In recent years, the advent of generative artificial intelligence (AI) has revolutionized numerous fields, including education. Among the most critical aspects of preparing future educators is the development of their cognitive capacities, particularly higher-order thinking and problem-solving skills. A groundbreaking study by researchers Zhang, Tian, and Lu, published in BMC Psychology in 2025, delves into the dynamic interplay between these cognitive abilities in pre-service teachers and the role generative AI plays in this developmental process. Utilizing sophisticated moderated mediation analysis, the study offers nuanced insights into how emerging AI technologies can augment teacher education, potentially transforming pedagogical practices worldwide.
Higher-order thinking involves complex cognitive processes such as analysis, evaluation, synthesis, and creation, which go beyond mere memorization or basic comprehension. For pre-service teachers, the ability to engage in higher-order thinking is vital, as it underpins their capacity to design effective instructional strategies and respond adaptively to classroom challenges. Problem-solving skills, closely linked to higher-order thinking, enable educators to navigate multifaceted problems that arise in educational contexts, fostering student engagement and conceptual mastery. The study in question underscores the critical relationship between these competencies, positing that generative AI tools can serve as catalysts for their development.
Generative AI systems, powered by advanced neural networks, are capable of producing novel content spanning text, images, and even pedagogical scenarios. These systems provide pre-service teachers with individualized, adaptive learning experiences that prompt reflective thinking and active problem-solving. Zhang and colleagues investigated how interaction with generative AI influences the cognitive development of teachers in training, particularly focusing on the mediated pathways through which AI engagement enhances higher-order thinking and subsequently problem-solving abilities. Employing a moderated mediation framework allowed the researchers to capture both direct and indirect effects, as well as the conditions under which these effects are strengthened or weakened.
The methodology incorporated rigorous quantitative assessments alongside qualitative feedback, situating the research at the intersection of cognitive psychology, educational technology, and teacher education. Participants, drawn from teacher training programs, engaged with AI-facilitated learning modules designed to challenge assumptions, encourage metacognitive awareness, and simulate real-world teaching dilemmas. Data analysis revealed that higher-order thinking significantly mediated the relationship between generative AI usage and problem-solving skill enhancement, indicating that AI’s influence operates predominantly through cognitive complexity rather than rote procedural knowledge.
Moreover, the study identified moderators that influenced this mediation effect, including individual differences in prior technological proficiency and pedagogical experience. Namely, pre-service teachers with greater baseline familiarity with digital tools derived more pronounced benefits from AI integration, suggesting the necessity of scaffolding AI literacy within teacher education curricula. These findings reflect a broader pedagogical imperative: to design learning environments that are both technology-enhanced and human-centered, fostering not just skill acquisition but also cognitive agility and adaptive resilience.
The implications of Zhang et al.’s research extend beyond teacher training. Educators equipped with enhanced higher-order thinking and problem-solving skills are more likely to innovate instructional methods, adapt to diverse student needs, and cultivate critical thinking among their pupils. Generative AI, when strategically employed, can democratize access to high-quality, expert-level cognitive challenges previously unavailable to many pre-service teachers due to resource limitations. This democratization can serve as a lever for equity in education, enabling teacher candidates from varied backgrounds to develop competencies essential for 21st-century classrooms.
Additionally, the integration of generative AI in teacher education presents an opportunity to rethink traditional pedagogical paradigms. Rather than passively receiving knowledge, pre-service teachers actively co-construct understanding with AI tools, engaging in iterative cycles of problem identification, hypothesis testing, and solution refinement. This mirrors authentic teaching scenarios and prepares educators for the complex decision-making required in real classroom settings. The study provides empirical support for the potential of AI-enhanced learning environments to simulate such conditions effectively.
Despite these promising results, Zhang and colleagues caution against uncritical adoption of generative AI technologies. Ethical considerations, such as data privacy, algorithmic bias, and overreliance on automated feedback, must be addressed meticulously. Responsible AI integration demands transparent, explainable AI systems and ongoing professional development to equip educators with the skills necessary to harness AI’s potential thoughtfully. The study advocates for collaborative efforts among AI developers, educators, and policymakers to create frameworks that balance innovation with ethical safeguards.
Furthermore, the research contributes to theoretical frameworks underpinning cognitive development in adult learners. By elucidating the mediating role of higher-order thinking in problem-solving skill development within AI-enhanced contexts, the study enriches our understanding of how technology can scaffold cognitive complexity. This insight invites future research to explore longitudinal trajectories of cognitive growth and the sustainability of AI-induced gains in educational settings.
The study also prompts reevaluation of assessment strategies within teacher education. Traditional evaluation techniques may inadequately capture the depth and nuance of higher-order thinking stimulated by generative AI interactions. The authors suggest that dynamic, formative assessments integrated within AI platforms could provide real-time feedback loops, promoting continuous self-regulation and metacognitive reflection among pre-service teachers. This paradigm shift in assessment aligns with the broader goals of fostering adaptive expertise and lifelong learning attitudes.
Importantly, Zhang et al.’s work underscores the value of personalized learning pathways. Generative AI’s capacity to tailor challenges and support according to individual learners’ profiles allows teacher education programs to accommodate diverse cognitive styles and developmental stages. Personalization enhances engagement, motivation, and efficacy, critical factors for optimal skill acquisition. Future iterations of AI tools will likely incorporate more sophisticated user modeling and affective computing to further refine personalized experiences.
The intersection of AI and teacher education heralds a transformative era in educational research and practice. Zhang and collaborators’ moderated mediation analysis offers robust evidence that generative AI is not merely a technological novelty but a potent enhancer of essential cognitive functions. As pre-service teachers gain higher-order thinking and problem-solving skills through AI-mediated experiences, the ripple effects will extend to their future classrooms, shaping generations of learners equipped to face complex societal challenges.
In summary, this pioneering study spotlights the symbiotic relationship between human cognition and artificial intelligence within teacher preparation programs. The researchers’ nuanced examination of cognitive mechanisms and contextual moderators provides a roadmap for integrating AI thoughtfully into pedagogical frameworks. By harnessing generative AI to elevate higher-order thinking, the educational community can cultivate resilient, reflective, and innovative educators ready to thrive in an ever-evolving landscape.
These findings invite a call to action for educational stakeholders to invest in AI infrastructure, professional development, and research that prioritizes cognitive development alongside technological adeptness. Zhang, Tian, and Lu’s work marks a milestone in understanding how AI can serve as both a tool and a collaborator in intellectual growth, paving the way for a future where human and artificial intelligence synergistically empower education.
Subject of Research: The interaction between higher-order thinking skills and problem-solving abilities development in pre-service teachers through the use of generative AI, investigated using moderated mediation analysis.
Article Title: The relationship between higher-order thinking and problem-solving skills development among pre-service teachers using generative AI: an analysis based on moderated mediation.
Article References:
Zhang, Y., Tian, H. & Lu, J. The relationship between higher-order thinking and problem-solving skills development among pre-service teachers using generative AI: an analysis based on moderated mediation. BMC Psychol 13, 1094 (2025). https://doi.org/10.1186/s40359-025-03404-6
Image Credits: AI Generated
