The authors began from the scale of fungal diversity: global species richness has been estimated at between 2.2 million and 13.2 million species, and most remain undescribed. They also noted that land-use intensification, habitat destruction and uneven protection of fungal diversity hotspots increase the urgency of documenting species. Their study was a structured review and assessment of publication content, rather than a new sampling or laboratory experiment, so its conclusions apply to the selected genera, publications and scoring criteria rather than all fungal taxonomy.

Using MycoBank, the researchers identified new species described from 2020 through 2024 in six genera. The sample comprised the ectomycorrhizal basidiomycetes Amanita, Cortinarius sensu lato and Russula, and the filamentous ascomycetes Aspergillus, Fusarium and Pestalotiopsis. These genera were selected to represent contrasting ecological strategies, taxonomic practices and publication cultures. The three basidiomycete genera accounted for 591 new species in 219 publications, while the three ascomycete genera accounted for 322 species in 138 publications.

Publications were divided into four formats: online releases, cumulative studies containing multiple unrelated descriptions, single-lineage studies and monographs. Of the 219 basidiomycete publications, 167 were single-lineage studies, whereas 67 of the 138 ascomycete publications used that format. The detailed scoring analysis was restricted to single-lineage publications because their introductions generally address hypotheses and objectives within one taxonomic group.

Many of these introductions did not clearly state why a new species was considered distinct. The review reported that 134 basidiomycete studies, or 80 percent, and 38 ascomycete studies, or 57 percent, did not articulate an explicit species hypothesis and instead emphasized descriptive information. A new species was named directly in the introduction in 24 percent of the basidiomycete publications and 22 percent of the ascomycete publications assessed.

The researchers evaluated each introduction across five categories: phylogenetic placement, closely related species, morphology, ecology and biogeography, and hypothesis and aims. Each category could receive up to 20 points. Phylogenetic placement was generally the most complete element, whereas morphology was the least complete. Introductions often gave broad descriptions of a genus, but they did not consistently explain diagnostic characters, known diversity, ecological differences or the mechanisms that might account for divergence. Geographic information was commonly descriptive and was not consistently connected to a species hypothesis or testable aim.

The analysis suggests that sequence information was prominent in many publications, but the scoring itself does not establish that reliance on molecular data caused the introduction deficiencies. The authors observed that morphology, ecology, biogeography and reproductive evidence were less fully represented and were not always linked to study aims. This pattern matters because a species name in modern systematics functions as a hypothesis about a distinct lineage, and that hypothesis may be evaluated using phylogenetic, morphological, geographic, ecological or reproductive evidence. An integrative or polyphasic approach combines available lines of evidence when delimiting species.

Several species in the dataset were later recognized as junior synonyms. The review did not establish a causal relationship between introduction quality and later synonymization, but it noted that some junior synonyms had appeared in data releases or cumulative publications, while others had been described in single-lineage papers with limited introductions. The authors stated that hypotheses supported by multiple data sources can help authors compare proposed taxa with type specimens and other closely related material, although obtaining loans, securing permission for destructive sampling and consulting scarce specialist expertise can complicate such work.

Based on the review, the authors recommend that introductions provide concise background linked to current literature, define the focal group and its placement, identify close relatives, summarize relevant morphology, and state a specific hypothesis and aims. They advise against announcing the names or number of new species in the introduction and recommend reserving “sp. nov.” for the section in which the nomenclatural act is made, consistent with the International Code of Nomenclature for algae, fungi, and plants. For papers describing more than five species, they suggest organizing the treatment by infrageneric groups where appropriate. They also recommend interpreting sequence similarity alongside morphology, culture-based evidence, ecology or biogeography when available.

The review acknowledges that online and cumulative formats can efficiently document opportunistic discoveries and provide a foundation for later work. Nevertheless, the authors favour single-lineage or comprehensive studies when higher-quality data and systematic comparisons are possible. Their bounded conclusion is that the sampled introductions often resembled summaries or data releases more than hypothesis-driven studies. Clear aims and integration of evidence could improve transparency and species delimitation, but the study did not directly test whether revised introductions would reduce taxonomic errors or improve acceptance rates.

