The 2025 3rd International Conference on Innovation Management, Psychology, Education and Sociology (IMPES 2025) was held in Sapporo, Japan during December 27-28, 2025. The main objectives of the conference are to promote research and development activities in innovation management, social psychology, educational technology, cultural studies, social policy, and sociology. Another goal is to promote the exchange of scientific information among researchers, developers, students, and practitioners around the world. IMPES 2025 can be a scientific forum to accommodate fruitful discussion among international researchers in various fields. We would like to thank all speakers, invited guests, and also other parties and donors for their continuous support to the success of this event. Hopefully, this event provides many benefits for us all. Conference Organizing Chair Sapporo, Japan

The COVID-19 pandemic catalyzed a dramatic reshaping of the educational technology landscape, creating an urgent need to analyze the field’s new trajectories and inform future directions. This study addresses that need by uniquely combining bibliometric analysis with machine learning forecasting to map and predict research trends using a comprehensive dataset of 9,630 articles from 20 high-impact journals (2020–2024). Our analysis reveals three significant findings: (1) research is heavily concentrated on scalable technologies like MOOCs and AI, while critical gaps persist in equity, open educational resources, and micro-learning; (2) methodological sophistication is increasing, with a rise in mixed-methods and meta-analytic approaches; and (3) nascent interdisciplinary connections with fields like healthcare offer promising new research frontiers. Forecasting models predict exponential growth in AI and computational thinking research through 2030, while pandemic-specific topics are expected to decline. These results provide a strategic roadmap for researchers, policymakers, and practitioners to prioritize underexplored areas, foster impactful interdisciplinary collaborations, and critically address the ethical implications of emerging technologies to build a more equitable and effective educational future.

This study presents a technically grounded design and implementation of an AI- and AR-enabled immersive learning environment for outdoor education. Moving beyond conceptual descriptions, the study develops an executable system framework that integrates adaptive navigation and positioning, context-aware virtual tours, task-driven scenario simulation, and real-time feedback mechanisms. Each functional module is explicitly linked to algorithmic implementations, including multi-sensor state estimation, constrained generative scene construction, and reinforcement-based adaptive control, enabling reproducible system behavior in real outdoor settings. A controlled field experiment was conducted using an experimental group and a control group under identical instructional conditions. Quantitative evaluation based on pre–post testing, behavioral logging, and statistical analysis demonstrates that the proposed system achieves statistically significant improvements in learning interest, participation, knowledge mastery, and problem-solving ability. Experimental conditions, data characteristics, and methodological limitations are explicitly reported to support result verification and generalizability. The findings indicate that the proposed immersive learning environment constitutes a validated system-level contribution rather than a purely conceptual framework, offering practical and scientific value for computer science–oriented educational technology research.

Background/Objectives: This systematic review examines neuroplasticity-informed approaches to learning under cognitive load, synthesizing evidence from functional imaging, brain stimulation, and educational technology research. As digital learning environments increasingly challenge learners with complex cognitive demands, understanding how neuroplasticity principles can inform adaptive educational design becomes critical. This review examines how neural mechanisms underlying learning under cognitive load can inform the development of evidence-based educational technologies that optimize neuroplastic potential while mitigating cognitive overload. Methods: Following PRISMA guidelines, we synthesized 94 empirical studies published between 2005 and 2025 across PubMed, Scopus, Web of Science, and PsycINFO. Studies were selected based on rigorous inclusion criteria that emphasized functional neuroimaging (fMRI, EEG), non-invasive brain stimulation (tDCS, TMS), and educational technology applications, which examined learning outcomes under varying cognitive load conditions. Priority was given to research with translational implications for adaptive learning systems and personalized educational interventions. Results: Functional imaging studies reveal an inverted-U relationship between cognitive load and neuroplasticity, with a moderate challenge in optimizing prefrontal-parietal network activation and learning-related neural adaptations. Brain stimulation research demonstrates that tDCS and TMS can enhance neuroplastic responses under cognitive load, particularly benefiting learners with lower baseline abilities. Educational technology applications demonstrate that neuroplasticity-informed adaptive systems, which incorporate real-time cognitive load monitoring and dynamic difficulty adjustment, significantly enhance learning outcomes compared to traditional approaches. Individual differences in cognitive capacity, neurodiversity, and baseline brain states substantially moderate these effects, necessitating the development of personalized intervention strategies. Conclusions: Neuroplasticity-informed learning approaches offer a robust framework for educational technology design that respects cognitive load limitations while maximizing adaptive neural changes. Integration of functional imaging insights, brain stimulation protocols, and adaptive algorithms enables the development of inclusive educational technologies that support diverse learners under cognitive stress. Future research should focus on scalable implementations of real-time neuroplasticity monitoring in authentic educational settings, as well as on developing ethical frameworks for deploying neurotechnology-enhanced learning systems across diverse populations.

