Robert Whyte, Manni Cheung, Katharine Childs
et al.
Data literacy skills are fundamental in computer science education. However, understanding how data-driven systems work represents a paradigm shift from traditional rule-based programming. We conducted a systematic literature review of 84 studies to understand K-12 learners' engagement with data across disciplines and contexts. We propose the data paradigms framework that categorises learning activities along two dimensions: (i) logic (knowledge-based or data-driven systems), and (ii) explainability (transparent or opaque models). We further apply the notion of learning trajectories to visualize the pathways learners follow across these distinct paradigms. We detail four distinct trajectories as a provocation for researchers and educators to reflect on how the notion of data literacy varies depending on the learning context. We suggest these trajectories could be useful to those concerned with the design of data literacy learning environments within and beyond CS education.
This study proposes a quantitative framework to enhance curriculum coherence through the systematic alignment of Course Learning Outcomes (CLOs) and Program Learning Outcomes (PLOs), contributing to continuous improvement in outcome-based education. Grounded in accreditation standards such as ABET and NCAAA, the model introduces mathematical tools that map exercises, assessment questions, teaching units (TUs), and student assessment components (SACs) to CLOs and PLOs. This dual-layer approach-combining micro-level analysis of assessment elements with macro-level curriculum evaluation-enables detailed tracking of learning outcomes and helps identify misalignments between instructional delivery, assessment strategies, and program objectives. The framework incorporates alignment matrices, weighted relationships, and practical indicators to quantify coherence and evaluate course or program performance. Application of this model reveals gaps in outcome coverage and underscores the importance of realignment, especially when specific PLOs are underrepresented or CLOs are not adequately supported by assessments. The proposed model is practical, adaptable, and scalable, making it suitable for academic programs. Its systematic structure supports institutions in implementing evidence-based curriculum improvements and provides a reliable mechanism for aligning teaching practices with desired learning outcomes. Ultimately, this framework offers a valuable tool for closing the feedback loop between instructional design, assessment execution, and learning outcomes, thus promoting greater transparency, accountability, and educational effectiveness. Institutions that adopt this model can expect to strengthen their quality assurance processes and help ensure that students graduate with the competencies required by academic standards and professional expectations.
Osama A. Marzouk, Walid Ali Marjan Haje Rhaim Jul, Amjad Masoud Khalfan Al Jabri
et al.
We developed a vacuum generation system composed of a reciprocating compressor (3 tons of refrigeration) with an inverted-function that is ready to be hooked flexibly to a gas-tight container to create an evacuated enclosed atmosphere, without strict limitation of the size of that container. The evacuated container (or vacuum chamber) can serve in different purposes such as educational demonstration of the vacuum properties, extraction of perfumes from herbal resources, and preserving food. We tested the device and found it can reach a vacuum level of 26 inches of mercury in an environment with an atmospheric pressure of 28.5 inches of mercury. We compared the performance of our vacuum device to a rotary-vane vacuum pump of 1/4 horsepowers and found that the vacuum pump reaches a set test vacuum level of 25 inches of mercury before the compressor. We then demonstrated experimentally some features of the vacuum using the inverted compressor or the vane vacuum pump. These experiments serve some topics in physics for school students as well as two core subjects of mechanical engineering, namely fluid mechanics and thermodynamics.
Learning to learn is becoming a science, driven by the convergence of knowledge tracing, signal processing, and generative AI to model student learning states and optimize education. We propose CoTutor, an AI-driven model that enhances Bayesian Knowledge Tracing with signal processing techniques to improve student progress modeling and deliver adaptive feedback and strategies. Deployed as an AI copilot, CoTutor combines generative AI with adaptive learning technology. In university trials, it has demonstrated measurable improvements in learning outcomes while outperforming conventional educational tools. Our results highlight its potential for AI-driven personalization, scalability, and future opportunities for advancing privacy and ethical considerations in educational technology. Inspired by Richard Hamming's vision of computer-aided 'learning to learn,' CoTutor applies convex optimization and signal processing to automate and scale up learning analytics, while reserving pedagogical judgment for humans, ensuring AI facilitates the process of knowledge tracing while enabling learners to uncover new insights.
This study investigates AI integration in architectural education through a teaching experiment in Zhejiang University's 2024-25 grade three undergraduate design studio. Adopting a dual-module framework (20-hour AI training + embedded ethics discussions), the course introduced deep learning models, LLMs, AIGC, LoRA, and ComfyUI while maintaining the original curriculum structure, supported by dedicated technical instructors. Findings demonstrate the effectiveness of phased guidance, balanced technical-ethical approaches, and institutional support. The model improved students' digital skills and strategic cognition while addressing AI ethics, providing a replicable approach combining technical and critical learning in design education.
