The integration of artificial intelligence (AI) into open educational resource (OER) development presents new opportunities for enhancing educational experiences. However, the factors shaping pre-service teachers’ adoption of AI-assisted OER development still unexplored. This study examines the key determinants of pre-service teachers’ (psts) behavioral intentions to develop and use AI-assisted OER, drawing from Uses and Gratifications Theory and related constructs. Data were collected from 240 psts. Participants received 10 weeks training on AI-supported OER development. Data analysis was conducted performing structural equation modeling and bootstrapping. The findings reveal that compatibility, enjoyment, task efficiency, and information seeking significantly influence both trust and behavioral intention. However, trust did not. Social factors yielded mixed results. While social influence positively affected behavioral intention, it did not significantly impact compatibility or trust. Similarly, social interaction fostered trust but did not directly influence behavioral intention. While peer and instructor opinions encourage adoption, they do not necessarily enhance AI’s trustworthiness or suitability for educational tasks. The study contributes to the theoretical understanding of AI acceptance in education by refining existing adoption models with social and cognitive factors. Findings suggest that training programs should emphasize AI’s alignment with teaching tasks and focus on enhancing user enjoyment to drive adoption.

The research process is undergoing a paradigm shift toward openness, revolutionizing the way research outputs are shared, accessed, and utilized. This shift promotes transparency and accelerates scientific discovery and development, as appropriate dissemination of research enhances global knowledge exchange. This is so because scientific knowledge is the foundation upon which societies build progress, yet access to this knowledge remains a privilege rather than a right. Specifically, there is a concerning trend of building walls around scientific knowledge, impacting higher education teaching and learning. This conceptual paper argues that limiting access to research creates educational inequities, stifles innovation, and undermines the potential of open science to democratize knowledge. This goes against the principles of open science, which advocate free and open sharing of scientific knowledge to maximize its impact. The paper presents the implications of restricting access to scientific knowledge in higher education teaching and learning (including online and distance learning, MOOCs, OERs, etc) and advocates for open science practices, including a proposed “open science pedagogy model” grounded in the concept of openness and transparency to better enhance teaching and learning in higher education.

The rapid expansion of online education highlights the importance of high-quality teacher feedback in enhancing student motivation and academic performance. However, there remains a lack of effective models and tools for systematically evaluating the quality of online teacher feedback. This study develops a comprehensive evaluation model based on Environment-Based Design principles, a systematic literature review, and expert consultation. The model identifies three essential dimensions of feedback quality: cognitive support, emotional care, and relational connection. A validated scale for measuring these dimensions is introduced, offering a robust tool for future educational research. The study further examines the influence of teacher characteristics, revealing that only teaching experience significantly affects feedback quality, whereas gender, age, and educational background have no significant effects. By presenting a theoretically grounded and empirically validated model, this research advances the understanding and assessment of online teacher feedback and offers implications for improving online teaching practices.

This study investigated the relationship between online course interactivity and perceptions of instructional quality, engagement, and learning experiences among undergraduate students, and whether these relationships differ by college generation status. Guided by Moore’s interaction framework and the Community of Inquiry model (Garrison et al.) a latent profile analysis was conducted on survey data from 577 students enrolled in various asynchronous online courses. Four distinct instructional profiles emerged, each characterized by varying degrees of student-student, student-content, and student-instructor interactions. Students in highly interactive environments, with a balanced integration of course activities that promote cognitive, social, and teaching presence, reported significantly higher instructional quality, engagement, and perceived learning outcomes. These relationships did not differ by college generation status. Our findings underscore the importance of diverse, interaction-rich instructional activities in promoting positive online learning experiences and offer practical implications for online course design and instructional development.

Online collaborative learning (OCL) is widely recognized for its potential to enhance students’ collaboration skills, problem-solving abilities, and critical thinking, yet learners often struggle to sustain deep cognitive engagement without adequate support. With the emergence of generative AI (GAI), new opportunities have arisen to scaffold collaboration, but little is known about how learner characteristics such as prior knowledge shape cognitive engagement in GAI-supported OCL contexts. This study investigated the frequency and co-occurrence patterns of cognitive engagement across groups with distinct prior knowledge distributions (i.e., high vs. low mean; high vs. low variation) in a GAI-enhanced OCL environment. Thirty-nine students from a key Chinese university participated in a GAI-supported collaborative writing task. Findings revealed that High–High groups demonstrated more knowledge exchange, High–Low groups engaged in deeper knowledge co-construction, Low–High groups prioritized planning and strategy formulation, while Low–Low groups focused more on process monitoring. These insights guide instructors in providing personalized cognitive support or designing tailored GAI agents to enhance cognitive engagement and learning outcomes.

