The integration of Artificial Intelligence (AI) in education has underscored the urgent need to equip educators with essential AI literacy and related competencies. This paper highlights the critical importance of advancing research on the development of teachers' AI literacy, particularly through targeted professional development programs. To address this need, the study piloted a training program involving 19 mid-career teachers in Singapore. Over the course of six weeks, participants engaged in an intensive 18-hour program designed to enhance their ability to integrate AI into educational practices, with a strong emphasis on ethical considerations. Mixed methods were employed. Data collection included pre-and post- intelligent Technological Pedagogical Content Knowledge (TPACK) surveys, teachers’ perceptions on AI, in-class group discussions, and post-individual written assignments. Data analyses included content analysis and quantitative data analysis. The results showed a significant enhancement in i-TPACK, accompanied by a shift in their overall perceptions of AI. The teachers not only acquired a good understanding of ethical frameworks but also demonstrated adept application in envisioning innovative AI in teaching, schools, and assessment. They formulated tailored action plans for implementing AI in their respective schools. Furthermore, the study employed a novel analytical matrix based on the Aristotelian tripartite division of knowledge—episteme, techne, and phronesis—to compare action plans between teachers with higher and lower perceived i-TPACK levels, focusing on AI’s application in teaching, schools, and assessment. This study contributes valuable insights into teacher professional development concerning AI in education and informs the implementation of AI in teaching practices.

This study examines the effects of integrating adaptive learning (AL) and expectancy-value theory of motivation (EVTM) interventions on second language (L2) learners' writing skills, error tolerance (ET), and feedback tolerance (FT). By investigating integration of EVTM instruction within AL environments, the study addresses the need to harness the transformative potential of adaptivity and EVTM in language learning, contributing to effective pedagogical approaches that promote students' writing proficiency and resilience. A total of 120 intermediate-level English proficiency students were randomly assigned to two experimental groups (AL-application use and AL-application use enriched with EVTM instruction) and one control group (technology-enhanced non-AL application use). The study employed writing tasks, ET, FT, EVTM questionnaires, and semi-structured interviews as data collection instruments. Findings indicate that the AL application significantly improves participants' writing outcomes, ET, and FT. Moreover, integration of EVTM interventions within AL environments enhances not only writing skills, ET, and FT but also EVTM. Furthermore, qualitative results suggest a positive impact of AL and EVTM interventions on students' learning processes. Results highlighted that integration of AL and EVTM instruction enhances students' motivation, beliefs, and metacognitive awareness, provides personalized instruction and immediate feedback, and creates a comprehensive and supportive learning environment.

Video-based training has proven useful for motor learning, particularly when combined with motion feedback. However, the integration of real-time feedback into instructional videos has not been sufficiently explored. This study aimed to develop and explore innovative real-time feedback methods to enhance video-based motor learning. Twenty-seven participants (15 women, 12 men) were assigned to three feedback groups and one control group, who learned a choreography in an initial pilot study. The feedback groups received real-time comparisons of their own motions with those of an instructor. Group A was provided with a proportionally adjusted virtual instructor skeleton superimposed on their movements. Group B’s motions were transparently overlaid on the instructor's video. Group C viewed the instructor’s demonstration alongside a mirror view displayed of themselves. Group D (control) trained using only the instructor’s video, mimicking home-based tutorial formats. Motion tests performed without feedback revealed adaptation across all groups. Temporal motion adaptation was highest in Group A, while spatial motion adaptation was highest in Group B. Findings suggest that motion superimposition is a promising approach for visualizing motion discrepancies. Each method exhibited unique characteristics in the learning process, including different learning curves (e.g., Group A showing adaptation in the second half of the training) and varying levels of adaptation across different exercises and body parts (e.g., Group B experienced arm motion adaptation in squats). While these novel real-time feedback techniques demonstrate potential, further research is required to examine the relationships between feedback modalities and motor learning outcomes, specifically regarding the visualization of motion comparisons.

Digital educational games (DEGs) have been utilized for some time across various learning domains. In light of the ongoing debate concerning their impact on skills and knowledge, particularly among very young learners, a study was undertaken to assess their effectiveness compared to conventional educational materials. The study involved 81 kindergarten students and followed a between-subjects design. This research focused on basic spatial and mathematical skills, specifically directional concepts (such as "up-down," "left-right," "in front-behind," and "above-below"), as well as students' abilities to organize and compare numbers and quantities up to ten. Evaluation tests and a questionnaire were employed to gather data on students' knowledge acquisition, motivation, enjoyment, and ease of use. The findings indicated that, compared to conventional materials, DEGs significantly enhanced the skills that were examined. Although DEGs were rated higher in terms of enjoyment and motivation, they were perceived as less easy to use. Furthermore, enjoyment emerged as the sole factor having a significant influence on learning outcomes with DEGs. The study calls for further exploration to optimize the usability and efficacy of DEGs and may assist educators in integrating them into their daily practice.