Subject of Research: Structured review of hypothesis framing and completeness in fungal new-species description introductions

Article Title: Should describing a new species be science or data release? Rethinking introductions in fungal taxonomy

Article References: Caboňová, M., Maharachchimbukura, S. S. N., Noffsinger, C. R., Caboň, M., Li, X.-L., Verbeken, A., Stadler, M., Phukhamsakda, C., Marin-Felix, Y., Guzman-Davalos, L., Wijayawardene, N. N., Adamčík, S., & Hyde, K. D. (2026). Should describing a new species be science or data release? Rethinking introductions in fungal taxonomy. IMA Fungus, 17, Article e186895. https://doi.org/10.3897/imafungus.17.186895

Image Credits: AI Generated

DOI: 10.3897/imafungus.17.186895

Keywords: fungal taxonomy, species descriptions, systematics, publication quality, species delimitation, integrative taxonomy, phylogenetics, morphology, research review, Should, describing, species

The notion of AI literacy borrows deliberately from older literacies. Just as digital literacy, media literacy and data literacy expanded the original idea of reading and writing into broader competencies, AI literacy describes the knowledge, skills and attitudes that non-experts need to engage with artificial intelligence in daily life. The most widely cited definition, from Long and Magerko, frames it as a set of competencies that enables individuals to critically evaluate AI technologies, communicate and collaborate effectively with AI, and use AI as a tool online, at home and in the workplace. That framing matters, because it shifts the educational focus away from hands-on technical skills alone and toward an understanding of, and critical reflection on, the role of AI in society. In recent years the research conversation has also migrated from university computer science departments, where AI literacy was first studied, down into K-12 classrooms, a shift driven largely by the sudden availability of generative AI applications such as ChatGPT.

To establish the state of the art, the authors conducted a qualitative evidence synthesis, searching the Scopus database for the most cited international articles on AI literacy in STEM education. After screening titles, abstracts and full texts, they settled on the ten most highly cited papers published between 2022 and 2026, restricted to prestigious, high-impact journals. The approach has limitations the authors acknowledge candidly: highly cited papers attract further citations, newer and more peripheral studies may be obscured, and a small sample cannot represent an entire field. Still, by examining the most visible scholarly output, the review captures what the field currently considers important, and where its blind spots lie. The selected studies span early childhood through higher education, confirming that AI literacy is now seen as relevant at every level of schooling.

Three broad dimensions emerged from the analysis. The first concerns instructional focus. In K-12 settings, researchers have tested AI literacy programs with preschoolers, designed summer workshops for middle schoolers, and piloted AI-integrated science lessons in lower secondary classrooms. One study examined whether upper primary students could develop AI literacy through digital story writing, with children imagining robots that recycle waste, care for injured animals, or help elderly people with household chores. In higher education, researchers developed and validated AI literacy tests, explored ChatGPT as a virtual tutor in university physics, and modeled how undergraduates’ technological readiness, self-efficacy and attitudes predict their use of AI tools. Notably, almost all of the top-cited studies focused on students rather than teachers, a gap the authors flag as urgent.

The second dimension, how AI literacy is characterized, reveals striking conceptual variation. Some frameworks emphasize technical concepts and processes, others ethical and societal implications, and still others future careers. In early childhood contexts, AI literacy has been described as learning about AI as content, using it as a tool in hands-on activities, and being aware of its limitations and incorrect output. One framework developed specifically for technology education divides AI literacy into conceptual, procedural and contextual technological knowledge, treating it as part of a broader technological multiliteracy. Another widely used structure includes four components: knowing and understanding AI, using and applying it, evaluating and creating with it, and considering ethics. Despite the differences, most conceptions now combine technical understanding with critical and ethical awareness, echoing the influential definition that treats critical evaluation as central.

The third dimension, STEM disciplinary content, exposes an uneven landscape. Interdisciplinary projects with environmental and sustainability themes dominate, particularly in early childhood and primary education, where children have learned basic AI inference through tools like Teachable Machine and connected robotics activities to topics such as ocean pollution and recycling. Biology and physics appear most often as disciplinary anchors, mathematics less so. Teachers piloting AI-integrated science lessons saw complementary roles: science provides the context for a problem, such as determining the proper conditions for solar panels on a Mars Rover, while AI contributes a predictive model that helps solve it. Yet technology as a disciplinary subject in its own right was almost entirely absent from the reviewed research, mentioned only in the framework developed for technology education. In higher education, meanwhile, STEM content often receded into the background, with studies focused on AI tools themselves rather than on how AI reshapes disciplinary understanding.

