantitative data describing UX quality. However, a questionnaire should not include too

many items to capture the UX impression of users to avoid being too time-consuming. Since UX question-

naires typically capture only high-level impressions, their results often do not provide clear suggestions for

designers or developers on how to improve an application. Linking design recommendations to questionnaire

scales would help connect UX evaluation results more directly to design changes that are likely to improve

users’ UX impressions. We describe a study establishing such a mapping for the application domain of AR in

corporate training. Preliminary results provide an initial classiﬁcation of AR design recommendations across

relevant UX dimensions.

1 INTRODUCTION

Augmented Reality (AR) supplements the real world

with virtual content (Azuma, 1997). Digital infor-

mation (for example, directions, labels, or 3D mod-

els) integrates into the user’s view of the real world

through smart glasses or smartphone displays. This

enables a new way of conducting and experiencing

tasks across various application domains, such as edu-

cation, training, or cultural heritage. The ﬁeld of Cor-

porate Training (CT), in particular, shows great poten-

tial for applying AR as the technology enables inter-

active and immersive learning scenarios. As a result,

https://orcid.org/0000-0002-5221-2959

https://orcid.org/0000-0003-0649-3750

https://orcid.org/0000-0001-7855-2524

https://orcid.org/0000-0002-6435-1497

https://orcid.org/0000-0003-3580-1038

various aspects, such as training engagement, motiva-

tion, and effectiveness, can be improved (Billinghurst

and Duenser, 2012; Chang et al., 2020; Criollo-C

et al., 2021).

AR introduces new learning formats differing

from classical methods, resulting in a distinct user

experience (UX) for learners. In the software devel-

opment process, UX is a key factor for the accep-

tance, use, and success of digital systems (Hinderks

et al., 2019). Thus, it is essential to ensure a positive

UX (Rauschenberger et al., 2013). Therefore, prod-

uct designers and developers typically apply design

recommendations as standards and best practices for

system design and development, intending to achieve

a good UX by designing the product features in ac-

cordance with the respective guidelines (Hassenzahl,

2004). Regarding AR, a large number of AR-speciﬁc

design recommendations exist in the literature (Fu

et al., 2016; Krauß et al., 2021a; Krauß et al., 2021b).

Graser, S., Kollmorgen, J., Schrepp, M., Escalona, M. J. and Böhm, S.

Classiﬁcation of Augmented Reality Design Recommendations on User Experience Dimensions: Preliminary Study Results.

DOI: 10.5220/0013778000003985

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 21st International Conference on Web Information Systems and Technologies (WEBIST 2025), pages 511-520

ISBN: 978-989-758-772-6; ISSN: 2184-3252

511

However, the actual UX perceived by the users may

differ signiﬁcantly from the designer’s intention (Has-

senzahl, 2004). Understanding and gathering insights

into the actual experience, therefore, requires apply-

ing methods of UX measurement. However, current

research highlights different challenges in integrating

UX into the software development process (Petters-

son et al., 2018; Kashﬁ et al., 2019; Kollmorgen et al.,

2025).

Various methods for measuring and quantifying

the UX exist in literature (Albert and Tullis, 2022).

Standardized UX questionnaires are the most com-

monly applied. These, however, often lack context

speciﬁcity. As the relevant UX dimensions differ de-

pending on the product, it is crucial to apply context-

speciﬁc methods. Only a limited number of AR-

speciﬁc UX measurement methods are available, dif-

fering in structure and focus (Graser et al., 2024a;

Graser et al., 2025c). Moreover, empirical ﬁndings

from UX evaluation methods are often challenging

to interpret and translate into actionable design im-

provements. Quantitative results, which are usually

based on concise and time-efﬁcient questionnaires,

often capture only high-level impressions and may

indicate dissatisfaction without revealing underlying

causes. On the other hand, qualitative data is often too

general and lacks clear references to speciﬁc system

weaknesses. This highlights a fundamental misalign-

ment: While designers rely on design recommenda-

tions to develop AR applications, it remains unclear

whether these recommendations actually enhance the

UX, as they often lack the tools to validate this. In

turn, there is a lack of understanding of the applica-

tion of empirical measurement methods and the in-

terpretation of their results (Pettersson et al., 2018;

Kashﬁ et al., 2019).

To sum up, even with domain-speciﬁc ques-

tionnaires, it often remains unclear how measured

weaknesses and problems can be speciﬁcally reme-

died. There is often an insufﬁcient connection

between empirical UX measurement and concrete

design measures. Although numerous design rec-

ommendations exist, their impact on speciﬁc UX

dimensions has not been proven, making it challeng-

ing for designers and software developers to select

the most suitable recommendation for the respective

UX weakness. Mapping design recommendations to

UX dimensions is, therefore, useful and necessary.

