Kenneth Yang Teck Lim

Understanding advanced pre-tertiary mathematics, particularly three-dimensional vectors, demands robust spatial reasoning skills that many students find challenging to develop through traditional pedagogical methods. This study proposes and evaluates an innovative educational tool that leverages large vision models to automate the conversion of handwritten vector equations into accurate 3D graphical representations. By interpreting students’ handwritten input using advanced computer vision, the system provides immediate, interactive visual feedback to bridge the cognitive gap between abstract symbolic notation and tangible geometric concepts. We evaluated the system using a dataset of 1000 handwritten vector equations typical of the Singapore-Cambridge GCE ‘A’ Level H2 Mathematics syllabus. Our findings demonstrate that while GPT-4o serves as a capable baseline, achieving 84.6% accuracy with multi-shot prompting, newer variants such as GPT-4.1-mini offer superior performance, reaching 91.4% accuracy with significantly higher computational efficiency. The results confirm that AI-powered visualisation tools can effectively interpret complex spatial mathematical layouts when guided by optimal prompt engineering. Implementing such technology in educational settings presents a viable, scalable, and cost-effective method to democratise learning support, fostering independent study and enhancing students’ conceptual comprehension of spatial mathematics.

Background: this paper reports an investigation into the cognitive and emotional states of adolescents while learning from an LLM. It seeks to address a relative dearth in empirical evidence which might otherwise facilitate informed decisions being made by curriculum designers, school leaders and policy makers regarding the use of Generative AI, amidst the wider discourse about the effectiveness of AI in teaching and learning. Methods: in this paper, we analyze electrodermal activity (EDA) in the context of students’ scholastic engagement using LLMs in comparison to curated texts. In our 27-min-long experiment, we recorded the EDA of participants learning from both learning methods, for 8 min each. A quiz was also conducted to assess the effectiveness of the learning method. We collected 23 samples of EDA from the experiment, and 42 samples of quiz results. Results: we have found that learning with an LLM results in greater Skin Conductance Response (p = 0.09404), which is linked to more positive emotional valence, and lower Skin Conductance Level (p = 0.09473), which is linked to lower cognitive load, compared to curated texts. We also discovered that learning with an LLM correlates to a higher quiz result (p = 0.02053). While this suggests that learning and absorbing information with an LLM could be more effective than curated texts, results from self-reported data indicate that there are few perceived differences between the effectiveness of LLM and curated texts. Conclusions: this exploratory and preliminary study revealed empirical insights between LLM usage and learning effectiveness in situ via physiological indicators, in contrast to prior work that has adopted post hoc frames over the medium- to long-term.

This chapter argues for a re-balancing of teaching and learning interactions towards a ‘deepening’ process. Traditional university teaching and learning often have a strong focus on ‘transfer’ processes that essentially involves building up a body of disciplinary knowledge. While this remains important, freeing up time to focus more on ‘deepening’ processes, particularly within interdisciplinary contexts, represents a more relevant educational preparation for students who are expected to graduate into a world where they will likely face complex challenges that require fast-pace and continual updating of skills and knowledge. ‘Deepening’ processes would generally involve both asynchronous learning, where students largely learn on their own, and synchronous learning, where application and extension of learnings can take place. The former requires the development of a learning support ecology, whereas the latter necessitates shifting pedagogies towards ‘deepening’.

While educational excursions are widely acknowledged to enhance student learning through immersive, real-world experiences, there is limited research on how students can best capture and retain knowledge during such activities. Traditional note-taking methods, such as pen and paper or digital devices, may be inadequate for recording spatial or multimodal information encountered in these dynamic environments. With the emergence of mixed reality (MR) technologies, there is an opportunity to explore spatial, immersive note-taking that aligns with the dynamic nature of field-based learning. This study compares the effectiveness of mixed reality, pen and paper, and digital note-taking during educational excursions. A total of 50 participants in grades 7 through 12 used the Apple Vision Pro headset for mixed reality notes, mobile phones for digital notes, and clipboards paired with a pen and paper for traditional notes. The information encountered was categorised as physical, textual, or video-based. The effectiveness was evaluated through three measures: content extracted and organised in notes, post-activity quizzes on retention and critical thinking, and participant feedback. For physical information, mixed reality significantly improved the content extraction and retention. For textual information, mixed reality yielded more content, but pen and paper outperformed it in terms of organisation. Statistically, all the note-taking methods were equally effective in the remaining aspects. Although mixed reality shows potential to be integrated into educational excursions, participant feedback highlighted discomfort with the headset, suggesting that mixed reality should complement, not replace, traditional approaches.