The potential of Augmented Reality (AR) for enhancing learning has long been discussed, however, existing research primarily relies on media comparisons that provide little basis for robust theoretical development. The Cognitive Theory of Multimedia Learning (CTML) proposes design principles for learning materials to support cognitive processes leading to learning, which have been shown to be effective in different media, though have very rarely been investigated in AR.
This study aims to investigate the effectiveness of two important learning principles, that is signalling and modality, on learning outcomes in AR, while examining the potential moderating impact that cognitive-affective factors caused by the technology may have.
Eighty adults participated in this pre-registered 2 × 2 experimental study. To test the signalling and modality principles, the presence of virtual signals (absent/present) and the content modality (text/audio) was manipulated between-subjects during an AR learning session. Declarative and procedural knowledge were measured after the learning session and additionally, learners' motivation and the system usability were included as moderators in the analysis to account for AR-specific influences.
The findings revealed no significant main or interaction effects of the signalling and modality principles on either declarative or procedural knowledge, and motivation and system usability did not moderate these relationships. This indicates that the expected benefits of these design principles may not translate effectively to AR contexts, underscores the complexity of educational AR, and highlights the need for further research to better align instructional design with the unique affordances of immersive technologies.
The rise of artificial intelligence (AI) has reshaped English as Foreign Language (EFL) instruction, offering new opportunities for language learning. While much of the existing research focuses on AI integration in classroom settings, limited research has explored EFL learners' autonomous engagement with AI beyond the classroom.
This study aims to address this gap by examining the relationships between EFL students' engagement in AI-mediated informal digital learning of English (AI-IDLE), growth mindset, foreign language enjoyment (FLE), and adaptability, while also exploring the factors that influence long-term AI-IDLE engagement.
Employing an explanatory sequential mixed-methods design, this study surveyed 357 Chinese EFL students and conducted 20 post-survey interviews. Quantitative data were analysed using structural equation modelling with SPSS 26.0 and AMOS 26.0, while qualitative data were analysed thematically with MAXQDA 24.
Quantitative results indicated that growth mindset has a significant direct effect on AI-IDLE engagement, with adaptability serving as a robust mediator. However, FLE did not significantly mediate the relationship. Qualitative results highlighted the complex nature of long-term AI-IDLE engagement, shaped by both external factors and individual student characteristics.
In the Information Age, prospective teachers increasingly rely on online sources for research and lesson preparation. This entails dynamic, situation-specific interactions within digital environments, which are increasingly shaped by artificial intelligence.
This study demonstrates how prospective teachers' information reception and interpretation in online environments can be made accessible through the complementary qualitative methods of narratology and reconstruction.
Through the exemplary analysis of two contrasting case studies, the study highlights mostly hidden yet salient textual and visual elements of a complex digital environment. It identifies the narrative framings, stylistic devices, meanings, and normative assumptions present in the online sources used by prospective teachers, as reflected in their written essays on the health benefits of e-bikes.
The qualitative analysis indicates whether, and in what ways, the reception of Internet information by prospective educators is influenced by specific features of the sources, such as narrative framings and normative assumptions. The study employs a novel methodological combination of narrative analysis and objective hermeneutics and outlines potential directions for further research on digital learning in higher education.
Computational thinking (CT) is an essential 21st-century competency, which has already been embedded into K-12 education globally and is regarded as a fundamental competency that applies to multiple domains. It is important to adopt non-programming-based instruments to assess CT in various domains, but few studies systematically examine non-programming-based CT assessments.
This study aims to conduct a scoping review of non-programming-based CT assessments to provide guidance for future research on CT assessments. The summary of non-programming-based CT assessments could reveal their state and gaps, and provide insights for choosing, implementing, or developing appropriate CT assessments.
Following a scoping review approach, this study examined 39 empirical studies published on journal articles between 2006 and 2022. The landscapes, audiences, contexts, and measures of non-programming-based CT assessments are summarised and discussed.
Non-programming-based CT assessments have covered all K-12 grade levels except for kindergarten, with most studies conducted in China and the United States of America. However, research is limited in Africa, Western Asia, South America, and Oceania. Most reviewed studies were conducted in formal education settings using online platforms and were integrated into various subjects—primarily STEM-related. While many assessments were conducted together with interventions, most of these were programming-related, and corresponding assessments were often misaligned. Problem-test method and self-reported scales were the most frequently used measures. Moreover, single-assessment tools were more commonly adopted than multi-method assessment tools. Future research could focus on informal education settings, leverage online assessments, implement CT assessments in non-STEM subjects, and develop effective non-programming-based CT interventions and aligned assessments.
The modality and redundancy principles are well-established within the cognitive theory of multimedia learning for their effectiveness in traditional media. However, previous studies applying these principles in virtual reality have shown contradictory results when immersive technologies are involved.
This study investigates the effects of modality and redundancy principles from the cognitive theory of multimedia learning on cognitive load and learning outcomes in head-mounted display augmented reality.
Using a between-subjects experimental design, 104 male participants were randomly assigned to three conditions based on the way verbal instructions related to a T-shirt folding procedure were presented (audio-only, text-only, text-and-audio). The presentation of the verbal instructions served as the manipulated variable; cognitive load levels and participants' performance were measured as outcome variables. Bayesian analyses were conducted to verify that there were no significant differences between the text-and-audio group and the audio-only group, and a Partial Least Squares Structural Equation Model (PLS-SEM) examined the relationship between the experimental condition, cognitive load levels, and participants' performance.
