Acceptability of Neuroscientific Interventions in Education

Abstract

Researchers are increasingly applying neuroscience technologies that probe or manipulate the brain to improve educational outcomes. However, their use remains fraught with ethical controversies. Here, we investigate the acceptability of neuroscience applications to educational practice in two groups of young adults: those studying bioscience who will be driving future basic neuroscience research and technology transfer, and those studying education who will be choosing among neuroscience-derived applications for their students. Respondents rated the acceptability of six scenarios describing neuroscience applications to education spanning multiple methodologies, from neuroimaging to neuroactive drugs to brain stimulation. They did so from two perspectives (student, teacher) and for three recipient populations (low-achieving, high-achieving students, students with learning disabilities). Overall, the biosciences students were more favorable to all neuroscience applications than the education students. Scenarios that measured brain activity (i.e., EEG or fMRI) to assess or predict intellectual abilities were deemed more acceptable than manipulations of mental activity by drug use or stimulation techniques, which may violate body integrity. Enhancement up to the norm for low-achieving students and especially students with learning disabilities was more favorably viewed than enhancement beyond the norm for high-achieving students. Finally, respondents rated neuroscientific applications to be less acceptable when adopting the perspective of a teacher than that of a student. Future studies should go beyond the acceptability ratings collected here to delineate the role that concepts of access, equity, authenticity, agency and personal choice play in guiding respondents’ reasoning.

Full text

Vol.:(0123456789)
Science and Engineering Ethics (2021) 27:52
https://doi.org/10.1007/s11948-021-00328-3
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ORIGINAL RESEARCH/SCHOLARSHIP
Acceptability ofNeuroscientific Interventions inEducation
A.Schmied1· S.Varma2· J.M.Dubinsky3
Received: 24 February 2020 / Accepted: 6 July 2021 / Published online: 5 August 2021
© The Author(s), under exclusive licence to Springer Nature B.V. 2021
Abstract
Researchers are increasingly applying neuroscience technologies that probe or
manipulate the brain to improve educational outcomes. However, their use remains
fraught with ethical controversies. Here, we investigate the acceptability of neuro-
science applications to educational practice in two groups of young adults: those
studying bioscience who will be driving future basic neuroscience research and
technology transfer, and those studying education who will be choosing among neu-
roscience-derived applications for their students. Respondents rated the acceptabil-
ity of six scenarios describing neuroscience applications to education spanning mul-
tiple methodologies, from neuroimaging to neuroactive drugs to brain stimulation.
They did so from two perspectives (student, teacher) and for three recipient popu-
lations (low-achieving, high-achieving students, students with learning disabilities).
Overall, the biosciences students were more favorable to all neuroscience applica-
tions than the education students. Scenarios that measured brain activity (i.e., EEG
or fMRI) to assess or predict intellectual abilities were deemed more acceptable
than manipulations of mental activity by drug use or stimulation techniques, which
may violate body integrity. Enhancement up to the norm for low-achieving students
and especially students with learning disabilities was more favorably viewed than
enhancement beyond the norm for high-achieving students. Finally, respondents
rated neuroscientific applications to be less acceptable when adopting the perspec-
tive of a teacher than that of a student. Future studies should go beyond the accept-
ability ratings collected here to delineate the role that concepts of access, equity,
authenticity, agency and personal choice play in guiding respondents’ reasoning.
Keywords Neuroethics· Cognitive enhancement· tCDS· Neuroeducation·
Educational neuroscience
* J. M. Dubinsky
Dubin001@umn.edu
1 Department ofEducational Psychology, University ofMinnesota, Minneapolis, MN, USA
2 School ofInteractive Computing, College ofComputing & School ofPsychology, College
ofSciences, Georgia Institute ofTechnology, Atlanta, GA, USA
3 Department ofNeuroscience, University ofMinnesota, 6-145 Jackson Hall, 321 Church St SE,
Minneapolis, MN55455, USA

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Introduction
An essential set of neuroethical questions has been identified by the 2018 Global
Neuroethics Summit for national brain research initiatives to address while
unraveling the inner workings of the brain (Rommelfanger etal., 2018). Their
last question focuses upon recognizing the cultural contexts in which novel
neurotechnologies or innovations may be deployed, raising issues of proper vs
improper use, equity of access, and considerations for all stakeholders (Rommel-
fanger etal., 2018). While this body was envisioning medical applications, edu-
cation provides a potential setting in which neuroscience-derived technologies or
innovations could also be productively and readily applied. A further ethical issue
is that of using brain research as a justification for applying a proposed interven-
tion in a classroom (Zocchi & Pollack, 2013). The dangers here include valid-
ity (i.e., the original research was exploratory and not definitive), generalizability
(i.e., the original research was laboratory- and not classroom-based, limited by
sample population characteristics, and applicable to groups, not individuals) and
pragmatics (i.e., the proposed intervention has been overly popularized) (Zocchi
& Pollack, 2013). Such limitations may not be appreciated or deeply understood
by administrators, teachers, parents or students. Thus national and international
policies have encouraged neuroscience application to education with caution, in
an evidence-based manner (Ansari etal., 2017; Royal Society, 2011).
Practically, neuroscientific methodologies may become integrated into class-
rooms for either research or evaluative purposes, with the goal of attempting to
optimize students’ educational experiences. As the demand for evidence-based
interventions increases, researchers are applying medical technologies that
probe or manipulate the brain (drugs, EEG, fMRI, tDCS, etc.) to human behav-
iors related to education. Utilization of physiological measures during classroom
activity furthers the neuroscience research agenda (Dikker etal., 2017) but may
not provide readily usable feedback for students or teachers. While the applicabil-
ity of such research interventions may be limited in real world classrooms, the
shift from research to common application can be swift when driven by com-
mercial interests which act as vectors for disseminating educational innovations.
Indeed, the fast pace of neuroscience research, discovery and innovation has been
likened to a ‘speedway’ (Giordano, 2017). The rush to apply and market neuro-
science ideas and techniques to educators has resulted in creation of neuromyths
(Geake, 2013; Im etal., 2018). Therefore, determining the ethical acceptability of
proposed neuroscience applications by educational stakeholders becomes para-
mount for initiating discussions around their use.
While the benefits of incorporating neuroscience concepts into teacher educa-
tion and pedagogical practice are apparent (Coch, 2018), how individual meas-
urements and interventions derived from neuroscience might directly be used in
a classroom setting remains fraught with ethical controversies. A common theme
among neuroscience educational applications is the idea of increasing student
learning, outcomes, or performance—in other words, enhancement. Addressing
the attentional control problems of students with ADHD by neuroactive drugs