To our knowledge, no approach exists combining

development recommendations with measurement

scales in research. This study addresses this gap in

the speciﬁc context of AR in CT. We aim to bridge

the gap between AR authors and users. Therefore,

our objective is to provide an initial setup for a

bidirectional, more evidence-based way to develop

AR applications in CT while fostering a positive

UX. We classify AR-speciﬁc design recommenda-

tions based on empirically validated, relevant UX

dimensions. By empirically mapping AR design

recommendations to UX dimensions, we aim to

provide actionable insights supporting both the

development and evaluation of AR applications in

CT. We present preliminary ﬁndings from a quan-

titative online survey. Based on this, we propose a

structured approach for linking design improvements

to measured UX deﬁciencies. Thus, this research

contributes to closing the gap between AR design

theory and practical UX evaluation in the domain of

Corporate Training. Moreover, this contributes to the

innovative development of user-centric, interactive

systems within the ﬁeld of Human-Computer Inter-

action (HCI). This article is based on our previous

research (Graser et al., 2025a; Graser et al., 2025c).

Against this background, we address the following

research question:

RQ: Which relationships exist between AR design

recommendations and the relevant UX dimen-

sions?

The article is structured as follows: Section 2 in-

troduces the related work regarding our previous re-

search relevant for this study. Section 3 describes the

methodological approach of this study. Preliminary

study results are presented in Section 4. Section 5

provides the discussion. Section 6 concludes includ-

ing insights for future work.

2 RELATED RESEARCH

This section presents an overview of the related re-

search, providing the basis of this pre-study. We intro-

duce UX evaluation in 2.1. Based on this, we present

the UXARcis questionnaire in 2.2. Moreover, we il-

lustrate existing design recommendations for AR in

CT (see 2.3).

2.1 Quantifying UX

UX is deﬁned as the ”user’s perceptions and re-

sponses that result from the use and/or anticipated

use of a system, product or service” (ISO9241-210,

2020). Thus, UX does not describe objective quality

criteria, but rather the individual subjective impres-

sions of users, including emotions and beliefs, result-

ing from their interaction with an application. Users

have corresponding expectations for interaction with

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512

systems, which raises the question of how individual

UX can be continuously evaluated in iterative soft-

ware development and how generalizable conclusions

can be drawn (Kollmorgen et al., 2024).

UX questionnaires are an established evaluation

method for this purpose. They enable systematic eval-

uation to identify weaknesses, strengths, and poten-

tial improvements, and to design user-centered devel-

opment (Lohse, 2011). The evaluation is carried out

using so-called UX dimensions or scales. These are

one-dimensional constructs that can positively or neg-

atively inﬂuence the user’s perception of the product

(Provost and Robert, 2013).

Depending on the system and use case, various

UX dimensions may be relevant. For example, while

users of online banking tools place a high value on di-

mensions such as trust and dependability, stimulation

and novelty are more relevant for online games (Koll-

morgen et al., 2024). UX questionnaires such as the

UEQ+, therefore, allow the relevant dimensions to be

individually compiled and queried depending on the

system (Schrepp and Thomaschewski, 2019). Over

the years, different dimensions have been added, such

as those speciﬁcally related to voice user interfaces

(Klein et al., 2020). Attempts have also been made to

develop domain-speciﬁc questionnaires, such as for

virtual reality (Tcha-Tokey et al., 2016). Research

highlights the necessity of domain-speciﬁc measure-

ment approaches (Pettersson et al., 2018). The ad-

vantage lies in quantitatively evaluating perception-

related and system-related UX dimensions and mak-

ing them comparable at the same time (Mortazavi

et al., 2024).

2.2 UXARcis Questionnaire

Only a limited number of AR-speciﬁc questionnaires

exist in research, also differing in structure and fo-

cus (Graser et al., 2024a). Against this background,

we identiﬁed the relevant UX dimensions for AR in

CT. Based on the UX model by (Hassenzahl, 2004),

we follow the understanding of breaking the UX di-

mensions down into product features and product

character. Regarding the product features, the AR-

cis criteria by (Kr

uger et al., 2019; Kr

uger, 2023)

represent the relevant system characteristics of AR

in relation to the display of information (Azuma,

1997) re-framed from a user’s perspective. Concern-

ing the product character, we followed the approach

by (Schrepp et al., 2023) describing UX with a set of

UX Quality Aspects (UX-QA). We evaluated the im-

portance of the UX-QA in relation to AR in CT and

identiﬁed ﬁve relevant UX-QAs (Graser et al., 2024c;

Graser et al., 2024b). The descriptions of the UX di-

mensions are shown in the following:

• Contextuality (Con): Integrated presentation of

virtual and physical elements.

• Spatiality (Spa): Assignment of unique spatial

properties to virtual elements by positioning them

within the physical environment.

• Interactivity (Int): Modiﬁcation and manipula-

tion of virtual components by interacting with

physical objects.

• Perspicuity (PE): The user easily gets familiar

with the product and to learn how to use it.

• Efﬁciency (EF): The user can solve their tasks

without unnecessary effort. The product reacts

fast.

