Alongside the rapid and broad uptake of online learning in higher education, fully online students report feeling isolated and disconnected from their institutions. Although formal course content may be expertly designed to engage online learners, much of the information provided to support higher education students’ orientation to the institution and to study is presented online in a written static form. Such presentations may not be accessible and engaging and may contribute to feelings of disconnection. Technologies such as virtual reality (VR) are being used in higher education to engage, motivate and connect students in their learning. This paper reports on the early design stages for a VR that aims to support initial teacher education students to connect and engage with key orienting information. The design of the VR was achieved by following a user-centred, iterative engineering design process and design principles of spatiality, interaction and narrative. The VR environment emulates the School of Education’s physical, on-campus reception area to provide an immersive experience where students have a choice in the types and format of key study information they receive. This experience was designed to be utilised in online orientation but also throughout students’ first year of study. Future research directions include collecting student responses to the VR to inform how students can be involved in enhancing the VR so that it supports their learning and sense of connection. Furthermore, future research can aim for the expansion of the VR inclusive of additional information, rooms and buildings and increased capabilities such as gamification and mobile access. This will enable the creation of a valuable teaching resource for online programs.

Despite increasing interest in using Generative Artificial Intelligence (GenAI) in education, little is known about how students with disability engage with GenAI to support their own learning. This study investigates the potential of ChatGPT to support the learning agency of adolescents with disability in a secondary science classroom in Australia. Guided by sociocultural and socio‐material conceptualisations of agency, the study explored the mediated choices and capabilities of three students with disability to use ChatGPT to facilitate their learning. The study was conducted in a class comprising students of varying ages clinically diagnosed with diverse learning needs. Data sources included student interviews, the students' conversations with ChatGPT, teachers' lesson worksheets and video recordings of the lesson. Thematic analyses reveal that while students expressed clear and meaningful choices to use ChatGPT to support their learning, they faced metacognitive challenges and cognitive constraints, resulting in a misalignment between their choices and actual capability. The findings identify key theoretical perspectives and practical considerations for supporting students with disability in using GenAI to develop their learning agency. The study recommends customising GenAI for specific learning needs in line with its function as a cognitive prosthesis for students with disability and for better alignment with Universal Design for Learning, thereby supporting students' learning agency. Practitioner notes: What is already known about this topic The development of students' learning agency has been widely explored in the secondary context, but not for students with disability. There is increasing interest in using Generative AI (GenAI) to support inclusive education. Current theoretical frameworks can inform the exploration of the learning agency of students with disability using GenAI. What this paper adds Foregrounds the learning choices of students with disability as indicators of their emergent learning agency. Identifies learning challenges facing students with disability that misalign their learning choices and capabilities. Examines GenAI's potential as a socio‐material mediator facilitating the material and relational agency of students with disability. Implications for practice and/or policy Consulting students with disability on how GenAI can support their learning, providing opportunities for them to express their learning preferences. Customising GenAI tools in line with the Universal Design for Learning guidelines to address the specific learning challenges of students with disability. Clarifying the roles of teachers and education assistants in facilitating the learning agency of students with disability in light of customising GenAI to mediate their learning agency.

Science teacher educators have a complex role in preparing future science teachers, giving them the content and pedagogical and technological knowledge. This research examines using a gamified laboratory simulation tool (Labster), where access was given to a cohort of science pre-service teachers (pst s) in the third year of their Initial Teacher Education for a semester. A mixed-method approach was adopted to generate data for this case study. Schulman’s pedagogical content knowledge provided the theoretical background, whilst Makransky and Petersen’s (2019) framework was deployed to identify factors in implementing Labster, including usability, motivation, and perceived use. The data determined that despite the ongoing engagement and interest in games and gamification in the wider community, the perceived engagement and interest were not reflected in the responses of the undergraduate science pst s in the study. pst s reported that they did not feel that it improved their content knowledge.

Lesson planning is a core professional practice for pre-service teachers, yet opportunities for timely, individualized feedback are frequently constrained by educator workload. While generative AI has the potential to enhance planning processes and expand opportunities for individualized feedback, the provision of comprehensive lesson plans may lead to excessive reliance. This conceptual design paper details the development and theoretical underpinnings of an artificial intelligence-assisted feedback tool that provides self-efficacy-strengthening feedback on lesson plans for pre-service teachers. To promote constructive feedback, the AI-assisted feedback tool integrates principles from educational feedback research and structures feedback to foster teachers’ lesson-planning self-efficacy through mastery-oriented affirmations, vicarious examples, social persuasions, and emotional reassurance. Curriculum alignment is incorporated to support content validity and contextual appropriateness. While the initial implementation of the feedback tool focuses on Western Australian teacher education, an explicit transfer perspective is considered for the German vocational education context. The paper describes the iterative development process that follows a design-based research approach including platform evaluation, internal refinement, and expert review by teacher educators in Western Australia. The resulting system prompt architecture comprises 11 dimensions including general baselines, the interaction between the Lesson Planning Coach and PSTs and the theoretical foundations mentioned above. The tools’ environment, including examples for provided feedback on lesson plans, is presented and discussed. Finally, an outlook is given on the planned empirical research to evaluate the effectiveness of the tool.

Educational attitude research is theoretically diverse but methodologically fragmented, with most studies relying on single-mechanism models that limit how variation and stability in attitudes are interpreted. This paper proposes a unified interpretive framework that integrates four established accounts of attitudinal processes: Self-Efficacy, Expectancy–Value, Planned Behaviour, and Habitual Action. The framework organises these theories into four interpretive vectors: belief activation, value construction, normative alignment, and patterned enactment, situated within a layered structure that links antecedent conditions to attitudinal expressions.The framework’s methodological contribution lies in providing a structured basis for construct alignment, scale development, and interpretive analysis across quantitative, qualitative, and mixed-methods designs. Three illustrative vignettes drawn from a multi-phase STEM attitudes study demonstrate how the four vectors clarify demographic, contextual, and developmental patterns that single-theory approaches cannot fully explain.The framework is adaptable across domains and research designs, offering a systematic way to analyse attitudinal data by identifying the mechanisms through which beliefs, values, norms, and habitual processes interact. It supports more coherent interpretation, reduces reliance on single-theory attribution, and offers researchers a practical scaffold for investigating why attitudes shift, stabilise, or persist across educational contexts.

Artificial intelligence (AI)-powered chatbots promised to streamline administrative tasks and offer just-in-time support within higher education institutions. However, many existing chatbots prioritised simple information delivery over the cultivation of deeper student engagement and intrinsic motivation. This paper argued that Self-Determination Theory (SDT), a prominent framework for understanding motivation, offered a robust model for designing chatbots that went beyond passive answering to become active facilitators of student agency. SDT emphasised three core psychological needs: autonomy, competence, and relatedness. This paper explored how the intentional integration of these needs into chatbot design could transform administrative support interactions into opportunities to empower students as self-directed learners.