Problem-Based Learning Shows Striking Gains in Student Creativity, But Scientists Urge Caution

Problem-based learning, the student-centred teaching method born in Canadian medical schools in the 1960s, has long been praised for turning passive lecture halls into rooms of active inquiry. Now a systematic review and meta-analysis published in Discover Education offers the most focused quantitative look yet at whether that approach actually makes university students more creative, particularly within STEAM disciplines spanning science, technology, engineering, arts and mathematics. The verdict is intriguingly two-sided: the headline numbers are dramatic, but the researchers themselves warn that the evidence base is too fragile to declare victory.

The research team, led by Shuiling Lu of Wuzhou University together with colleagues at UCSI University and Zhengzhou Normal University, followed PRISMA 2020 guidelines to sift the literature published between January 2015 and May 2025. Starting from 1,964 database records drawn from Web of Science, Scopus, Sage, Springer Link and JSTOR, and applying a search string that combined problem-based learning terms with creativity and higher-education keywords, the team ultimately retained fifteen peer-reviewed studies for systematic review. Only six of those studies, all quasi-experimental designs with pretest-posttest control groups, provided the complete statistical data needed to enter the meta-analysis.

The pooled result from those six studies was eye-catching. Compared with traditional instruction, problem-based learning was associated with a large improvement in creativity outcomes, yielding a pooled Hedges’ g of 1.64 with a 95 percent confidence interval running from 0.96 to 2.32. The overall effect was statistically significant, with a Z-statistic of 4.72 and a p-value below 0.001. In every one of the six studies, the problem-based learning group outperformed the control group on creativity measures, though the magnitude of the advantage varied considerably from one context to another.

That variation is where the caution begins. The heterogeneity statistic, I-squared, came in at 88.5 percent, meaning that the overwhelming majority of the variability between studies reflects real differences in contexts, interventions and measurements rather than random noise. The Q-test confirmed this, returning a value of 43.29 with five degrees of freedom and a p-value below 0.001. With only six studies available, the authors could not run formal subgroup analyses or meta-regression to statistically disentangle those sources of variance. They describe their pooled estimate as an upper-bound figure, potentially inflated by methodological weaknesses, and frame the primary contribution of their work not as a single number but as a demonstration that positive effects appeared consistently across diverse settings.

Methodological quality was a persistent concern. Using the ROBINS-I tool for non-randomized studies, two independent reviewers rated all fifteen included studies as carrying serious or critical risk of bias. The main culprits were inherent to educational research: the absence of randomization, which leaves the confounding domain at serious risk, and the impossibility of blinding students and instructors to the teaching method they were receiving. Missing outcome data and selective reporting, by contrast, were consistently rated low risk. A funnel plot showed slight asymmetry, hinting that small negative studies may be under-represented in the literature, though with only six points the plot has limited reliability.

The individual study effects tell a story of their own. Problem-based learning in engineering education produced the largest single effect, a Hedges’ g of 2.83, though that figure was converted from a reported Cohen’s d because raw standard deviations were unavailable, introducing uncertainty. Writing education yielded a g of 2.52, while PBL combined with digital mind-mapping in mathematics produced a moderate-to-large g of 1.40. Two engineering studies reported comparable effects of 0.99 and 1.41, and the smallest effect, 0.75, came from a dance-based intervention, suggesting that topic relevance, facilitator expertise and implementation fidelity all moderate outcomes. The authors stress that these subgroup comparisons are purely descriptive and hypothesis-generating, since no statistical comparisons between variants were possible.

One of the review’s most striking findings concerns how creativity itself is measured, and how differently that happens across disciplines. In arts and humanities contexts such as fashion design, collage making, dance and writing, assessors foregrounded aesthetic originality, narrative coherence, emotional connection and self-expression. In science and engineering settings, by contrast, creativity was judged through solution feasibility, technical viability, collaborative problem-solving and inventiveness in tackling authentic design problems. This pattern supports the domain-specificity view of creativity, treating it not as a single unified construct but as something that manifests differently depending on disciplinary expectations, a distinction educators must respect when designing both activities and assessment rubrics.

The measurement problem runs deeper still. The included studies operationalized creativity through markedly different instruments, from the Torrance Tests of Creative Thinking to self-report Likert scales and researcher-developed rubrics, each capturing distinct facets of fluency, flexibility, originality and usefulness. Two of the six meta-analyzed studies relied on self-report scales, which research suggests are vulnerable to social desirability bias and may correlate only moderately with demonstrated creative performance. Notably, the studies reporting the largest effects tended to use self-report measures or researcher-developed rubrics, raising the possibility that measurement characteristics inflated the observed magnitudes. None of the included studies employed widely validated, standardized creativity instruments with established psychometric properties, and all follow-ups were short-term, with none exceeding six months, leaving the durability of any creativity gains unknown.

Despite these caveats, the review identifies three mechanisms that appear to amplify problem-based learning’s effect on creativity. First, integrating the arts introduces non-verbal modes of expression, allowing students to externalize tacit knowledge through concept mapping, collage and dance that is difficult to articulate in words alone. Second, digital augmentation through platforms such as MATLAB, Genially and gamification tools creates interactive environments that scaffold creative risk-taking and sustain motivation, as seen in studies of matrix algebra and gamified mathematics training. Third, hybrid models combining problem-based learning with project-based learning generate synergistic gains across the so-called 4C competencies of creativity, collaboration, communication and critical thinking. The authors note these mechanisms are not mutually exclusive, though because most interventions bundled multiple components, the unique contribution of each cannot yet be isolated.

For educators and curriculum designers, the practical message is that problem-based learning shows genuine promise as a creativity-oriented strategy in higher education, particularly when enriched with digital tools or arts integration, but recommendations must remain tentative pending stronger validation. The authors call for future research built on randomized controlled designs, validated standardized creativity assessments and delayed post-tests to establish both causality and durability of effects. They also highlight gaps in the evidence base: disciplinary concentration in engineering and STEAM, sparse representation from the liberal arts and natural sciences, an exclusively undergraduate population, and incomplete reporting of key moderators such as intervention duration and facilitator expertise. Until such rigorous studies arrive, the dramatic effect size of 1.64 should be read not as a guarantee but as an encouraging, preliminary signal that when students are handed real problems to solve together, their creative capacities may genuinely flourish.

Subject of Research: The effect of problem-based learning on creativity outcomes in STEAM higher education

Article Title: Enhancing creativity in STEAM higher education through problem-based learning: a systematic review and meta-analysis

Article References: Lu, S., Phang, S. M., Judge, S. K., Liu, Y., & Fu, K. (2026). Enhancing creativity in STEAM higher education through problem-based learning: a systematic review and meta-analysis. Discover Education, 5(1), Article 1075. https://doi.org/10.1007/s44217-026-02125-1

Image Credits: AI Generated

DOI: 10.1007/s44217-026-02125-1

Keywords: problem-based learning, creativity, STEAM education, higher education, meta-analysis, systematic review, pedagogy, divergent thinking, digital technology, arts integration, quasi-experimental design, educational assessment