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Examining the Use of Game-Based Assessments for Hiring Autistic Job Seekers

HireVue, Inc., South Jordan, UT 84095, USA
Integrate Autism Employment Advisors, New York, NY 10022, USA
Department of Psychology, Colorado State University, Fort Collins, CO 80523, USA
Author to whom correspondence should be addressed.
Submission received: 15 September 2021 / Revised: 22 October 2021 / Accepted: 26 October 2021 / Published: 3 November 2021


Although people with autism are protected under the Americans with Disabilities Act of 1990, there is little theoretical or practical effort to determine whether traditional pre-employment assessments unfairly impact autistic job seekers. Due to the lack of emphasis on social communication, game-based assessments (GBAs) may offer a way of assessing candidate ability without disadvantaging autistic candidates. A total of 263 autistic job seekers took one of two game-based assessment packages designed to measure cognitive ability. After comparing their results to 323 college-graduate job seekers in the general population, we found that performance on the GBAs was generally similar in both populations, although some small differences were detected. Implications for hiring decisions are discussed.

1. Introduction

Attitudes toward who is qualified to work are undergoing a shift. Organizations today are reeling from an unprecedented labor shortage (Spiggle 2021) that, coinciding with a rapidly warming attitude toward the inclusion of “disabled” talent in the workforce, has led to an explosion of interest into tapping this large untapped labor market (Schur et al. 2014). Through the lens of neurodiversity, autistic individuals, in particular, are increasingly thought of as a skilled and reliable part of this untapped talent pool who can bring diverse perspectives and valuable traits, including a strong work ethic, heightened attention, and analytical and critical thinking skills, to an organization (Hensel 2017). This shift in thinking posits that the disability mindset makes false dichotomies (“abled, disabled”, “high-functioning, low-functioning”, and “typical, atypical”) that create social biases that are the only meaningful burdens upon neurodivergent individuals, including autistic individuals, from succeeding in the workplace. Furthermore, neurodivergent individuals are no different than neurotypical individuals in the value they derive from work or the rate at which each population wants to work (Ali et al. 2011).
The neurodiversity movement is, however, meaningfully distinct from typical diversity, equity, and inclusion initiatives (Chen et al. 2015; Volkers 2021). As organizations increase their diversity hiring efforts, many organizations assume that inclusion looks the same for neurodiversity as it does for race, gender, age, or sexuality (Bonaccio et al. 2020). Hughes (2020) argued that simply viewing autism through the neurodiversity paradigm may leave one without an awareness of the very real challenges an autistic individual may have, paying mind to the whole spectrum of the diagnosis, at work. Nevertheless, as organizations build out diversity initiatives that at least include disabled workers if not outright focus on them (Hoque et al. 2014; Waterhouse et al. 2010), navigating the balance between the growing social push for inclusion with the, many times, real challenges of accommodating neurodivergent workers across the talent lifecycle is coming into focus for research and applied work.
Inclusion starts with the beginning of the talent lifecycle: attracting candidates to the organization and making hiring decisions about those candidates. A critical component of the talent lifecycle, and of particular interest here, is simply choosing whom to hire. The selection process is a process of intentional barriers—attraction and selection hurdles—meant to attract a qualified candidate pool and winnow down the pool to the best talent. Nevertheless, any stage of the recruiting and selection process may also include unintentional barriers to attracting neurodivergent talent and retaining them through the selection process. Bonaccio et al. (2020) noted that organizations that do not mention disabilities in their diversity initiatives are less likely to attract disabled talent and, even more tactically, job boards themselves may discourage candidates if their designs are not accessible to people with disabilities. The selection procedures discussed throughout the rest of the paper are directly impacted by assumptions made in the recruiting process; an organization that endeavors to include neurodivergent workers into their workforce must first attract them.
Moving into the selection phase of the process, the emphasis changes from not only signaling that the process or tools are inclusive but also that the selection decisions themselves are fair to all candidates. Organizations implement many forms of screening to winnow down their applicant pool. Despite an increasingly virtual and asynchronous world, the most common selection tool likely remains the face-to-face or two-way interview (Campion et al. 1997; Huffcutt et al. 2001). They are so ubiquitous, in fact, that most previous hiring and autism research has focused on how to prepare the autistic candidate for the interview (e.g., Higgins et al. 2008; Kumazaki et al. 2019; Smith et al. 2014; Strickland et al. 2013) with only a recent shift to how the interview can change to be more inclusive (Maras et al. 2021). Growing in parallel to this literature is a growing body of evidence that, put simply, traditional interviews are poorly suited for assessing autistic candidates.
An autistic job candidate may be concerned that their interviewer will form a negative opinion about them for reasons outside of the candidate’s control. The traditional interview is a complex, high-pressure social situation. Past research suggests that managers are uncomfortable interviewing disabled candidates due to a lack of proper training, possible legal implications, or an inability to ask certain questions (Bonaccio et al. 2020). Indeed, Hebl and Skorinko (2005) found that managers react negatively to disability disclosure and efforts have been made to study how to make interviewing less biased against disabled candidates (Reilly et al. 2006). Autistic candidates, meanwhile, are likely to be quite aware of the possible stigma associated with presenting as autistic. Alongside this, research has found that autistic individuals have difficulty understanding non-verbal cues and reciprocal exchanges (Müller 2007), have a harder time processing implied meaning than their neurotypical peers (Wilson and Bishop 2021), and generally experience measurable cognitive disruption when put into social situations (Curioni et al. 2017; Dichter and Belger 2007). Considering both awareness of stigma and possible social-cognitive challenges, it becomes clear that the demands of a traditional job interview set autistic candidates up for failure.
Organizations may consider alternate selection hurdles to be more inclusive or to collect specific job-related information that is difficult to obtain in an interview. Tests, or, more generally, assessments, are typically computer-proctored questions or statements whose content is aligned to the knowledge, skills, abilities, or other characteristics required to perform a job. Often assessments will be built around the job itself (e.g., a work sample, a job simulation, a specific knowledge or skills-based test, etc.) The content of an assessment, however, can vary widely based on the job and organizational needs and constraints. Uniquely job-specific assessments can be expensive and time consuming to develop or impractical (e.g., in the case of entry-level rotational programs), and organizations have opted for assessments of more general job-related traits to circumvent these issues. Personality testing, for example, is frequently used in organizations but many scholars are quick to point out the risks of using personality testing when disabilities are in scope (Hensel 2017; Melson-Silimon et al. 2019) due to the association between some personality traits and mental disabilities.