The integration of contemporary digital technologies and education has called for a review of the form and function of the assessment of learning achievement. Non-disposable assignments have become an effective form of assessment in distance education due to their openness, inclusiveness, and participation, as well as their emphasis on process and continuity for the cultivation of metacognitive skills. To be able to clarify the bidirectional relationship between metacognition and non-disposable assignments, this study constructed a non-disposable assignment teaching model to promote the development of college students’ metacognition. It adopts a design-based research methodology and conducts three rounds of non-disposable assignment teaching practice and model adjustment and modification. With the help of precise and visualized cognitive network analysis tools to provide timely feedback on students’ metacognitive development level in the effect evaluation, an effective and complete set of non-disposable homework teaching models for promoting college students’ metacognitive development was finally obtained.

Student disengagement during online video-based learning, known as “in-video dropout,” negatively impacts learning outcomes. Understanding the factors contributing to dropout is critical for improving video design and engagement. This study employs a mixed-methods approach to investigate dropout behavior among secondary students, analyzing data from 14,251 users on an online math platform. Survival analysis revealed that higher-achieving students and those actively interacting with videos are less likely to drop out. Conversely, longer lecture videos, slower tutor speech, and videos followed by a quiz are associated with higher dropout rates. A qualitative analysis of video content further identified three potential triggers for dropouts: pauses, visual transitions, and occlusions. These findings provide actionable insights for reducing dropout rates through strategic video design, such as optimizing video length, tutor delivery, and visual clarity. The study contributes to the growing body of research on enhancing instructional video effectiveness in online learning environments.

Online education is increasingly popular and frequently being used in higher education. However, the quality of online interpersonal interaction (QOII) remains one of its main concerns. While the need for implementing high-quality online interpersonal interaction is broadly acknowledged, there is no consensus on how to define, operationalize, or improve it. Therefore, we explored the higher education literature for definitions, operationalizations, and interventions regarding QOII. Our literature search resulted in 1408 studies, out of which 80 were included. Text selections were extracted and analyzed descriptively and qualitatively. We found three main clusters of definitions, several operationalizations, and five main clusters of interventions. Our study revealed that QOII can be defined as the degree to which participants perceive that they are building meaningful relationships and knowledge together. To improve QOII, students and teachers should be increasingly provided with guidelines and skills training. More research is needed on the effectiveness of such interventions.

The aim of this study is to examine university students’ self-efficacy, enjoyment, and experience in using Metaverse in their learning. To achieve the goal of the study, the Technology Acceptance Model (TAM) was used to investigate the effect of the variables (self-efficacy, enjoyment, and experience) in the use of Metaverse. This study followed a quantitative methodology, and the data was collected from 314 students at the University of Ha’il. The results of the study revealed that self-efficacy significantly influences Perceived Usefulness, Perceived Ease of Use, and Perceived Experience. Enjoyment influences Perceived Usefulness, Perceived Ease of Use, and Perceived Experience. However, Perceived Usefulness and Perceived Experience do not significantly influence Behavioral Intention to use Metaverse. Recommendations, practical implications, and future directions were discussed.

Guided by the framework of students’ internal mechanisms of feedback processing, this convergent mixed methods research aimed to understand the differences between working adult learners’ perceptions and adoption of instructor and peer feedback in online project-based learning. We distributed an online survey containing a 6-point Likert scale, yes/no question, and open-ended questions to collect quantitative and qualitative data simultaneously. The quantitative results showed student perceptions and adoption of instructor feedback were significantly higher than that of peer feedback. The qualitative findings indicated five themes: 1) perceived easiness to improve the readability of projects, 2) understandable suggestions for project improvement, 3) supporting reflective practices, 4) confidence building in project development, and 5) promoting generative practices to strengthen projects. Data merging further provided insights into distinct differences in their perceptions and utilization of feedback, including instructor feedback was primarily valued for fostering a growth mindset to support project enhancement, while peer feedback as a complementary tool contributed to project development. We concluded by discussing the importance of cultivating a growth mindset culture for continuous improvement and implications for feedback practices and interventions tailored to the needs of working adult learners in online higher education.