Allysson Allex Araújo, Marcos Kalinowski, Maria Teresa Baldassarre
In recent years, Software Engineering (SE) scholars and practitioners have emphasized the importance of integrating soft skills into SE education. However, teaching and learning soft skills are complex, as they cannot be acquired passively through raw knowledge acquisition. On the other hand, hackathons have attracted increasing attention due to their experiential, collaborative, and intensive nature, which certain tasks could be similar to real-world software development. This paper aims to discuss the idea of hackathons as an educational strategy for shaping SE students' soft skills in practice. Initially, we overview the existing literature on soft skills and hackathons in SE education. Then, we report preliminary empirical evidence from a seven-day hybrid hackathon involving 40 students. We assess how the hackathon experience promoted innovative and creative thinking, collaboration and teamwork, and knowledge application among participants through a structured questionnaire designed to evaluate students' self-awareness. Lastly, our findings and new directions are analyzed through the lens of Self-Determination Theory, which offers a psychological lens to understand human behavior. This paper contributes to academia by advocating the potential of hackathons in SE education and proposing concrete plans for future research within SDT. For industry, our discussion has implications around developing soft skills in future SE professionals, thereby enhancing their employability and readiness in the software market.
Powerful artificial intelligence (AI) tools that have emerged in recent years -- including large language models, automated coding assistants, and advanced image and speech generation technologies -- are the result of monumental human achievements. These breakthroughs reflect mastery across multiple technical disciplines and the resolution of significant technological challenges. However, some of the most profound challenges may still lie ahead. These challenges are not purely technical but pertain to the fair and responsible use of AI in ways that genuinely improve the global human condition. This article explores one promising application aligned with that vision: the use of AI tools to facilitate and enhance education, with a specific focus on signal processing (SP). It presents two interrelated perspectives: identifying and addressing technical limitations, and applying AI tools in practice to improve educational experiences. Primers are provided on several core technical issues that arise when using AI in educational settings, including how to ensure fairness and inclusivity, handle hallucinated outputs, and achieve efficient use of resources. These and other considerations -- such as transparency, explainability, and trustworthiness -- are illustrated through the development of an immersive, structured, and reliable "smart textbook." The article serves as a resource for researchers and educators seeking to advance AI's role in engineering education.
This paper aims to describe the digitalization in Educative system in Côte d’Ivoire according to the Education 4.0 paradigm to promote digital appropriation and build social resilience in a challenging context. Through a multisite ethnography case study in Côte d’Ivoire, a West African country, observation, interviews, and focus groups were used to collect data from a convenience sample of 20 participants (students, educators, parents, administrators) settled in 3 cities of the country. Based on Education 4.0 lenses, findings underlined the benefits and challenges of techno-pedagogy and technology integration according to Educative institutions' capacities in terms of Competencies, Learning methods, Information and communication technologies (ICT) categories, and Infrastructure levels. Moreover, participants show the integration of 21st century learning as an approach to support digital as a tool of educative resilience. As recommendations, an educative system approach including techno-pedagogy and 21st century pedagogy is a strategic approach for an effective educative system in Côte d’ Ivoire in this changing world, according to SDGs and internationalization of education trends.
Listiani Listiani, Ágnes Hódi and Marianne Nikolov
In recent years, there has been an increase in research on English as a foreign language (EFL) learners’ engagement with teachers’ feedback; however, little is known about how students engage with feedback combining written and audio feedback. Previous research has primarily focused on a single mode of feedback addressing specific writing issues, although findings have indicated the potential of combined modes of feedback (CMF) for addressing a broader range of writing problems across different levels. The purpose of this study was to address this gap by examining how 23 low-proficient university students behaviorally engaged with their teacher’s CMF in an EFL writing class in Indonesia. Datasets included students’ initial and final drafts of their descriptive and narrative tasks and their teacher’s CMF (audio and written). The findings revealed that successfully used feedback was more frequent than partially and unused feedback. The level of behavioral engagement varied across the language features addressed in the teacher’s feedback and the error categories also varied between the two writing tasks. Students used several strategies, including Revision, No Revision, Deletion, Substitution, and Addition. These strategies generally concerned micro level errors, which did not require extensive understanding and knowledge to implement the feedback. This article discusses the study’s pedagogical implications, limitations, and potential directions for future research.
