Traditional professional development (PD) often fails to meet teachers’ diverse needs or support effective integration of technology. This mixed-methods study examined the motivations of K–12 teachers for voluntarily engaging in microlearning professional development (mLPD). Guided by Self-Determination and Adult Learning theories, the research examined intrinsic and extrinsic motivational factors through survey data (Work Task Motivation Scale for Teachers) and interviews. Results showed teachers were highly intrinsically motivated, valuing autonomy, competence, and contextual relevance. Qualitative findings highlighted the importance of choice, practicality, and professional identity. While mLPD’s brevity and flexibility supported participation, time remained a key barrier. The study suggests that short, job-embedded, teacher-driven learning aligned with autonomy and relevance can enhance engagement in digital professional learning.
This practice-oriented paper presents three classroom-ready mathematics activities that repurpose simple educational robots – Bee-Bot, Ozobot, and Dot – for older learners and pre-service teachers. Rather than arguing that simple robots are inherently better than more sophisticated tools, the paper shows how instructional design, task structure, and teacher orchestration shape the motivational value of robotics in mathematics lessons. The article is grounded in professional literature on motivation, educational robotics, and mathematics learning, and it interprets the activities through a concise design lens centred on autonomy, competence, relatedness, self-efficacy, and gameful challenge. For each activity, the paper describes the instructional purpose, materials, classroom flow, typical misconceptions, scaffolds, and adaptation options. The contribution is practical rather than empirical: the paper offers transferable design principles and implementation guidance for practitioners who wish to integrate educational robotics into mathematics instruction in ways that are cognitively demanding, motivationally supportive, and feasible in ordinary school settings.
This study examines how national artificial intelligence (AI) strategies articulate educational priorities and readiness across countries with different income classifications. Using an integrated framework combining the Diffusion of Innovation (DOI) and the EdTech Readiness Index (ETRI), the study analyzes quantitative readiness indicators from 2020 to 2024 alongside qualitative policy content from national AI strategies. Findings reveal substantial cross-national variation in AI education readiness. High-income countries tend to adopt more comprehensive and coordinated approaches emphasizing governance, ethics, and advanced skills development, while lower-middle-income and upper-middle-income countries focus on foundational digital capacity, AI literacy, and access. Across income groups, policies prioritize higher education and workforce development over K–12 implementation. Overall, the results show persistent readiness gaps alongside a shared movement toward more targeted guidance for integrating generative AI in education.
Artificial intelligence (AI) is transforming educational practices and prompting renewed reflection on teacher identity. This qualitative study investigates how Vietnamese secondary school teachers perceive AI and reinterpret their professional roles and classroom practices in response. Data from in-depth interviews with 15 teachers reveal that AI is widely viewed as both pervasive and inevitable, yet requiring careful human oversight. Participants emphasized that AI should support rather than replace teachers, reinforcing the importance of professional judgment. Teachers reported a shift from traditional roles as knowledge transmitters toward epistemic mediators who guide students in evaluating and using AI-generated information. Despite this emerging identity transformation, classroom integration remains cautious and uneven due to centralized curriculum constraints, uncertainty about pedagogical use, and limited institutional guidance. The findings suggest that Vietnamese teachers are at an early stage of negotiating AI integration, balancing technological possibilities with established educational norms. The study highlights the need for context-sensitive professional support to facilitate responsible and meaningful AI adoption in centralized educational systems.
Generative artificial intelligence (AI) is transforming reading and writing practices in and out of educational contexts, yet few frameworks exist to support students' responsible engagement with these tools. This conceptual paper proposes a taxonomy of literacy practices for engaging with AI, grounded in the new literacies of online reading comprehension (Leu et al., 2004, 2015); Coiro, 2021). Using Leu et al.'s (2015) five processing practices and Coiro's (2021) multifaceted heuristic as an analytical lens, we identify seven interconnected practices students enact when reading and writing with AI. We conceptualize these not as hierarchical competencies but as socially situated practices enacted differently across contexts and purposes. For each practice, the taxonomy provides a description, an observable indicator, and ethical considerations embedded as intrinsic dimensions. This framework extends established new literacies scholarship into AI-mediated environments, providing educators with language and observable markers for supporting students' evolving literacy practices.
