Launched in 2014, the International Journal of STEM Education was established to advance educational research across diverse scientific disciplines. Over the past twelve years, the journal’s development and performance have provided valuable insights into the field’s response to the growing demand for multidisciplinary and interdisciplinary scholarship in STEM education. In this article, I review the journal’s development trajectory, offering a retrospective account of its growth, scholarly impact, and contributions to the advancement of STEM education research. Since its inception, the journal has also been committed to internationalizing STEM education scholarship and fostering a global community of researchers. This article thus seeks to engage the international STEM education community in reflecting on the journal’s evolution, strengthening a shared understanding of its development, and discussing its continuing role in shaping and advancing STEM education as a distinct and globally established field of scholarship.
Abstract Background College students’ persistence in science, technology, engineering, and mathematics (STEM) fields remains a longstanding concern in higher education. STEM persistence reflects a complex interplay of individual characteristics, including academic preparation (academic skills and knowledge), academic behaviors (engagement with academic work), and contextual factors (e.g., pedagogy and support systems). Despite the prevalence of attention-deficit/hyperactivity disorder (ADHD) among college students, evidence on how ADHD status relates to STEM persistence and through what mechanisms remains limited. This study explores whether ADHD status is associated with STEM persistence and whether academic preparation and academic behaviors help explain that association. Results Chi-square tests using matched student-level first-year and senior-year survey data from 26,199 college students indicated that students with ADHD were as likely as peers without ADHD to intend to major in STEM in the first year. However, among the 4,644 first-year students who intended to major in STEM, students with ADHD were less likely to persist in STEM majors through the senior year. A generalized structural equation model was specified and estimated to examine the association between ADHD status and STEM persistence. Students who were more academically prepared and exhibited fewer disengaged academic behaviors were more likely to persist in STEM majors. Academic preparation and academic behaviors together accounted for 98.88% of the total association between ADHD status and STEM persistence, with academic preparation exhibiting a dominant effect. Conclusions Students with ADHD intended to major in STEM at a similar rate to their peers without ADHD, but they were less likely to persist in STEM majors. The association between ADHD status and STEM persistence was primarily explained by academic preparation. These findings suggest that supporting STEM persistence for students with ADHD may involve (1) integrating traditional accommodations with targeted supports to address academic under-preparation for STEM coursework and (2) establishing STEM learning-environment features (e.g., course structure, pedagogy, and assessment design) that support students with a diverse range of academic preparation.
Abstract This article examines the state of the art in AI literacy and STEM education and identifies emerging frontiers and research priorities to inspire cutting-edge empirical and theoretical research. The reviewed research is grounded in established AI literacy frameworks and shows that there is potential for students’ development of a critical and technologically informed AI literacy. However, the research is unevenly spread across the STEM disciplines and the potential for AI to transform STEM learning remains unclear. Thus, while the field has expanded rapidly, particularly with the emergence of generative AI (GenAI), fundamental challenges remain. The conceptual ambiguity surrounding AI literacy, combined with the rapid evolution of AI technologies, demands continued theoretical refinement and empirical investigation. Critical questions remain regarding how AI literacy intersects with disciplinary STEM literacies, how GenAI tools both support and reshape teaching and learning processes, and what opportunities and trade-offs arise when students engage with AI as learning partner. The ethical dilemmas of AI integration, including bias, data privacy, and environmental sustainability, also warrant urgent attention. As STEM education increasingly engages with AI as both a subject of learning and a learning tool, future research must move beyond one-size-fits-all frameworks toward context-sensitive approaches that empower educators and learners to critically and responsibly engage with AI while preserving core STEM learning goals. This article highlights key research frontiers and priorities for advancing scholarship at the intersection of AI literacy and STEM education.
Abstract Background Generative AI (GenAI) holds great potential for STEM education, especially in the context of problem-solving tasks, but unguided use often leads to metacognitive laziness, where students treat the technology as an answer machine rather than a collaborative partner. On the basis of Zimmerman’s self-regulated learning (SRL) framework and cognitive load theory (CLT), our randomized controlled trial (RCT; N = 95) investigated whether a structured, SRL-aligned scaffolding training session could improve students’ AI-assisted revision performance in electromagnetism problems. The experimental group ( n = 50) received the designed training, which emphasized the nature of GenAI, the SRL-aligned prompting strategy, and the remediation strategy for GenAI deficiencies. The control group ( n = 45) received problem-solving instruction without specific AI guidance. After completing the Conceptual Survey of Electricity and Magnetism (CSEM) independently, both groups subsequently conversed with the large language model Doubao to revise their responses. Results Independent samples t tests revealed that the experimental group showed greater AI-assisted revision performance ( d = 0.81, p < 0.001) than did the control group. The experimental group reported comparable overall cognitive load, despite a marginal elevation in the intrinsic cognitive load. Robust linear models showed that training attenuated the predictive effect of prior GenAI experience on revision performance. Qualitative analysis revealed that trained participants employed more diverse higher-order strategies and deployed systematic error remediation upon identifying AI deficiencies. The trained students achieved an incorrect-to-correct revision rate of 69.3%, compared with 41.3% for the control group. Conclusion Effective GenAI integration requires pedagogical scaffolding rather than mere access to the tool itself. The results suggest that training can enhance students’ revision performance and foster more strategic engagement with GenAI in the short term. These findings not only extend the theoretical applications of SRL and CLT in the human-AI context but also underscore the practical need for student development in AI literacy.
The rise of generative artificial intelligence (GenAI) in higher education and the resulting institutional responses have prompted research to support evidence-based policy decisions. As policies change to adjust to this disruptive technology, it demands agility from educators. The impacts of these policy changes on student behaviours and outcomes are currently poorly understood. This study examines how students used GenAI across three assessments in an undergraduate physics unit after assessment redesign forced by institutional policy that permitted purpose-specific and open use. Using a mixed-methods approach, student declarations of GenAI use were coded into four functional categories and mapped to the Substitution, Augmentation, Modification, and Redefinition (SAMR) framework. Learning and Formatting dominated early use and aligned with substitution and augmentation for a presentation assessment type. Later assessments showed a marked growth in Feedback uses, which spanned all SAMR levels and became the most common pattern. ‘Doing Task’ uses appeared in smaller numbers but illustrated modification and redefinition when GenAI generated step-by-step solutions for unfamiliar problems. Across all assessments, GenAI users achieved slightly higher scores, although differences were not statistically significant, so this trend should be interpreted as a tentative observation. Findings suggest that assessment design, which rewards understanding and requires student judgement, aligns with the theoretical markers of evaluative judgement. While this study did not directly measure internal cognitive states, the observed change in functional behaviour, specifically the transition toward iterative feedback loops, suggests that GenAI could serve as a catalyst for learning. This study provides empirical evidence on how assessment redesign based on policy shifts influences GenAI use patterns and proposes design principles for authentic assessment in science, technology, engineering, and mathematics (STEM) contexts. It highlights authentic assessment and constructive alignment as guiding principles for sustainable policy and pedagogy in the GenAI era, ensuring coherence among learning outcomes, activities, and assessment.