Cognitive ability testing is another general assessment option available to organizations. Cognitive ability tests assess a candidate’s ability to reason, verbal and mathematical ability, problem-solving skills, memorization, and perceptual and processing speed. Although not without its own problematic history (Hunter and Schmidt 1996; Ployhart and Holtz 2008), cognitive ability has remained an important construct to measure in job selection due to its strong and well-established relationship to job performance (Kuncel et al. 2010; Schmidt and Hunter 1998; Schmidt and Hunter 2004; Schmidt et al. 2016) and is widely used in applied settings today (Bertua et al. 2005; Schmidt and Hunter 2004). The tests usually administered by organizations are typically shorter and narrower in scope than the intelligence tests administered by medical professionals (e.g., Kuo and Eack 2020). Cognitive ability tests used for hiring are designed to estimate an applicant’s potential to use mental processes to solve work-related problems or acquire and apply new job knowledge rather than be sensitive enough to diagnose a disability.
Gamification has emerged as a new format for assessing cognitive ability in response to the conventional long paper-and-pencil-type formats historically used for cognitive-ability assessment. Research has suggested that gamification improves upon traditional formats by offering a more engaging candidate experience and capturing more information via gameplay and trace behaviors (Lumsden et al. 2016; Quiroga et al. 2016). Games have unique features that drive engagement and motivation (Connolly et al. 2012) by providing instantaneous feedback to players (i.e., through level progression, win or loss indicators, and timers; (Burgers et al. 2015; Wood et al. 2004)). Consequently, research has shown that games may reflect true scores better than conventional tests (Miranda and Palmer 2014), games receive more positive reactions and engagement from candidates (Tremblay et al. 2010; Tso et al. 2015), and games may present a challenge that alleviates some of the anxiety associated with traditional tests (Alter et al. 2010; McPherson and Burns 2008). Due to these features, the game-based medium is a promising approach for assessing candidates.
Serious games, or games without an entertainment purpose (Michael and Chen 2006), have been developed from psychometric theory to predict cognitive ability and outcomes related to cognitive ability (Luft et al. 2013; Quiroga et al. 2015). These types of serious games have been found to be strong measures of typical cognitive abilities including spatial reasoning, working memory, and reasoning (Atkins et al. 2014), with a conventional measure of cognitive ability, the Wechsler Adult Intelligence Scales (McPherson and Burns 2008), and with g, or the general mental ability factor (Quiroga et al. 2015). Furthermore, game-based cognitive ability assessments have been shown to be as effective when delivered on a smartphone as on a computer (Brown et al. 2014). Game-based assessments not only provide enhanced candidate experiences but also are similarly effective at measuring cognitive ability as traditional tests.
Games have also been considered in multiple contexts in the autism literature. Most frequently, research has focused on games as either an intervention medium to deliver early treatment or skill development in autistic children (e.g., Bai et al. 2014; Herrera et al. 2008; Malinverni et al. 2017; Murdock et al. 2013; Simut et al. 2016) or, recently, as a way to deliver traditional diagnostic assessments earlier to autistic children, who struggle with the length of these assessments (Mash et al. 2020). Game-like smartphone applications have also been explored as a method for supporting autistic workers by delivering instructions to them in real time (Burke et al. 2010). In short, games appear to be a promising medium for delivering content to autistic individuals in a variety of conditions and applications.
Although cognitive ability is a strong predictor of job performance, the question becomes whether assessing cognitive ability will introduce group differences in passing rates between autistic and neurotypical candidates. In a meta-analysis of cognition and autism spectrum disorder, Velikonja et al. (2019) found that, in general, autistic individuals showed impairments in cognitive functioning relative to neurotypical individuals. These differences were greatest for social cognitive functions, including emotion perception and processing, verbal memory and learning, and processing speed, and the differences were least, and non-significant, for non-social cognitive functions including working memory, attention, and vigilance. Importantly, these are findings based on traditional cognitive batteries and diagnostic settings across the full spectrum of the disorder. As Hughes (2020) noted, there are real medical concerns to consider with this population; however, cognitive impairment varies across the spectrum (Müller 2007) and, within the framework of neurodiversity, removing the social cognitive aspects that unnecessarily and uniquely challenge autistic individuals may attenuate any differences between autistic and neurotypical individuals one could expect from the medical literature. For example, Ozonoff (1995) found that the performance difference between autistic and neurotypical individuals was minimized on a pattern recognition and attention task when it was proctored via a computer instead of an experimenter.
Consequently, the purpose of the present study is to determine whether game-based measures of cognitive ability have potential for assessing job seekers regardless of autism status. Following a review of the medical literature (e.g., Velikonja et al. 2019), one might expect that autistic job seekers will perform worse than their neurotypical peers. However, cognitive ability testing has traditionally been laden with long proctored assessments. Given advancements in technology-proctored delivery modalities (i.e., gamification) that not only remove the need to interact with another person but also make the test-taking experience shorter, less threatening, and capable of being more narrowly focused on non-social cognitive traits, it is predicted that autistic job seekers will have no difficulty performing as well as other job seekers.
Hypothesis 1.
Scores on game-based measures of non-social cognitive ability will not significantly vary across job seekers drawn from an autistic population and the general population.
The present study is furthermore novel in having the opportunity to study real-world candidates, which comes with its own strengths and limitations. The autistic-candidate data come from a partnership with an organization that helps organizations identify, recruit, and retain professionals (typically college graduates) on the autism spectrum. As part of the application process, candidates are asked to take a hiring assessment which includes game-based assessments. The results are not used to screen out candidates. To match comparison data as closely as possible, general-population applicant scores were sampled from a database of graduate-level job applicants who took the same assessment when applying to similar graduate jobs as the autistic candidates. These general-population candidates are not explicitly neurotypical—hence the use of the “general population” term; it is expected that autistic candidates may be in the general sample in a similar proportion to the base rate of autism in the general population (i.e., approximately 2%; Centers for Disease Control and Prevention 2021).