This study probes the transformative potential of integrating developing technological tools—including artificial intelligence (AI), virtual reality (VR), augmented reality (AR), and learning management systems (LMS)—with collaborative reflective practice (CRP) in English as a Foreign Language (EFL) teacher education. Building on butterfly effect, this study explores the process through which small-scale technological innovations, when processed through CRP, catalyze large-scale instructional transformations. Using an explanatory case study design, the study probes the experiences of 15 EFL student teachers and two teacher educators at Iran's Teacher Education University. Data were collected through reflective journals, semi-structured interviews, classroom observations, and artifacts, and analyzed thematically to expose the mechanisms directing these transformations. Findings indicate that immersive technologies including AR and VR make safe, simulated environments for practicing pedagogical strategies, whereas AI-driven tools support personalized learning and feedback. CRP and technological tools can develop critical reflection, collaboration, and continuous professional development, producing innovative teaching practices. However, challenges like resistance to change, technical issues, ethical concerns, and inequities in access to technology were acknowledged as barriers to active application. The study highlights the ripple effect of these novelties, representing how small-scale modifications in classroom management and lesson planning can produce inclusive shifts in organizational policies and learner-centered learning atmospheres. This research underscores the importance of leveraging technological integration with CRPs, emphasizing culturally sensitive approaches and systemic support to guarantee equitable and continuous development.
Artificial intelligence (AI) has transformed the learning process, offering new opportunities and challenges for self-regulated learning (SRL). While autonomy during the SRL process can be satisfied by external support, such as AI and teachers’ guidance, critical thinking is a crucial internal prerequisite skill for SRL with AI. However, the impact of students’ perception of AI on their SRL remains unclear. This study aims to investigate the mediating effects of students’ perception of AI on the relationship between students’ autonomy, critical thinking, and SRL by involving 333 university students. The findings indicate that students’ perceived autonomy and critical thinking have direct effects on their SRL with AI, whereas value-based perceptions of AI, especially ethics and social good, serve as key mediating mechanisms in these relationships. However, the positive attitude toward AI did not serve as a mediator. These findings suggest that SRL with AI requires strong critical thinking and autonomy support, and that value perceptions related to ethics and social good may more effectively engage this process.
Despite the proliferation of asynchronous video instruction across higher education and K–12 contexts, educational technology lacks an integrated, design-oriented framework that explicitly addresses how cinematic composition choices influence the mechanisms through which learning occurs. This conceptual paper introduces Cinematic Instructional Design (CID), a provisional integrative framework that synthesizes insights from multimedia learning, instructor-presence research, multimodal communication, and film and media theory. Rather than treating cinematic form as pedagogy in itself, CID conceptualizes framing, sound design, editing rhythm, narrative structure, and visual signaling as design variables that may shape attention allocation, perceived social presence, affective engagement, and cognitive load, mechanisms that, in turn, may support or impede learning in asynchronous environments. Drawing on four decades of empirical research, CID articulates eight interrelated design dimensions, each linked to hypothesized causal mechanisms and operationalizable indicators. Unlike procedural production rubrics or purely cognitive-load frameworks, CID attends to the visual-compositional and social-affective dimensions of instructional video that existing frameworks address incompletely. The framework is offered as a provisional, design-oriented synthesis rather than a validated theory: its value lies in clarifying a neglected design problem, specifying plausible mechanisms, and generating testable propositions for future design-based research, with particular attention to equity-relevant outcomes. The framework synthesizes research-based design principles drawn from multimedia learning, instructor-presence, multimodal communication, and narrative cognition research into a structured vocabulary that practitioners can apply and researchers can test.
This paper conceptualizes good teaching as a continual process of teachers’ professional growth, grounded in reflection on who they are as educators and how this understanding shapes their technology-enhanced instructional design and practice. Using the tree metaphor, the paper introduces the Tree Model of teacher identity and practice, outlining three major components that represent different dimensions of teachers’ professional growth. The Tree Model provides a reflective lens through which educators can align their values, professional development, and instructional design in an era of rapid technological change. Finally, the paper proposes reflective questions to guide educators in examining the relationship between their identity and technology-enhanced teaching practice.
While peer feedback is crucial for developing professional skills such as teamwork, students often struggle to provide high-quality evaluations of their peers. This study analyzed 5,534 written feedback entries from a year-long engineering capstone course to examine how student feedback aligns with the five teamwork dimensions of the CATME evaluation system. Our results show that most comments focused on "Contributing to Team’s Work," while "Expecting Quality" was rarely addressed. Alignment with the CATME dimensions was generally low, and as teams matured, students shifted their focus toward interpersonal and project management behaviors. Notably, we discovered that students consistently emphasized leadership and creativity, which are not currently captured by CATME’s dimensions. This reveals a significant gap between formal academic rubrics and the specific qualities students value in their peers. These findings suggest that technology-enabled feedback tools must be refined to include these student-perceived dimensions and better support the development of professional teamwork skills.
