This study explores the complex dualities in digital education, focusing on the case study of Singapore. It highlights the ethical issues surrounding the integration of information and communication technology (ICT), especially artificial intelligence, in the education sector. The paper presents a theoretical framework to explore these dualities, examining how they have been navigated in Singapore’s policy reforms to enhance digital education. These dualities include centralisation vs. decentralisation of resource orientation; customisation vs. standardisation of curriculum, formal vs. informal learning with respect to pedagogical approaches; human agency vs. technological automation for data interpretation; and peaks of excellence vs. equity in achievement outcomes. These aspects significantly impact the outcomes of ICT-enabled reforms. The study draws upon Singapore’s longitudinal trajectory of integrating ICT in education, illustrating its efforts in reconciling these dualities. The findings underscore the importance of careful consideration and balance in integrating ICT in education, emphasising the need to transcend these dualities to build a more inclusive digital learning environment.
Amid the digital revolution, this research explores a groundbreaking topic—the potential impact of metaverse services on the future of computing and engineering education. The transformative potential of metaverse services in education is a beacon of the future, promising new learning modes in digital environments. This work poses two questions: Will metaverse services affect computing and engineering education learning? If so, to what extent has computing and engineering education adopted metaverse services in its curricula? To address these queries, the authors researched several metaverse activities affecting computing and engineering education. The new concepts of metaverse services, metaverse education services, and metaverse education service space are presented and analyzed. This research also discusses the influences of metaverse and services on computing and engineering education. The research showed a transformation toward metaverse service education in the evolving digital era. Academic and industry professionals must recognize the critical need to prepare students and graduates for the digital era adequately. The future is coming whereby metaverse, higher education, and services will generate a new destiny for computing and engineering education with new learning modes in digital environments. The transformative potential of metaverse services in education cannot be overstated, and the academic and industry communities must recognize and embrace this phenomenon.
The digital transformation is driving profound changes in education and teaching, with immersive technologies such as virtual reality (VR), augmented reality (AR), and mixed reality (MR), representing the future form of the internet, set to lead a new round of innovation in primary and secondary education applications. This study first elaborates on the connotation and typical characteristics of immersive technologies, analyzes their potential applications in primary and secondary education scenarios such as space, resources, curriculum, models, and evaluation. It then points out the key issues in the application of immersive technologies in primary and secondary education from four aspects: educational system, innovative technology, application orientation, and privacy ethics. Finally, from the perspective of multi-party collaboration among government departments, educational entities, industries, and enterprises, we propose application strategies such as top-level planning and design, core technology research, typical case cultivation, and regulatory governance. Such strategies aim to promote the deep integration of immersive technologies and primary and secondary education, shaping a new educational climate that aligns with the talents demands of the digital era.
Educational digitalization is a trend in both domestic and international educational development. The importance of educational digitalization lies in its potential to improve the efficiency, engagement, and equity of education. However, it also encounters challenges in terms of macro-planning, support infrastructure, and regional balance, which necessitate proactive responses. As a crucial contributor to the process of educational digitalization, the community of educators, notably those teaching foundational mathematics, must adapt to the evolving landscape. By shifting their mindsets, enhancing their capabilities, and guiding students accordingly, they can effectively enhance the quality and effectiveness of teaching, thereby making substantial contributions to the advancement of educational digitalization.
Large language models (LLMs) have emerged as powerful tools in natural language processing (NLP), showing a promising future of artificial generated intelligence (AGI). Despite their notable performance in the general domain, LLMs have remained suboptimal in the field of education, owing to the unique challenges presented by this domain, such as the need for more specialized knowledge, the requirement for personalized learning experiences, and the necessity for concise explanations of complex concepts. To address these issues, this paper presents a novel LLM for education named WisdomBot, which combines the power of LLMs with educational theories, enabling their seamless integration into educational contexts. To be specific, we harness self-instructed knowledge concepts and instructions under the guidance of Bloom’s Taxonomy as training data. To further enhance the accuracy and professionalism of model’s response on factual questions, we introduce two key enhancements during inference, i.e., local knowledge base retrieval augmentation and search engine retrieval augmentation during inference. We substantiate the effectiveness of our approach by applying it to several Chinese LLMs, thereby showcasing that the fine-tuned models can generate more reliable and professional responses.
After the Overall Plan for Deepening the Reform of Education Evaluation in the New Era has been released for over two years, the reform of education evaluation has achieved a good start and important phased outcomes. Promoting the digital transformation of education evaluation and developing Big Data-based education evaluation are the main measures of current evaluation reform. Based on the case study of the Minzu University of China, this paper systematically sorts out the relevant research, constructs the factor model and process model of Big Data-based education evaluation from the perspectives of factors and process of evaluation, puts forward the application idea of Big Data-based education evaluation from the perspectives of full business, full process and full factors, and puts forward the practical path of Big Data-based education evaluation from the aspects of application traction, teacher training and safe operation.
With the advent of the digital age, cultivating students’ digital media literacy has become an important educational mission. A standardized digital media literacy scale was developed to study the digital media literacy of primary school students in urban and rural areas. Through stratified random sampling, a total of 2,848 urban and rural primary school students participated in this research. Primary school students exhibited moderate proficiency in digital media literacy, with a notable deficiency in their ability to create and disseminate information effectively. Subsequent investigations revealed disparities in the level of digital media literacy between urban and rural students, which appeared in their proficiency to access and use digital tools, as well as their ability to understand and evaluate media messages. Parental education, parental mediation, and time spent using digital devices all have a substantial positive influence on students’ digital media literacy, with parental mediation having the greatest impact. Consequently, it is imperative to prioritize the development of higher-order digital media literacy skills in students. Efforts should also be directed toward enhancing the basic digital media literacy of rural primary school students while fostering parental engagement in students’ digital education. In this regard, the Chinese government, enterprises, and schools have launched measures to promote rural students’ digital media literacy and parents’ involvement in students’ digital education. Future research should prioritize investigations of the efficacy of these measures to ascertain their impact on the holistic development of digital media literacy among all students.
This paper proposes a novel approach to use artificial intelligence (AI), particularly large language models (LLMs) and other foundation models (FMs) in an educational environment. It emphasizes the integration of teams of teachable and self-learning LLMs agents that use neuro-symbolic cognitive architecture (NSCA) to provide dynamic personalized support to learners and educators within self-improving adaptive instructional systems (SIAIS). These systems host these agents and support dynamic sessions of engagement workflow. We have developed the never ending open learning adaptive framework (NEOLAF), an LLM-based neuro-symbolic architecture for self-learning AI agents, and the open learning adaptive framework (OLAF), the underlying platform to host the agents, manage agent sessions, and support agent workflows and integration. The NEOLAF and OLAF serve as concrete examples to illustrate the advanced AI implementation approach. We also discuss our proof of concept testing of the NEOLAF agent to develop math problem-solving capabilities and the evaluation test for deployed interactive agent in the learning environment.