As the Australasian Journal of Educational Technology’s (AJET) 40th anniversary year draws to a close, we look back over the year to consider what we have learnt from our journey through the past 40 years of AJET articles. We also explore the bibliometric data for 2025 where we see that AJET has experienced the highest number of submissions in its 40-year history. Impact is an important goal for AJET and this year we have seen an increase in our metrics across the various ranking systems that show that AJET is a leading venue for educational technology research and practice as a Q1 journal. We end with a look towards the future for what is to come in 2026 and beyond. Overall, this year has taught us the value of learning from our history, keeping learning at the centre of our research and practice, and the value of community in fostering innovation and quality. We look forward to the next 40 years of AJET’s journey with great interest and enthusiasm as we take inspiration from the past and continue in our quest to provide access to quality educational technology research and practice into the future.

This study aims to implement Augmented Reality (AR)–based learning media for introducing ecosystem concepts at SMP Negeri 4 Bajo and to analyze its effect on students’ conceptual understanding and learning motivation in science learning. This research employed a sequential mixed methods design, integrating qualitative and quantitative approaches in successive phases. The initial qualitative stage involved interviews and classroom observations to identify students’ prior understanding of ecosystem concepts and the need for digital learning media. The findings informed the design and development of an AR application incorporating image marker and audio marker features. The quantitative phase evaluated the effectiveness of the AR media through classroom implementation and learning outcome analysis. The results indicate that the use of AR significantly improved students’ understanding of ecosystem concepts, particularly food chains, food webs, and interactions among living organisms. Students showed increased enthusiasm, critical thinking, and active participation during learning activities. Teachers reported that AR media enhanced the interactivity and contextual relevance of science instruction. The findings provide empirical evidence that AR-based learning media can effectively support science instruction at the junior high school level. Teachers are encouraged to integrate AR into lesson planning to enhance student engagement and conceptual understanding, supported by appropriate training and technological infrastructure. This study contributes to educational technology research by demonstrating the effectiveness of a sequential mixed methods approach in developing and evaluating AR-based learning media for ecosystem learning.

IntroductionThe rapid adoption of artificial intelligence (AI) in higher education has increased college students’ reliance on AI tools. While AI enhances learning efficiency, it may also undermine key cognitive processes required for innovation.MethodsUsing survey data from 1,032 students, this study employed partial least squares structural equation modeling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA) to examine how AI dependence, cognitive inertia, employment pressure, and academic utilitarian atmosphere shape students’ innovation capability.ResultsAI dependence significantly increases cognitive inertia, with cognitive dependence (β = 0.570, p < 0.001) exerting a stronger effect than tool dependence (β = 0.161, p < 0.001). Cognitive inertia reduces innovation capability (β = −0.111, p < 0.001) and serves as a key mediator linking AI dependence to innovation. Employment pressure strengthens the positive effect of AI dependence on cognitive inertia (β = 0.045, p < 0.05). A stronger academic utilitarian atmosphere further amplifies the negative impact of cognitive inertia on innovation capability (β = 0.052, p < 0.05). The fsQCA results reveal multiple pathways to high innovation capability, with low cognitive inertia emerging as a core condition across all effective configurations.DiscussionThis study clarifies the cognitive mechanisms and contextual conditions through which AI dependence affects innovation. The findings extend research in educational technology and innovation psychology and offer practical guidance for universities to optimize learning environments, promote rational AI use, ease employment pressure, and improve academic culture.