• Dependability (DE): The user feels in control of

the interaction. The product reacts predictably

and consistently to user commands.

• Usefulness (US): Using the product brings advan-

tages to the user. Using the product saves time and

effort.

• Clarity (CL): The user interface of the product

looks ordered, tidy, and clear.

Both product features and character were com-

bined, resulting in an adjusted UX model for AR in

CT (Graser and B

ohm, 2025) (see Figure 1).

Figure 1: Adjusted UX model for AR in CT (Graser and

ohm, 2025).

Based on this, the UXARcis questionnaire repre-

sents a context-speciﬁc UX questionnaire for AR in

CT, measuring both product features and character

(Graser et al., 2025c).

2.3 AR Design Recommendations

AR authoring as part of software engineering refers

to the development of AR applications. Within the

software engineering process, requirements engineer-

ing is an initial step in the development process

(Brooks, 1987; Hull et al., 2011; Pacheco et al., 2018;

de Almeida Pacheco et al., 2019). Over time, design

practices and lessons learned emerge based on expe-

rience gained during the development of the respec-

tive technology. This results in so-called design rec-

ommendations (sometimes referred to as ”guidelines”

Classiﬁcation of Augmented Reality Design Recommendations on User Experience Dimensions: Preliminary Study Results

513

or even ”heuristics”), which serve as orientation and

standards for designers and developers. Similar to

the UX evaluation, it is necessary to apply context-

speciﬁc design recommendations to avoid neglecting

the speciﬁcs of a particular technology (Krauß et al.,

2021a; Krauß et al., 2021b).

In the context of AR, various design recommen-

dations exist. (Krauß et al., 2021b) identiﬁed a com-

prehensive set of recommendations regarding mixed

reality applications. They classiﬁed all design rec-

ommendations into thematically similar topics. How-

ever, due to technological development, AR is devel-

oping rapidly, resulting in new design practices and

recommendations. We applied the dataset by (Krauß

et al., 2021b) as a foundation. Based on a multi-

method approach, we identiﬁed new design recom-

mendations regarding AR published since 2020. We

further classiﬁed them into thematically similar top-

ics. For this, we applied an NLP approach, measur-

ing the semantic textural similarity between the state-

ments. Moreover, all resulting topics were evaluated

based on the importance of AR in CT. This results

in a total of 32 relevant topics. Lastly, for all topics

and the corresponding design recommendations, we

added summarizing descriptions to make them com-

municable (Graser et al., 2025a). The comprehensive

data set, including all topic descriptions, is available

online (See (Graser et al., 2025b)).

3 METHODOLOGICAL

APPROACH

This article presents a preliminary study based on our

prior research (Graser et al., 2025a; Graser et al.,

2025c). We aim to identify the relationships between

the identiﬁed AR design recommendation topics (see

2.3, (Graser et al., 2025a; Graser et al., 2025b)) and

the UX dimensions of the UXARcis (2.2, (Graser

et al., 2025c)). In particular, AR authors with expe-

rience in developing and designing AR applications

evaluate the inﬂuence of the design recommendation

topics on the UX dimensions when applied.

We conducted a quantitative online survey using

Unipark. To gather participants, we shared the sur-

vey on LinkedIn within our professional environment,

including different international AR-related develop-

ment groups. To ensure that only AR authors with ex-

pertise in developing and designing AR applications

were eligible to participate, we ﬁltered based on their

experience with AR. We ensured this by asking two

ﬁlter questions. We considered whether the partici-

pant had already developed an AR application, fol-

lowed by the question of how long participants have

worked in the ﬁeld of AR authoring. Participants who

had not yet created an application and therefore had

no experience were excluded. Moreover, participants

were asked to specify their role within the authoring

process, and we examined the speciﬁc AR applica-

tion domain. Afterwards, the participants were auto-

matically divided into two groups. The assignment

was performed randomly. Each group was shown 16

topic descriptions (Group 1: Topic descriptions 1–16;

Group 2: Topic descriptions 17–32) to be evaluated

in terms of their inﬂuence on the UX dimensions. In

particular, the participants could select the four op-

tions: (1) product features (ARcis), (2) product char-

acter (UX quality aspects), (3) both product features

and product character, or (4) none. Option (1) means

that the application of the respective topic only im-

pacts the ARcis criteria, whereas option (2) indicates

an inﬂuence solely on the UX quality aspects. Option

(3) implies an inﬂuence on both. Choosing option (4)

means that the topic has no inﬂuence on any UX di-

mension and, thus, is irrelevant. An illustrative ex-

ample from the survey is shown in the Appendix. A

quality assurance question was included in the mid-

dle of each group after the eighth topic. The group

division was performed to reduce the survey duration

and, thus, the abandonment rate. Finally, demograph-

ics regarding the age group, level of education, and

employment relationship were considered.

