The emergence of Chat GPT has once again sparked a wave of information revolution in generative artificial intelligence. This article provides a detailed overview of the development and technical support of generative artificial intelligence. It conducts an in-depth analysis of the current application of generative artificial intelligence in the field of education, and identifies problems in four aspects: opacity and unexplainability, data privacy and security, personalization and fairness, and effectiveness and reliability. Corresponding solutions are proposed, such as developing explainable and fair algorithms, upgrading encryption technology, and formulating relevant laws and regulations to protect data, as well as improving the quality and quantity of datasets. The article also looks ahead to the future development trends of generative artificial intelligence in education from four perspectives: personalized education, intelligent teaching, collaborative education, and virtual teaching. The aim of the study is to provide important reference value for research and practice in this field.
This article outlines, by means of a critique of film criticism, the processes of self-formation and subject-formation as affected by unconscious education. The basic definition of the unconscious used is: something that happens to someone without them being consciously aware of it happening, unless their attention to it is actively drawn. This is a functional definition used to develop a critical argument specific to the filmic and theoretical texts used in the article and is not an attempt to capture other definitions of the unconscious within its scope except where explicitly mentioned. While Freud is engaged indirectly through more recently theoretical work, including my own, the deep relationship to Freud’s thought is beyond the scope of this article and is therefore – consciously – only gestured towards. Unconscious education, as defined in the context of this article, is as much about what in our experience might be out of focus , which, in aural terms might be heard without listening . As such, this article charts a theoretical and practical movement from hearing to listening and passive inattention to active attention in the context of film and film criticism, with a specific focus on film music. This specific focus, though, is principally a means of illustrating (note the visual metaphors) a theoretical position that can be considered across the full range of educational experiences and practices, including research and criticism.
Special education teachers often report having heavy workloads and problematic working conditions that lead to a desire to leave their campus. Some attention has been given to the role of principal especially since administrative support has been reported by special education teachers as a working condition influencing their decisions to leave. In this study, we use Texas administrative data from 1995–2024 academic years to identify the extent to which principals have special education teaching experience and examine the relationship between that experience and special education teacher retention. We find that roughly 22% of principals had some special education teaching experience in their careers. We also find that principals with special education teaching experience may be positively correlated with special education teacher retention, but our findings suggest that the influence of principal experience is partially moderated by school-level factors. We discuss these findings and implications for future research and practice.
As Artificial Intelligence (AI), particularly Large Language Models (LLMs), becomes increasingly embedded in education systems worldwide, ensuring their ethical, legal, and contextually appropriate deployment has become a critical policy concern. This paper offers a comparative analysis of AI-related regulatory and ethical frameworks across key global regions, including the European Union, United Kingdom, United States, China, and Gulf Cooperation Council (GCC) countries. It maps how core trustworthiness principles, such as transparency, fairness, accountability, data privacy, and human oversight are embedded in regional legislation and AI governance structures. Special emphasis is placed on the evolving landscape in the GCC, where countries are rapidly advancing national AI strategies and education-sector innovation. To support this development, the paper introduces a Compliance-Centered AI Governance Framework tailored to the GCC context. This includes a tiered typology and institutional checklist designed to help regulators, educators, and developers align AI adoption with both international norms and local values. By synthesizing global best practices with region-specific challenges, the paper contributes practical guidance for building legally sound, ethically grounded, and culturally sensitive AI systems in education. These insights are intended to inform future regulatory harmonization and promote responsible AI integration across diverse educational environments.
Generative AI tools - most notably large language models (LLMs) like ChatGPT and Codex - are rapidly revolutionizing computer science education. These tools can generate, debug, and explain code, thereby transforming the landscape of programming instruction. This paper examines the profound opportunities that AI offers for enhancing computer science education in general, from coding assistance to fostering innovative pedagogical practices and streamlining assessments. At the same time, it highlights challenges including academic integrity concerns, the risk of over-reliance on AI, and difficulties in verifying originality. We discuss what computer science educators should teach in the AI era, how to best integrate these technologies into curricula, and the best practices for assessing student learning in an environment where AI can generate code, prototypes and user feedback. Finally, we propose a set of policy recommendations designed to harness the potential of generative AI while preserving the integrity and rigour of computer science education. Empirical data and emerging studies are used throughout to support our arguments.
Cynthia Zastudil, David H. Smith, Yusef Tohamy
et al.
