With the rapid development of entity recognition technology, animal recognition has gradually become essential in modern society, supporting labour‐intensive agriculture and animal husbandry tasks. Severe problems such as maintaining biodiversity can also benefit from animal identification technology. However, certain invasive recognition systems have resulted in permanent harm to animals, while noninvasive identification methods also exhibit certain drawbacks. This paper conducts a systematic literature review (SLR), presenting a comprehensive overview of various animal recognition technologies and their applications. Specifically, it examines methodologies such as deep learning, image processing and acoustic analysis used for different animal characteristics and identification purposes. The contribution of machine learning to animal feature extraction is highlighted, emphasising its significance for animal taxonomy and wild species monitoring. Additionally, this review addresses the challenges and limitations of current technologies, including data scarcity, model accuracy and computational requirements, and suggests opportunities for future research to overcome these obstacles.

Using computer image-processing technology for animal identification has improved labour-intensive agriculture and animal husbandry efficiency and accuracy. Additionally, camera traps have provided researchers with abundant biodiversity and wildlife habitat conservation data. However, despite these advancements, there is still a need for taxonomic rank-based animal classification studies, and fewer datasets are available to train AI models based on taxonomic ranks. The importance of taxonomy was ignored by researchers and dataset creators in previous animal classification studies, which is the basis and prerequisite of classifying wild animal images. To this end, this paper presents a dataset labelled with the taxonomic ranking. The dataset is focused on Australian animals and includes 47 taxonomically significant animal species, each with a maximum of 600 manually labelled images. This dataset will contribute significantly to the field of animal identification, as it provides researchers and practitioners with a valuable tool to improve the efficiency and accuracy of animal identification, which can have significant implications for agriculture, animal husbandry, biodiversity research, and wildlife conservation.

Developments in digital technology and the wide-spread use of digital devices makes digital storytelling a powerful instructional tool. Fortuitously, digital storytelling has received increasing attention in recent years, and it has the power to be a powerful and popular teaching and learning tool in the future. This paper reports partial findings of a case study conducted to investigate the impact of digital storytelling on students' engagement and their learning outcomes.

The lack of systematic processes for authoring educational multimedia content is impeding the realization of its full potential. Catering to different learning styles is also a challenge in creating effective educational multimedia content. Storytelling has been recognized as an effective pedagogical technique to keep the learners engaged. Movement Oriented Design (MOD) paradigm provides a framework for systematically developing educational multimedia stories using good storytelling principles articulated by the masters of storytelling, such as Aristotle and Robert McKee. This paper introduces Movement Oriented Design principles, and imperatives for creating good educational multimedia stories. Next it presents the workshop process for developing educational multimedia narratives.

Most multimedia creation models and tools are disjoint entities. It remains the multimedia author's responsibility to integrate the functionality of these models and tools. MUDPY facilitates the creation of meaningful multimedia (stand-alone or online) by exposing the semantic connections between the various design components. The paper describes the multimedia design and planning pyramid (MUDPY), a meta-design framework for multimedia project planning and design.