As artificial intelligence (AI) continues to advance, it demonstrates capabilities comparable to human intelligence, with significant potential to transform education and workforce development. This study evaluates OpenAI o1-preview's ability to perform higher-order cognitive tasks across 14 dimensions, including critical thinking, systems thinking, computational thinking, design thinking, metacognition, data literacy, creative thinking, abstract reasoning, quantitative reasoning, logical reasoning, analogical reasoning, and scientific reasoning. We used validated instruments like the Ennis-Weir Critical Thinking Essay Test and the Biological Systems Thinking Test to compare the o1-preview's performance with human performance systematically. Our findings reveal that o1-preview outperforms humans in most categories, achieving 150% better results in systems thinking, computational thinking, data literacy, creative thinking, scientific reasoning, and abstract reasoning. However, compared to humans, it underperforms by around 25% in logical reasoning, critical thinking, and quantitative reasoning. In analogical reasoning, both o1-preview and humans achieved perfect scores. Despite these strengths, the o1-preview shows limitations in abstract reasoning, where human psychology students outperform it, highlighting the continued importance of human oversight in tasks requiring high-level abstraction. These results have significant educational implications, suggesting a shift toward developing human skills that complement AI, such as creativity, abstract reasoning, and critical thinking. This study emphasizes the transformative potential of AI in education and calls for a recalibration of educational goals, teaching methods, and curricula to align with an AI-driven world.
The development of large language models has ushered in new paradigms for education. This paper centers on the multi-Agent system in education and proposes the von Neumann multi-Agent system framework. It breaks down each AI Agent into four modules: control unit, logic unit, storage unit, and input-output devices, defining four types of operations: task deconstruction, self-reflection, memory processing, and tool invocation. Furthermore, it introduces related technologies such as Chain-of-Thought, Reson+Act, and Multi-Agent Debate associated with these four types of operations. The paper also discusses the ability enhancement cycle of a multi-Agent system for education, including the outer circulation for human learners to promote knowledge construction and the inner circulation for LLM-based-Agents to enhance swarm intelligence. Through collaboration and reflection, the multi-Agent system can better facilitate human learners' learning and enhance their teaching abilities in this process.
This research explores the integration of chatbot technology powered by GPT-3.5 and GPT-4 Turbo into higher education to enhance internationalization and leverage digital transformation. It delves into the design, implementation, and application of Large Language Models (LLMs) for improving student engagement, information access, and support. Utilizing technologies like Python 3, GPT API, LangChain, and Chroma Vector Store, the research emphasizes creating a high-quality, timely, and relevant transcript dataset for chatbot testing. Findings indicate the chatbot's efficacy in providing comprehensive responses, its preference over traditional methods by users, and a low error rate. Highlighting the chatbot's real-time engagement, memory capabilities, and critical data access, the study demonstrates its potential to elevate accessibility, efficiency, and satisfaction. Concluding, the research suggests the chatbot significantly aids higher education internationalization, proposing further investigation into digital technology's role in educational enhancement and strategy development.
This paper examines the historical foundations, current practices, and emerging challenges for Artificial Intelligence in Education (AIED) within broader AI practices. It highlights AIED's unique and rich potential for contributing to the current AI policy and practices, especially in the context of responsible AI. It also discusses the key gaps in the AIED field, which need to be addressed by the community to elevate the field from a cottage industry to the level where it will deservedly be seen as key to advancin AI research and practical applications. The paper offers a five-point manifesto aimed to revitalise AIED' contributions to education and broader AI community, suggesting enhanced interdisciplinary collaboration, a broadened understanding of AI's impact on human functioning, and commitment to setting agendas for human-centred educational innovations.This approach positions AIED to significantly influence educational technologies to achieve genuine positive impact across diverse societal segments.
Contribution: This Full paper in the Research Category track describes a practical, scalable platform that seamlessly integrates Generative AI (GenAI) with online educational forums, offering a novel approach to augment the instructional capabilities of staff. The platform empowers instructional staff to efficiently manage, refine, and approve responses by facilitating interaction between student posts and a Large Language Model (LLM). This contribution enhances the efficiency and effectiveness of instructional support and significantly improves the quality and speed of responses provided to students, thereby enriching the overall learning experience. Background: Grounded in Vygotsky's socio-cultural theory and the concept of the More Knowledgeable Other (MKO), the study examines how GenAI can act as an auxiliary MKO to enrich educational dialogue between students and instructors. Research Question: How effective is GenAI in reducing the workload of instructional staff when used to pre-answer student questions posted on educational discussion forums? Methodology: Using a mixed-methods approach in large introductory programming courses, human Teaching Assistants (AI-TAs) employed an AI-assisted platform to pre-answer student queries. We analyzed efficiency indicators like the frequency of modifications to AI-generated responses and gathered qualitative feedback from AI-TAs. Findings: The findings indicate no significant difference in student reception to responses generated by AI-TAs compared to those provided by human instructors. This suggests that GenAI can effectively meet educational needs when adequately managed. Moreover, AI-TAs experienced a reduction in the cognitive load required for responding to queries, pointing to GenAI's potential to enhance instructional efficiency without compromising the quality of education.
Lauren D. Branditz, Andrew P. Kendle, Cynthia G. Leung
et al.
