Robert Otto Davis

Educational video podcasts are increasingly used in higher education, yet the cognitive mechanisms underlying their effectiveness remain underexplored. This experimental study examined how working memory capacity and additive contextual features influence situational interest, cognitive load, and learning from educational video podcasts. Sixty-two university students viewed video podcasts on lightning with standard narration with visuals (control), elaborative details, or seductive details. Working memory did not significantly predict learning outcomes or moderate the effects of additive features, and no differences were observed across conditions for cognitive load or situational interest. In free recall, the control condition outperformed the seductive details condition (p = .004, g = 1.03), though this difference was not significant when controlling for working memory. Transfer did not differ across conditions. Follow-up interviews revealed that students across conditions emphasized visuals as critical for comprehension, while those in additive conditions reported feeling overwhelmed despite similar cognitive load ratings. These findings suggest that well-designed multimedia environments may stabilize learning outcomes across differences in working memory capacity, extending cognitive theory of multimedia learning. Designers of educational video podcasts should prioritize narration aligned with supportive visuals over additive contextual features.

This study examined how cultural background and self-reported gender influence student–faculty email communication in English-Medium Instruction (EMI) settings. Advanced international language learners (N = 113) wrote emails in English to either Korean or international professors without prior instruction. The emails were analyzed for framing elements and request strategies using holistic assessment. The findings revealed significant patterns in formality and strategy use based on professor nationality and student gender. Emails to Korean professors exhibited higher formality levels, especially among students with better framing appropriateness scores. Cultural differences emerged in request strategies: international students favored performative requests, while Korean students preferred disarmers. Self-reported gender also correlated with different framing strategies, particularly when communicating with Korean professors. These findings highlight the complex interaction among culture, gender, and pragmatic awareness in EMI academic correspondence. The study underscores the importance of understanding cross-cultural communication patterns in diverse educational environments and suggests the need for further research into multilingual communication practices in higher education to better support international student populations.

This study examined how 59 English as a foreign language (EFL) students utilize generative artificial intelligence (AI) tools in English writing and their perceptions of AI-assisted instruction. Using pre- and post-intervention surveys, the quantitative research measured writing strategies across three dimensions and AI experiences. Results showed significant improvements in idea generation (η² = .08) and content refinement (η² = .09) strategies, with students frequently using translation applications (mean = 4.07) and AI tools (mean = 3.94) in conjunction with traditional resources. However, perceptions of AI tools remained stable (p = .124), indicating consistently positive attitudes. Findings suggest that AI is an effective supplementary tool for EFL writing, particularly for idea generation and content refinement, while students maintained a critical awareness of AI limitations and preferred AI as a complement to, rather than a replacement for traditional instruction. The study supports the balanced integration of AI in EFL curricula, promoting the reflective and ethical use of AI.

The split-attention principle suggests that separating onscreen information sources can overburden working memory and impede learning. While research has traditionally focused on the separation of images and text, relatively little is known about the impact of multiple simultaneous visual inputs. This study examined the split-attention principle in a multimedia environment featuring a pedagogical agent performing gestures, with hologram-like images either integrated centrally with the agent or spatially separated. A within-subjects design (N = 80) investigated the impact on satisfaction, cognitive load, and cued recall. The quantitative findings revealed no significant differences between the two spatial conditions. Preliminary qualitative insights from a limited sample of six individual interviews suggested that some participants may employ strategies to simplify complex designs and manage perceived cognitive load. Based on these limited qualitative observations, this research tentatively proposes the “pruning principle”, a metacognitive strategy where learners actively “prune” extraneous information to optimize cognitive resources. These findings underscore the importance of considering individual differences and metacognitive strategies in multimedia design.

Artificial intelligence (AI) has emerged as a critical subject in global educational contexts, not only within computing majors but also across all academic disciplines. This shift mirrors the rise of digital literacy in the late 20th century, positioning AI literacy as a needed skill for future generations. Despite its importance, there is ongoing debate about what exactly AI literacy entails and the skills it requires. While previous research has explored how computational thinking and varying educational levels affect AI literacy, there is a gap in related research on the impact of coding experience and the age at which students first learn about AI, especially for university students in non-computer-based majors. This exploratory study revealed that South Korean university students with prior coding experience consistently demonstrated significantly greater AI literacy than did those without prior coding experience. However, the age when students first learn about AI does not seem to play a major role in their overall perception of AI. These results suggest that the idea that coding experience might not be necessary for AI literacy needs additional investigation. However, further research is needed to fully understand the factors that contribute to AI literacy in non-computer-based university majors.

