Simulation facilitators routinely invoke the ‘Socratic method’ when describing their questioning approach, yet this invocation often lacks philosophical grounding and practical specificity. Whilst Socratic questioning features prominently in debriefing standards, its application has become what scholars describe as "extraordinarily vague", with conflicting interpretations proliferating across the literature. Facilitators need clear guidance for important decisions: when to challenge versus support, when to profess ignorance versus share expertise, when to create discomfort versus maintain psychological safety. This article returns to Plato’s dialogues to construct a contemporary pedagogical framework through close textual analysis. We developed five distinct facilitation orientations drawn from specific passages in the original texts: the Gadfly (challenging assumptions through persistent questioning), the Professed Ignorant (modelling intellectual humility), the Midwife (facilitating emergence of tacit knowledge), the Stingray (inducing productive cognitive dissonance), and the Co-inquirer (fostering collaborative discovery). These orientations function as philosophical stances rather than algorithmic techniques, providing meta-level guidance that complements existing debriefing frameworks. Each orientation addresses different aspects of productive uncertainty, the deliberate cultivation of intellectual discomfort as a catalyst for deeper thinking. When facilitators position themselves as fellow learners, debriefing can shift from teaching learners what to think towards teaching them how to think. Engagement with Socratic principles expands facilitators’ repertoires for creating meaningful learning conversations. These orientations offer simulation educators a philosophically grounded alternative to vague appeals to ‘being Socratic’. They emerge from interpretive choices calibrated specifically to healthcare simulation contexts rather than claims of historical authenticity.
There is currently no universally adopted definition of rural either by government entities or by rural special education researchers. Yet, the replicability, generalizability, transferability, and validity of rural special education research is dependent upon the definition of rural researchers’ use to situate their work. Examining the terminology used to describe rural settings can help identify for whom and in which contexts study results may be relevant when making policy and practice decisions. The purpose of this descriptive study was to examine current definitions of rural used in rural special education research. Thirty-five empirical articles published from 2020 through 2024 were examined for their rural definitions and the locale characteristics used to describe rural settings. Results indicated that a majority of authors of included studies did not use any formal definition of rural to classify their study setting. Recommendations are provided on ways to define rural and describe study setting characteristics.
Anthony A. Essien, Tanya Evans, Maitree Inprasitha
et al.
The theme of the PME-48 conference, Making sure that mathematics education research reaches the classroom, highlights a key concern: not all mathematics education research informs classroom practice. This raises several fundamental questions: Which research fails to reach the classroom? Why? And should all research be expected to do so? Accordingly, it is fitting that the plenary panel engages with these important issues by debating the following motion: Mathematics education research must be useful for the classroom. This paper presents the debate as structured for the purposes of this publication. Following an introduction, Anthony Essien and Salome Martinez Salazar argue against the motion, while Maitree Inprasitha and Demetra Pitta-Pantazi argue in support. We hope that this debate will stimulate ongoing dialogue and encourage the mathematics education research community to critically engage with this issue - one that is central to the relevance and impact of research in the field.
Valeria Cesaroni, Eleonora Pasqua, Piercosma Bisconti
et al.
AI-based technologies have significant potential to enhance inclusive education and clinical-rehabilitative contexts for children with Special Educational Needs and Disabilities. AI can enhance learning experiences, empower students, and support both teachers and rehabilitators. However, their usage presents challenges that require a systemic-ecological vision, ethical considerations, and participatory research. Therefore, research and technological development must be rooted in a strong ethical-theoretical framework. The Capability Approach - a theoretical model of disability, human vulnerability, and inclusion - offers a more relevant perspective on functionality, effectiveness, and technological adequacy in inclusive learning environments. In this paper, we propose a participatory research strategy with different stakeholders through a case study on the ARTIS Project, which develops an AI-enriched interface to support children with text comprehension difficulties. Our research strategy integrates ethical, educational, clinical, and technological expertise in designing and implementing AI-based technologies for children's learning environments through focus groups and collaborative design sessions. We believe that this holistic approach to AI adoption in education can help bridge the gap between technological innovation and ethical responsibility.
Using nationally representative data for India, this paper examines the incidence of education occupation mismatch and returns to education and EOM for internal migrants while considering the heterogeneity among them. In particular, this study considers heterogeneity arising because of the reason to migrate, demographic characteristics, spatial factors, migration experience, and type of migration. The analysis reveals that there exists variation in the incidence and returns to EOM depending on the reason to migrate, demographic characteristics, and spatial factors. The study highlights the need of focusing on EOM to increase the productivity benefits of migration. It also provides the framework for minimizing migrants' likelihood of being mismatched while maximizing their returns to education.
Rubaina Khan, Tammy Mackenzie, Sreyoshi Bhaduri
et al.
