The study, published in Artificial Intelligence Review, examined peer-reviewed quantitative research involving a comparison or control group. The researchers searched ERIC, PsycINFO and the Web of Science Core Collection, then expanded their results through forward and backward citation tracking. Their search identified 85 eligible studies, most of them conducted in higher education and involving text-based generative AI systems. Of these, 49 studies provided 59 effect sizes suitable for statistical meta-analysis. The analysis focused specifically on cognitive outcomes—what students learned and how well they performed on externally assessed measures—rather than satisfaction, usability or attitudes toward AI.

At first glance, the results appeared encouraging. A conventional random-effects meta-analysis indicated an overall positive effect of generative AI on STEM learning. In this model, the effect was expressed as Hedges’ g, a standardized measure designed to compare results across studies using different tests and scales. However, the studies differed dramatically from one another. Statistical heterogeneity was extremely high, with an I² value of 96.32 percent. In practical terms, this means that most of the variation in reported effects was not simply random sampling error; it reflected major differences among studies, including the learners, tasks, AI systems, teaching designs and outcome measures. The prediction interval ranged from g = −1.52 to g = 3.20, indicating that a future comparable study could plausibly find a substantial harm, little effect or a very large benefit.

The researchers also found signs that publication bias may be influencing the literature. Publication bias occurs when studies with striking positive findings are more likely to be published, noticed or included than studies showing no effect or negative outcomes. Funnel-plot asymmetry, a common diagnostic in meta-analysis, suggested that the available evidence may not represent the full distribution of results. To test how sensitive the headline finding was to this problem, the team used Robust Bayesian Meta-Analysis, or RoBMA. This approach compares models that allow for publication bias with models that do not, while estimating the underlying effect and uncertainty.

The RoBMA results substantially changed the interpretation. After accounting for the possibility that positive studies were overrepresented, the overall effect was estimated at approximately μ = 0.076 with a standard deviation of 0.254—a result close to zero and highly uncertain. The authors conclude that the positive average effect found in the conventional model can largely be attributed to publication bias. Importantly, however, the disappearance of a reliable overall effect does not mean that generative AI is ineffective in every educational setting. The analysis still revealed very large residual heterogeneity, with an estimated τ of approximately 1.190. In other words, the key scientific question is not whether AI “works” in general, but under what conditions, for which learners and through which kinds of learning activity it works.

Two factors explained part of this variation: the type of learning outcome and the way AI changed the cognitive activity required from students. The first distinction was between knowledge and skills. Knowledge outcomes included conceptual understanding and related forms of learning, while skills involved abilities such as problem-solving, procedures or performance. The second factor was captured by the ISAR framework, which compares the cognitive activity performed by students in an AI-supported intervention with that performed by students in the control condition. The framework draws on the ICAP model, which ranks learning activities from passive to active, constructive and interactive. In simplified terms, AI can substitute for a student’s activity, augment it, or enable a redefinition of the task that changes what students do cognitively.

The moderator analyses suggested that knowledge-focused interventions generally produced larger effects than skill-focused interventions. The meta-regression estimated that studies targeting skills had an effect approximately 0.73 standard deviations lower than studies targeting knowledge, relative to the reference category. ISAR level also mattered: substitution was associated with an estimated effect about 1.05 standard deviations higher than the reference category of redefinition in the statistical model, a result the authors interpret cautiously because many of the largest apparent effects arose when the intervention and control groups were not performing comparable cognitive activities. The moderators explained only part of the variance—about 12.5 percent and 13 percent in separate analyses, and 18.66 percent in a combined meta-regression—leaving most of the heterogeneity unexplained.

That warning is central to the study. The review identified 33 studies reporting large effects, but the authors found that these effects often reflected unequal learning conditions rather than a clean test of AI’s added value. In some cases, students using AI were allowed to engage in a substantially different activity from those in the control group. Comparing an AI-assisted learner who receives automated explanations, solution suggestions or generated examples with a control learner who receives none of these resources may measure the advantage of additional support, not the unique educational value of generative AI. Without carefully matched tasks, time, feedback and instructional support, a large effect can be statistically real while still being difficult to interpret.

