Higher-order thinking skills (HOTS) play a central role in 21st-century education, yet the neural mechanisms underlying HOTS during speech production remain poorly understood. This study investigated superior frontal gyrus (SFG) activity during HOTS-related verbal tasks using standardized low-resolution electromagnetic tomography (sLORETA). Twenty-four right-handed university students (12 males, 12 females; mean age = 19.1 ± 0.8 years, range 18–20) performed nine HOTS verbal production tasks while a 19-channel EEG was recorded. sLORETA source analysis localized peak neural activity to Brodmann Area 10 (BA10) of the SFG across all conditions. The highest peak activation occurred during the Comparing task (M = 4.82 A/m², SD = .45), Detailing (M = 4.67 A/m², SD = .38), and Clarifying (M = 4.53 A/m², SD = .42), with all HOTS conditions showing very large effect sizes relative to baseline (Cohen’s d = 5.10–8.04). Temporal dynamics revealed sustained SFG engagement during Detailing (M = 12.4 seconds, SD = 3.2) compared to other HOTS tasks (M = 8.6 seconds, SD = 2.7; t(23) = 4.8, p < .001). SFG activation during the Clarifying stage was significantly correlated with task performance quality (r = .68, p < .001). These results establish the SFG BA10 as a functional hub for cognitive integration during verbal higher-order thinking, with direct implications for educational neuroscience, neurolinguistics, and instructional design. Acknowledgements This research was funded by the Directorate of Research and Community Service (Direktorat Penelitian dan Pengabdian kepada Masyarakat), Universitas Pendidikan Indonesia. CRediT Statement Jatmika Nurhadi: Conceptualization, Methodology, Software, Formal Analysis, Investigation, Writing – Original Draft, Writing – Reviewing & Editing, Visualization; Dadang Sudana: Validation, Writing – Reviewing & Editing, Supervision. Disclosure Statement The authors reported no potential conflicts of interest. Generative AI Statement Artificial intelligence-based language tools (DeepL and Grammarly) were used to assist with the translation, drafting, and editing of the manuscript. All EEG data collection, sLORETA analysis, statistical calculations, and scientific interpretation were performed entirely by the research team. The text drafted with the assistance of AI has been reviewed, verified, and approved by all authors.
While individual semantic map structure has been proposed as a foundation for creative text generation, conventional group-aggregated network metrics obscure the idiosyncratic features that may drive creative output. The main objective of current study was to test whether the specific features of individual semantic structure may impact the subjective perception of text creativity level. To test our hypotheses, we used the following methods: text generation (essays), natural language processing, behavioral experiment. During the first stage of the study, we collected essays on a free topic from 41 participants (35 females, M age = 18.2 years, SD = 0.19, range = 18-20). All texts were processed, and the 15 most frequent and 15 most rare noun pairs used within the same sentence were selected, along with the corresponding sentences. During the second stage, the selected word pairs and sentences were presented to another 71 participants (60 females, M age =18.93 years, SD = 0.36, range = 18-38). Participants rated the semantic distance between the words in each word pair and the level of creativity of the presented sentences. Our results showed that rare word pairs were perceived as being significantly more semantically distant, while sentences containing rare word pairs were perceived as significantly more creative. We conclude that nodes of semantic map, which show person-specific features significantly impact creative text generation.We further discuss how these individual differences can be used in the design of behavioral and neuroimaging experiments on creativity. Acknowledgements Illia Kuznietsov was supported by Purdue Ukrainian Scholars Initiative during this work. The authors thank Dr. Sébastien Hélie and the members of the Purdue Laboratory for Computational Cognitive Neuroscience for their assistance with design of the experiment and valuable feedback. CRediT Author Statement Illia Kuznetsov: Conceptualization, Methodology, Software, Formal Analysis, Investigation, Data Curation, Writing – Original Draft, Writing – Review & Editing, Visualization, Supervision, Project Administration; Tetiana Masytska: Methodology, Validation, Data Curation; Writing – Reviewing & Editing; Oleksandr Zhuravlov: Validation, Formal Analysis Data Curation, Writing – Reviewing & Editing; Oleksandr Kazmirchuk: Methodology, Software, Data Curation, Visualization; Stanislav Revko: Software, Validation, Formal Analysis, Writing – Original Draft, Writing – Review & Editing, Visualization; Nataliia Kozachuk: Conceptualization, Methodology, Validation, Formal Analysis, Investigation, Resources, Writing – Original Draft, Writing – Review & Editing, Supervision, Project Administration. Disclosure Statement The authors reported no potential conflicts of interest. Generative AI Statement During the preparation of this work the authors (Illia Kuznietsov, Stanislav Revko, Nataliia Kozachuk) used Claude Opus 4.8 in order to correct spelling and grammar errors in the manuscript text and remove tautologies, Julius 1.2 for initial prototyping of Python code for data and statistical processing, Undermind for literature search. All Python code was tested on sample datasets and was corrected, if necessary. After using these tools and services, the authors reviewed and edited the content as needed and takes full responsibility for the content of the publication.