As the survey is still ongoing to collect more

data, we will present a preliminary empirically de-

rived classiﬁcation between design recommendation

topics and related UX dimensions.

4 STUDY RESULTS

This section presents the preliminary results of this

study. We illustrate demographics and information

regarding the study participants in Section 4.1. More-

over, we provide the initial classiﬁcation of the topics

on the UX dimensions in Section 4.2.

The results were collected between June 18 and

June 30, 2025. In summary, 207 people started the

questionnaire, and 138 submitted it. We conducted

data cleaning by dropping all participants who failed

to answer the ﬁlter and quality question correctly.

This results in a ﬁnal sample size of 106. These are

divided into 56 participants for group 1 (N1) and 50

participants for group 2 (N2).

4.1 Demographics & Experience

The age distribution, based on the 95 that provided an

answer for this question in the total sample (N=106),

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514

highlights a focus on the so-called ”Generation Y,”

which was born between 1981 and 1996 and thus

ranges in age from 29 to 44. Two respondents be-

longed to ”Generation Z” (aged 18-28), while nine

participants belonged to ”Generation X” (aged 45-

60). No person of the ”Baby Boomer” generation (61-

79) was represented.

In terms of educational attainment, the majority

of respondents had an academic degree. No partici-

pant stated that a high school diploma was their high-

est level of education. Thirteen people had completed

an apprenticeship. The majority of the sample had

a bachelor’s degree (76 participants), followed by 15

participants with a master’s degree. Two people had a

doctorate.

There was also a wide range of professional back-

grounds. 23 participants were employed in small

or medium-sized companies, 28 in corporations, and

another 28 in (media) agencies. Forty respondents

worked at research institutions, and eight at public in-

stitutions, such as universities. Two people were self-

employed.

We further examined the participants’ experience

as AR authors. It should be noted that multiple as-

signments were possible, as many participants take on

several roles in AR projects. For this, we followed the

classiﬁcation into four roles by (Krauß et al., 2021a):

• Concept developers: They create the ﬁrst con-

cepts and drafts of an application. In some cases,

they manage to ignore technical limitations and

focus on the problem to be solved. 52 participants

(49.06%) assigned themselves to this role.

• Interaction designers: They focus on the mechan-

ics and interactivity of the application, with an

emphasis usually on the conceptual level. Their

tasks include conceiving mechanisms for locomo-

tion, navigation, and input and output, as well

as designing the interaction between the various

modalities used in AR systems. With 66 men-

tions (62.26%), this was the most frequently cho-

sen role.

• Content author: They focus on the creation of

animations, 3D models, visualizations, and ele-

ments such as shadows, textures, color schemes,

or sound design. 33 participants (31.13%) chose

this option.

• Technical developer: They do not focus exclu-

sively on the production of code and the de-

velopment of functions, but are usually also in-

volved in the technical feasibility and practicality

of concepts. This role was chosen by 29 people

(27.36%).

With regard to experience in developing AR appli-

cations, the distribution was as follows: the majority

of respondents (n = 42; 39.62%) had one to two years

of experience. Eighteen people reported two to three

years of experience, while 15 participants had three

to four years of professional experience. Another 17

authors had four to ﬁve years of experience, and 14

reported more than ﬁve years of experience in devel-

oping AR applications. Overall, the distribution of

authors with more than two years of experience is rel-

atively balanced.

There was a wide range of application contexts in

which the participants implemented AR projects: 42

people were active in academic teaching, 38 in the

context of cultural heritage (e.g., museums), 43 in the

ﬁeld of games, 48 in industry and business, 32 in cor-

porate training, 34 in marketing and entertainment,

three people in the ﬁeld of medicine, and a few men-

tions in the ﬁeld of “digital twins” (other). This again

underscores the diversity of application ﬁelds in the

sample.

The broad range of professional backgrounds,

roles, and areas of application of the participants pro-

vides a comprehensive picture of the requirements

and challenges involved in designing AR applica-

tions. This diversity increases the validity of the fol-

lowing classiﬁcation, as different perspectives and ex-

periences have been incorporated into the evaluation

of the design recommendations. Building on this ba-

sis, the next section presents the preliminary assign-

ment of the design recommendations to the UX di-

mensions.

4.2 Preliminary Classiﬁcation

In the following, we present a preliminary classiﬁca-

tion. Figure 2 illustrates the distribution of evaluation

results by the authors, exemplarily for topic (1). This

indicates that AR authors assume that the topic (1)

Appropriate interplay of virtual content and physical

environments mainly inﬂuences the product features

(ARcis criteria) of AR applications.