Ensuring equitable access to computing education for all students-including those with autism, dyslexia, or ADHD-is essential to developing a diverse and inclusive workforce. To understand the state of disability research in computing education, we conducted a systematic literature review of research on neurodiversity in computing education. Our search resulted in 1,943 total papers, which we filtered to 14 papers based on our inclusion criteria. Our mixed-methods approach analyzed research methods, participants, contribution types, and findings. The three main contribution types included empirical contributions based on user studies (57.1%), opinion contributions and position papers (50%), and survey contributions (21.4%). Interviews were the most common methodology (75% of empirical contributions). There were often inconsistencies in how research methods were described (e.g., number of participants and interview and survey materials). Our work shows that research on neurodivergence in computing education is still very preliminary. Most papers provided curricular recommendations that lacked empirical evidence to support those recommendations. Three areas of future work include investigating the impacts of active learning, increasing awareness and knowledge about neurodiverse students' experiences, and engaging neurodivergent students in the design of pedagogical materials and computing education research.
Fiona Preston-Whyte , Toshka Barnardo , Danica Marlin
et al.
Data gaps limit solutions and policy development for environmental issues. Citizen science offers a possible solution to reduce data gaps at a limited cost while enhancing environmental education (EE). While highly effective in the latter, citizen science campaigns rarely produce reliable, comparable, and meaningful data. This often results from fragmented awareness, varying data collection methods, and little training prior to data collection. This article explores how Massive Open Online Courses (MOOCs) can be used to train citizen scientists, increase the value of citizen science data, and ensure that resources invested in citizen science initiatives are used more efficiently. We use a beach macrolitter monitoring course developed by Sustainable Seas Trust (SST) (NGO/NPO) and GRID-Arendal (a research foundation) as a case study in Africa, since the marine litter issue has widespread public support, and beaches are pleasant locations that attract potential citizen scientists. Beach macrolitter surveys utilise everyday equipment, and monitoring methods are simple if individuals are supported with appropriate training. This is especially relevant in Africa, where plastic pollution is forecasted to increase faster than other regions, and resources for research can be limited. This article gives a modified problemsolution model (mPSM) perspective, considering the challenges and solutions of MOOC development by two organisations working in the same space with limited resources. Challenges to inclusivity for online training in Africa included language barriers and limited technological access. Using Africa as a case study, we show that by combining professional abilities, inclusive digital education can be achieved using data-light MOOCs, offline engagement and other inclusive strategies to overcome the challenges of m- (mobile) and e- (electronic) learning. This kind of EE can be a powerful tool in developing reliable data while enhancing citizens’ agency in working towards Sustainable Development Goals (SDGs).
Using look-alike avatars may enhance the likeability and realism of avatars in 3D virtual learning environments. This paper explores perception of the features of the look-alike avatar representations of an instructor in virtual environments in two studies. In a pilot study, an instructor was represented as a look-alike, stick, and video avatar, allowing us to investigate students’ perceptions of teaching effectiveness in virtual and augmented reality environments. The main study seeks to determine the influence of three specific features of a look-alike avatar (head shape, texture fidelity and head orientation) on perception of likeability and visual realism, especially when judged by other people. Two textured look-alike avatars were generated using: (i) three-dimensional (3D) stereophotogrammetry; and (ii) 3D face reconstruction from a single full-face image. Participants compared three different head orientations (0°, 45°, 90°) of the look-alike avatars’ textured heads to their corresponding head silhouettes, to emphasize the differences in head shapes. Results suggest that participants prefer geometrically-accurate photorealistic avatars of the instructor due to the accuracy of the head shape and texture fidelity. In line with studies on face recognition, participants ranked the likeability and realism of the look-alike avatars similarly regardless of the head orientation. We discuss the implications of these findings for 3D virtual learning environments.
The COVID-19 pandemic had a significant impact on school education, as confirmed by numerous studies produced at the international level. One of the most profound effects was the potential change to the political mandates of schools and resulting alterations in professional teaching practices, given the proliferation of remote (online and blended) teaching. This article aims to explore the extent to which the pandemic crisis has reconfigured schools’ educational priorities, with an emphasis on democratisation and inclusion at the expense of learning outcomes and meritocratic approaches. Based on an extensive study as part of a wider international project, the results of a questionnaire survey of Portuguese teachers (n = 3983) during the initial lockdown period in 2020 are analysed. The empirical evidence suggests that the pandemic resulted in a strengthening of the democratising pole, underpinned by the principles of equal opportunities, inclusion, and social justice, even though the practices and priorities of teachers as a whole are not homogeneous. This heterogeneity reflects pre-existing professional and school cultures, which vary depending on level of education, gender, school type, and career length, among other important factors.