2. Materials and Methods

2.1. Power Analysis

Proving a null hypothesis is problematic, and, as the intent of this hypothesis tracks closely to that structure (i.e., two groups will not vary in scores), an a priori step taken was to find what effect size this study could reasonably detect via a power analysis. Velikonja et al. (2019) reported Hedges’ g effect sizes ranged from 0.23 (working memory) to 1.09 (theory of mind), with significant effect sizes beginning at 0.33; interpretation of Hedges’ g can follow the same magnitude interpretation as Cohen’s d. Mean scores on the cognitive ability range from 0 to 5 with a typical standard deviation of 0.6. With sample sizes for each group set to 120, the power analysis indicated that the design would have 80% power to detect a Hedges’ g effect size of 0.36 (approximately a mean difference of 0.22), which is sufficient power to find a significant effect for any significant difference in cognitive ability reported by Velikonja et al. (2019). To reliably find effect sizes as small as 0.23, group sample sizes would need to be approximately 600 with all other parameters remaining equal.

2.2. Participants

A total of 586 college-aged participants completed the game-based cognitive ability assessment. A total of 263 candidates were autistic (190 male, 50 female, 23 undisclosed; 179 White, 23 Asian, 23 Hispanic, 11 Black, and 27 undisclosed) and 323 candidates were drawn from the general population (193 male, 107 female, 13 undisclosed; 181 White, 47 Black, 44 Hispanic, 28 Asian, and 13 undisclosed). Sampling from the general population involved searching the assessment database for candidates to entry-level college-graduate jobs who took the same assessment as the autistic candidates. From this group, participants were randomly selected by using a random-number generator to create a roughly equal comparison sample. A detailed demographic breakdown of participants and measures completed is provided in Table 1.

2.3. Measures

The games, which are described below, are combined into “packages” of two games, which are taken in sequence. One package, “Disconumbers and Shapedance” combined these two games, and the second, “Digitspan and Shapedance”, switches Disconumbers for Digitspan. Shapedance, which is used in both packages, does not change in any way between the two packages. Scores on the games range from zero to five, with higher scores indicating higher levels of performance on each game. Scores are true interval scores (i.e., values in between whole numbers are possible).

2.3.1. Disconumbers

Disconumbers is a memory and math-based game that asks players to observe a set of numbers on the screen, answer options on the bottom of the screen, and a sequence of highlighted numbers. The purpose of the game is to memorize and tap the sequence of numbers highlighted on the screen in the same order and then calculate the sum of those numbers and select the right option at the bottom of the screen. For example, the screen may show the numbers 1, 6, 3, 8, and 9 in the middle of the screen, 13, 18, 19, and 15 as answer options at the bottom of the screen, and then highlight 1, 8, and 9 in order. Players have a limited time (ten seconds) to tap 1, 8, and 9 in order, correctly add up the number to 18, and select the answer at the bottom. The levels become more challenging as players progress (longer sequences of numbers to memorize, larger numbers, and moving number stimuli in the middle of the screen), and players have a limited time (three minutes) to progress. Losing a level brings a player down to a lower level; only by running out of time does the game end.

2.3.2. Shapedance

Shapedance is a visuospatial ability task similar to the Mental Rotation task (Vandenberg and Kuse 1978) in gameplay. Players are asked to observe a set of matrices, three cells by three cells each, on the screen. Each matrix has an assortment of colored shapes in some of the cells (e.g., a red circle or a blue triangle). In contrast to Mental Rotation tasks, which asks individuals whether two static images would match if rotated, Shapedance asks players to identify the matrices that match one another among several moving matrices. As the player progresses through the game, the number of matches may change, the size of the matrices change, and the matrices may rotate or move on screen, requiring the player to more carefully attend to the stimuli. There is a limited time per level (ten seconds) to correctly identify matches, and players have a limited time (three minutes) to progress as far as possible in the game. Losing a level drops the player down to a lower level. The game only ends when the three minutes have elapsed.

2.3.3. Digitspan

Digitspan is a memorization game very similar to its namesake, the digit span task on the Wechsler Adult Intelligence Scale (Wechsler 1997). The game prompts players with a string of numbers or letters that disappear quickly from the screen. Then, players are asked to use a dial pad on the screen to input the sequence they just saw in a specific manner (e.g., front to back, back to front). The game becomes more challenging as players successfully recall strings; the strings become longer (up to nine digits or letters) and the order in which they must be recalled changes. Similar to the above games, players have a limited time to complete each level (ten seconds) and the entire game (three minutes), losing a level drops one down to a lower level, and the game only ends when time has expired.

2.4. Procedure

Two game-based packages were administered across the four samples as part of a longer hiring assessment. After applying to the respective job or program, candidates received an email inviting them to complete the hiring assessment. The broader assessment included, in addition to the games of interest here, five competency-based structured behavioral interview questions that were completed prior to taking the games. At the discretion of the hiring organization, additional unscored questions may have also been included (depending on the organization, these questions are asked to build comfort with the process, inquire about minimum requirements, or ask other job-related questions). Between the interview questions and the games, candidates had an opportunity to take a break (there is no specified limit to the break), so candidates had the opportunity to prepare to take the games.

3. Results

To test whether cognitive ability measurement varied significantly between autistic and general-population candidates, scores obtained in each game-based assessment were compared by group. Two one-way analyses of variance (ANOVA) were conducted to determine whether mean scores on the assessments varied as a function of group membership. Scores on the Disconumbers and Shapedance package did not significantly vary between the general-population (n = 169, M = 2.81, SD = 0.67) and autistic candidates (n = 120, M = 2.84, SD = 0.85): F(1,287) = 0.065, p = 0.80, and Cohen’s d = −0.04. Scores on the Digitspan and Shapedance package varied significantly between the general-population (n = 154, M = 2.65, SD = 0.49) and autistic candidates (n = 143, M = 2.53, SD = 0.58): F(1,295) = 3.721, p = 0.05, and Cohen’s d = 0.22. As shown in Figure 1 and Figure 2, the proportional distribution of scores appear roughly equal across both game packages. The hypothesis of the study was partially supported.

