Adaptive testing can boost motivation while increasing anxiety

Computerised adaptive testing, or CAT, promises to make high-stakes assessment feel more personal by changing the questions a person receives as their performance becomes clearer. But a major new synthesis suggests that the technology can deliver a psychological double edge: test-takers report slightly more motivation and positive emotion, yet they also become more anxious and, on average, obtain lower scores. The finding is not a verdict on digital testing, but it does challenge the assumption that faster and more personalised quizzes automatically produce better learning or calmer students.

The finding comes from a pre-registered umbrella review published in npj Science of Learning. Rather than analysing one experiment, the researchers brought together evidence from 10 meta-analyses reported in six documents, then combined their effect estimates and conducted a new meta-analysis. The studies represented a total sample range of 2,830 to 32,735, with a median sample size of 16,842. Searches covered PubMed, ProQuest PsycINFO, Scopus and Web of Science, while the review protocol was registered in PROSPERO as CRD420251207547.

CAT differs from a conventional computer-delivered test because the next question is selected in response to the test-taker’s answers. A software algorithm estimates ability from the items already completed, then chooses a subsequent item that is expected to provide useful information about that ability. In principle, this means fewer questions for some candidates, more efficient measurement for test developers and a shorter assessment experience. It also creates a distinctive psychological setting: the test appears to react continuously to the individual, which can make the process feel more interactive and tailored than a fixed test administered to everyone in the same order.

Across the reviewed evidence, CAT was associated with a small improvement in test-takers’ motivation, with an effect size of d = 0.08. It was also linked to more positive emotions, with d = 0.13. These are statistically discernible benefits, but the magnitudes are modest. In behavioural research, effect sizes are often easier to interpret through their size than through a simple pass-or-fail threshold: a value near 0.1 generally indicates a small difference between groups. The result therefore supports the idea that adaptive testing can make an assessment somewhat more engaging, without establishing a transformation in motivation.

That positive signal came with a parallel increase in anxiety, also measured at d = 0.08. The combination may seem contradictory, but it reflects a plausible feature of adaptive assessment: a more responsive and interactive system may make a test feel more relevant and motivating while simultaneously making it feel more consequential. The algorithm’s real-time adjustments can heighten the impression that the test is monitoring and evaluating each answer closely. CAT is therefore associated not with a single emotional profile, but with simultaneous gains in engagement and rises in pressure.

Most strikingly, the synthesis found lower test scores under CAT, with a median effect of d = −0.08. The negative sign indicates that scores tended to be lower in adaptive-testing conditions than in comparison conditions, while its small magnitude shows that the average difference was limited. Performance therefore moved in an unfavourable direction even as motivation and positive emotion improved. This combination complicates the idea that feeling more engaged during a test necessarily means knowing more, performing better or making fewer errors. It also suggests that CAT should be evaluated on several outcomes at once rather than through a single measure of test quality or user satisfaction.

The authors assessed the quality of the evidence using the Joanna Briggs Institute Critical Appraisal of Systematic Reviews and Research Synthesis tool. An umbrella review of this kind is valuable because the immediate question is often not whether one study works, but what the accumulated evidence says after accounting for a wider body of results. By synthesising meta-analyses rather than individual experiments, the approach can reveal recurring patterns across different populations, testing platforms and research designs. It also makes inconsistencies easier to see, such as the simultaneous presence of stronger motivation and greater anxiety, or the disconnect between emotional improvement and reduced scores.

Even so, the conclusions should be interpreted at the scale the effects allow. The small average effects do not mean that every learner is helped or harmed by CAT, nor do they show that adaptive algorithms inevitably reduce achievement. They summarise average differences across the evidence available at the time of the review, where results can vary with the subject being tested, the sophistication of the adaptive procedure, the purpose of the assessment and the characteristics of the test-takers. A technology that is valuable for precise measurement may still be uncomfortable for some users, while another implementation may produce different benefits. The umbrella review identifies a pattern across studies; it does not prescribe a single design for every educational or high-stakes setting.

The findings also arrive as CAT is being used increasingly for large-scale assessments worldwide. Its technical appeal is closely connected to the prevalence of digital infrastructure: tests can be delivered at scale, item selection can be automated and data can be collected during the assessment itself. Those capabilities may be especially valuable when many candidates need testing and a fixed-length paper test would be inefficient. But the evidence reviewed here adds an important constraint to the technology narrative. Personalisation at the level of the next question does not necessarily create a personalised emotional experience, and an apparently smoother interface can coexist with a less favourable performance result.

For schools, universities, testing organisations and policymakers, the practical lesson is not to abandon adaptive testing, nor to treat it as a self-evident solution to conventional exam design. The evidence supports testing it as a tool, with attention to implementation and to the experience of the people being assessed. Designs that provide reassurance, explain how item selection works, reduce abrupt changes in difficulty and monitor anxiety could be worth evaluating. Outcomes should include not only scores and measurement precision, but also motivation, emotion, accessibility and the burdens placed on test-takers.

The authors call for more high-quality meta-analyses covering CAT in broader contexts, a wider range of application settings and more diverse outcome variables. That recommendation matters because the current evidence already suggests there is no single, uniformly positive or negative effect. CAT can make testing more engaging and emotionally rewarding for some users, yet it can also make the same experience more stressful and coincide with lower performance. The most important question is therefore not whether adaptive testing beats a fixed test in every setting, but when its dynamic and personalised format improves the balance between efficient measurement, learning and psychological wellbeing.

Subject of Research: The benefits and risks of computerised adaptive testing in large-scale assessments

Article Title: Umbrella review of meta-analyses on the benefits and risks of computerised adaptive testing

Article References: Song, Y., Li, H., & Zheng, Q. (2026). Umbrella review of meta-analyses on the benefits and risks of computerised adaptive testing. npj Science of Learning. https://doi.org/10.1038/s41539-026-00460-4

Image Credits: AI Generated

DOI: 10.1038/s41539-026-00460-4

Keywords: computerised adaptive testing, adaptive assessment, digital testing, test motivation, test anxiety, student emotions, test scores, educational technology, meta-analysis, large-scale assessment, Umbrella, review