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Acceptability of Neuroscientific Interventions in Education Page 3 of 27 52
such as methylphenidate remains an established practice. By improving impulse
control, students focus better and achieve according to their own potential (Rajala
et al., 2012; Sprague & Sleator, 1977). However, students without ADHD also
use these drugs to enhance their performance, raising issues of authenticity, per-
sonhood, fairness in testing, and equal access to the drugs (de Jongh etal., 2008).
Novel approaches like transcranial direct current stimulation (tDCS) also prom-
ise cognitive enhancement with do-it-yourself technology (Landhuis, 2017). Such
devices raise ethical issues around safety and efficacy.
Other advances with potential to predict and measure learning are under investiga-
tion for transfer to education. Assessment of individual learning or learning capacity
becomes a corollary to the theme of enhancement. Techniques such as EEG or fMRI
have been proposed for assessment or diagnosis of impairments in cognitive skills
(Allen & Fong, 2008; Volkmer & Schulte-Korne, 2018); (Cetron etal., 2019; Seghier
etal., 2019). They can be used to predict dyslexia in infants (Langer etal., 2017; Volk-
mer & Schulte-Korne, 2018) and developmental dyscalculia in children (Peters & De
Smedt, 2018; Butterworth etal., 2011). These predictive technologies bring the advan-
tage of being able to tailor instructional strategies to individual needs but also raise
the possibility that stigmatization may result from diagnosing learning disabilities (Ball
& Wolbring, 2014; Illes & Raffin, 2005). Additionally, pharmaceuticals and imaging
technologies raise ethical questions concerning consent, privacy and incidental findings
(Lalancette & Campbell, 2012; Maxwell & Racine, 2016).
More invasive, futuristic applications of neuroscientific devices involve use of surgi-
cally implanted neuroprostheses for cognitive enhancement. Memory enhancement has
been demonstrated with both hippocampal and non-hippocampal depth electrode place-
ments (Deadwyler etal., 2017; Hampson etal., 2018; Widge etal., 2019) like those
currently used for treatment of Parkinson’s Disease, dystonia and other psychiatric or
neurological illnesses (Suthana etal., 2018). To date, improvement in specific aspects
of memory have been reported, but so have failures to enhance, indicative of the early
stages of such research and imprecisions in understanding of the multiple networks
contributing to memory encoding and retrieval (Clausen et al., 2017; Jacobs et al.,
2016; Suthana & Fried, 2014; Suthana etal., 2018). In addition, implanted electronic
circuitry mimicking hippocampal function can improve visual identification memory
in epileptic patients, opening the door for both correcting memory loss and enhancing
memory function through brain-machine interfaces (Hampson etal., 2018). While the
hope is that precision electrode placement will provide better results than pharmaco-
logical global enhancement, the ethical issues of safety, tolerability, longevity, off-target
effects, security and privacy conflate the issue of enhancement alone (Clausen et al.,
2017; Ragan etal., 2013). With respect to education, these devices could be used to
induce learning in students’ brain, boosting the cerebral functions beyond the natural
capacities.

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Research Questions
As a first step, we investigate here the acceptability of applying such methodolo-
gies, measurements, and treatments arising from the field of neuroscience to educa-
tional practice. Whether considering individual diagnoses, educational interventions,
or assessments of learning, the ultimate goal is to improve student experiences and
achievement. Determining attitudes towards the ethics of such applications now will
guide their future development, implementation, and uptake. To determine the readi-
ness of the next generation to deal with these transitions, we surveyed young adults
with differing but relevant backgrounds: those studying bioscience, who will be driv-
ing the basic neuroscience research and technology transfer, and those studying educa-
tion, who will be selecting among various available neuroscience-based assessments
and interventions, and administering these to students. These study populations will be
faced with making such decisions as they enter the workforce or choose to start families
in the first half of the 21st century.
The current study evaluated four research questions:
1. Do students in the biosciences and in education differ in their judgments of the
acceptability of applying neuroscience findings to improve student learning?
2. Do the two groups differ in their judgments based on the scenario—whether the
application is non-invasive, pharmacological, or involves physical hardware?
3. Do the two groups differ in their judgments based on the perspective they adopt—
that of a student receiving the application or a teacher approving it?
4. Do the two groups differ in their judgments based on the recipient population—
high-achieving students, low-achieving student, or students with learning dis-
abilities?
Public approval of what constitutes a benefit from cognitive enhancement, and by
extension more invasive technologies, may be context-dependent (Shook etal., 2014).
Opinions were gathered on six different scenarios addressing interventions or assess-
ments derived from neuroscience as applied to education. These scenarios cross a num-
ber of dimensions, from already in use to highly experimental, from diagnostic to inva-
sive. They also span multiple methodologies, from neuroimaging to neuroactive drugs
to brain stimulation. Realizing that acceptability of proposed applications may vary
depending upon the school population involved, the survey specifically included appli-
cability to students performing at different levels. To uncover how participants’ opin-
ions might change as they transition from student into working adult, the survey asked
them to take the perspective of distinct actors in educational settings: themselves as a
student or that of a teacher. Thus respondents were asked to project how they expected
each of these actors to rate the acceptability of the proposed scenarios for each of the
school populations.

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Acceptability of Neuroscientific Interventions in Education Page 5 of 27 52
Methods
Participants
The questionnaire and methodology for this study was approved by the University of
Minnesota Institutional Review Board (protocol 1503P66601). All participants gave
their informed consent prior to taking the survey.
The participants were students in two undergraduate courses at a large public
university in the American Midwest. The original sample of 183 students com-
pleted an online survey. A Survey Response Time analysis determined the subset
of data for further analysis; those who completed the survey in less than 8min were
excluded from future consideration. The final sample consisted of 166 participants.
Biosciences students (82) were enrolled in a capstone course for undergraduate
Neuroscience majors and minors.1 Many were also majoring or minoring in other
biological sciences or in psychology. Education students (84) were enrolled in the
core educational psychology course for trainee teachers.2 Many were majoring in
elementary education.3 Demographic data about both groups are presented in the
Results section.
Design
The study adopted a factorial design with four factors that were varied orthogonally.
Respondent Group (biosciences, education) was a between-subjects factor, whereas
Scenario (non-invasive, pharmacological, physical hardware), Perspective (student,
teacher), and recipient Population (high ability students/HAS, low ability students/
LAS, students with learning disabilities/SLD) were within-subjects factors. The
dependent variable was acceptability rating. The within-subjects factors and the
dependent variable were embedded in the structure of the materials, as described
next.
Instrument
The materials consisted of a new instrument (see the supplementary materials) con-
ceptually divided into two parts. The first part consisted of background questions.
The second part contained the six scenarios that participants read about neurosci-
ence applications to improve educational outcomes, and the six items they rated for
each scenario.
1 The Biosciences students completed the survey as a course assignment. Their data was then summa-
rized and used as the basis for a class discussion led by the research team.
2 The Education students completed the survey for extra credit on their next exam.
3 Note that few if any of the Education students were training to be science or mathematics teachers.
Those students take the educational psychology course during the summer session, whereas the current
participants were recruited during the academic year.