4. Discussion

The present study examined whether test scores varied between autistic and general-population graduate job seekers on two game-based cognitive-ability assessments. Three games were used in the two packages: Digitspan, a working memory task similar to the typical used digit span task; Shapedance, a visuospatial ability task that is similar to the Mental Rotation task; and Disconumbers, a working memory and math-based task. Each game is a “serious game” (Michael and Chen 2006), meaning that the intent of the games is not to entertain but rather to leverage game elements to make a functional task more enjoyable and engaging. Game-based measures of cognitive ability were considered in this study for several reasons. First, prior research indicates that cognitive ability is one of the strongest predictors of future job performance (Schmidt et al. 2016), making it a valuable tool in job selection. Second, assessing non-social cognitive traits in a non-social manner may provide a fair way to assess both autistic and neurotypical candidates’ readiness for the same job, and each game measures non-social traits either not typically studied in the literature or found to have the smallest group differences (Velikonja et al. 2019). Although the medical literature reports that cognitive ability is lower in autistic than neurotypical individuals (e.g., Velikonja et al. 2019), other evidence suggests that, in the absence of socially laden cognitive tasks (e.g., being assessed by another person or assessing social cognition, Curioni et al. 2017; Dichter and Belger 2007; Ozonoff 1995), cognition improves in autistic individuals. Third, gamification has been shown to work well in other accommodations and job-related contexts for autistic individuals (e.g., Burke et al. 2010) and in general for increasing candidate engagement and motivation and collecting more data than typical tests (Connolly et al. 2012).
Consistent with the hypothesis of the study, no significant differences or meaningful effect sizes were found between scores on the Disconumbers and Shapedance package between autistic and general population candidates. Inconsistent with the hypothesis of the study but consistent with meta-analytic findings, a significant but small difference was found between autistic and general population candidates’ scores on Digitspan and Shapedance, essentially equivalent to the meta-analytic effect size reported for working memory in Velikonja et al. (2019), which was the smallest effect size found in their study. Although the results are evaluated within the framework of supporting a null hypothesis, it bears noting that the difference between the two groups on the latter game package is small and likely not to introduce problematic group differences at cut scores typically used by organizations (i.e., via empirically set cut scores derived from group passing rates or rationally set cut scores, such as failing the bottom third of candidates). This study’s hypothesis was partially supported: there appears to be a basis to the idea that gamified cognitive-ability assessments can provide a fair means for evaluating autistic candidates.
Studying autistic job seekers is rarely practiced outside of preparing them for job interviews (Higgins et al. 2008; Kumazaki et al. 2019; Smith et al. 2014; Strickland et al. 2013); therefore, this study contributes to the literature by capturing the use of game-based assessments for job selection purposes in an autistic population. More generally, it can be difficult to quantify job-related outcomes for autistic individuals due to a relatively low base rate in the population, a reasonable fear about disclosing a disability status (Bonaccio et al. 2020; Chen et al. 2015), and a reliance on mock procedures meant to prepare autistic candidates, which lack the motivation components of an actual job application, over actual hiring scenarios which are often not feasible. Consequently, the strengths of this study included observing nearly 300 autistic job seekers in a real-world job-application setting. In addition, although game-based technologies have been used both in hiring studies and in autism-specific accommodations and training, this is the first study, to the authors’ knowledge, to explore game-based assessments for selecting autistic job seekers.
As discussed earlier, studies are typically designed to detect a difference or an effect due to some experimental manipulation or relationship to another variable. Although this study was designed in this manner, the study’s hypothesis was a null hypothesis, or a prediction that differences would not exist between groups. Establishing evidence for a lack of a difference is difficult. Simply increasing the sample size increases the power of a test; thus, a statistical test can find even the smallest difference between two groups with sufficient data. In situations such as these, studies look for negligible to small effects sizes coupled with sufficient power to detect meaningful effect sizes in order to rule out accepting the lack of an effect that is due to an underpowered test simply failing to find that effect when it is in fact there (i.e., a type II error). Consequently, the results of this study cannot simply be understood as finding an effect or not, but rather that sufficient data were collected to find meaningful effects. Meaningful effects were not found, as hypothesized, although small effects were detected. Given that studies of this design are unusual and this is the first study to explore this application of game-based cognition assessments to autistic candidates, caution is advised in applying these results without further study and evidence.
Second, the one-way interviews (interviews where the candidate records an answer to a question presented on a screen) that preceded the games may have drained cognitive resources from autistic candidates more so than the general population (Curioni et al. 2017; Dichter and Belger 2007). Given that small group differences were observed for only one package and all candidates could have taken a break between the interviews and games, if desired, it is not expected that the interviews had this effect, although it is a methodological limitation. Future research should consider adding a games-only condition to eliminate the effects any preceding assessment could have on candidates.
Another limitation that should be mentioned is that the current results are not compared to conventional measures of general intelligence. Thus, we cannot demonstrate the extent to which the correspondence in scores between groups is due to the gamification of these tasks or the result of similar standings of actual cognitive ability. However, the gamified tasks in the present research closely resemble non-gamified traditional measures of specific cognitive abilities (Vandenberg and Kuse 1978, Wechsler 1997). Further, we incorporated measures that were shown to exhibit minimal group differences when assessed traditionally, as shown in prior meta-analytic work (Velikonja et al. 2019). Based on this, we suspect similar conclusions would be drawn had a general intelligence measure been evaluated. However, this would be important to confirm via future research.