This quasi-experimental study investigated the primary factors contributing to English-language demotivation among first-year Saudi undergraduates. It evaluated the longitudinal impact of a structured Gemini intervention on student perceptions of remotivation. Baseline metrics identified perceived instructional distance and evaluation-related classroom anxieties, specifically fear of negative peer judgment, as the most severe demotivators. Following a 12-week intervention, a parametric Two-Way Mixed ANOVA revealed a monumental, highly significant longitudinal interaction effect in motivational recovery, with an independent Exploratory Factor Analysis confirming excellent psychometric construct validity for the AI remotivation subscale. Qualitative interviews triangulated these findings, demonstrating that participants perceived Gemini's intervention as a primary restorative catalyst for functional, individualistic remotivation. It established a private, zero-anxiety digital sandbox that unblocked finite cognitive processing resources and elevated perceived autonomy and competence. However, the results revealed a critical "efficiency–relatedness paradox": the automated intervention strained perceptions of social relatedness and instructional delivery. The study concludes that for generative AI to serve as a sustainable motivational driver, institutions should move beyond technological substitution models in favor of a blended, human-in-the-loop framework in which instructors leverage automated interfaces for technical feedback while intentionally repurposing saved classroom hours to deepen human mentorship, socio-affective rapport, and empathetic interpersonal validation. Disclosure Statement The author reported no potential conflicts of interest. Generative AI Statement During the preparation of this work, the author used Gemini 3.1 Pro and Grammarly to refine language and improve clarity. The author critically reviewed and edited all AI-generated suggestions and remains fully responsible for the integrity, accuracy, and original contributions of the final manuscript.
This article examines the intentions of English children’s nonfiction discourse from communicative and cognitive-pragmatic perspectives and identifies the linguistic means of their realization at lexical, syntactic, and stylistic levels. The study is based on a corpus of English children’s nonfiction books, including encyclopedias and biographies represented by covers, blurbs, and main texts. The research applies pragmalinguistic, descriptive, cognitive, and quantitative methods grounded in speech act theory and discourse studies. The communicative perspective includes representative, directive, commissive, expressive, declarative, and quesitive intentions, whereas the cognitive-pragmatic perspective is represented by informing, influencing, and engaging intentions. The results demonstrate that communicative and cognitive-pragmatic intentions, together with their linguistic means of representation, form an interconnected system in children’s nonfiction discourse. Representative and informing intentions are predominantly realized through scientific terminology, declarative constructions, and descriptive statements, while directive and influencing intentions are expressed through imperative constructions, modal verbs, and direct address. Engaging and expressive intentions are represented through emotionally marked vocabulary, rhetorical questions, intensifiers, and exclamatory constructions aimed at maintaining readers’ interest and stimulating curiosity. The findings provide new insights into the intentional organization of children’s nonfiction discourse and its role in shaping children’s perception of scientific knowledge and interaction with the surrounding world. CRediT Statement Maryna Chernyk: Conceptualization, Methodology, Software, Investigation, Resources, Data curation, Writing – Original Draft, Visualization, Project Administration; Hanna Prihodko: Conceptualization, Methodology, Investigation, Resources, Writing – Review & Editing, Supervision, Formal Analysis, Investigation, Data Curation, Project Administration; Vladyslava Kulish: Methodology, Validation, Investigation, Writing – Original Draft; Oleksandra Prykhodchenko: Validation, Formal analysis, Investigation, Writing – Original Draft, Writing – Review & Editing. Disclosure Statement The authors reported no potential conflicts of interest. Generative AI Statement During the preparation of this work, Maryna Chernyk used Grammarly, an AI-assisted writing tool, to improve the grammar, spelling, and stylistic clarity of the manuscript. After using this tool, all co-authors reviewed and edited the content as needed and take full responsibility for the content of the publication.
This study explores the potential of young adult fiction addressing controversial topics to support theory of mind in EFL contexts. It examines how engagement with emotionally and socially complex literary narratives may foster perspective-taking, empathy, intrinsic motivation, and language learning among adolescent readers. The research employed a mixed-methods design. Initial quantitative data were collected via an online questionnaire administered to 169 young adults aged 18 to 30 to investigate reading motivation, engagement, and format preferences. The main phase consisted of a five-week extensive reading intervention conducted with ten female bilingual secondary school students, aged 17- 18, in Slovakia. Data were gathered through reflective written responses posted on Padlet and semi-structured interviews, enabling examination of both cognitive and affective dimensions of literary engagement. The findings indicate that sustained engagement with young adult fiction addressing controversial topics encouraged students to engage in deeper perspective-taking, mental state attribution, and empathetic reasoning. Students demonstrated an increasing ability to interpret characters’ motivations, emotions, and decisions, suggesting theory-of-mind-related skills. Emotional engagement emerged as an important mediating factor, while intrinsic motivation and guided reflection further supported students’ involvement in the reading process. The findings suggest that young adult fiction with controversial topics can serve as a valuable pedagogical resource in EFL classrooms by supporting linguistic development, empathy, critical reflection, and intercultural understanding. Disclosure Statement The author reported no potential conflicts of interest. Generative AI Statement AI tools were used solely for language editing and proofreading during manuscript preparation, while all research design, data collection, analysis, interpretation, and conclusions remained the responsibility of the author.