Results support the validity of the modality principle in head-mounted display augmented reality, showing that applying the modality principle significantly decreases intrinsic cognitive load and improves learning performances. The null hypothesis was supported by comparing audio-only and text-and-audio conditions for both cognitive load and learning outcomes. These findings suggest that cognitive theory of multimedia learning principles work similarly in head-mounted display augmented reality as in traditional media, and they offer valuable insights into multimedia learning in immersive augmented reality, extending our understanding of how these principles function in immersive technologies. Contradicting results using immersive technologies could be explained by considering task complexity and perceived intrinsic cognitive load; however, further studies testing different learning materials are necessary to support this hypothesis.
Virtual reality (VR) offers immersive and authentic learning opportunities that can transform lecture delivery and student engagement. Despite growing adoption in education, little is known about how learners' cultural identity, self-efficacy, and perceived authenticity shape their sustained use of VR learning environments.
This study examined how cultural identity, self-efficacy, and perceived authenticity predict Chinese university students' continued use of VR, with prior VR experience tested as a potential moderator.
A correlational survey was conducted with 833 students using validated instruments on cultural identity, VR self-efficacy, authenticity gap, and VR usage. Data were analysed through structural equation modelling.
The findings reveal that cultural identity (β = 0.32), self-efficacy (β = 0.52), and perceived authenticity (β = 0.41) positively influence continued VR use. Prior VR experience strengthened these relationships. Beyond theoretical contributions extending expectation-confirmation theory (ECT) with cultural and psychological dimensions, these results offer clear design and pedagogical implications. Developers should embed culturally responsive content and realistic environments, while educators can strengthen self-efficacy through scaffolded VR learning and feedback.
Despite growing attention to English education in Bangladesh, conventional classroom instruction often fails to meet learners' diverse needs or ensure effective language development. Informal digital learning of English (IDLE) has emerged as a promising avenue for fostering learners' affective experience and enhancing proficiency beyond formal classroom settings. Nevertheless, empirical evidence on how Bangladeshi learners participate in and experience self-directed language learning remains scarce, underscoring the need for further investigation.
Drawing on the broaden-and-build theory, this study examines how foreign language enjoyment (FLE) and foreign language anxiety (FLA) shape learners' IDLE and (AI-mediated) IDLE (AI-IDLE) behaviours and learning confidence.
An explanatory sequential mixed-methods design was adopted, integrating quantitative data from 1045 university students with qualitative insights from 26 semi-structured interviews. Structural equation modelling (SEM) was employed to test the hypothesized relationships among variables, followed by thematic analysis to enrich the interpretation.
Quantitative findings revealed that FLE positively predicted IDLE behaviour and learning confidence, whereas FLA, traditionally viewed as debilitating, also exerted a facilitating effect by directly promoting participation in IDLE and AI-IDLE activities and indirectly enhancing learning confidence. IDLE significantly predicted both AI-IDLE and learning confidence, while FLE did not directly predict AI-IDLE. Qualitative findings corroborated these results and further highlighted additional factors (e.g., instrumental motivation and cognitive ability) that contribute to the complexity of the emotion-(AI) IDLE-confidence link. This study provides pedagogical implications for optimising learners' technology-mediated informal learning in the Bangladeshi context.
English as a Foreign Language (EFL) learners often struggle to develop robust academic writing, especially in online settings with limited feedback and interaction. Existing AI writing tools show promise but are often tested in brief, isolated interventions, so little is known about which AI-supported strategies are most effective.
In a four-arm randomised controlled trial (N = 383), three AI-enhanced nudges (goal-setting, feedback, social norm) were compared with an active control across 18 × 90-min sessions (total dose = 27 h). Posttest group differences were large (Welch's F (3, 188.454) = 1447.280, p < 0.001, η2 = 0.895), with the goal-setting nudge yielding the highest writing performance (M = 20.24/25, 81.0% of maximum).
University EFL students were randomly assigned to three AI-enhanced nudge conditions (goal-setting, feedback, social norm) or an active control in a pretest–posttest design. Interventions drew on social cognitive theory (SCT) and nonlinear dynamic language learning theory (NDLLT) and were delivered over 18 × 90-min sessions. Writing performance, motivation and self-efficacy were assessed with validated instruments, complemented by interviews and reflective essays.
Goal-setting nudges produced the largest gains in writing performance, motivation and self-efficacy, with feedback and social norm nudges also outperforming the active control. Effect sizes were large: goal-setting versus control ΔM = 11.01, 95% CI [10.51, 11.52] for performance; ΔM = 3.50, 95% CI [3.16, 3.83] for motivation; ΔM = 3.22, 95% CI [2.95, 3.49] for self-efficacy. Students described AI-enhanced goal-setting as making tasks more manageable and progress more visible, while AI feedback and peer comparisons supported revision and engagement. Overall, the findings suggest that carefully designed, instructor-mediated AI nudges can substantially enhance EFL academic writing when used to supplement, rather than replace, human teaching. Centring on three research questions, RQ1 tested differential effects of the three nudges versus control on writing performance, motivation and self-efficacy; RQ2 examined learners' perceived effectiveness and experiences across modalities; RQ3 explored the correspondence between quantitative performance gains and qualitative learner insights.
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