Table 1 presents a comprehensive classiﬁcation of

all topics in relation to the UX dimensions. We high-

lighted the category with the most ratings in bold. In

summary, ﬁve topics were evaluated to inﬂuence the

product features (ARcis criteria), whereas 13 topics

refer to the product character (UX Quality Aspects,

short: UX-QA). Moreover, 13 topics were assigned to

both product features and character. Topic (31) Head-

locked content has the same number of ratings (n =

16) concerning product features and character, simi-

lar to topic (15) Encourage to explore, which has a

rating of 19 each. Thus, we chose both. In this case,

Classiﬁcation of Augmented Reality Design Recommendations on User Experience Dimensions: Preliminary Study Results

515

ARcis UX-QA Both

None

Number of ratings of the AR authors

Figure 2: Distribution of classiﬁcation by AR authors for

topic (1) Appropriate interplay of virtual content and phys-

ical environments.

a chi-square test would be necessary to reject the as-

sumption of a random distribution. Figure 3 shows

the topic distribution among the UX dimensions. A

comprehensive classiﬁcation, including topic names,

is presented in the Appendix.

ARcis

UX-QA

Both

Figure 3: Classiﬁcation of the 32 AR design recommenda-

tion topics to the UX dimensions.

5 DISCUSSION

The reﬁnement and practical applicability of the iden-

tiﬁed relationships to improve the UX must be tested

in case studies involving AR authors. Design recom-

mendations naturally align with various UX criteria.

Thus, if we want to improve a dedicated UX scale,

a checklist of potential design improvements will re-

sult. If this checklist is manageable and interpretable,

allowing for clear design decisions, and if AR authors

are comfortable with the method, it can be effectively

applied in practical applications.

We currently have only a limited number of re-

sponses. This will be enhanced by further data col-

lection. Moreover, our study focuses on the applica-

Table 1: Overview of the classiﬁcation of the AR design

recommendation topics to the UX dimensions. The num-

bers represent the number of authors rated for this classiﬁ-

cation option.

AR Design Recommendation

Topics

ARcis

UX-QA

Both

None

(1) Appropriate interplay of vir-

tual content and physical envi-

ronments

29 15 8 4

(2) Attention directors 12 20 21 3

(3) Instructions 15 17 18 6

(4) Onboarding 19 14 21 2

(5) Hand & ﬁnger gestures 19 17 16 4

(6) Textures – Visual Realism

and Appearance of Objects

19 14 17 6

(7) Occlusion 18 16 18 4

(8) Image detection 17 16 19 4

(9) Handling Interruptions / Re-

localization

15 20 19 2

(10) Surface Detection 14 18 22 2

(11) Affordance 14 16 23 3

(12) Visual cues for object ma-

nipulation

17 18 16 5

(13) Object Placement 15 21 17 3

(14) Object Manipulation 17 17 20 2

(15) Encourage to explore 19 19 18 0

(16) Keep the focus on AR ex-

perience, but use 2D-UI On-

Screen elements when needed

15 19 20 2

(17) Error prevention & recov-

ery

22 16 10 2

(18) Consider and show User’s

required Effort

16 15 12 7

(19) Law of practice 13 21 13 3

(20) Inform about Waiting Time 11 14 20 5

(21) Text / Font 11 16 14 9

(22) Accessibility (visuals) 14 15 14 7

(23) Ergonomics (avoid muscle

fatigue)

16 18 16 0

(24) Ergonomics (avoid head &

neck fatigue)

14 19 11 6

(25) Pause / Breaks 8 21 17 4

(26) Performance 15 19 11 5

(27) Audio Feedback 13 15 17 5

(28) Feedback 16 18 12 4

(29) FOV 11 18 14 7

(30) Content Placement 15 20 14 1

(31) Headlocked content 16 16 12 6

(32) Anchored UI 14 16 17 3

tion domain of AR in CT. However, applications from

this domain are not so different from other learning

applications (for example, AR applications that sup-

port learning in museums or exhibitions). Thus, it

is promising to investigate whether this approach can

also be applied to other AR domains. Even if this may

not always be possible, the developed method can be

used to provide a similar mapping. Moreover, we did

not consider the classiﬁcation results in relation to the

different roles of AR authors or the professional ex-

perience. This could also provide interesting differ-

ences.

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516

5.1 Implications

From a practical perspective, this further enables a

bidirectional application: On the one hand, it be-

comes evident how speciﬁc design recommendations

and their implementation affect the resulting UX. This

provides AR authors with concrete starting points for

enhancing the UX. On the other hand, when UX

deﬁcits are identiﬁed, AR authors can trace them back

to associated design recommendations and adapt their

AR applications accordingly. This contributes to a

more evidence-based development, design, and eval-

uation process of AR applications. The internal com-

munication within the team among all participants is

facilitated, as the relationship between weaknesses

and approaches for improvement becomes transpar-

ent and clear.

In terms of interpretation, we want to illustrate a

concrete example by revisiting the topic (1) Appro-

priate interplay of virtual content and physical en-

vironments. Concerning the ﬁrst classiﬁcation, topic

(1) refers to the product features of AR applications.