A further analysis reinforced the limits of simple conclusions. Among 59 effect sizes, 23 were classified as large, meaning Hedges’ g exceeded 0.6. Nineteen of those large effects came from knowledge-oriented studies, while four came from skill-oriented studies. Large effects appeared most frequently when AI use was categorized at the redefinition level, especially in knowledge-focused settings. Yet no combination of outcome type and ISAR level met the researchers’ threshold for a sufficient condition that reliably produced a large effect. Knowledge was an almost, but not strictly, necessary condition: 19 of the 23 large effects involved knowledge outcomes, yielding a necessity consistency of about 0.83, but only 19 of 41 knowledge-focused studies produced large effects. The pattern points to a tendency, not a guarantee.

The review also examined learner challenges and instructional interventions, although the evidence was too inconsistent to support pooled quantitative estimates. Across the literature, researchers frequently failed to report variables that could determine whether AI helps or harms learning, including students’ AI literacy, metacognitive skills, prompt quality, verification behavior and the extent to which learners delegated tasks to the system. These omissions matter because generative AI can produce fluent but inaccurate explanations, incomplete reasoning or fabricated references. A student who treats an answer as authoritative may achieve short-term task completion without developing durable understanding. By contrast, a student who critiques, checks and revises AI output may use the same system as a cognitive partner rather than a replacement for thinking.

The authors propose six testable hypotheses and an integrative framework for future research, urging investigators to measure the learning process rather than focusing only on final scores. Stronger studies should compare equivalent activities, preregister outcomes, include negative and null findings, document the exact AI system and prompts used, and examine how students verify or challenge generated content. They should also distinguish between immediate performance and lasting learning, because AI may improve a student’s answer during an intervention without improving what the student can independently recall or apply later. For educators, the emerging message is neither an unqualified endorsement nor a rejection of generative AI: use it to augment students’ cognitive work, make verification visible and target clearly defined learning goals. Generative AI may be a powerful tool for STEM education, but the evidence suggests that its benefits depend less on the technology itself than on whether it preserves—and deepens—the intellectual work students must do.

Subject of Research: Generative artificial intelligence and cognitive learning outcomes in STEM education

Article Title: Evidence of impact and interpretational limits of generative AI in STEM education: a systematic review and meta-analysis on cognitive learning outcomes

Article References: Boolzen, C., Kuhn, J., Flegr, S. et al. “Evidence of impact and interpretational limits of generative AI in STEM education: a systematic review and meta-analysis on cognitive learning outcomes.” Artificial Intelligence Review (2026).

Image Credits: AI Generated

DOI: 10.1007/s10462-026-11665-9

Keywords: Generative artificial intelligence; STEM education; learning outcomes; educational technology

Generative AI refers to systems capable of producing novel content—from text and images to complex problem solutions—based on learned data patterns. In secondary STEM education, such AI systems can potentially transform how subjects are taught and learned, offering personalized assistance, creating tailored learning materials, and stimulating creative problem-solving among students. However, the literature also indicates a nuanced tension between using AI as a productivity and engagement tool and ensuring that the technology supports holistic human development rather than diminishing critical cognitive and social skills.

At the core of Fock and Siller’s review lies the philosophical framework of human flourishing, a concept grounded in Aristotle’s idea of eudaimonia, which emphasizes living a fulfilling, virtuous, and meaningful life. Applying this lens to secondary STEM education signals an educational paradigm that prioritizes student well-being, autonomy, intellectual growth, creativity, and ethical understanding alongside academic achievement. The authors argue that evaluating generative AI’s role in classrooms requires more than a focus on efficiency or content mastery; it demands a systemic view of AI’s impact on the cognitive, emotional, and social dimensions involved in human flourishing.

Technically, generative AI models used in STEM education often employ sophisticated architectures such as large-scale transformers and deep learning networks. These systems can generate intricate explanations, simulate experiments, and even help with coding or mathematical proofs. The computational underpinnings allow AI to analyze vast datasets, adapt to individual learner profiles, and respond dynamically to evolving educational needs. Yet, integrating AI tools is not merely a matter of deploying algorithms; it necessitates educators’ pedagogical reorientation and robust infrastructure to harness these technologies effectively without compromising the educational experience.