Background The transition from medical student to intern is a recognized educational gap. To help address this, the Association of American Medical Colleges developed the Core Entrustable Professional Activities for entering residency. As these metrics outline expectations for all graduating students regardless of specialty, the described procedural expectations are appropriately basic. However, in procedure-heavy specialties such as emergency medicine, the ability to perform advanced procedures continues to contribute to the disconnect between undergraduate and graduate medical education. To prepare our graduating students for their internship in emergency medicine, we developed a simulation-based mastery learning curriculum housed within a specialty-specific program. Our overall goal was to develop the students’ procedural competency for central venous catheter placement and endotracheal intubation before graduation from medical school.Methods Twenty-five students participated in a simulation-based mastery learning procedures curriculum for ultrasound-guided internal jugular central venous catheter placement and endotracheal intubation. Students underwent baseline assessment, deliberate practice, and post-test assessments. Both the baseline and post-test assessments used the same internally developed checklists with pre-established minimum passing scores.Results Despite completing an emergency medicine rotation and a critical care rotation, none of the students met the competency standard during their baseline assessments. All twenty-five students demonstrated competency on both procedures by the end of the curriculum. A second post-test was required to demonstrate achievement of the central venous catheter and endotracheal intubation minimum passing scores by 16% and 28% of students, respectively.Conclusions Students demonstrated procedural competency for central venous catheter placement and endotracheal intubation by engaging in simulation-based mastery learning procedures curriculum as they completed their medical school training. With three instructional hours, students were able to achieve basic procedural competence for two common, high-risk procedures they will need to perform during emergency medicine residency training.
Sasirangan is a typical cloth from the South Kalimantan which is produced by the Banjarist people in home industries. The production of sasirangan has a very positive impact on the welfare of Banjarist people. However, the processing and liquid waste resulting from the production process can have a negative impact on workers' health and the environment because it contains synthetic dyes and heavy metals. This is caused by poor worker behavior in processing and disposing of liquid waste from sasirangan cloth. Worker behavior can be related to worker education and knowledge. This research aims to analyze the relationship between education, knowledge and the behavior of sasirangan workers in processing and disposing of waste in the home-based sasirangan industry in Banjarmasin. This research is an analytical observational study with a cross sectional approach, carried out at 3 (three) sasirangan production locations: Sungai Jingah, Seberang Masjid Village and Surgi Mufti subdistricts. Sampling used a purposive sampling technique with a sample size of 30 workers. Data analysis was carried out using descriptive and statistical analysis using the Chi Square test with the alternative Fisher Exact Test. The results of data analysis show the p value of the education variables (p=0.032) and knowledge (p=0.049). There is a significant relationship between education and knowledge and worker behavior in processing and disposing of waste in the sasirangan home industry in Banjarmasin. This is in accordance with Lawrence Green's theory, the better the worker's education and knowledge, the better the worker's behavior. The existence of a significant relationship between education and knowledge and workers' behavior in processing and disposing of waste in the sasirangan home industry in Banjarmasin requires follow-up in the form of efforts to increase education and knowledge regarding the processing and disposal of sasirangan waste for workers in the sasirangan home industry in Banjarmasin.
The integration of artificial intelligence (AI) into education has the potential to transform the way we learn and teach. In this paper, we examine the current state of AI in education and explore the potential benefits and challenges of incorporating this technology into the classroom. The approaches currently available for AI education often present students with experiences only focusing on discrete computer science concepts agnostic to a larger curriculum. However, teaching AI must not be siloed or interdisciplinary. Rather, AI instruction ought to be transdisciplinary, including connections to the broad curriculum and community in which students are learning. This paper delves into the AI program currently in development for Neom Community School and the larger Education, Research, and Innovation Sector in Neom, Saudi Arabia s new megacity under development. In this program, AI is both taught as a subject and to learn other subjects within the curriculum through the school systems International Baccalaureate (IB) approach, which deploys learning through Units of Inquiry. This approach to education connects subjects across a curriculum under one major guiding question at a time. The proposed method offers a meaningful approach to introducing AI to students throughout these Units of Inquiry, as it shifts AI from a subject that students like or not like to a subject that is taught throughout the curriculum.
Kacper Łodzikowski, Peter W. Foltz, John T. Behrens
We discuss the implications of generative AI on education across four critical sections: the historical development of AI in education, its contemporary applications in learning, societal repercussions, and strategic recommendations for researchers. We propose ways in which generative AI can transform the educational landscape, primarily via its ability to conduct assessment of complex cognitive performances and create personalized content. We also address the challenges of effective educational tool deployment, data bias, design transparency, and accurate output verification. Acknowledging the societal impact, we emphasize the need for updating curricula, redefining communicative trust, and adjusting to transformed social norms. We end by outlining the ways in which educational stakeholders can actively engage with generative AI, develop fluency with its capacities and limitations, and apply these insights to steer educational practices in a rapidly advancing digital landscape.