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Background questions
The background questions concerned demographics (i.e., age and gender), prior
coursework taken (i.e., neuroscience, other natural sciences, psychology), educa-
tional attainment, parent status, and prior experience with neuroscience applications
for improving learning.
Scenarios
Participants read six scenarios about the application of neuroscience findings to
improve educational outcomes – two that were non-invasive, two that were pharma-
cological, and two that involved physical hardware (Table1).
The following applications were selected from a broader set of applications that
are widely discussed in the educational neuroscience literature.
• EEG (electroencephalography) is a spatially coarse measure of filtered electri-
cal activity from the underlying brain, aggregated over many independent neural
events, that is recorded with fine temporal precision. Aberrant EEG responses to
auditory word stimuli in infants predict later reading disabilities upon entering
school (Molfese, 2000). In educational settings, students could wear EEG caps to
provide real-time information about their attention during learning from instruc-
tion (Gamez, 2018; Kuo etal., 2017).
• fMRI (functional magnetic resonance imaging) measures local blood flow as
a proxy for neural activity. Its combination of moderate spatial resolution and
moderate temporal resolution makes it an effective tool for investigating math-
ematical thinking and scientific reasoning (Mason & Just, 2015; Butterworth
etal., 2011) and also language understanding (Richards et al., 2017, 2018). In
educational settings, fMRI could potentially be used to identify which students
might struggle with new content and why, and to evaluate the effects of new
instruction for resolving these struggles (Varma etal., 2008).
• Adderall is a mixed amphetamine salt currently prescribed as a first-line pharma-
cotherapy for impulsivity reduction in ADHD (de Jongh etal., 2008). Cognitive
enhancement may result from increased attention at low doses but this may be
lost when doses increase to the point of controlling excessive motor behavior
in affected individuals (Rajala etal., 2012; Sprague & Sleator, 1977). The per-
ception that Adderall generally enhances cognition has resulted in a black mar-
ket on college campuses (Benson etal., 2015; Colaneri et al., 2018; Smith &
Farah, 2011). Teachers’ inputs are important components of recognizing effec-
tive ADHD treatments (MTA Cooperative Group, 1999).
• Oxytocin, a brain peptide, has been found to play multiple roles in regulating
human prosocial behaviors (Dolen, 2015). As a result, it becomes a possible tar-
get for interventions with people with emotional and social impairments (Parker
etal., 2017). Oxytocin is available as a nasal spray. In educational settings, oxy-
tocin can potentially be used to minimize students’ misbehavior and to facilitate
peer interaction and group work (Hyman, 2011).

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Table 1 The six scenarios that participants read
Scenario Level Summary
EEG Non-invasive Measurement of the brain’s electrical signals can predict future academic disabilities and can monitor
learning during instruction
fMRI Non-invasive Indirect measurement of blood flow to different brain areas can be used to predict ability, to diagnose
academic disabilities and to assess learning after instruction
Adderall Pharmacological Pharmacological stimulation of normal brain chemicals is currently used to decrease impulsivity and
improve attention in children with ADHD. These cognitive enhancing effects may extend to others to
improve educational outcomes
Oxytocin Pharmacological Introducing oxytocin as a nasal or air spray can potentially be used to improve cooperation and to
facilitate peer interaction and group work
tDCS Physical hardware Application of electrical current to the scalp during instruction can potentially be used to improve
learning and memory
Neuroprosthesis Physical hardware Stimulation through embedding electrodes in the brain can potentially be used to improve learning and
memory during instruction

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• tDCS (anodal transcranial Direct Current Stimulation) passes a weak electrical
current through electrodes placed on the scalp, creating a circuit that may influ-
ence the activity of underlying brain regions. tDCS has been shown to improve
mathematical performance (Cohen Kadosh et al., 2010) and creative problem
solving (Ruggiero etal., 2018), although overall the results are mixed (Axelrod
etal., 2015; Westwood & Romani, 2017). tDCS can potentially be used in edu-
cational settings because it requires inexpensive, portable equipment (Landhuis,
2017). However, such applications are in their infancy and will require regu-
latory control given their possible adverse effects on children (Cohen Kadosh
etal., 2012; Fitz & Reiner, 2015).
• Neuroprosthesis refers to surgically implanted depth electrodes for stimulating
specific brain regions. Originally developed for clinical applications (e.g., treat-
ing Parkinson’s), this technique has been recently applied to improving cogni-
tive abilities, albeit with mixed success (Jacobs etal., 2016; Suthana & Fried,
2014). The invasiveness of neuroprosthesis and the safety and ethical issues that
surround its use likely limit its educational applicability (Clausen etal., 2017).
However, technological breakthroughs are difficult to forecast, and could poten-
tially lead to broader deployment (Hampson etal., 2018).
The scenarios were written following the Contrastive Vignette Technique (Burstin
etal., 1980). This technique systematically organizes keywords and text structures
so that readers can better identify the key informational elements. See Table2 for
the organization used across all scenarios and its instantiation for the EEG scenario.
Six selected-response items immediately followed each scenario. Each asked par-
ticipants to rate the acceptability of the neuroscience application on a seven-point
rating scale ranging from “Unacceptable (1)” to “Acceptable (7)”. The items crossed
the two levels of Perspectives factor and the three levels of the recipient Populations
factor.4 See Table3 for the six items for the Adderall scenario. The terms “ethics”
and “ethical” were avoided because people do not necessarily know or agree on their
definitions (e.g., (Ball & Wolbring, 2014)).
Procedure
The study was implemented electronically using the Qualtrics tool. Participants
received an email invitation providing a link to the online survey. The first screen
was the consent form. After reading each scenario in turn, they made six accept-
ability ratings. Finally, the participants answered the background questions. Time-
to-completion was recorded.
4 Note that participants actually rated four Perspectives: student, classmate, teacher, and parent. The
classmate and parent data are omitted here; the former added little beyond the student perspective and the
latter was too hypothetical because participants were 20years old on average and very few were parents.
See (Schmied 2017) for the complete data set.