Lindsay et al. (2019) laid out an empirical supported framework for moving employers from disability discomfort to disability confidence, which involves employers broadening their perspectives through minimizing biases, challenging stigmas, and focusing on the abilities of all candidates. Although nearly ubiquitous in the job-selection process, interviews not only uniquely challenge autistic candidates with non-verbal communication, reciprocal exchanges, and implied meaning (Müller 2007; Wilson and Bishop 2021) but also make hiring managers uncomfortable when the interviewee is disabled (Bonaccio et al. 2020). Ability assessments, when implemented following an alignment between job needs and test content via a job analysis, present an opportunity for organizations to focus on the abilities of their candidates and present unbiased data to hiring managers before they gain an impression of a candidate.
Furthermore, non-traditional assessments can be an opportunity for organizations to signal that they are forward-thinking in their selection process (Bonaccio et al. 2020). Computer-proctored gamified assessments not only possess features that make them more engaging and accessible, but they can also be taken at any place and at any time, making them highly accommodating relative to traditional screening methods, such as interviews. Hence, as the proportion of autistic job seekers only expected to grow (Chen et al. 2015; see also Centers for Disease Control and Prevention 2021), the results of this study suggest that measuring cognition via game-based assessments cannot only fairly assess autistic and neurotypical candidates but may give organizations a head start when hiring in a neurodiverse world.
Nevertheless, caution should be exercised when considering assessments for a neurodiverse population. The games selected for this study measured cognitive traits that aligned with the literature on what traits are least likely to differentiate between autistic and neurotypical individuals (Velikonja et al. 2019). Specifically, these findings do not generalize to other cognitive abilities or other content that can be assessed via games (e.g., emotional intelligence or personality). Others have cautioned against assessing content that has been linked to disabilities—such as emotional intelligence or personality (Hensel 2017; Melson-Silimon et al. 2019)—and although gamification appeared to have helped in this study for a narrow set of cognitive abilities, it is not expected that that pattern will hold with content that autistic individuals typically struggle with, such as social or emotional assessments (Velikonja et al. 2019). For jobs that appear to require social and emotional processing, it may be less a matter of assessment and more a matter of exploring whether accommodations can be made to allow for neurodiverse individuals to productively contribute (e.g., Burke et al. 2010).
The consideration of hiring assessments to promote neurodiversity should also weigh decisions against the risk of adverse impact for other protected classes. Of note, cognitive-ability testing leading to racial differences in hiring rates (i.e., adverse impact) has been so well documented, it has been referred to as a “classic problem” in industrial–organizational psychology (Cottrell et al. 2015, p. 1713; see also Goldstein et al. 2010; Zedeck 2010). Thus, evaluation of the application of the results here should bear this in mind. However, recent research has demonstrated a reason for optimism with regard to racial differences in hiring rates as a function of cognitive-ability testing (Wee et al. 2014), as described further below.
Future research can expand upon this study in several ways. Returning to the discussion of the neurodiversity movement that began the paper, it is important to consider not only the spectrum of autism from a medical perspective (e.g., Hughes 2020), but also who seeks employment from the autistic population in light of the current results. This study’s results are drawn from a subgroup of the autism population, specifically individuals who pursue a university degree. According to Hurley-Hanson and Giannantonio (2016), 35% of autistic individuals pursue a degree. Of them, it is estimated that 85% of autistic college-level job seekers are either unemployed or underemployed, underscoring the need to explore the barriers that prevent this group from successfully attaining employment. That said, the results should be considered within the constraints of this sample: autistic job seekers who are completing or have completed a degree. Future research should consider whether similar interventions (i.e., gamified assessments) are appropriate for non-college-level autistic job seekers where the social barriers of the hiring process are not critical for actually performing the role.
Building from the above discussion of racial differences in hiring due to cognitive-ability testing, recent work has demonstrated that assessment of second-stratum cognitive abilities can be used to select similar quality hires with less risk of adverse impact than general measures of intelligence (Wee et al. 2014). Given that the cognitive assessments packages studied here are specific abilities rather than general intelligence measures, future work should investigate the use of the strategy investigated by Wee et al. (2014) to evaluate whether these game-based assessments can promote equity across multiple protected classes (i.e., both race and mental disability). Moreover, the encouraging results generally found in this study pave the way for exploring other assessment content—i.e., other cognitive abilities or other job-related knowledge, skills, or abilities—as possible improvements to the current selection processes that make finding jobs and persevering through the job-selection process difficult for autistic individuals. Second, gamified assessments may attract neurodiverse candidates to organizations, in line with the signaling effects that Bonaccio et al. (2020) noted that shape candidate perceptions of organizations when searching for jobs. Given a choice between a traditional assessment or a gamified assessment, future research could consider measuring whether the gamified option is more attractive to neurodiverse job seekers. Additionally, future study of gamified assessments, cognitive or otherwise, should evaluate what elements of gamification maintain measurement reliability and validity across neurodiverse job seekers and their neurotypical peers. Lastly, what drives the efficacy of the gamified cognitive-ability assessment remains untested: Future research should explore whether and to what extent the specific cognitive abilities, gamification, and/or on-demand nature of the assessment are responsible for the similar test outcomes between autistic and general-population candidates.
In summary, there are very real barriers that organizations commonly apply to their screening processes that unnecessarily block autistic job seekers from getting jobs that they are otherwise qualified to do. Organizations can make efforts to reduce the unintentional barriers and make fairer decisions grounded in psychometric theory. Game-based assessments of cognitive ability show promise for empowering organizations to make fair, evidence-driven decisions about whom to hire in an increasingly neurodiverse workforce.