Generative AI and virtual reality are transforming educational and cultural experiences by enabling interactive, personalized, and immersive environments. This study employed a mixed-method sequential explanatory design to investigate gender-specific differences in user engagement, interaction patterns, and learning outcomes within AI-powered virtual museum environments. Sixty university students (30 males and 30 females) participated, engaging with AI-driven virtual assistants through VR headsets. Quantitative analyses using independent t-tests revealed that while both genders achieved significant knowledge gains, females demonstrated higher task-focused engagement and more fact- and clarification-oriented interactions, whereas males favored exploratory and entertainment-oriented queries. Qualitative thematic analysis further highlighted gender-specific preferences in system design and AI communication styles. The findings underscore the importance of designing adaptive AI systems that account for diverse interaction styles and learning needs. Recommendations include integrating gamification, enhancing AI responsiveness, and tailoring communication approaches to foster inclusive and equitable educational experiences.
This study extends the Unified Theory of Acceptance and Use of Technology (UTAUT) to examine university students' adoption of Large Language Model (LLM) Agents for self-directed learning. Using a mixed-methods approach (306 questionnaires, 9 interviews) incorporating Information Accuracy and Personal Innovativeness, results show that Performance Expectancy and Social Influence jointly exert the strongest direct impact on Behavioral Intention. Crucially, Information Accuracy acts as a cognitive contract influencing usage directly and indirectly. Personal Innovativeness indirectly drives intention by significantly enhancing Performance Expectancy. Conversely, Effort Expectancy is neutralized, showing no significant impact, while Facilitating Conditions and intention jointly explain over half the variance in actual Usage Behavior. These findings rigorously refine the UTAUT framework for the agentic AI era, underscoring that functional utility, factual trustworthiness, and robust institutional support, rather than mere usability, are the true catalysts for integrating LLM Agents into autonomous learning ecosystems.
This study builds on a previous pilot study to further investigate the relationship between students’ satisfaction and the four types of interactions in asynchronous courses: learner-learner, learner-content, learner-instructor, and learner-interface. It also examines how students prioritize these interactions. Data were collected from 378 students via an online survey. The data were analyzed using correlation analyses and multiple regression models in SPSS. The results revealed that learner-content interaction was the strongest predictor of satisfaction, emphasizing the importance of providing high-quality and well-structured course materials. Learner-instructor interaction also significantly influenced satisfaction, highlighting the need for timely feedback, clear guidance, and an active instructor presence. Additionally, user-friendly technological platforms and opportunities for peer interaction were found to support positive learning experiences by fostering engaging and collaborative learning environments. These findings suggest that improving the design of instructional materials, strengthening instructor presence, and adopting accessible learning technologies are important considerations in the development and implementation of asynchronous online courses.
FIRST LEGO League (FLL) has long functioned as one of the world’s most recognizable educational robotics ecosystems, connecting classrooms, competitions, and STEM learning communities across more than 100 countries. Recent announcements regarding the redesign of FLL around interactive gameplay, wireless technologies, and AI-oriented learning systems, followed by the dissolution of the nearly 30-year partnership between FIRST and LEGO Education, signal a major transition in the global educational robotics landscape. This article examines how these developments reflect broader technological, pedagogical, and institutional shifts occurring during the AI era. The analysis explores the movement from deterministic robotics toward more interactive and coordination-oriented learning environments, the introduction of semi-cooperative gameplay structures, and emerging tensions surrounding accessibility, infrastructure, interoperability, and governance. The article further considers how the fragmentation of a long-standing educational robotics ecosystem may create both uncertainty and new opportunities for international educators and regional programs navigating increasingly divergent technological pathways.