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Results
Comparison ofBiosciences andEducation Students
The biosciences and education groups were largely comparable on a number of vari-
ables measured by the background questions (Table4).
With respect to the demographic variables, the bioscience and education students
were comparable in age (t(92.58) = − 1.44, d = 0.22, p = 0.152).5 There was an asso-
ciation between group and gender, with the biosciences group having a larger pro-
portion of male students than the education group (X2(1) = 14.54, p < 0.001).6
With respect to the educational variables, the highest level of educational attain-
ment in the two groups was comparable, with most students having earned high
school degrees (i.e., were currently enrolled as undergraduates). A X2 test to evaluate
Table 2 The Contrastive Vignette Technique as applied in the EEG scenario
Structure Scenario
Title Electroencephalography (EEG)
Current use in medicine EEG has been used to diagnose the cause of sei-
zures in people with epilepsy
Stage of development and technical description EEG is an approved technique that measures
electrical activity coming from the brain using
electrodes placed on the scalp
Transition to education and current or potential use Recent research suggests that EEG might also be
useful for predicting the verbal ability of children
and adults without epilepsy
Extent of applicability Thus, in the field of education, EEG could be indi-
rectly applied to improve student learning
Specific example for use in education For example, EEG might be used to identify infants
who are at-risk for future reading difficulties when
they enter school, so that they can benefit from
early intervention programs
Precaution in translating the finding However, at the present time, the potential of using
EEG to improve educational outcomes is still
unclear
Additional research is needed to establish whether
or not using EEG improves educational outcomes
for students who do not have epilepsy
Level of invasiveness and short-term side effects So far, the most known common side effect of EEG
is irritation of the skin where the electrodes are
placed
Long-term side effects However, its potential long-term effects are
unknown
5 Fractional degrees of freedom on t-tests indicate that the Levene’s test for equal variances was not
passed, and a correction was applied.
6 The Yates correction for continuity was applied to all 2 × 2 X2 tests.

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Table 3 The six items for the Adderall scenario
Perspective Population Item
Student How acceptable would it be for you to use Adderall…
HAS …if you are performing well in class, but want to be the best?
LAS …if you are performing poorly in class, and want to perform better?
SLD …if you have been diagnosed with a learning difficulty, and want to perform as well as other students?
Teacher If you are a teacher, how acceptable would it be for your student to use Adderall…
HAS …if your student is performing well in class, but you want him or her to be the best?
LAS …if your student is performing poorly in class, and you want him or her to perform better?
SLD … if your student has been diagnosed with a learning difficulty, and you want him or her to perform as
well as other students?

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the association between group and education level could not be performed because
the expected count in two cells was fewer than 5. As expected, the biosciences stu-
dents had taken more neuroscience courses (t(157.10) = 11.71, d = 1.81, p < 0.001)
and more courses in the other natural sciences (t(112.80) = 9.88, d = 1.54, p < 0.001)
than the education students. The two groups had comparable coursework in psychol-
ogy (t(148.75) = 0.842, d = 0.12, p = 0.401).
We also measured several variables relevant to participants’ reasoning about the
scenarios. The first was parental status. Parents might reason differently about their
own children, and thus about all students, then non-parents. However, very few stu-
dents were parents—too few students, in fact, to evaluate the association between
group and parental status using a X2 test because the expected count in two cells was
fewer than 5. The second variable was whether participants had been prescribed a
drug (e.g., Adderall) that might improve their educational achievement. A high pro-
portion of participants responded that they had been prescribed such drugs, although
there was no association between this variable and group membership (X2(1) = 1.20,
p = 0.273). The final variable was whether participants had experienced brain stimu-
lation for the purpose of improving educational achievement. A number of partici-
pants had, and this was more frequently the case for education students (X2(1) = 7.24,
p = 0.007).
We note that all observed differences between the two groups on the background
questions should be interpreted with caution. This is because all responses were self-
reports, and we did not collect independent and corroborating evidence for reasons
of practicality and confidentiality. We return to this limitation in the Discussion.
Acceptability Ratings
The research questions were addressed in a four-way repeated measures ANOVA
of the acceptability ratings with between-subjects factor Group (biosciences, edu-
cation) and with within-subjects factors Scenario (non-invasive, pharmacological,
Table 4 Comparison of the biosciences and education groups on a broad set of variables, M (SD)
Variable Biosciences Education
N 82 84
Age (years) 19.83 (.96) 20.49 (4.06)
Gender 53 F, 29M 76 F, 8M
Highest education level attained 72 high school,
10 college,
0 graduate
67 high school,
13 college,
4 graduate
Neuroscience courses 2.74 (.85) 1.31 (.71)
Other natural science courses 4.28 (1.63) 2.32 (.74)
Psychology courses 3.49 (1.75) 3.29 (1.29)
Parental status 1 yes, 81 no 5 yes, 79 no
Prescribed drug to improve learning 35 yes, 47 no 44 yes, 40 no
Prescribed brain stimulation to improve learning 3 yes, 79 no 15 yes, 69 no

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hardware), Perspective (student, teacher), and recipient Population (HAS, LAS,
SLD). Where the sphericity assumption was violated, the Greenhouse–Geisser cor-
rection was applied and the F values reported with fractional degrees of freedom.
There was a main effect of Group (F(1,164) = 24.61, MSE = 72.37, p < 0.001,
ηp2 = 0.13, Fig. 1). Biosciences students (M = 3.79, SE = 0.13) judged neuroscience
applications to be more acceptable than education students (M = 2.85, SE = 0.1).
Thus, with respect to the first research question, the two groups reasoned differently,
with biosciences students more accepting of applying neuroscience treatments and
interventions to improve student learning.
There was a main effect of Scenario (F(1.81, 297.94) = 55.47, MSE = 270.41,
p < 0.001, ηp2 = 0.25, Fig. 2). An embedded contrast found that non-invasive
scenarios (M = 3.85, SE = 0.11) were judged more acceptable than pharma-
cological scenarios (M = 3.23, SE = 0.10) (F(1, 164) = 40.86, MSE = 128.57,
p < 0.001, ηp2 = 0.19), which were judged more acceptable than hardware scenar-
ios (M = 2.87, SE = 0.10) (F(1,164) = 20.79, MSE = 42.84, p < 0.001, ηp2 = 0.11).
This main effect was qualified by a significant Scenario x Group interaction
Fig. 1 Acceptability judgments
for the two respondent groups.
Error bars represent 95% CIs
Fig. 2 Acceptability judgments
for the different scenarios for the
two respondent groups. Error
bars represent 95% CIs