Author Contributions

Conceptualization, C.W., T.P.-R., K.C. and J.P.; Data curation, C.W.; Formal analysis, C.W.; Investigation, T.P.-R. and K.C.; Methodology, C.W.; Project administration, T.P.-R.; Writing—original draft, C.W.; Writing—review & editing, T.P.-R. and J.P. All authors have read and agreed to the published version of the manuscript.


This research received no external funding.

Institutional Review Board Statement

Ethical review and approval were waived for this study, due to the use of archival data associated with job candidates who agreed to the use of their assessment scores, collected by the recruiting-technology company, for research purposes as part of the company’s terms of service agreement.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy concerns.

Conflicts of Interest

The authors declare no conflict of interest.


  1. Ali, Mohammad, Lisa Schur, and Peter Blanck. 2011. What types of jobs do people with disabilities want? Journal of Occupational Rehabilitation 21: 199–210. [Google Scholar] [CrossRef] [PubMed]
  2. Alter, Adam L., Joshua Aronson, John M. Darley, Cordaro Rodriguez, and Diane N. Ruble. 2010. Rising to the threat: Reducing stereotype threat by reframing the threat as a challenge. Journal of Experimental Social Psychology 46: 166–71. [Google Scholar] [CrossRef]
  3. Atkins, Sharona M., Amber M. Sprenger, Gregory J. H. Colflesh, Timothy L. Briner, Jacob B. Buchanan, Sydnee E. Chavis, Sy-yu Chen, Gregory L. Iannuzzi, Vadim Kashtelyan, Eamon Dowling, and et al. 2014. Measuring working memory is all fun and games: A four-dimensional spatial game predicts cognitive task performance. Experimental Psychology 61: 417–38. [Google Scholar] [CrossRef] [Green Version]
  4. Bai, Zhen, Alan F. Blackwell, and George Coulouris. 2014. Using augmented reality to elicit pretend play for children with autism. IEEE Transactions on Visualization and Computer Graphics 21: 598–610. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  5. Bertua, Cristina, Neil Anderson, and Jesús F. Salgado. 2005. The predictive validity of cognitive ability tests: A UK meta-analysis. Journal of Occupational and Organizational Psychology 78: 387–409. [Google Scholar] [CrossRef]
  6. Bonaccio, Silvia, Catherine E. Connelly, Ian R. Gellatly, Arif Jetha, and Kathleen A. Martin Ginis. 2020. The participation of people with disabilities in the workplace across the employment cycle: Employer concerns and research evidence. Journal of Business and Psychology 35: 135–58. [Google Scholar] [CrossRef] [Green Version]
  7. Brown, Harriet R., Peter Zeidman, Peter Smittenaar, Rick A. Adams, Fiona McNab, Robb B. Rutledge, and Raymond J. Dolan. 2014. Crowdsourcing for cognitive science–the utility of smartphones. PLoS ONE 9: e100662. [Google Scholar] [CrossRef] [PubMed]
  8. Burgers, Christian, Allison Eden, Mélisande D. van Engelenburg, and Sander Buningh. 2015. How feedback boosts motivation and play in a brain-training game. Computers in Human Behavior 48: 94–103. [Google Scholar] [CrossRef] [Green Version]
  9. Burke, Raymond V., Melissa N. Andersen, Scott L. Bowen, Monica R. Howard, and Keith D. Allen. 2010. Evaluation of two instruction methods to increase employment options for young adults with autism spectrum disorders. Research in Developmental Disabilities 31: 1223–33. [Google Scholar] [CrossRef]
  10. Campion, Michael A., David K. Palmer, and James E. Campion. 1997. A review of structure in the selection interview. Personnel Psychology 50: 655–702. [Google Scholar] [CrossRef]
  11. Centers for Disease Control and Prevention. 2021. Data & Statistics on Autism Spectrum Disorder; Washington, DC: U.S. Department of Health and Human Services. Available online: (accessed on 31 August 2021).
  12. Chen, June L., Geraldine Leader, Connie Sung, and Michael Leahy. 2015. Trends in employment for individuals with autism spectrum disorder: A review of the research literature. Review Journal of Autism and Developmental Disorders 2: 115–27. [Google Scholar] [CrossRef]
  13. Connolly, Thomas M., Elizabeth A. Boyle, Ewan MacArthur, Thomas Hainey, and James M. Boyle. 2012. A systematic literature review of empirical evidence on computer games and serious games. Computers & Education 59: 661–86. [Google Scholar]
  14. Cottrell, Jonathan M., Daniel A. Newman, and Glenn I. Roisman. 2015. Explaining the black–white gap in cognitive test scores: Toward a theory of adverse impact. Journal of Applied Psychology 100: 1713–36. [Google Scholar] [CrossRef] [Green Version]
  15. Curioni, Arianna, Ilaria Minio-Paluello, Lucia Maria Sacheli, Matteo Candidi, and Salvatore Maria Aglioti. 2017. Autistic traits affect interpersonal motor coordination by modulating strategic use of role-based behavior. Molecular Autism 8: 1–13. [Google Scholar] [CrossRef] [Green Version]
  16. Dichter, Gabriel S., and Aysenil Belger. 2007. Social stimuli interfere with cognitive control in autism. Neuroimage 35: 1219–30. [Google Scholar] [CrossRef] [Green Version]
  17. Goldstein, Harold W., Charles A. Scherbaum, and Kenneth P. Yusko. 2010. Revisiting g: Intelligence, adverse impact, and personnel selection. In Adverse Impact. Abingdon: Routledge, pp. 122–61. [Google Scholar]
  18. Hebl, Michelle R., and Jeanine L. Skorinko. 2005. Acknowledging one’s physical disability in the interview: Does “when” make a difference? Journal of Applied Social Psychology 35: 2477–92. [Google Scholar] [CrossRef] [Green Version]
  19. Hensel, Wendy F. 2017. People with autism spectrum disorder in the workplace: An expanding legal frontier. Harvard Civil Rights-Civil Liberties Law Review (CR-CL) 52: 73–102. [Google Scholar]
  20. Herrera, Gerardo, Francisco Alcantud, Rita Jordan, Amparo Blanquer, Gabriel Labajo, and Cristina De Pablo. 2008. Development of symbolic play through the use of virtual reality tools in children with autistic spectrum disorders: Two case studies. Autism 12: 143–57. [Google Scholar] [CrossRef]
  21. Higgins, Kristin K., Lynn C. Koch, Erica M. Boughfman, and Courtney Vierstra. 2008. School-to-work transition and Asperger syndrome. Work 31: 291–98. [Google Scholar] [PubMed]