The evidence on outcomes is mixed, and this is where the review becomes most provocative. Some interventions report genuine gains, including improved conceptual and epistemic understanding of AI among upper elementary students. Others are more cautionary. In one study, primary students who used ChatGPT to support STEM programming gained confidence and programming knowledge but showed no improvement in computational-thinking skills or independent thought. A mixed-methods study of ChatGPT as a physics tutor found that students must learn to recognize AI-generated content and develop prompt engineering skills, and identified a persistent misconception that AI is super-human and infallible. The authors of that study concluded that AI literacy education is first and foremost about improving students’ critical thinking in assessing information, a conclusion that resonates throughout the review. Anthropomorphism, the humanlike quality of chatbot communication, may foster unwarranted trust in AI responses, compounding the risk.

Underlying all of this is a deeper tension the authors articulate with unusual clarity: AI enters STEM education in two distinct roles, as a domain of knowledge to be taught and as a tool for learning, and these roles can interact or even conflict. Students should understand how neural networks recognize patterns and how algorithms process data, but they should also use intelligent tutoring systems, automated feedback and generative AI to support inquiry. The danger is that AI used as a crutch may undermine the learning of foundational problem-solving skills, precisely the competencies that deep disciplinary understanding requires. Whether AI literacy can genuinely transform STEM learning, rather than simply accompany it, remains an open question that few studies have directly investigated, though some point to increased authenticity when AI literacy is woven into real-life STEM problems.

The ethical stakes receive pointed attention. The authors call for urgent research on bias, data privacy, cybersecurity, the energy footprint of AI systems, data ownership and rights, and the responsiveness of education systems to rapid technological change. They also note that prominent AI competency frameworks from UNESCO, the European Commission and the OECD often lack connection to empirical research or sufficient theoretical underpinning, and few are specific enough to be usable in classrooms. Cultural and contextual factors add another layer of complexity: given differences in AI governance between, for example, the United States and the European Union, AI literacy is likely conceived differently across national contexts, yet comparative scholarship is scarce. Gender differences in AI literacy development also warrant closer study.

The editorial closes with a call to action that doubles as a research agenda. Future work must move beyond one-size-fits-all frameworks toward context-sensitive approaches that empower educators and learners to engage critically and responsibly with AI while preserving core STEM learning goals. Teachers, the authors argue, need to be AI literate themselves before they can deliver effective instruction, yet research on promoting AI literacy among teachers is strikingly thin. Assessment presents its own frontier: generative AI threatens traditional assessment formats, inviting multimodal and interactive alternatives, while AI-generated feedback demands a new form of teacher expertise the authors call AI feedback literacy, the capacity to judge machine inferences and adapt instruction accordingly. As AI becomes simultaneously a subject of study and a learning partner in STEM classrooms, the field’s central challenge is to ensure that technical competence and critical reflection grow together, and that the promise of AI does not come at the cost of the deep disciplinary understanding that STEM education exists to cultivate.

Subject of Research: AI literacy and its role in STEM education research

Article Title: AI literacy and STEM education: emerging frontiers and research priorities

Article References: Hallström, J., Chiu, T. K. F., Ding, M., Koretsky, M. D., & Li, Y. (2026). AI literacy and STEM education: emerging frontiers and research priorities. International Journal of STEM Education, 13(1), Article 63. https://doi.org/10.1186/s40594-026-00653-8

Image Credits: AI Generated

DOI: 10.1186/s40594-026-00653-8

Keywords: AI literacy, STEM education, generative AI, ChatGPT, critical thinking, K-12 education, higher education, AI ethics, educational assessment, teacher training, technological literacy, research review

The study, conducted by Iván Sánchez-López and Gemma San Cornelio of the Universitat Oberta de Catalunya in Barcelona together with Heleny Méndiz-Rojas of the Catholic University of the North in Chile, took a deliberately targeted approach. Rather than attempting an exhaustive census of the literature, the team mined Scopus and Web of Science for publications explicitly engaging both artificial intelligence and creativity, screened them through a PRISMA-informed procedure, de-duplicated the results down to 71 unique records, and then ranked them by citation prominence. The final corpus comprised the 40 most citation-prominent studies, spanning journal articles and full conference papers, all published between 2020 and 5 February 2025.

Once the corpus was assembled, the researchers analyzed it in ATLAS.ti using Grounded Theory and the Constant Comparative Method. Initial coding produced 227 descriptive code entries, refined into 203 first-order codes, which axial coding then organized into nine categories through 230 code-category assignments. A final narrative synthesis distilled eight cross-cutting transformation features. The team used the software’s AI-assisted coding only as a supplementary first pass; every machine suggestion was manually checked, and the final codes, categories, and interpretations were settled by the researchers themselves.