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Acceptability of Neuroscientific Interventions in Education Page 13 of 27 52
(F(2,328) = 4.03, MSE = 17.86, p = 0.019, ηp2 = 0.024). Biosciences students
judged neuroscience applications more acceptable than education students, a dif-
ference that was larger for the non-invasive scenarios (t(164) = 5.31, d = 0.82,
p < 0.001) than for the pharmacological (t(164) = 3.71, d = 0.57, p < 0.001) and
hardware (t(164) = 3.76, d = 0.58, p < 0.001) scenarios. Thus, with respect to the
second research question, the two groups reasoned differently based on the sce-
nario, with bioscience students more accepting of non-invasive applications such
as using EEG and fMRI to detect impaired cognitive function.
Both groups rated the hardware scenarios as least acceptable. A follow-up
analysis evaluated the prediction that tDCS is more acceptable than neuropro-
sthesis because the former has been widely promoted for improving student
learning whereas the latter has been largely limited to clinical applications.
This prediction was tested in a two-way ANOVA with between-subjects factor
Group and repeated measure Scenario (tDCS, Neuroprosthesis). As predicted,
there was a main effect of Scenario (F(1,164) = 68.23, MSE = 57.41, p < 0.001,
ηp2 = 0.29), with tDCS more acceptable (M = 03.29, SE = 0.12) than neuropros-
thesis (M = 2.46, SE = 0.11). However, this effect was comparable across groups
and not driven, for example, by the biosciences students, as revealed by the non-
significant interaction (F(1,164) = 1.31, MSE = 1.11, p = 0.253, ηp2 = 0.01).
There was a main effect of Perspective (F(1,164) = 24.85, MSE = 72.60,
p < 0.001, ηp2 = 0.13). Neuroscience applications were more acceptable when
taking the perspective of students (M = 3.48, SE = 0.09) than that of teachers
(M = 3.16, SW = 0.10). This main effect was qualified by a significant Perspective
x Group interaction (F(1,164) = 8.67, MSE = 22.37, p = 0.004, ηp2 = 0.05, Fig. 3).
The interaction indicates that the main effect of Perspective was driven by the
biosciences students: the effect of Perspective was larger for them (t(81) = 4.45,
d = 0.49, p < 0.001) than for the education students (t(83) = 2.176, d = 0.24,
p = 0.032). Thus, with respect to the third research question, the two groups rea-
soned differently based on perspective, with the bioscience students more accept-
ing of neuroscience applications when taking the perspective of students.
Fig. 3 Acceptability judgments
for the different perspectives
for the two respondent groups.
Error bars represent 95% CIs

A. Schmied etal.
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52 Page 14 of 27
There was a main effect of the recipient Population (F(1.49, 244.37) = 363.81,
MSE = 1626.96, p < 0.001, ηp2 = 0.68). An embedded contrast found that neuro-
science applications were judged more acceptable for students with learning dis-
abilities (M = 4.53, SE = 0.11) than for low-achieving students (M = 3.07, SE = 0.10)
(F(1,164) = 314.00, MSE = 702.62, p < 0.001, ηp2 = 0.65), which were judged more
acceptable than for high-achieving students (M = 2.36, SE = 0.09; F(1,164) = 153.51,
MSE = 166.98, p < 0.001, ηp2 = 0.48). Note that the Population x Group interaction
was not significant (F(2,328) = 0.18, MSE = 0.61, p = 0.831, ηp2 = 0.001; Fig. 4).
Thus, with respect to the fourth research question, the two groups reasoned compa-
rably about the acceptability of neuroscience applications for different responding
populations.
There was also a Scenario x Population interaction (F(4,656) = 11.78,
MSE = 9.16, p < 0.001, ηp2 = 0.06), and it was qualified by a Scenario x Population
x Perspective interaction (F(4,656) = 3.04, MSE = 0.357, p = 0.017, ηp2 = 0.018). The
three-way interaction suggests that participants judged pharmacological interven-
tions for HAS and LAS students to be more unacceptable than for SLD students,
and this was particularly true when they adopted a teacher (vs. student) perspective.
Although these interactions are sensible, they were not predicted a priori and are
small in size. We therefore treat them as exploratory.
Discussion
A major finding was that across all scenarios, the bioscience students favored the use
of neuroscience-derived enhancers or measures in education more than the educa-
tion students. From non-invasive diagnostic techniques to predict use of individual-
ized instruction to pharmacological interventions to brain stimulation, the education
students were much more cautious in their acceptability ratings than the bioscience
students. Another notable finding is that, consistent with previous determinations
regarding who should receive cognitive enhancing treatments (Cabrera etal., 2015a;
Wagner etal., 2018), respondents rated enhancements for students with learning
Fig. 4 Acceptability judgments
for the different receiving popu-
lations for the two respondent
groups. Error bars represent
95% CIs

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Acceptability of Neuroscientific Interventions in Education Page 15 of 27 52
disabilities to be more acceptable than those for low performing students; high per-
forming students were the least deserving of intervention or cognitive enhancement.
Interestingly, respondents rated neuroscientific applications to be less acceptable
when adopting the perspective of a teacher compared with that of a student.
Notably, most participants responded with ratings in the lower half of the rat-
ing scale. The only scenario receiving a midrange acceptability rating was the use
of non-invasive measurements for SLD when viewed from the students’ point of
view; this was marginally the case for education respondents and decisive only for
biosciences students. The overall low acceptability ratings are in line with previ-
ous reports comparing acceptability for cognitive enhancement to or above the norm
(Cabrera etal., 2015a, 2015b) but less than the reported acceptability ratings favor-
ing enhancement in work over educational contexts (Dinh etal., 2020). Ambiva-
lence towards the use of pharmacological cognitive enhancers has been noted previ-
ously among university students, their parents and health care providers (Cynthia
Forlini & Racine, 2012). The current study probed a variety of different forms of
cognitive enhancement whereas these prior studies focused solely on pharmacologi-
cal enhancement. Teachers in England did not agree with the statement that grades
achieved with cognitive enhancers should be valued as much as those achieved with-
out (Howard-Jones & Fenton, 2012).
We can only speculate upon the reasons why the overall acceptability ratings
were not higher. In one sense, this is an encouragingly cautious sign that authentic-
ity was widely valued by respondents. Information sharing regarding ‘study drugs’
on social media platforms such as Instagram is often overly positive. Posts contain
positive sentiments 4 to 34 times more often than negative evaluations depending
upon their category (information vs motivational quotes) (Petersen et al., 2021).
Fear of the consequences of the various enhancement strategies presented in the
vignettes may be responsible for the low overall acceptability ratings observed here.
Youth and young adults report choosing not to use illicit drugs due to fear of their
long and short term effects or lack of interest altogether (Fountain etal., 1999). Note
that the low ratings do not mean that people would not adopt some of the neurosci-
ence applications considered here. Users of ‘study drugs’ recognize side effects and
possible lack of positive effects yet persist pursuing personal optimization despite
expressing uncertainty (Petersen etal., 2019).
An additional reason for respondents to be cautious is the possibility that
enhancement of one cognitive domain may come at the expense of performance in
another domain (Brem etal., 2014). The neurological tradeoffs or harms associated
with cognitive enhancement are becoming more recognized and openly discussed
(Davis, 2017). Pharmacologically enhanced chess players improved the quality of
their decision making, but at the expense of increasing their deliberation time; the
net result was negative, with the enhanced players losing more games on the basis
of time (Franke etal., 2017). tDCS of one part of cortex enhanced learning a novel
numerical task but automaticity was impaired, whereas tDCS of another area pro-
duced the opposite effect (Iuculano & Cohen Kadosh, 2013). Similarly, tDCS of
same brain area in people with low or high math anxiety increased or decreased
reaction times on simple arithmetic tasks, respectively, suggesting individual traits
or brain states may influence enhancement outcomes (Sarkar etal., 2014). Fewer