In experiential learning involving real-world activities, such as fieldwork and pre-service training, transferring knowledge into practice is essential. While reflection is a critical component of this process, it is challenging to review how previously learned knowledge has been utilized. To address this issue, this study connected student descriptions of real-world activities with relevant course contents using information retrieval techniques and large language models (LLMs). The validity of linking was evaluated for one approach without LLM and three approaches that employ LLMs differently. These approaches were applied to a dataset collected from a university course in Japan. There were conditions for the inclusion or exclusion of supplemental information. The results indicated the supremacy of LLM-featured approaches without supplemental information. However, we found that these performances have not yet been stable. The findings and discussions shed light on the potential of the LLM-featured retrieval approaches for data-enhanced reflection across in-class knowledge acquisition and real-world knowledge applications.

Personalized learning (PL) initiatives represent a powerful instructional strategy that prioritizes a learner-centered approach, allowing educators to tailor content to meet individual students’ characteristics and needs. Various technologies have been developed to support PL, the integration of Learning Management Systems (LMS) has emerged as a particularly effective to deliver adaptive materials and strategies in classroom settings. This study presents a systematic literature review on the application of LMS in facilitating PL, guided by PRISMA protocols to ensure rigorous screening and inclusion of relevant studies. Out of an initial 1,069 publications from 2014 to 2024, a total of 61 studies met the inclusion criteria. Findings highlight promising opportunities to enhance standard LMS features with data-driven tools that support personalized learning. Additionally, this study highlights the need for further research into learner attributes extending knowledge levels and learning styles. It also encourages exploring learning outcomes that transcend cognitive achievements.

This qualitative study investigates the opportunities and concerns regarding the integration of artificial intelligence (AI) in higher education. Through in-depth interviews with students, faculty, parents, administrators, policymakers, and employers, the research explores the complex landscape of AI adoption in colleges and universities. Thematic analysis reveals shared anxieties about equity and inclusion, the preservation of human interaction in teaching, potential job displacement, the ethical implications of AI, technical capabilities and resource requirements, the impact on educational quality and student outcomes, and the alignment of AI education with workforce demands. The findings underscore the need for a collaborative and transparent approach to AI integration that addresses stakeholder concerns and prioritizes ethical considerations, pedagogical effectiveness, and societal values.

Synchronous online learning (SOL) is becoming a common learning modality among students in higher education. However, concerns remain about student loneliness, stress, anxiety, and social isolation arising from reduced face-to-face interaction. Students’ language learning often depends on teacher-student interaction, an important element of language acquisition. While studies examine interaction types and their frequencies, how these occur in SOL needs more focus. This exploratory study explored various interaction patterns between a university teacher and students in an online English class delivered through Microsoft Teams. Interaction transcript data were extracted from fourteen SOL sessions and analyzed using Content and Thematic Analyses. The findings reveal five interaction patterns: Moving along, Coaxing, Degrading, Demanding, and Polling. Data were further analyzed for prevalence and frequencies. Moving along was the most prominent pattern observed in the data. In this pattern, the teacher tends to progress the learning activities after observing students performing satisfactorily on a given task. Coaxing was the second frequently observed pattern. It entails the teacher encouraging interaction among students when they sense students are delaying their response to particular activities, stimulating in-depth discussion. Degrading and Demanding were the least common patterns to students’ unsatisfactory responses. Polling interaction patterns occurred fairly often when students were given time and space to respond to the teacher’s query, intended to improve engagement. The study provides a generic and practical view of interaction patterns in SOL and implications for teaching and learning in SOL environments.

There is increasing demand to shift from intuition- and experience-based practices to evidence-based education. However, extracting meaningful evidence from real-world educational data poses significant challenges. Traditional approaches to evidence generation, such as randomized controlled trials and systematic reviews, face limitations in both the medical and educational domains due to high costs and ethical constraints. In response, the concept of real-world evidence has emerged as a promising alternative, particularly in medicine and, more recently, in education. Although this approach may be less robust than traditional methods, it offers the potential to uncover broad and practical insights from naturally occurring data. This study explores the use of deep learning for causal discovery in real-world educational data. Specifically, we apply Structural Agnostic Modeling, a method previously validated in biological datasets, to identify underlying causal relationships. In Study 1, we compare this data-driven approach to a traditional hypothesis-driven method. The results demonstrate that this technique can generate both interpretable and novel causal hypotheses, although it occasionally produces plausible relationships in the reverse direction. To address this limitation, we propose an enhanced model, SAM+, in Study 2. Our findings indicate that SAM+ effectively mitigates the identified shortcomings. This research contributes a new methodology for leveraging large-scale educational data and opens new possibilities for advancing evidence-based education.

In the present-day digital landscape, the internet serves as an essential medium for learning and acquiring information. As reliance on internet-based reading increases, it is necessary to address and resolve the difficulties encountered by the readers in this medium. Keeping this objective in mind, this study investigates the challenges faced by students and the affective responses they evoke while reading on the websites for educational purposes. Three focus group discussions were conducted with six participants each to explore the students’ perceptions and experiences while reading through websites. Four themes emerged related to the challenges of website reading such as: (i) distractions and interruptions, (ii) information overload, (iii) difficulty in using active reading strategies like highlighting and annotating, and (iv) difficulties while using hyperlinks. The results further show that these challenges evoked various unfavorable affective responses in the readers depending on the challenge they faced. The study concludes that these challenges and the ensuing emotional responses are likely to detract from the seamless reading experience and diminish the enjoyment derived from learning. Understanding such challenges faced by students while reading through websites is important for educators in making necessary interventions to enhance the effectiveness of online reading and learning.