Importantly, the literature reveals that while generative AI can augment learning by providing scaffolded support and fostering inquiry-based exploration, there are risks of overreliance. If students defer too readily to AI-generated answers, they may experience a decline in critical thinking skills and problem-solving resilience. Moreover, ethical concerns emerge around data privacy, algorithmic bias, and the potential erosion of the teacher-student relationship, which is pivotal for mentorship, motivation, and the cultivation of moral reasoning.

Fock and Siller emphasize that educators must negotiate a delicate balance: leveraging generative AI’s adaptive capacities to enhance engagement without allowing technology to supplant essential human elements in education. This involves curriculum redesigns that integrate AI as a collaborative partner rather than a mere provider of solutions. For example, tasks prompting students to critique AI-generated content or co-create solutions with AI tools can promote deeper learning and reflection, thereby aligning technological use with human flourishing principles.

From a policy perspective, the authors highlight the necessity for frameworks that guide ethical AI implementation in schools. Regulatory guidance should ensure transparency, inclusivity, and accountability while encouraging innovation. Furthermore, teacher professional development emerges as critical to equip educators with the skills to facilitate AI-enhanced learning environments responsibly and effectively. Investments in both technological infrastructure and human capacity building are pivotal for realizing AI’s promise in STEM education.

The review also surveys empirical findings indicating improved student motivation and personalized learning trajectories when generative AI tools are integrated thoughtfully. For instance, AI-driven simulations can make abstract scientific concepts tangible and stimulate curiosity through interactive experimentation. However, disparities in access to advanced technologies threaten to widen educational inequities, underscoring the need for equitable deployment strategies that consider diverse socio-economic contexts.

In addition to practical and ethical dimensions, there are significant cognitive science implications entwined with AI adoption. Generative AI may reshape students’ metacognitive strategies by providing instant feedback and prompting self-regulated learning. Yet, continuous exposure to AI assistance could potentially modulate neural pathways related to problem-solving and creativity, a phenomenon warranting further longitudinal investigation within educational neuroscience.

Fock and Siller also explore the societal impact of embedding generative AI in STEM education. By cultivating future generations fluent in AI-assisted methodologies, educational systems may accelerate innovation ecosystems and economic competitiveness. Nonetheless, this technological enthusiasm must be tempered by cultivating ethical and empathetic STEM professionals who can critically assess AI’s broader cultural and environmental consequences—a core tenet of education aimed at human flourishing.

Looking ahead, the paper identifies several research gaps: longitudinal studies on AI’s long-term effects on student development, cross-cultural analyses of AI integration in diverse educational traditions, and explorations of interdisciplinary curricula combining AI literacy with philosophy and ethics. Advancing understanding in these areas could help construct comprehensive educational models that future-proof STEM education amid technological upheaval.

In sum, Fock and Siller’s scoping review offers a timely and multifaceted perspective on generative AI’s role in secondary STEM education through the philosophical prism of human flourishing. It challenges stakeholders to move beyond instrumental views of AI, advocating for an education system that uses technology to amplify human potential holistically. The study serves as a clarion call for educators, policymakers, technologists, and researchers to collaboratively shape AI-enabled learning environments that nurture ethical, creative, and resilient learners equipped for the complexities of the 21st century.

This insightful synthesis not only elucidates the vast possibilities generative AI brings to STEM education but also cautions against uncritical adoption, underscoring the importance of values-driven implementation. By fostering inclusive dialogue and evidence-based policies grounded in human flourishing, societies can harness AI’s transformative power while safeguarding fundamental educational ideals. As AI continues to reshape our world, reaffirming education’s role in nurturing human dignity and holistic growth remains paramount. In this evolving paradigm, generative AI emerges not as a mere tool, but as a catalyst for reimagining how we learn, teach, and thrive together.

Article Title:
Generative artificial intelligence in secondary STEM education in the light of Human Flourishing: a scoping literature review

Article References:
Fock, A., Siller, HS. Generative artificial intelligence in secondary STEM education in the light of Human Flourishing: a scoping literature review. IJ STEM Ed (2025). https://doi.org/10.1186/s40594-025-00589-5

Image Credits:
AI Generated