Purpose This qualitative research study explores the design and development of innovative three-dimensional didactic materials for teaching pictorial appreciation, following Universal Design for Learning (UDL), as a conduit for effective knowledge transfer into society. Inclusive education and soft skills are two highly topical topics applicable to the aesthetic-artistic education of painting, including students with visual impairment or other difficulties. Design/methodology/approach The qualitative study examined the effectiveness of 3D models as educational resources in representing two-dimensional paintings. Eleven experts physically interacted with 3D-printed model, while listening to narrated explanations of the scene. Their feedback was collected through semi-structured interviews that identified 101 descriptors across 13 categories. Findings The experts concluded that the size, texture and spatial distribution of the models were appropriate for their intended purpose, while also being enjoyable to use. Their recommendations included adding colour, incorporating more figures and expanding audio description to provide details about the artist and contextual information. Research limitations/implications This research represents only one phase of a broader framework that encompasses the transfer of learning material designs. Practical implications The UDL approach might enhance accessibility in both educational and museum environments. Social implications This study sits within a broader development framework exploring educational technology when designing teaching resources to improve inclusion in classroom and society-based learning experiences. Originality/value This research contributes new criteria for the research of UDL and educational technology when designing inclusive didactic materials and solutions. It demonstrates how knowledge can be researched and materials can be prepared to transfer into society.

Abstract Background Generative artificial intelligence (AI) large‐language models (LLMs) have significant potential as research tools. However, the broader implications of using these tools are still emerging. Few studies have explored using LLMs to generate data for qualitative engineering education research. Purpose/Hypothesis We explore the following questions: (i) What are the affordances and limitations of using LLMs to generate qualitative data in engineering education, and (ii) in what ways might these data reproduce and reinforce dominant cultural narratives in engineering education, including narratives of high stress? Design/Methods We analyzed similarities and differences between LLM‐generated conversational data (ChatGPT) and qualitative interviews with engineering faculty and undergraduate engineering students from multiple institutions. We identified patterns, affordances, limitations, and underlying biases in generated data. Results LLM‐generated content contained similar responses to interview content. Varying the prompt persona (e.g., demographic information) increased the response variety. When prompted for ways to decrease stress in engineering education, LLM responses more readily described opportunities for structural change, while participants' responses more often described personal changes. LLM data more frequently stereotyped a response than participants did, meaning that LLM responses lacked the nuance and variation that naturally occurs in interviews. Conclusions LLMs may be a useful tool in brainstorming, for example, during protocol development and refinement. However, the bias present in the data indicates that care must be taken when engaging with LLMs to generate data. Specially trained LLMs that are based only on data from engineering education hold promise for future research.

ABSTRACT This study investigates the impact of inclusive and accessible learning technologies on student engagement and academic performance in higher education. Grounded in Universal Design for Learning (UDL) and Technology-Enhanced Learning (TEL) frameworks, the research explores how assistive technologies, AI-driven systems, and intelligent tutoring platforms contribute to equitable and effective learning environments. Employing a mixed-methods design integrating bibliometric and content analyses, the study systematically examines 56 peer-reviewed articles from the Web of Science database. Quantitative indicators, including co-authorship networks, keyword co-occurrences, and citation patterns, were combined with qualitative thematic analysis to identify research trends and trajectories. Results reveal a steady global increase in inclusive educational technology research, with notable emphasis on AI-based personalization, assistive tools, and intelligent tutoring systems. Findings indicate significant regional variations: institutions in technologically advanced contexts demonstrate higher adoption rates, while socio-economic disparities and limited faculty training impede equitable access elsewhere. The study concludes that inclusive learning technologies significantly enhance engagement and performance when supported by robust institutional strategies, professional development, and equity-focused policies. These findings advance the discourse on inclusive education by providing an integrated empirical overview and identifying pathways for future research and policy development.