  22. Hoque, Kim, Nick Bacon, and Dave Parr. 2014. Employer disability practice in Britain: Assessing the impact of the Positive about Disabled People ‘Two Ticks’ symbol. Work, Employment and Society 28: 430–51. [Google Scholar] [CrossRef]
  23. Huffcutt, Allen I., James M. Conway, Philip L. Roth, and Nancy J. Stone. 2001. Identification and meta-analytic assessment of psychological constructs measured in employment interviews. Journal of Applied Psychology 86: 897–913. [Google Scholar] [CrossRef] [PubMed]
  24. Hughes, Jonathan A. 2020. Does the heterogeneity of autism undermine the neurodiversity paradigm? Bioethics 35: 47–60. [Google Scholar] [CrossRef] [PubMed]
  25. Hunter, John E., and Frank L. Schmidt. 1996. Intelligence and job performance: Economic and social implications. Psychology, Public Policy, and Law 2: 447–72. [Google Scholar] [CrossRef]
  26. Hurley-Hanson, Amy E., and Cristina M. Giannantonio. 2016. Autism in the Workplace (Special Issue). Journal of Business and Management 22: 1–156. [Google Scholar]
  27. Kumazaki, Hirokazu, Taro Muramatsu, Yuichiro Yoshikawa, Blythe A. Corbett, Yoshio Matsumoto, Haruhiro Higashida, Teruko Yuhi, Hiroshi Ishiguro, Masaru Mimura, and Mitsuru Kikuchi. 2019. Job interview training targeting nonverbal communication using an android robot for individuals with autism spectrum disorder. Autism 23: 1586–95. [Google Scholar] [CrossRef]
  28. Kuncel, Nathan R., Deniz S. Ones, and Paul R. Sackett. 2010. Individual differences as predictors of work, educational, and broad life outcomes. Personality and Individual Differences 49: 331–36. [Google Scholar] [CrossRef]
  29. Kuo, Susan S., and Shaun M. Eack. 2020. Meta-analysis of cognitive performance in neurodevelopmental disorders during adulthood: Comparisons between autism spectrum disorder and schizophrenia on the Wechsler Adult Intelligence Scales. Frontiers in Psychiatry 11: 1–16. [Google Scholar] [CrossRef]
  30. Lindsay, Sally, Joanne Leck, Winny Shen, Elaine Cagliostro, and Jennifer Stinson. 2019. A framework for developing employer’s disability confidence. Equality, Diversity and Inclusion: An International Journal 38: 40–55. [Google Scholar] [CrossRef]
  31. Luft, Caroline Di Bernardi, July Silveira Gomes, Daniel Priori, and Emilio Takase. 2013. Using online cognitive tasks to predict mathematics low school achievement. Computers & Education 67: 219–28. [Google Scholar]
  32. Lumsden, Jim, Elizabeth A. Edwards, Natalia S. Lawrence, David Coyle, and Marcus R. Munafò. 2016. Gamification of cognitive assessment and cognitive training: A systematic review of applications and efficacy. JMIR Serious Games 4: e11. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  33. Malinverni, Laura, Joan Mora-Guiard, Vanesa Padillo, Lilia Valero, Amaia Hervás, and Narcis Pares. 2017. An inclusive design approach for developing video games for children with autism spectrum disorder. Computers in Human Behavior 71: 535–49. [Google Scholar] [CrossRef]
  34. Maras, Katie, Jade Eloise Norris, Jemma Nicholson, Brett Heasman, Anna Remington, and Laura Crane. 2021. Ameliorating the disadvantage for autistic job seekers: An initial evaluation of adapted employment interview questions. Autism 25: 1060–75. [Google Scholar] [CrossRef]
  35. Mash, Lisa E., Raymond M. Klein, and Jeanne Townsend. 2020. A gaming approach to the assessment of attention networks in autism spectrum disorder and typical development. Journal of Autism and Developmental Disorders 50: 2607–15. [Google Scholar] [CrossRef]
  36. McPherson, Jason, and Nicholas R. Burns. 2008. Assessing the validity of computer-game-like tests of processing speed and working memory. Behavior Research Methods 40: 969–81. [Google Scholar] [CrossRef] [Green Version]
  37. Melson-Silimon, Arturia, Alexandra M. Harris, Elizabeth L. Shoenfelt, Joshua D. Miller, and Nathan T. Carter. 2019. Personality testing and the Americans with Disabilities Act: Cause for concern as normal and abnormal personality models are integrated. Industrial and Organizational Psychology 12: 119–32. [Google Scholar] [CrossRef]
  38. Michael, David R., and Sandra L. Chen. 2006. Serious Games: Games That Educate, Train, and Inform. Boston: Thomson Course Technology. [Google Scholar]
  39. Miranda, Andrew T., and Evan M. Palmer. 2014. Intrinsic motivation and attentional capture from gamelike features in a visual search task. Behavior Research Methods 46: 159–72. [Google Scholar] [CrossRef] [PubMed]
  40. Müller, Ralph-Axel. 2007. The study of autism as a distributed disorder. Mental Retardation and Developmental Disabilities Research Reviews 13: 85–95. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  41. Murdock, Linda C., Jennifer Ganz, and Jessica Crittendon. 2013. Use of an iPad play story to increase play dialogue of preschoolers with autism spectrum disorders. Journal of Autism and Developmental Disorders 43: 2174–89. [Google Scholar] [CrossRef] [PubMed]
  42. Ozonoff, Sally. 1995. Reliability and validity of the Wisconsin card sorting test in studies of autism. Neuropsychology 9: 491–500. [Google Scholar] [CrossRef]
  43. Ployhart, Robert E., and Brian C. Holtz. 2008. The diversity–validity dilemma: Strategies for reducing racioethnic and sex subgroup differences and adverse impact in selection. Personnel Psychology 61: 153–72. [Google Scholar] [CrossRef]
  44. Quiroga, María Ángeles, F. J. Román, J. de la Fuente, Jesús Privado Zamorano, and Roberto Colom. 2016. The measurement of intelligence in the XXI Century using video games. The Spanish Journal of Psychology 19: 1–13. [Google Scholar] [CrossRef]
  45. Quiroga, María Ángeles, Sergio Escorial, Francisco J. Román, Daniel Morillo, Andrea Jarabo, Jesús Privado, Miguel Hernández, Borja Gallego, and Roberto Colom. 2015. Can we reliably measure the general factor of intelligence (g) through commercial video games? Yes, we can! Intelligence 53: 1–7. [Google Scholar] [CrossRef]
  46. Reilly, Nora P., Shawn P. Bocketti, Stephen A. Maser, and Craig L. Wennet. 2006. Benchmarks affect perceptions of prior disability in a structured interview. Journal of Business and Psychology 20: 489–500. [Google Scholar] [CrossRef]