The central finding is that creativity, as portrayed across this influential literature, has become a socio-technical assemblage rather than an individual capacity. Creative outcomes now emerge from the interplay of human-model interaction, institutional gatekeeping, infrastructural asymmetries, and normative conflict. Generative AI is consistently associated with expanded productivity: faster ideation, rapid prototyping, broader exploration of possibility spaces, and new formats of human-AI collaboration. But the same studies repeatedly document a parallel intensification of trouble around authorship, originality, recognition, labour, bias, access, legitimacy, and sustainability.

The reorganization of creative work follows a recognizable pattern. As the cost of producing drafts and variants collapses, the bottleneck migrates from generation to framing, prompting, selecting, editing, and justifying outputs. In human-computer interaction and design research, this appears as iterative prompt craft, co-creative interfaces, and curation as the central creative acts. In education, generative tools are framed simultaneously as scaffolds for writing, feedback, and idea generation, and as threats to assessment, academic integrity, and critical thinking. In management and innovation studies, the emphasis falls on productivity and capability building, with experimental and empirical work showing that augmentation outcomes depend heavily on task conditions, organizational resources, and workflow integration rather than unfolding as a uniform boost to creativity.

The review argues that these expansions do not distribute their benefits evenly. Generative tools may lower the barriers to producing plausible artifacts, yet sector reviews suggest they can concentrate advantage around computing infrastructure, technical expertise, and validation authority. Barriers shift from production toward evaluation, credibility, and institutional endorsement, and access to tools does not automatically translate into recognition or effective use. The review also identifies a mismatch between productive acceleration and evaluative capacity: when systems flood the pipeline with candidates, criteria for quality, originality, and appropriateness do not scale automatically, and selection can drift toward popularity defaults, raising the risk of homogenization even as output volume rises.

Questions of evaluation are further complicated by evidence of human bias against machine-made work. Studies in the corpus position evaluators as gatekeepers whose identity-based perceptions of whether an artifact is human- or AI-produced shape recognition and uptake. In Csikszentmihalyi’s systems view of creativity, creative status is conferred by a field of evaluators, and generative AI disrupts that field function by multiplying and obscuring the sites of evaluation: merit, originality, and responsibility are now negotiated across platforms, curators, clients, reviewers, and model providers, while institutions cling to accountability logics designed for individualized authorship.

Reading the corpus against contemporary scholarship exposes two significant gaps. First, sustainability receives comparatively thin treatment. While the reviewed studies address it mainly through urban and infrastructural perspectives, recent research has developed systematic accounts of the material externalities of large-scale generative systems, including electricity demand, carbon and water footprints, data-centre infrastructure, and disclosure obligations. Second, Global South and minoritized perspectives remain weakly integrated into the dominant corpus, even though training data distributions, dominant-language resources, and uneven access to computation may act as technical and cultural priors that determine which outputs and epistemic positions become visible in the first place.

The authors are candid about the limits of their design. Focusing on the most-cited publications maps dominant framings but may reproduce existing epistemic hierarchies, since citational prominence is treated as an indicator of visibility and influence rather than quality or truth. The corpus also reflects indexed, anglophone, predominantly Global North academic infrastructures, and the 2025 slice of coverage is partial by construction. The researchers call for future work using ethnographies of everyday use, comparative studies across languages and cultures, and mixed methods connecting computational traceability with critical analysis of labour and environmental costs.

The study’s principal interpretive conclusion is that expanded productivity and contested authority are not separate outcomes but interrelated dimensions of AI-mediated creativity. Artificial intelligence does not simply automate creative tasks; it relocates where creative agency sits, changes how contribution is evaluated, and reshapes which actors and infrastructures participate in producing legitimacy. High-impact scholarship, the authors argue, does not merely describe this transformation. It also helps stabilize influential definitions of creative value and redistribute forms of recognition, making it all the more important to examine how authority, visibility, and material conditions determine which forms of AI-mediated creativity become visible and legitimate.

Subject of Research: A citation-informed review of how highly cited research frames the transformation of creativity under artificial intelligence

Article Title: Expanded productivity, contested authority: creativity and AI in high-impact research

Article References: Sánchez-López, I., Méndiz-Rojas, H., & San Cornelio, G. (2026). Expanded productivity, contested authority: creativity and AI in high-impact research. AI & SOCIETY. https://doi.org/10.1007/s00146-026-03213-4

Image Credits: AI Generated

DOI: 10.1007/s00146-026-03213-4

Keywords: artificial intelligence, creativity, generative AI, authorship, human-AI collaboration, creative industries, evaluation, legitimacy, bias, sustainability, research review, AI & Society