This study analyzes global research trends on Technology-Enhanced Learning (TEL) from 2020 to 2024 using a bibliometric mapping approach. Data were extracted from the Scopus database with the query TITLE-ABS-KEY (“technology-education” AND “learning”), yielding 747 valid documents consisting of journal articles, conference papers, and book chapters. The analysis employed Microsoft Excel, VOSviewer, and Biblioshiny to examine publication performance, collaboration networks, and thematic evolution. Results indicate a steady growth of research output, reflecting an increasing global commitment to educational transformation driven by digital innovation and artificial intelligence. The United States, China, and India emerged as the most productive countries, while leading sources such as Computers &amp; Education and Educational Technology Research and Development dominated the publication landscape. Keyword co-occurrence mapping revealed seven thematic clusters, with e-learning, artificial intelligence, digital pedagogy, and STEM education as the most prominent themes. The findings highlight a paradigm shift from emergency remote learning during the pandemic to a sustainable integration of technology into pedagogical design. This study provides a foundational map for researchers and policymakers to navigate the evolving intellectual structure of TEL and highlights underexplored areas such as ethical AI implementation in Global South contexts and the limited empirical evidence on teacher readiness for advanced learning analytics.

Purpose This study investigates public sentiment and discourse on YouTube regarding the integration of generative artificial intelligence in education, using the technology acceptance model framework. The research explores themes such as democratized access, personalized learning and ethical implications, offering insights into the perceived role and societal impact of GenAI in educational settings. Design/methodology/approach Leximancer’s Topic Guide is employed to analyse user-generated comments on YouTube, identifying key themes and evolving sentiments over time. This approach enabled a detailed examination of GenAI’s perceived usefulness (PU) and perceived ease of use within educational contexts. Findings The analysis reveals strong appreciation for GenAI’s potential to democratize education and personalize learning experiences, with minimal concerns over usability. However, as discourse evolved, ethical concerns around privacy, job displacement and reduced human interaction emerged, reflecting complex societal considerations. Originality/value This study offers a novel exploration of GenAI’s role in education by capturing unfiltered public perceptions from a broad audience beyond traditional educational stakeholders. Unlike most studies, which focus on educators, administrators and policymakers, this research uses YouTube comments to include perspectives from students, parents and the general public. By analysing these diverse voices through TAM, we reveal a nuanced understanding of GenAI’s perceived impact in real-world educational contexts. This approach broadens the scope of educational technology research, highlighting GenAI’s potential and challenges as experienced by the wider public.

This study addresses a critical gap in educational technology research by simultaneously examining the internal and external determinants of Artificial Intelligence (AI) integration in primary mathematics instruction. Using a second-order Structural Equation Modeling (SEM) framework, the study investigates how teachers’ attitudes and TPACK competencies (internal factors), alongside policy support, infrastructure, and community engagement (external factors), influence AI utilization among 516 primary school mathematics teachers in Jakarta, Indonesia. The results reveal that internal factors have a strong direct effect on AI utilization (β = 0.791; p &lt; 0.001), while external factors exert a significant indirect influence via internal mediators (β = 0.217; p &lt; 0.001), despite an insignificant direct effect (β = 0.008; p = 0.908). The model explains 78.1% of the variance in AI utilization (R² = 0.781) and shows high predictive relevance (Q² &gt; 0.70). These findings underscore the pivotal role of teacher readiness in AI integration, with systemic support enhancing its effectiveness through internal capacity-building. The study contributes an empirically validated instrument and a comprehensive ecological model, offering actionable insights for policymakers and educators in developing nations pursuing ethical, equitable, and sustainable AI integration in primary education.