  47. Schmidt, Frank L., and John E. Hunter. 1998. The validity and utility of selection methods in personnel psychology: Practical and theoretical implications of 85 years of research findings. Psychological Bulletin 124: 262–74. [Google Scholar] [CrossRef]
  48. Schmidt, Frank L., and John E. Hunter. 2004. General mental ability in the world of work: Occupational attainment and job performance. Journal of Personality and Social Psychology 86: 162–73. [Google Scholar] [CrossRef] [Green Version]
  49. Schmidt, Frank L., In-Sue Oh, and Jonathan A. Shaffer. 2016. The Validity and Utility of Selection Methods in Personnel Psychology: Practical and Theoretical Implications of 100 Years…. Research Paper. Philadelphia: Fox School of Business, pp. 1–74. [Google Scholar]
  50. Schur, Lisa, Lisa Nishii, Meera Adya, Douglas Kruse, Susanne M. Bruyère, and Peter Blanck. 2014. Accommodating employees with and without disabilities. Human Resource Management 53: 593–621. [Google Scholar] [CrossRef] [Green Version]
  51. Simut, Ramona E., Johan Vanderfaeillie, Andreea Peca, Greet Van de Perre, and Bram Vanderborght. 2016. Children with autism spectrum disorders make a fruit salad with Probo, the social robot: An interaction study. Journal of Autism and Developmental Disorders 46: 113–26. [Google Scholar] [CrossRef]
  52. Smith, Matthew J., Emily J. Ginger, Katherine Wright, Michael A. Wright, Julie Lounds Taylor, Laura Boteler Humm, Dale E. Olsen, Morris D. Bell, and Michael F. Fleming. 2014. Virtual reality job interview training in adults with autism spectrum disorder. Journal of Autism and Developmental Disorders 44: 2450–63. [Google Scholar] [CrossRef] [Green Version]
  53. Spiggle, Tom. 2021. What does a Worker Want? What the Labor Shortage Really Tells Us. Forbes. Available online: (accessed on 31 August 2021).
  54. Strickland, Dorothy C., Claire D. Coles, and Louise B. Southern. 2013. JobTIPS: A transition to employment program for individuals with autism spectrum disorders. Journal of Autism and Developmental Disorders 43: 2472–83. [Google Scholar] [CrossRef]
  55. Tremblay, Jonathan, Bruno Bouchard, and Abdenour Bouzouane. 2010. Adaptive game mechanics for learning purposes-making serious games playable and fun. CSEDU 2: 465–70. [Google Scholar]
  56. Tso, Leslie, Christos Papagrigoriou, and Yannic Sowoidnich. 2015. Analysis and comparison of software-tools for cognitive assessment. OPUS 1: 1–40. [Google Scholar]
  57. Vandenberg, Steven G., and Allan R. Kuse. 1978. Mental rotations, a group test of three-dimensional spatial visualization. Perceptual and Motor Skills 47: 599–604. [Google Scholar] [CrossRef] [PubMed]
  58. Velikonja, Tjasa, Anne-Kathrin Fett, and Eva Velthorst. 2019. Patterns of nonsocial and social cognitive functioning in adults with autism spectrum disorder: A systematic review and meta-analysis. JAMA Psychiatry 76: 135–51. [Google Scholar] [CrossRef] [PubMed]
  59. Volkers, Nancy. 2021. When a Co-Worker’s Atypical… Can We Be Flexible? Leader Live. Available online: (accessed on 31 July 2021).
  60. Waterhouse, Peter, Helen Kimberley, Pam Jonas, and John Glover. 2010. What would it take? Employer perspectives on employing people with a disability. In A National Vocational Education and Training Research and Evaluation Program Report. Adelaide: National Centre for Vocational Education Research Ltd. [Google Scholar]
  61. Wechsler, David. 1997. Wechsler Adult Intelligence Scale-III (WAIS-III) Manual. New York: The Psychological Corporation. [Google Scholar]
  62. Wee, Serena, Daniel A. Newman, and Dana L. Joseph. 2014. More than g: Selection quality and adverse impact implications of considering second-stratum cognitive abilities. Journal of Applied Psychology 99: 547–63. [Google Scholar] [CrossRef]
  63. Wilson, Alexander C., and Dorothy V. M. Bishop. 2021. “Second guessing yourself all the time about what they really mean…”: Cognitive differences between autistic and non-autistic adults in understanding implied meaning. Autism Research 14: 93–101. [Google Scholar] [CrossRef]
  64. Wood, Richard T. A., Mark D. Griffiths, Darren Chappell, and Mark N. O. Davies. 2004. The structural characteristics of video games: A psycho-structural analysis. CyberPsychology & Behavior 7: 1–10. [Google Scholar]
  65. Zedeck, Sheldon. 2010. Adverse impact: History and evolution. In Adverse Impact. Abingdon: Routledge, pp. 33–57. [Google Scholar]
Figure 1. Distribution of Performance on Disconumbers & Shapedance by Sample Proportion.
Figure 1. Distribution of Performance on Disconumbers & Shapedance by Sample Proportion.
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Figure 2. Distribution of Performance on Digitspan & Shapedance by Sample Proportion.
Figure 2. Distribution of Performance on Digitspan & Shapedance by Sample Proportion.
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Table 1. Detailed Demographic Breakdown of Participants and Cognitive Measure Packages.
Table 1. Detailed Demographic Breakdown of Participants and Cognitive Measure Packages.
Autistic Participant SampleGeneral Population Sample
Disconumbers and ShapedanceDigitspan and ShapedanceDisconumbers and ShapedanceDigitspan and Shapedance
Undisclosed Gender1310013
Race and Ethnicity
Undisclosed Race/Ethnicity1315013
Note: Cognitive assessment packages are denoted by the column heading “Disconumbers and Shapedance” as well as “Digitspan and Shapegance”. Cell values denote samples size in each contingency of demographics and cognitive measure package completed.
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Willis, C.; Powell-Rudy, T.; Colley, K.; Prasad, J. Examining the Use of Game-Based Assessments for Hiring Autistic Job Seekers. J. Intell. 2021, 9, 53.

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Willis C, Powell-Rudy T, Colley K, Prasad J. Examining the Use of Game-Based Assessments for Hiring Autistic Job Seekers. Journal of Intelligence. 2021; 9(4):53.

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Willis, Colin, Tracy Powell-Rudy, Kelsie Colley, and Joshua Prasad. 2021. "Examining the Use of Game-Based Assessments for Hiring Autistic Job Seekers" Journal of Intelligence 9, no. 4: 53.

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