While generative Artificial Intelligence (GAI) has shown considerable potential in improving writing quality, little is known about both how and to what extent students cognitively engage with GAI during writing tasks, for example, whether they critically evaluate or simply accept its suggestions. This mixed-methods study examined 41 undergraduate students’ interactions with ChatGPT-4o during academic writing revision using an adapted Critical Interaction with GAI for Writing framework. Thematic analysis of 102 prompts identified four engagement dimensions: content revision, information seeking, writing presentation, and conversational engagement, each coded for three levels of engagement (deep, moderate, or shallow). While most students engaged at shallow to moderate levels, a few students demonstrated deeper critical thinking engagement in the dialogic and evaluative use of GAI feedback. Additionally, results from linear mixed-effects models showed that content-focused engagement significantly facilitated the improvement in the idea presentation of academic writing. Findings from this study call for future pedagogical effort that promotes students’ critical thinking engagement when using GAI in academic writing.
In this continuation of a series, we provide an analysis of scholarship trends over the last five years in the field of educational technology, drawing from the Scopus and ERIC databases. We report an analysis of the keywords reported for articles during this timeframe, including trends about which keywords are rising or falling in importance. We also report the top-cited papers, scholars, and institutions. Finally, we share findings about the impact of open access on the field. We interpret these results, including the seismic impact that generative artificial intelligence has had on the scholarship of the field, and conclude with recommendations for future research.
Incorporating student-generated questions (SGQ) into teacher education powerfully promotes preservice teachers’ (PSTs) critical thinking, agency, and instructional design skills. Despite these benefits, SGQ remains underutilized in many educational contexts. Fortunately, structured interventions for SGQ that integrate scaffolding frameworks such as Bloom’s Taxonomy can significantly enhance students’ ability to think reflectively and critically. This study investigates how PSTs develop critical thinking through SGQ using Ask.Smile across three semesters (n ≈ 90; 8,576 questions). Data analyses included descriptive statistics, linear mixed-effects modeling, correlation analyses, and structural topic modeling. Results show that PSTs’ question levels improved with repeated attempts, supporting the iterative nature of critical thinking and the value of AI feedback. Topic modeling revealed that when topical constraints were absent, students often posed personally oriented or real-world questions, while later semesters showed more academic focus under more instructional guidance. Findings highlight how instructional design shapes both the quality and nature of inquiry, suggesting that balancing autonomy with scaffolding can foster creativity. Moreover, weak correlations between question levels and assignment grades highlight the complementary role of reflective writing in capturing critical thinking. Overall, this study demonstrates SGQ’s potential, particularly when paired with AI tools, to enhance PSTs’ inquiry and critical thinking.
The study explores the use of Generative Artificial Intelligence (GenAI) for visualisation to foster students’ critical thinking and reading comprehension in second language (L2) reading. Using a task based approach in which students needed to read a text, identify the key ideas, and make prompts asking GenAI to create images to reflect the main contents of the text, the study then asked learners to reflect on the task and images created by AI. There were 32 undergraduate students whose English level was from B2 to C2 (CEFR) participating in the research. Applying surveys to collect students’ opinions, the study analysed their ideas using qualitative content analysis with the application of Excel, adopting phenomenology as the philosophical stance. The findings indicate that approximately one third of the students perceived GenAI-generated images as lacking accuracy and offering limited variation despite different prompts. They also noted that the images tended to appear idealised, even when the source text conveyed negative realities. In contrast, about one fifth of the participants found the images helpful in making the text more concrete and easier to understand, while a few acknowledged that they expanded imaginative possibilities. However, expecting GenAI to replicate human mental imagery may generate extraneous cognitive load. These findings suggest that GenAI should be used not as a tool for exact reproduction of human imagination, but as a means to extend knowledge, foster critical engagement with both text and image, and encourage evaluative feedback that supports human-AI co-creation of meaningful outcomes.
This transcendental phenomenological study explored how four first-year teachers experienced the transition from preservice to in-service teaching while implementing technology-enabled learning (TEL). Drawing on Ajzen's Theory of Planned Behavior (TPB) as a sensitizing framework, data were collected through interviews, observations, and teaching artifacts during participants' student teaching and first year of teaching. Findings revealed that while participants' thinking about TEL shifted from idealistic to realistic as responsibilities increased, their commitment to TEL for equitable, student-centered instruction remained steadfast. Despite facing barriers including infrastructure challenges and unsupportive administrators, participants leveraged high self-efficacy, self-regulation strategies, and peer support through an informal community of practice to sustain their TEL practices. The essence of their experience highlighted how affective factors, particularly self-efficacy and equity-focused motivation, outweighed external barriers. This study addresses gaps in understanding novice teachers' TEL use and suggests teacher preparation programs should intentionally cultivate communities of practice and TEL self-efficacy to support successful transitions into teaching.