This paper offers novel methodological insights into a meta-autoethnography of metadiscourse and implications for interdisciplinary qualitative research on generative artificial intelligence (AI) models. Two authors represent divergent subjectivities in science, technology, engineering, arts, and mathematics (STEAM), business, management, and economics (BME), and higher education research and development (HERD). Thus, the authors attempt to design the meta-autoethnography of metadiscourse on the use of generative AI models and demonstrate the specific research design procedures. The two authors articulated the research thesis, addressed research gaps and methodological issues of relevance and applicability, and employed hermeneutic data collection and analysis strategies. They discussed key concerns that may arise from recontextualization and the trustworthiness needed by qualitative researchers. This paper provides methodological implications by suggesting meta-auto ethnographic writings and potential themes within the meta discourse. Each of the co-authors revealed their relevant issues as critical pedagogues, academic editors, business entrepreneur trainers, and developed scholarly discourses on potential human capital crises in STEAM academia and the information and communication technology (ICT) industry, as well as pragmatist research and business ethics, and grounded rules. Therefore, this paper concludes by addressing the paradoxical use of Generative AI models, highlighting methodological insights and implications for diverse audiences in interdisciplinary qualitative research.

ABSTRACT This study combines Scherer's Component Process Model (CPM) with a generative AI tool to analyse emotional appraisals of 80 engineering students in a team-based graduate-level subject focused on innovation, professional skills and professional identity transformation. Despite emotions being central to learning, engineering education research has limited theoretical frameworks for analysing students' emotions reported in reflective tasks. We employed a novel combination of manual and AI-assisted thematic coding to analyse students' reflective writings using CPM. The methodology proved effective for coding emotional components. It successfully produced patterns of appraisals within reflections, and differences of appraisals between international/domestic and male/female reflections, consistent with theoretical expectations. The methodology demonstrated feasibility and utility, whilst revealing important limitations and affordances. This research provides a foundation for combining theoretical frameworks and AI-assisted analytic methods to understand students' emotional reflections. It offers directions for future empirical research based on the observed strengths and limitations of our approach.

This narrative review critically examines the methodological landscape of educational technology and ICT integration research through the lens of evolving educational paradigms. Utilising Scopus AI's analytical capabilities, this paper examines how shifts from behaviourist to constructivist to connectivist paradigms have fundamentally reshaped research methodologies in the field. The review identifies methodological strengths, persistent weaknesses, and critical gaps in current EdTech research, proposing a paradigm-conscious methodological framework that aligns research approaches with underlying philosophical assumptions about learning, technology, and educational management. Drawing on sources spanning 2010-2025, the analysis documents the emergence of design-based research, learning analytics, mixed methods, and AI-enhanced approaches while identifying concerning patterns including theoretical thinness, paradigm-method incoherence, quality limitations, and the systematic overestimation of technology effects. The review contributes a comprehensive mapping of paradigm-methodology relationships, a comparative analysis of contemporary methodological approaches, quality criteria across research traditions, and practical guidelines for paradigm-coherent research practice. Implications for researchers, doctoral training, and the field's methodological development are discussed, with an emphasis on achieving "principled diversity" - the embrace of multiple approaches united by shared commitments to rigour, transparency, and the improvement of technology-enhanced learning.

This study presents a comprehensive bibliometric analysis of Computers &amp; Education, a leading academic journal in the field of educational technology, covering the period from 1978 to 2025. Drawing upon data from the Web of Science database, a total of 4,715 articles were examined using bibliometric techniques, including co-citation, co-authorship, keyword co-occurrence, and thematic evolution analysis. The findings reveal a significant increase in publication volume and citation rates, particularly after 2008, indicating a growing scholarly interest in digital education. Thematic clustering analyses identified three dominant research foci: student performance and achievement, technology-enhanced learning and motivation, and user acceptance and self-efficacy. Furthermore, collaboration networks show that Taiwan, China, the USA, and the UK are central hubs in the global educational technology research landscape. Thematic evolution analysis reveals a paradigm shift from early discussions on "computer and distance education" toward more recent emphases on "digital content," "virtual learning," and "student-centered design." This study contributes to the literature by mapping structural patterns and identifying key trends, prolific authors, institutions, and countries, offering a macro-level understanding of how Computers &amp; Education has shaped the educational technology discourse over nearly five decades.