This research investigated the effects of generative AI on affective factors (motivation, interest, and confidence) of English as a foreign language (EFL) learners enrolled in Korean university-level general English courses. During the Spring 2024 semester, this study involved 89 participants exposed to a generative AI-based instruction model. Compared to traditional methodologies, these results highlight the potential effectiveness of generative AI-based English instruction for writing and speaking in supporting linguistic proficiency and positive affective attitudes toward language learning among EFL learners.

Background: Gestures are an integral component in human‐to‐human communication when the speaker is visually present to the listener. In the past several years, research has examined how computer‐generated pedagogical agents can be designed to perform the four main gesture types and what this means for agent persona and learning outcomes. The research into agent gesturing has only explored gestures without other presentation strategies such as visual aids or verbal redundancy to properly explore the impact of gestures, and to avoid overly “rich” displays of information. Objective: The objective of this study is to explore the use of static images and varying frequencies of gestures to assess whether two visual inputs increases the risk of the split‐attention effect, and to investigate the potential for visual redundancy when two visual inputs coincide with narration. Data on cognitive load, agent persona, and learning outcomes (recall and transfer) will be collected to measure participants' learning experience while acquiring procedural knowledge, specifically regarding the principles of lightning, in comparison to previous research. Method: A mixed methods approach consisted of three gesture frequency conditions (enhanced, average, no) with 118 participants. Quantitative data were analysed using a random‐effect linear regression model; whereas qualitative data was collected through individual interviews that lasted 15–20 min. Results and Conclusions: The use of enhanced gesture frequency and images may significantly increase intrinsic cognitive load, but gestures and images do not cause extraneous cognitive load. The enhanced gesture condition significantly outperformed the no‐gesture condition. Interviews indicated that depending upon the gesture condition, students selectively attended to information that they perceived as offering them the greatest learning opportunity. Using two visual inputs does not cause split‐attention, nor does it provide evidence of a visual redundancy effect.

This research explores the perceptions of Korean university students regarding artificial intelligence (AI)-based writing tools that include tools guided by machine learning, such as Google Translate and Naver Papago, and generative AI tools, such as Grammarly. A mixed methodology was used, including both quantitative and qualitative data. Among students who have taken English writing courses, 80 Korean university students volunteered for the online survey. After the survey, the research team recruited interview participants, and five volunteered participants joined the focus group interview. The study results indicate that these AI-based writing tools could improve English language learners (ELLs) writing skills. ELLs also noted the strengths and weaknesses of each AI-based tool, including the accessibility of translation machine learning and the error-checking capabilities of generative AI. However, interview data analysis indicates that the excessive use of AI-based writing tools could interfere with ELLs’ English writing process. This study highlights the need to effectively integrate AI-based tools in English language teaching for adult ELLs worldwide.

Previous studies have mostly focused on Artificial Intelligence (AI) education from students’ perspectives. However, it is necessary to understand teachers’ perceptions of AI convergence education because they are essential personnel for K-12 education. To fill the gap in the literature, this study aims to understand in-service teachers’ perceptions regarding AI education for teaching in schools and their AI teacher training programs. Data collection included an online survey (n = 20) and a focus group interview (n = 4). The survey investigated teachers’ perceptions of AI education for teaching within the educational setting, while the focus-group interview explored teachers’ perceptions of AI teacher training programs. Survey results showed that teachers hold favorable attitudes toward AI education for teaching and future use. The interview findings reveal the advantages and disadvantages of AI teacher training programs. Although most participating teachers expressed satisfaction, there was a notable call for a more practice-oriented curriculum. Specifically, the respondents found that many theory-centric courses failed to offer applicable skills or knowledge for real-world classroom settings. The results from this study have novelty and uniqueness in that understanding teachers’ perspectives of AI convergence education for K-12 is essential for teacher education programs. Thus, this study can provide valuable insights and suggestions to structure teacher professional development more effectively in AI convergence education, particularly within diverse higher educational contexts.