This autoethnographic study explores the need for interdisciplinary education spanning both technical and philosophical skills - as such, this study leverages whole-person education as a theoretical approach needed in AI engineering education to address the limitations of current paradigms that prioritize technical expertise over ethical and societal considerations. Drawing on a collaborative autoethnography approach of fourteen diverse stakeholders, the study identifies key motivations driving the call for change, including the need for global perspectives, bridging the gap between academia and industry, integrating ethics and societal impact, and fostering interdisciplinary collaboration. The findings challenge the myths of technological neutrality and technosaviourism, advocating for a future where AI engineers are equipped not only with technical skills but also with the ethical awareness, social responsibility, and interdisciplinary understanding necessary to navigate the complex challenges of AI development. The study provides valuable insights and recommendations for transforming AI engineering education to ensure the responsible development of AI technologies.
Mathematics education, a crucial and basic field, significantly influences students' learning in related subjects and their future careers. Utilizing artificial intelligence to interpret and comprehend math problems in education is not yet fully explored. This is due to the scarcity of quality datasets and the intricacies of processing handwritten information. In this paper, we present a novel contribution to the field of mathematics education through the development of MNIST-Fraction, a dataset inspired by the renowned MNIST, specifically tailored for the recognition and understanding of handwritten math fractions. Our approach is the utilization of deep learning, specifically Convolutional Neural Networks (CNNs), for the recognition and understanding of handwritten math fractions to effectively detect and analyze fractions, along with their numerators and denominators. This capability is pivotal in calculating the value of fractions, a fundamental aspect of math learning. The MNIST-Fraction dataset is designed to closely mimic real-world scenarios, providing a reliable and relevant resource for AI-driven educational tools. Furthermore, we conduct a comprehensive comparison of our dataset with the original MNIST dataset using various classifiers, demonstrating the effectiveness and versatility of MNIST-Fraction in both detection and classification tasks. This comparative analysis not only validates the practical utility of our dataset but also offers insights into its potential applications in math education. To foster collaboration and further research within the computational and educational communities. Our work aims to bridge the gap in high-quality educational resources for math learning, offering a valuable tool for both educators and researchers in the field.
Zhonghao Shi, Allison O'Connell, Zongjian Li
et al.
As artificial intelligence (AI) is playing an increasingly important role in our society and global economy, AI education and literacy have become necessary components in college and K-12 education to prepare students for an AI-powered society. However, current AI curricula have not yet been made accessible and engaging enough for students and schools from all socio-economic backgrounds with different educational goals. In this work, we developed an open-source learning module for college and high school students, which allows students to build their own robot companion from the ground up. This open platform can be used to provide hands-on experience and introductory knowledge about various aspects of AI, including robotics, machine learning (ML), software engineering, and mechanical engineering. Because of the social and personal nature of a socially assistive robot companion, this module also puts a special emphasis on human-centered AI, enabling students to develop a better understanding of human-AI interaction and AI ethics through hands-on learning activities. With open-source documentation, assembling manuals and affordable materials, students from different socio-economic backgrounds can personalize their learning experience based on their individual educational goals. To evaluate the student-perceived quality of our module, we conducted a usability testing workshop with 15 college students recruited from a minority-serving institution. Our results indicate that our AI module is effective, easy-to-follow, and engaging, and it increases student interest in studying AI/ML and robotics in the future. We hope that this work will contribute toward accessible and engaging AI education in human-AI interaction for college and high school students.
Technology enhanced learning analytics has the potential to play a significant role in higher education in the future. Opinions and expectations towards technology and learning analytics, thus, are vital to consider for institutional developments in higher education institutions. The Sheila framework offers instruments to yield exploratory knowledge about stakeholder aspirations towards technology, such as learning analytics in higher education. The sample of the study consists of students (N = 1169) and teachers (N = 497) at a higher education institution in Germany. Using self-report questionnaires, we assessed students and teachers attitudes towards learning analytics in higher education teaching, comparing ideal and expected circumstances. We report results on the attitudes of students, teachers, as well as comparisons of the two groups and different disciplines. We discuss the results with regard to practical implications for the implementation and further developments of learning analytics in higher education.
Dauda Abdu, Almustapha Abdullahi Wakili, Lawan Nasiru
et al.
Over a decade there has been a rapid growth in Nigerian educational system particularly higher education. Various institutions have come up both from public and private sector offering many of courses both under and post graduate students. Therefore, rates of students enroll for higher educational institutions in Nigeria have also increased. Hence it is very important to understand the roles play by data mining in analyzing the collected data of students and their academic progression. It is a concern for today's education system and this gap has to be identified and properly addressed to the learning community. Data Mining it helps in various ways to resolve issues face in predictions students and staff performances within Nigerian education system. This paperwork we discuss the roles of Data Mining tools and techniques which can be used effectively in resolving issues in some functional unit of Nigerian tertiary institutions.