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parents were willing to use tDCS to enhance a child’s long term factual memory if
the scenario included the possibility of a slight reduction in working memory (Wag-
ner etal., 2018). Thus the skepticism reflected in the low acceptability ratings in this
survey may well be warranted.
Future Scientists versusFuture Teachers
Overall, the bioscience students appeared more favorable to all interventions than
the education students. Educational attainment and age did not differ between the
responding groups, thus their different attitudes could not generally be ascribed to
demographic variables (though see Limitations). Several factors may have contrib-
uted to this result. First, this predisposition towards accepting neuroscientific-based
interventions may stem from their knowledge of (neuro)scientific mechanisms or,
less positively, a belief that biologically-based practices are inherently good. As
neuroscience students, they may have been unduly influenced by the neuroscience
covered in the course (McCabe & Caster, 2008; Weisberg etal., 2008). Second, edu-
cation students may value authenticity (versus technical mediation) more than bio-
scientists, as authenticity is a critical characteristic of teachers and their instruction
(De Bruyckere & Kirschner, 2016; Newmann & Wehlage, 1993). Educators may
also value caring for children when guiding their learning more than treating learn-
ing ability as a condition that can be manipulated (Stein, 2010). Third, cognitive
enhancement beyond normal has become normalized in the US workplace (Sales
et al., 2019) and is desired among professionals such as surgeons, lawyers, trans-
portation and construction workers (Leon etal., 2019), and even scientists (Maher,
2008). Competitive bioscience students trying to gain admission to highly selective
post-graduate and medical programs may also think similarly. By comparison, there
are more on-ramps to the teaching profession. Education students may have felt less
pressure or need for cognitive enhancement.
Enhance uptotheNorm
As expected from previous studies (Cabrera et al., 2015a), for current respond-
ents, enhancement up to the norm for LAS and especially SLD students was more
favorably viewed than enhancement beyond the norm for HAS students. Many of
the approaches considered here were initially developed for the purpose of medi-
cal diagnosis or treatment. Their current or potential use for cognitive enhancement,
educational diagnosis or assessment can be considered similarly to off-label appli-
cations of approved medicines and medical devices. There, treatment is defined as
interventions to improve or return functioning up to normal level, whereas enhance-
ment is defined as improvements above normal levels of functioning. However, in
practice this dichotomy really is a continuum, dependent upon the individual and
the context (Daniels, 2000; Shook etal., 2014; Singh & Kelleher, 2010). Medically,
some drugs are prescribed to ameliorate a risk state (e.g. high blood pressure) and
prevent an illness rather than to simply treat an illness (Hyman, 2011). Similarly,

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Acceptability of Neuroscientific Interventions in Education Page 17 of 27 52
cognitive enhancement might be less categorical and more continuous (Hyman,
2011).
Invasiveness ofNeuroscience Measures: Integrity Matters
Among the different scenarios, those that measured brain activity, EEG or fMRI, to
assess or predict intellectual abilities were deemed more acceptable than manipula-
tions of mental activity by drug use or stimulation techniques, which may violate
body integrity. We cannot distinguish between non-invasiveness and diagnostic util-
ity as reasons for the more favorable ratings as both were characteristics of only
the EEG and fMRI vignettes. Diagnosis ranges from predicting a learning disability
and a need for an individualized learning plan to actually assessing comprehension
of subject matter (Hoeft et al., 2011; Mason & Just, 2016). Short of truth serum
or biopsies, drugs or invasive devices do not currently lend themselves to educa-
tional diagnostic purposes. If diagnostic applications arise in the future for drugs
or invasive devices, the acceptability of invasive diagnosis for educational purposes
would have to be assessed separately. Safety issues may also have contributed to the
non-invasive/diagnostic preference. The scenarios utilizing drugs or devices were
less desirable. The experimental nature of the neuroprosthetics and tDCS may also
figure into their disfavor, but assessing student performance with EEG or fMRI or
determining if classrooms run better with oxytocin in the air are also experimen-
tal at this juncture. Thus, focusing on the further development of non-invasive neu-
roscience assessments should be a priority. Such measures are more acceptable to
bioscientists who will be asked to develop them than to the teachers who will be
asked to utilize them in their classroom practice. The risks of measuring educational
attainment using physiological measures disconnected from the actual performance
include projecting decreased expectations, stigmatization or incorrect categoriza-
tion for individual children. In addition, they are large-budget items. These prospects
threaten to diminish enthusiasm for using such techniques in place of traditional
educational testing.
The current use of prescription neuroenhancers in educational settings is com-
monly accepted for the treatment of ADHD. However, a number of ethical con-
siderations still exist surrounding this practice. Students feel social pressures to
self-enhance and maintain personal integrity yet they also perceive the use of neu-
roenhancers as a substitute for their own authentic performance, suffer from identity
issues, or social stigma associated with their use (C. Forlini & Racine, 2009; Singh
& Kelleher, 2010). From the teachers’ and schools’ points of view, encouraging neu-
roenhancement may have benefits beyond enhancing individual performance when
enhancers are used to control disruptive behaviors or when widespread enhancer use
promotes meeting mandated proficiency levels on standard exams (Singh & Kelle-
her, 2010).
Respondents expressed appropriate skepticism about the invasive scenarios that
were more experimental in nature. They are not alone. In a university-wide Irish
survey, only 15% favored using tDCS for enhancement in education (Karok &
Witney, 2017). Among Italian psychology and medical undergraduates, only 21%