If the UX evaluation revealed a deﬁcit in the ARcis

criteria, one approach would be to consider the re-

spective guidelines for this topic. In particular, practi-

tioners should check whether the scenes and objects

are natural, realistic, and simple in design. In this

way, the classiﬁcation can serve as a basis for itera-

tive product improvement, and even when resources

are scarce, it can be utilized in a way that positively

impacts the UX. This can be applied to all topics and

their classiﬁcation (see Appendix for the comprehen-

sive results). Thus, this provides an initial under-

standing of which topics are relevant for which UX

dimensions. Moreover, mapping the UX evaluation

to the design recommendations facilitates communi-

cation within the team, as the relationship between

the weakness and the solution is made transparent.

From a research perspective, this can serve as a

sound empirical basis for future studies on the in-

terplay between UX evaluation and design practices

as well as the development of new methods. The

mapping enables researchers to systematically inves-

tigate the effectiveness of individual design recom-

mendations in improving speciﬁc UX dimensions, for

instance, through correlation or regression analyses.

Moreover, the results allow for the derivation of new

hypotheses regarding the impact of design decisions

on UX, such as the comparison of topic (1) to topic

(15) Encourage to explore. In this context, the respec-

tive design recommendations can be applied to an AR

application and thus systematically modiﬁed. Fur-

thermore, a UX evaluation can be conducted. Based

on the evaluation results, the hypotheses could be

tested. Thus, the impact of the design recommen-

dations on the UX could be veriﬁed. Moreover, this

could be useful in identifying interdependencies be-

tween both aspects.

5.2 Limitations

Some limitations of this work should also be men-

tioned. During classiﬁcation, potential overlaps may

exist between individual UX dimensions or multiple

assignments, making interpretation difﬁcult. How-

ever, this was addressed by explicitly allowing multi-

ple assignments and making them transparent in order

to reﬂect the complexity of the design in practice.

6 CONCLUSION & FUTURE

WORK

This article is a pre-study extending our previous re-

search on AR design recommendations and context-

speciﬁc UX evaluation of AR in CT. We aimed to

classify AR design recommendations on relevant UX

dimensions. In particular, we conducted a quantita-

tive online survey. AR authors were asked to evaluate

the inﬂuence of the design recommendation topics on

the UX dimensions. As preliminary results, we pro-

vide an initial classiﬁcation. Thus, mapping design

recommendations to UX dimensions bridges the gap

between research and practice, as the ﬁndings of UX

evaluation become actionable. This closes the exist-

ing gap in research regarding the UX integration in

software development.

As this is a pre-study, we aim to further enhance

this approach by gathering additional data within the

online survey regarding the classiﬁcation. This is the

ﬁrst step in a multi-method approach. Based on this,

we aim to map the design recommendation topics to

the eight speciﬁc UX dimensions related to product

features and character in a second step. This allows us

to achieve a more speciﬁc differentiation. In the third

step, we aim to evaluate the classiﬁed design recom-

mendation topics in terms of their importance for each

respective UX dimension. By doing so, we will ob-

tain a prioritized, ﬁne-grained mapping of AR design

recommendation topics to the structure of the UXAR-

cis. This will enable AR authors to identify which

design aspects are most critical for achieving and im-

proving a positive UX based on identiﬁed UX deﬁcits.

Thus, this framework supports strategic and effective

design decisions, fostering the development of AR ap-

plications that are both user-centered and empirically

grounded.

Classiﬁcation of Augmented Reality Design Recommendations on User Experience Dimensions: Preliminary Study Results

517

REFERENCES

Albert, W. B. and Tullis, T. T. (2022). Measuring the User

Experience. Collecting, Analyzing, and Presenting UX

Metrics. Morgan Kaufmann.

Azuma, R. T. (1997). A survey of augmented reality. Pres-

ence: Teleoperators & Virtual Environments, 6:355–

385.

Billinghurst, M. and Duenser, A. (2012). Augmented Real-

ity in the Classroom. Computer, 45(7):56–63.

Brooks (1987). No silver bullet essence and accidents of

software engineering. Computer, 20(4):10–19.

Chang, Y.-S., Hu, K.-J., Chiang, C.-W., and Lugmayr, A.

(2020). Applying Mobile Augmented Reality (AR)

to Teach Interior Design Students in Layout Plans:

Evaluation of Learning Effectiveness Based on the

ARCS Model of Learning Motivation Theory. Sen-

sors, 20(1):105.

Criollo-C, S., Abad-V

asquez, D., Martic-Nieto, M.,

Vel

asquez-G, F. A., P

erez-Medina, J.-L., and Luj

an-

Mora, S. (2021). Towards a New Learning Experi-

ence through a Mobile Application with Augmented

Reality in Engineering Education. Applied Sciences,

11(11):4921.

de Almeida Pacheco, B., Guimar

aes, M., Correa, A. G.,

and Farinazzo Martins, V. (2019). Usability evalu-

ation of learning objects with augmented reality for

smartphones: A reinterpretation of nielsen heuris-

tics. In Agredo-Delgado, V. and Ruiz, P. H., editors,

Human-Computer Interaction, pages 214–228, Cham.