The recent surge in generative AI technologies, such as large language models and diffusion models, has boosted the development of AI applications in various domains, including science, finance, and education. Concurrently, adaptive learning, a concept that has gained substantial interest in the educational sphere, has proven its efficacy in enhancing students' learning efficiency. In this position paper, we aim to shed light on the intersectional studies of these two methods, which combine generative AI with adaptive learning concepts. By presenting discussions about the benefits, challenges, and potentials in this field, we argue that this union will contribute significantly to the development of the next-stage learning format in education.
This study explores the impact of appearance discrimination in the labor market and whether education can mitigate this issue. A statistical analysis of approximately 1.058 million job advertisements in China from 2008 to 2010 found that about 7.7% and 2.6% of companies had explicit requirements regarding candidates' appearance and height, particularly in positions with lower educational requirements. Literature review indicates that attractive job seekers typically enjoy higher employment opportunities and wages, while unattractive individuals face significant income penalties. Regression analysis of 1,260 participants reveals a significant positive correlation between attractiveness scores and wages, especially in low-education groups. Conversely, in high-education groups, the influence of appearance on income is not significant. The study suggests that enhancing education levels can effectively alleviate income declines associated with appearance, providing policy recommendations to reduce appearance discrimination in the labor market.
Zhipeng Yin, Sribala Vidyadhari Chinta, Zichong Wang
et al.
The integration of AI in education holds immense potential for personalizing learning experiences and transforming instructional practices. However, AI systems can inadvertently encode and amplify biases present in educational data, leading to unfair or discriminatory outcomes. As researchers have sought to understand and mitigate these biases, a growing body of work has emerged examining fairness in educational AI. These studies, though expanding rapidly, remain fragmented due to differing assumptions, methodologies, and application contexts. Moreover, existing surveys either focus on algorithmic fairness without an educational setting or emphasize educational methods while overlooking fairness. To this end, this survey provides a comprehensive systematic review of algorithmic fairness within educational AI, explicitly bridging the gap between technical fairness research and educational applications. We integrate multiple dimensions, including bias sources, fairness definitions, mitigation strategies, evaluation resources, and ethical considerations, into a harmonized, education-centered framework. In addition, we explicitly examine practical challenges such as censored or partially observed learning outcomes and the persistent difficulty in quantifying and managing the trade-off between fairness and predictive utility, enhancing the applicability of fairness frameworks to real-world educational AI systems. Finally, we outline an emerging pathway toward fair AI-driven education and by situating these technologies and practical insights within broader educational and ethical contexts, this review establishes a comprehensive foundation for advancing fairness, accountability, and inclusivity in the field of AI education.
Personalized learning (PL) aspires to provide an alternative to the one-size-fits-all approach in education. Technology-based PL solutions have shown notable effectiveness in enhancing learning performance. However, their alignment with the broader goals of modern education is inconsistent across technologies and research areas. In this paper, we examine the characteristics of AI-driven PL solutions in light of the goals outlined in the OECD Learning Compass 2030. Our analysis indicates a gap between the objectives of modern education and the technological approach to PL. We identify areas where the AI-based PL solutions could embrace essential elements of contemporary education, such as fostering learner's agency, cognitive engagement, and general competencies. While the PL solutions that narrowly focus on domain-specific knowledge acquisition are instrumental in aiding learning processes, the PL envisioned by educational experts extends beyond simple technological tools and requires a holistic change in the educational system. Finally, we explore the potential of generative AI, such as ChatGPT, and propose a hybrid model that blends artificial intelligence with a collaborative, teacher-facilitated approach to personalized learning.
ChatGPT, an implementation and application of large language models, has gained significant popularity since its initial release. Researchers have been exploring ways to harness the practical benefits of ChatGPT in real-world scenarios. Educational researchers have investigated its potential in various subjects, e.g., programming, mathematics, finance, clinical decision support, etc. However, there has been limited attention given to its application in data science education. This paper aims to bridge that gap by utilizing ChatGPT in a data science course, gathering perspectives from students, and presenting our experiences and feedback on using ChatGPT for teaching and learning in data science education. The findings not only distinguish data science education from other disciplines but also uncover new opportunities and challenges associated with incorporating ChatGPT into the data science curriculum.
The launch of ChatGPT in November 2022 precipitated a panic among some educators while prompting qualified enthusiasm from others. Under the umbrella term Generative AI, ChatGPT is an example of a range of technologies for the delivery of computer-generated text, image, and other digitized media. This paper examines the implications for education of one generative AI technology, chatbots responding from large language models, or C-LLM. It reports on an application of a C-LLM to AI review and assessment of complex student work. In a concluding discussion, the paper explores the intrinsic limits of generative AI, bound as it is to language corpora and their textual representation through binary notation. Within these limits, we suggest the range of emerging and potential applications of Generative AI in education.