A. Schmied etal.
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52 Page 18 of 27
favored using tDSC for self-enhancement (Karok & Witney, 2017). Even research-
ers working on tDCS viewed its effectiveness as moderate with clinical use favored
over research, and lastly enhancement applications (Riggall etal., 2015). The most
invasive scenario involved implantation of a device to stimulate brain activity with
hypothetical enhancement capabilities. This possibility is now not that far into the
future. Stimulation via electrodes used for localization of epileptic foci in brain areas
relevant to memory formation can enhance performance on specific experimental
memory tasks (Khan etal., 2019; Kucewicz etal., 2018). While current interest in
this technique focuses upon restoration of memory in neurodegenerative disease,
conceivably, a child with epilepsy might be in a position to receive such enhance-
ment now. However, ethical issues concerning loss of agency in adults with deep
brain stimulation remain unresolved (Goering etal., 2017), and will be even thornier
for students in education.
From Acceptability Ratings toEthical Judgments andPolicy
This study only measured acceptability ratings; it did not probe the ethical reason-
ing behind these choices by asking respondents to provide rationales. Thus the ethi-
cal positions of respondents can only be imputed from their ratings. This section
explores how the acceptability judgments captured in the current study can inform
discussions of regulation of these neuroscience applications in educational and
broader policies.
Most likely, the complex reasoning behind individual ratings was highly diverse,
as evidenced by an in-depth analysis of Dutch student, parent, and teacher focus
groups discussing acceptability of using neuroimaging in assessing and adapting
teaching methods to individual learners’ needs (Edelenbosch etal., 2015). Opinions
ranged from the benefits to be gained by tailoring teaching to individual needs to
the stresses this would place on teachers or the loss of social interaction among stu-
dents who would be segregated away from the class by this personalized instruction.
Critically, each stakeholder group emphasized different aspects of this tension. High
school students emphasized the social aspects of learning and the desire to choose
their own instructional paths. Parents worried about privacy and possible stigmati-
zation related to the consequences of using the brain scans. Teachers felt the person-
alized learning would undercut their roles as deliverer of stories and relegate them
to supervisors of computer use (Edelenbosch et al., 2015). As indicated in these
examples, future studies might adopt qualitative methods or structured interviews
to probe more deeply into how different educational stakeholders reason about the
ethics of different neuroscience applications to improve academic outcomes. Such
in depth discussions could be part of community processes leading to decision mak-
ing by appropriate regulatory bodies, whether educational, medical or governmental
(Escobar, 2014).
While youth may be the beneficiaries of these new technologies, teachers, par-
ents and administrators are the decision makers who opt to purchase or engage
with such practices (Horwitz, 2007). In most countries, national policies can
drive educational practices and purchasing decisions; in the US, such decisions

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Acceptability of Neuroscientific Interventions in Education Page 19 of 27 52
largely occur at the level of the local school or district. Accountability for stu-
dent performance has been a dominant educational policy theme both in the US
and internationally (Teltemann & Schunck, 2020; Mathis 2016) that could drive
policy decisions favoring the interventions in the current vignettes. Only at the
university level do students have the agency to seek or reject enhancements. As of
2017, only one out of 191 US universities specifically prohibited non-prescription
use of stimulants as a violation of academic integrity, while virtually all institu-
tions had broader policies complying with state and federal laws regarding alco-
hol and other drugs as part of the student code of conduct (Aikins etal., 2017).
Educational policy makers are under pressure to make decisions based upon
high quality educational research involving randomly controlled trials measuring
performance outcomes longitudinally that are scalable (Horwitz, 2007). As evi-
dence-based pedagogical practices spread, neuro-technologies may become more
commonplace in education. For example, EEG can detect attention or lack thereof
in real time. In a RCT study, an EEG-based attention training game reduced chil-
drens’ ADHD symptoms (Lim etal., 2019). EEG caps that assess mental work-
load and engagement, providing personal feedback, are being prepared for mar-
ket (So etal., 2017). More complex systems have already been incorporated into
computer-based intelligent tutoring systems which detect and respond to atten-
tion lapses (Kuo etal., 2017). Such developments are driven by combining neu-
roscience understanding of learning with commercial interests and may not be
incorporating input from all educational stakeholders. Recently, organized social
media campaigns have reacted against existing policies, leading to state by state
modifications of the Common Core standards (Daly 2019). So public opinion can
become a powerful determinant of adoption and should appropriately be taken
into consideration as part of educational innovation.
Regulatory issues surrounding the use of cognitive enhancers in education fall
upon doctors who prescribe these drugs as well as educational institutions (Ricci,
2020). Commercial regulation would fall under governmental auspices. The
American Medical Association policy discourages physicians from prescribing
cognitive enhancers in healthy individuals (American Medical Association 2016).
Both US and Italian governments have formally considered the ethical implica-
tions of enhancement up to and beyond normal, releasing reports, but neither
have formally enacted legal limits (Ricci, 2020). Regulatory authority over the
educational use of neurotechnologies is unclear as all of the applications in the
vignettes except the neuroprosthesis are available for purchase on open or private
markets (e.g. (So etal., 2017); (Kuo etal., 2017); (Fitz & Reiner, 2015; Smith &
Farah, 2011)). Major proponents of cognitive enhancer use are the businesses that
seek to market over-the-counter supplements and devices to audiences of “brain
hackers” (Glionna, 2017). As nutritional supplements, these are unregulated
in the US. Attempts to advance ethical policies around the use of brain imag-
ing technologies in Scotland involved extensive public discourse that eventually
failed to reach the threshold needed for legislation (Escobar, 2014). More recent
calls for policy making around use of devices such as fMRI or tDCS propose
an iterative process engaging multiple stakeholders willing to form communities
of practice to weigh the benefits and potential harms of using such technologies