This study presents an analysis of Science, Technology, Engineering, and Mathematics (STEM) education research in the Philippines, drawing from 251 documents published between 1959 and 2025 retrieved from the Scopus database. Using data from the Scopus database and visualization tools such as VOSviewer, the study explores publication trends, intellectual structures, thematic patterns, and the evolution of research priorities over time. Results reveal a marked increase in scholarly output post-2010, peaking in 2024, coinciding with national education reforms, the COVID-19 pandemic, and advances in digital learning. The citation analysis shows a parallel rise in research impact, indicating increasing academic engagement and visibility. Co-citation mapping identifies two core intellectual traditions: empirical, data-driven methodologies centered on assessment and benchmarking, and constructivist learning theories grounded in pedagogical foundations. Keyword co-occurrence analysis yielded five thematic clusters: engineering education and development, student learning in digital environments, pedagogy and teacher preparation, emerging technologies in STEM, and instructional design systems. The overlay visualization underscores a clear thematic evolution from foundational teaching and curriculum concerns toward digital transformation, AI integration, and learner-centered innovation. The study further highlights the underrepresentation of regional State Universities and Colleges (SUCs), underscoring the need for more inclusive and decentralized research development. The findings suggest that STEM education research in the Philippines is transitioning into a mature, future-ready domain, increasingly responsive to both global trends and localized educational needs. Keywords: bibliometric analysis, co-citation analysis, digital learning, educational policy, STEM education

The integration of artificial intelligence (AI) in higher education presents complex challenges for preservice teachers facing significant academic pressures. This study examines how academic workload affects AI adoption among preservice teachers in Peru and investigates the mediating roles of work stress and performance expectations in this relationship. AI models in this context encompass generative tools for content creation, automated assessment systems, and personalized learning platforms commonly utilized in educational settings. A cross-sectional study was conducted with 876 preservice teachers from 12 Peruvian universities. Data were collected through online questionnaires measuring workload, work stress, performance expectations, and AI usage patterns. Partial least squares structural equation modeling (PLS-SEM) was employed to analyze the hypothesized relationships. The findings confirmed that workload has a significant direct positive effect on AI model usage. Work stress significantly mediated the relationship between workload and AI usage. Additionally, work stress and performance expectations operate as serial mediators between workload and AI adoption, demonstrating how psychological mechanisms connect academic pressures to technological behaviors. This study provides novel insights into AI adoption among Peruvian preservice teachers, an underexplored population in educational technology research. The findings reveal the multifaceted relationship between academic workload and AI integration in teacher preparation. Educational institutions should implement stress management interventions including mindfulness training and time management workshops, while establishing realistic performance expectations through hands-on AI literacy programs that address both technical competencies and ethical considerations in technology adoption.

This research investigates the landscape of STEM education research in Africa using advanced bibliometric techniques. Using data from Scopus, we identified 1390 articles meeting the initial search criteria, refining the dataset to 449 articles after applying filters for article type, language and subject areas. The study employs various bibliometric methods, including performance evaluation, co-citation analysis, bibliographic coupling and scientific mapping, to identify key themes and conduct analyses. Through this extensive analysis, the research aims to uncover trends, collaboration networks and thematic patterns within the selected articles. The findings offer important information about the state of STEM education research in Africa, aiding evidence-based decision-making for policymakers, educators and researchers. The results present 187 scholarly articles from 1964 to 2024, having a 3.73% annual growth rate, an 11 years average document age, 11.84 average citations, 1276 author’s keywords, 925 authors and approximately 23% international collaborations. Overall, this dataset provides an important understanding of the evolution, trends and collaborative dynamics of Science, Technology, Engineering and Mathematics (STEM) education research in Africa. Notably, this study contributes to the broader objective of Sustainable Development Goal 4 (SDG 4) by elucidating the role of STEM education in fostering inclusive and equitable quality education for all.