Springer International Publishing.

Fu, K. K., Yang, M. C., and Wood, K. L. (2016). De-

sign principles: Literature review, analysis, and fu-

ture directions. Journal of Mechanical Design,

138(10):101103.

Graser, S. and B

ohm, S. (2025). The inﬂuence of system

characteristics on the user experience of augmented

reality applications: An experimental approach in cor-

porate training. In Proceedings of the 2025 9th Inter-

national Conference on Virtual and Augmented Real-

ity Simulations, ICVARS ’25, New York, NY, USA.

Association for Computing Machinery. [Unpublished

manuscript, accepted for publication].

Graser, S., Kirschenlohr, F., and B

ohm, S. (2024a). User

experience evaluation of augmented reality: A sys-

tematic literature review. CENTRIC 2024, The Sev-

enteenth International Conference on Advances in

Human-oriented and Personalized Mechanisms, Tech-

nologies, and Services.

Graser, S., Schrepp, M., and B

ohm, S. (2024b). Construc-

tion of the UXAR-CT – A User eXperience Question-

naire for Augmented Reality in Corporate Training.

CENTRIC 2024, The Seventeenth International Con-

ference on Advances in Human-oriented and Person-

alized Mechanisms, Technologies, and Services.

Graser, S., Schrepp, M., and B

ohm, S. (2024c). UXAR-CT

– An Approach for Measuring UX for Mobile Aug-

mented Reality Applications in Corporate Training.

In Wei, J. and Margetis, G., editors, Human-Centered

Design, Operation and Evaluation of Mobile Com-

munications, pages 211–234, Cham. Springer Nature

Switzerland.

Graser, S., Schrepp, M., and B

ohm, S. (2025a).

Identiﬁcation of design recommendations for aug-

mented reality authors in corporate training. arXiv.

https://arxiv.org/abs/2507.21722.

Graser, S., Schrepp, M., Kollmorgen, J., Escalona,

M. J., and B

ohm, S. (2025b). Classiﬁcation of

ar design recommendations on ux dimensions: Pre-

liminary study results: Research report & data.

10.13140/RG.2.2.28124.71047.

Graser, S., Schrepp, M., Kr

uger, J. M., and B

ohm, S.

(2025c). Development of an innovative ux mea-

surement approach for augmented reality in corpo-

rate training. [Unpublished manuscript, accepted for

publication in the HCII 2025 - Late Breaking Papers

Springer LNCS volumes of the Proceedings.

Hassenzahl, M. (2004). The Thing and I: Understanding the

Relationship Between User and Product, pages 31–

42. Springer Netherlands, Dordrecht.

Hinderks, A., Winter, D., Schrepp, M., and

Thomaschewski, J. (2019). Applicability of user

experience and usability questionnaires. Journal of

Universal Computer Science, 254(13):1717–1735.

Hull, E., Jackson, K., and Dick, J. (2011). Requirements

Engineering. Springer-Verlag, Berlin Heidelberg, 3

edition.

ISO9241-210 (2020). Ergonomics of human-system inter-

action - part 210: Human-centred design for interac-

tive systems.

Kashﬁ, P., Feldt, R., and Nilsson, A. (2019). Integrating ux

principles and practices into software development or-

ganizations: A case study of inﬂuencing events. Jour-

nal of Systems and Software, 154:37–58.

Klein, A. M., Hinderks, A., Schrepp, M., and

Thomaschewski, J. (2020). Construction of ueq+

scales for voice quality: measuring user experience

quality of voice interaction. In Proceedings of Mensch

Und Computer 2020, MuC ’20, page 1–5, New York,

NY, USA. Association for Computing Machinery.

Kollmorgen, J., Hinderks, A., and Thomaschewski, J.

(2024). Selecting the Appropriate User Experience

Questionnaire and Guidance for Interpretation: The

UEQ Family. International Journal of Interactive

Multimedia and Artiﬁcial Intelligence, pages 1–14.

Kollmorgen, J., Pilz, J., Silva da Silva, T., Thomaschewski,

J., and Sch

on, E.-M. (2025). Reframing challenges

in agile and ux: A new categorization approach. In

Schrepp, M., editor, Design, User Experience, and

Usability, pages 101–111, Cham. Springer Nature

Switzerland.

Krauß, V., Boden, A., Oppermann, L., and Reiners, R.

(2021a). Current Practices, Challenges, and De-

sign Implications for Collaborative AR/VR Applica-

tion Development. In Proceedings of the 2021 CHI

Conference on Human Factors in Computing Systems,

CHI ’21, pages 1–15, New York, NY, USA. Associa-

tion for Computing Machinery.