Samuel de Andrade Lima, José Borzacchiello da Silva
O referido artigo expõe sobre a fundamentação teórica e o percurso metodológico de um pesquisador em nível de pós-graduação para o desenvolvimento da sua tese de doutorado. O escrito traz os detalhes da temática a partir da pergunta problema. A apresentação da base teórica partindo dos conceitos chave da ciência Geográfica, espaço, território e região, assim como de outras áreas como economia, capitalismo e espaço agropastoril. A temática principal versa sobre as Rotas de Integração Nacional como política pública do Estado brasileiro, voltado ao desenvolvimento da cadeia produtiva que envolve a rota do leite no sertão central do semiárido cearense. No discorrer do artigo está descrito o percurso metodológico da pesquisa com a apresentação do método, abordagem, metodologia, coleta de dados, leitura e interpretação dos dados e apresentação dos resultados da investigação.
This paper details an outlook on modern constraint programming (CP) education through the lens of a CP instructor. A general overview of current CP courses and instructional methods is presented, with a focus on online and virtually-delivered courses. This is followed by a discussion of the novel approach taken to introductory CP education for engineering students at large scale at the Georgia Institute of Technology (Georgia Tech) in Atlanta, GA, USA. The paper summarizes important takeaways from the Georgia Tech CP course and ends with a discussion on the future of CP education. Some ideas for instructional methods, promotional methods, and organizational changes are proposed to aid in the long-term growth of CP education.
Anjan Ray Chaudhury, Dipankar Das, Sreemanta Sarkar
Decision to participate in education depends on the circumstances individual inherits and on the returns to education she expects as well. If one person from any socio-economically disadvantaged social group inherits poor circumstances measured in terms of family background, then she is having poor opportunities vis-à-vis her capability set becomes confined. Accordingly, her freedom to choose the best alternative from many is also less, and she fails to expect the potential returns from educational participation. Consequently, a complementary relationship between the circumstances one inherits and the returns to education she expects can be observed. This paper is an attempt to look at this complementarity on the basis of theoretical logic and empirical investigation, which enables us to unearth the origin of inter-group disparity in educational participation, as is existed across the groups defined by taking caste and gender together in Indian society. Furthermore, in the second piece of analysis, we assess the discrimination in the likelihood of educational participation by invoking the method of decomposition of disparity in the likelihood of educational participation applicable in the logistic regression models, which enables us to re-establish the earlier mentioned complementary relationship.
This paper explores the potential of artificial intelligence (AI) in higher education, specifically its capacity to replace or assist human teachers. By reviewing relevant literature and analysing survey data from students and teachers, the study provides a comprehensive perspective on the future role of educators in the face of advancing AI technologies. Findings suggest that although some believe AI may eventually replace teachers, the majority of participants argue that human teachers possess unique qualities, such as critical thinking, creativity, and emotions, which make them irreplaceable. The study also emphasizes the importance of social-emotional competencies developed through human interactions, which AI technologies cannot currently replicate. The research proposes that teachers can effectively integrate AI to enhance teaching and learning without viewing it as a replacement. To do so, teachers need to understand how AI can work well with teachers and students while avoiding potential pitfalls, develop AI literacy, and address practical issues such as data protection, ethics, and privacy. The study reveals that students value and respect human teachers, even as AI becomes more prevalent in education. The study also introduces a roadmap for students, teachers, and universities. This roadmap serves as a valuable guide for refining teaching skills, fostering personal connections, and designing curriculums that effectively balance the strengths of human educators with AI technologies. The future of education lies in the synergy between human teachers and AI. By understanding and refining their unique qualities, teachers, students, and universities can effectively navigate the integration of AI, ensuring a well-rounded and impactful learning experience.
Blockchain technologies for rewards in education are gaining attraction as a promising approach to motivate student learning and promote academic achievement. By providing tangible rewards for educational attainment and engagement, such as digital tokens, educators can motivate learners to take a more active role in their learning and increase their sense of ownership and responsibility for their academic outcomes. In this context, this work proposes the Software Engineering Skill (SES) token as a way of rewarding students in order to improve their experiences in Software Engineering Education (SEE). We performed a proof of concept and conclude that SES token can be deployed in a platform to support SEE.
The education system is getting diversified, challenged, and blended for the overwhelming advancement of disruptive technology. The core purpose of this chapter is to visualize the probable solutions of the modern education system using blockchain technology. The entire chapter has been discussed on the basis of present solution and projection of future inventions to smoothen the education system. The fourth industrial revolution (4IR) is changing our experiences in terms of education and other lifestyle. Delivering lectures, interacting between learners and educations, evaluating learning outcomes, and verifying educational credentials might be smoother, easier, faster, cheaper, and jollier than before. Blockchain technology can contribute to the education provider to tackle all those existing problems to create a comfortable learning environment to all irrespective to their economic backgrounds and geographic location. How this technology can contribute to improve Reviewing recent inventions in this technology, the chapter explains some of the strategies to go beyond the ongoing projects around the world. A set of models are arranged to enable the readers mind for future inventions in the realm of educationists. Keywords: -Blockchain, 4IR, educators, learning outcome.