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in educational settings on children (Ahmed & Hens, 2020; Schuijer etal., 2017;
Seghier etal., 2019).
With such discourse rightly centered at the community level, the acceptance data
gathered here can contribute to those discussions. Under current US law, cognitive
enhancement could conceivably be coercively administered to minors by parents or
private schools without further regulation, although challenges to safety and effi-
cacy might limit such practices (Jwa, 2019). Current regulatory policies in the UK
surrounding the use of cognitive enhancing pharmaceuticals, grounded in the idea
of a drug-free culture, do not appear to be inhibiting the use or growing accept-
ance of such practices by specific segments of the population (Coveney etal., 2019).
Changes in regulatory policies to deal with off label use of cognitive enhancers
should be both sensitive to public opinion, captured here and more broadly, and the
successes and failures in the regulation of psychoactive drugs (Collins, 2017; Dinh
etal., 2020; Greer & Ritter, 2020).
The current study is a first attempt to assess permissibility of these emerging edu-
cational neurointerventions among a community involved in education. However,
acceptability ratings are a coarse indicator. Future studies should adopt qualitative
methods or structured interviews to probe more deeply into how people reason about
the ethics of different neuroscience applications to improve academic outcomes
and who should regulate these. Such studies can investigate the importance of the
breadth of ethical values and issues relating to these applications to determine which
values underpin the moral acceptability observed here.
Limitations andConcerns
Two limitations derive from the composition of the sample. Demographically, fewer
males were present among the education respondents. As males are generally more
accepting of technology (Broos, 2005), it is an open question whether this resulted
in the lower acceptability ratings of the education students versus the biosciences
student.7 Thus, exploring whether there are gender effects in evaluating the accept-
ability of neuroscience applications to education remains a direction for future
research. Additionally, the reported acceptability of these hypothetical educational
applications of neuroscience reflects the cultural orientation of students attending
a large Midwestern university in the United States. Attitudes and acceptability of
7 At the suggestion of one of the anonymous reviewers, we undertook an exploratory analysis of gen-
der in our sample. There were simply too few men among the education students (8 of the 84 partici-
pants) to support a statistical analysis of gender in that group. However, the numbers were better for
the biosciences students (29 of the 82 participants). We conducted an ANOVA of acceptability ratings
with between-subjects factor Gender (female, male) and the within-subjects factors Scenario, Perspec-
tive, and recipient Population. There were two notable findings. First, there was a main effect of Gender
(F(1,80) = 11.08, p < 0.001, ηp2 = 0.12), with male participants judging neuroscience applications to be
more acceptable than female participants, consistent with the literature (Broos 2005). Second, there was
a Gender x Perspective interaction (F(1,80) = 4.97, p = .029, ηp2 = 0.06). Participants rated neuroscience
application more favorably when taking the perspective of a student (versus a teacher), and this was par-
ticularly the case for male participants.

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Acceptability of Neuroscientific Interventions in Education Page 21 of 27 52
all proposed interventions would vary considerably in the context of another coun-
try or culture (Rommelfanger etal. 2018). Engagement of larger and more diverse
respondents is necessary to explore these potential confounds.
Of concern, respondents reported a high incidence of having been prescribed
drugs or brain stimulation to improve their learning, ranging from 3 to 52%. By con-
trast, the reported prevalence of pharmacological enhancement in educational set-
tings ranges from 5 to 35% depending upon context (Benson etal., 2015; Wilens
etal., 2008) with prescription use recently increasing (Sales etal., 2019). This con-
cern could arise because the current set of respondents were all from a university
setting. The heavy prior use in our sample could be affecting the overall degree of
acceptability encountered. However, respondents differentiated acceptability across
a range of other variables, indicating their ability to reason independently of their
own experiences.
A final set of limitations concerns the materials. Although we devoted significant
effort to developing the six scenarios and piloting them along with the associated
items, they are limited in several ways. One limitation is that there are other dimen-
sions of variation across the six scenarios besides the one we focused on here (i.e.,
non-invasive vs. pharmacological vs. physical hardware). For example, both non-
invasive scenarios also share the property that they have no direct effect on student
learning. It is possible that this shared property drives participants’ acceptability rat-
ings. Such confounds were unavoidable given our goal of using naturally occurring
examples of neuroscience applications to education to ensure the external validity
of the results. Nevertheless, future studies should improve the scenarios to reduce
such confounds. Another limitation is a possible ambiguity in the items where par-
ticipants rate, from the perspective of the teacher, the acceptability of neuroscience
applications. This prompt is ambiguous because it does not specify who is mak-
ing the decision to use the intervention: the teacher, the student, a parent, or school
administrator (McCall etal., 2020). Future studies should improve these items to
explicitly name the decision maker; this will reduce this source of variability. (We
thank the anonymous reviewers for these suggestions.)
Conclusion
Providing teachers with background neuroscience knowledge may be necessary so
that they can critically appraise these futuristic technologies, which are practically
implemented, and interpret their results for students (Coch, 2018; Howard-Jones,
2009; Dubinsky etal., 2013, 2019; Schwartz etal., 2019). The work of improving
teacher understanding of how learning occurs in the brain is already being done
(Dubinsky etal., 2019). This raises the parallel question of educating the design-
ers of tomorrow’s neuroscience measures and treatments about the ethical dimen-
sions of their application to education. While they did not embrace these ideas
overwhelmingly, bioscience respondents were more favorable towards infusing
neuroscience-related technologies into classrooms than education respondents. In
the near future, these young adults will join students, parents, teachers, adminis-
trators, and policy makers in conversations about neuroscience interventions, and

A. Schmied etal.
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52 Page 22 of 27
they must be sensitive to the potentially conflicting views of different stakeholders
(Ahmed & Hens, 2020; Hardiman etal., 2012; Maxwell & Racine, 2016). We agree
with Ahmed and Fens (Ahmed & Hens, 2020) that follow-up qualitative studies
should extend the current findings to delve deeper into the prevailing reasons for and
against acceptance of these applications. Such studies should include potential cog-
nitive costs, probe the concepts of access, equity, authenticity, agency and safety and
utilize continuous rating scales.
Supplementary Information The online version contains supplementary material available at https:// doi.
org/ 10. 1007/ s11948- 021- 00328-3.
Acknowledgements We wish to thank Christopher Barbey for helpful discussions and Soo-hyun Im, Kat-
rina Schleisman, and Purav Patel for help in developing the scenarios.
Author Contributions AS: designed the experiment, designed the instrument, collected data, performed
data analysis, initially drafted the manuscript. SV: guarantor of integrity of the experiment, designed the
experiment, performed data analysis, wrote and edited the manuscript. JMD: designed the experiment,
wrote and edited the manuscript.
Funding Support for this work was provided by the Fulbright Program, Becas Chile, and the Interna-
tional Student and Scholar Services at the University of Minnesota to AS.
Declarations
Conflict of interest The authors declare that they have no conflict of interest.
Ethical Approval The questionnaire and methodology for this study was approved by the University of
Minnesota Institutional Review Board (protocol 1503P66601). All participants gave their informed con-
sent prior to taking the survey.
Data Repository https:// doi. org/ 10. 13020/ vn7a- m891.
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