Krauß, V., Jasche, F., Saßmannshausen, S. M., Ludwig, T.,

and Boden, A. (2021b). Research and practice recom-

WEBIST 2025 - 21st International Conference on Web Information Systems and Technologies

518

mendations for mixed reality design – different per-

spectives from the community. In Proceedings of the

27th ACM Symposium on Virtual Reality Software and

Technology, VRST ’21, New York, NY, USA. Associ-

ation for Computing Machinery.

uger, J. M. (2023). Augmented Reality for Learning -

The Role of Contextuality, Interactivity, and Spatiality

for AR-based Learning Experiences. Doctoral disser-

tation, University of Duisburg-Essen.

uger, J. M., Buchholz, A., and Bodemer, D. (2019). Aug-

mented reality in education: Three unique character-

istics from a user’s perspective. In Chang, M., So,

H.-J., Wong, L.-H., Yu, F.-Y., and Shih, J. L., edi-

tors, Proceedings of the 27th International Conference

on Computers in Education, pages 412–422, Taiwan.

Asia-Paciﬁc Society for Computers in Education.

Lohse, M. (2011). Bridging the gap between users’ expec-

tations and system evaluations. In 2011 RO-MAN 20th

IEEE International Symposium on Robot and Human

Interactive Communication, pages 485–490.

Mortazavi, E., Doyon-Poulin, P., Imbeau, D., Taraghi, M.,

and Robert, J.-M. (2024). Exploring the landscape of

ux subjective evaluation tools and ux dimensions: A

systematic literature review (2010–2021). Interacting

with Computers, 36(4):255–278.

Pacheco, C., Garc

ıa, I., and Reyes, M. (2018). Require-

ments elicitation techniques: a systematic literature

review based on the maturity of the techniques. IET

Software, 12(4):365–378.

Pettersson, I., Lachner, F., Frison, A.-K., Riener, A., and

Butz, A. (2018). A Bermuda Triangle? A Review

of Method Application and Triangulation in User Ex-

perience Evaluation. In Proceedings of the 2018 CHI

Conference on Human Factors in Computing Systems,

CHI ’18, page 1–16, New York, NY, USA. Associa-

tion for Computing Machinery.

Provost, G. and Robert, J.-M. (2013). The dimensions of

positive and negative user experiences with interac-

tive products. In Marcus, A., editor, Design, User

Experience, and Usability. Design Philosophy, Meth-

ods, and Tools, pages 399–408, Berlin, Heidelberg.

Springer Berlin Heidelberg.

Rauschenberger, M., Schrepp, M., Cota, M. P., Olschner, S.,

and Thomaschewski, J. (2013). Efﬁcient measurement

of the user experience of interactive products. how to

use the user experience questionnaire (ueq).example:

Spanish language version. Int. J. Interact. Multim. Ar-

tif. Intell., 2:39–45.

Schrepp, M., Kollmorgen, J., Meiners, A.-L., Hinderks,

A., Winter, D., Santoso, H., and Thomaschewski, J.

(2023). On the Importance of UX Quality Aspects for

Different Product Categories. International Journal

of Interactive Multimedia and Artiﬁcial Intelligence,

8:1.

Schrepp, M. and Thomaschewski, J. (2019). Design and

validation of a framework for the creation of user ex-

perience questionnaires. International Journal of In-

teractive Multimedia and Artiﬁcial Intelligence, 5(7).

Tcha-Tokey, K., Loup-Escande, E., Christmann, O., and

Richir, S. (2016). A questionnaire to measure the

user experience in immersive virtual environments. In

Proceedings of the 2016 Virtual Reality International

Conference, VRIC ’16, New York, NY, USA. Associ-

ation for Computing Machinery.

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APPENDIX

Exemplary excerpt from the survey. AR design

recommendation topics were presented to the partici-

pants, followed by the classiﬁcation question. At the

end of each questionnaire page, the UX dimensions

and their descriptions were listed, allowing partici-

pants to refer back to them as needed.

Product features (ARcis):

(1) Appropriate interplay of virtual content

and physical environments

(5) Hand & ﬁnger gestures

(6) Textures – Visual Realism and Appear-

ance of Objects

(17) Error prevention & recovery

(18) Consider and show User’s required Effort

Product character (UX Quality Aspects):

(9) Handling Interruptions / Relocalization

(12) Visual cues for object manipulation

(13) Object Placement

(19) Law of practice

(21) Text / Font

(22) Accessibility (visuals)

(23) Ergonomics (avoid muscle fatigue)

(24) Ergonomics (avoid head & neck fatigue)

(25) Pause / Breaks

(26) Performance

(28) Feedback

(29) FOV

(30) Content Placement

Product features and character (Both):

(2) Attention directors

(3) Instructions

(4) Onboarding

(7) Occlusion

(8) Image detection

(10) Surface Detection

(11) Affordance

(14) Object Manipulation

(15) Encourage to explore

(16) Keep the focus on AR experience, but

use 2D-UI On-Screen elements when needed

(20) Inform about Waiting Time

(27) Audio Feedback

(31) Headlocked content

(32) Anchored UI

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