Hasil untuk "Special aspects of education"

Jeong-Bae Son, N. K. Ružić, A. Philpott

Abstract Artificial intelligence (AI) is changing many aspects of education and is gradually being introduced to language education. This article reviews the literature to examine main trends and common findings in relation to AI technologies and applications for second and foreign language learning and teaching. With special reference to computer-assisted language learning (CALL), the article explores natural language processing (NLP), data-driven learning (DDL), automated writing evaluation (AWE), computerized dynamic assessment (CDA), intelligent tutoring systems (ITSs), automatic speech recognition (ASR), and chatbots. It contributes to discussions on understanding and using AI-supported language learning and teaching. It suggests that AI will be continuously integrated into language education, and AI technologies and applications will have a profound impact on language learning and teaching. Language educators need to ensure that AI is effectively used to support language learning and teaching in AI-powered contexts. More rigorous research on AI-supported language learning and teaching is recommended to maximise second and foreign language learning and teaching with AI.

Andy Nguyen, M. Kremantzis, Aniekan Essien et al.

The proliferation of artificial intelligence (AI) technologies and chatbots has the potential to significantly reshape higher education. It is now imperative for stakeholders in this sector to grasp the fundamental aspects of AI technologies and understand their implications. This paper not only introduces basic AI concepts but also explains their specific applications and relevance in the higher education context. Moreover, it outlines the prospects of using AI technologies and chatbots to boost student engagement, presenting a synthesis of the opportunities available. Concurrently, we discuss the concerns and challenges associated with integrating AI into higher education settings. Several articles included in this special issue explore these opportunities and challenges from diverse viewpoints and within various contexts, across countries such as Australia, the United Kingdom, Vietnam, Cyprus, and GCC nations. Finally, we propose several avenues for future research aimed at enhancing student engagement through AI, charting a path forward for empirical evidence and practical application of AI and chatbots in enhancing student engagement.

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.

Francisco J. Cuadrado Muñoz

Reseña de La práctica de la orientación educativa y sus desafíos para la comunidad Coordinadores: Córdoba-Alcaide, F., Romera-Félix, E.M., y Ortega-Ruiz, R. Editorial: Graó Educación

Dimitris Pantazatos, Mary Grammatikou, Vasilis Maglaris

The rapid evolution of technology in educational settings has opened new avenues for enhancing learning experiences, particularly in specialized fields like network management. This paper explores the novel integration of a GenAI-based virtual assistant in a university-level network management course, focusing on its impact on developing students' soft skills, notably critical thinking and problem-solving abilities. Recognizing the increasing importance of these skills in the digital age, our study aims to assess the empirical effectiveness of this artificial intelligence-driven educational tool in fostering these competencies among students.

Wang Jiaqi, Lan Yi, Chen Xiang

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.

Qiaolin Qin, Ronnie de Souza Santos, Rodrigo Spinola

Context. The rise of generative AI (GenAI) tools like ChatGPT and GitHub Copilot has transformed how software is learned and written. In software engineering (SE) education, these tools offer new opportunities for support, but also raise concerns about over-reliance, ethical use, and impacts on learning. Objective. This study investigates how undergraduate SE students use GenAI tools, focusing on the benefits, challenges, ethical concerns, and instructional expectations that shape their experiences. Method. We conducted a survey with 130 undergraduate students from two universities. The survey combined structured Likert-scale items and open-ended questions to investigate five dimensions: usage context, perceived benefits, challenges, ethical and instructional perceptions. Results. Students most often use GenAI for incremental learning and advanced implementation, reporting benefits such as brainstorming support and confidence-building. At the same time, they face challenges including unclear rationales and difficulty adapting outputs. Students highlight ethical concerns around fairness and misconduct, and call for clearer instructional guidance. Conclusion. GenAI is reshaping SE education in nuanced ways. Our findings underscore the need for scaffolding, ethical policies, and adaptive instructional strategies to ensure that GenAI supports equitable and effective learning.

Surat Teerakapibal, Poompak Kusawat

Since its recent debut, ChatGPT has become a global sensation and significantly impacted the field of education. Both educational researchers and practitioners have identified opportunities as well as risks associated with the use of this novel tool in educational settings. Despite the ongoing debate, there is still no research exploring occupational differences in the perception of ChatGPT in education. In this paper, we analyzed Twitter data using topic modeling and sentiment analysis to investigate how ChatGPT is perceived and discussed differently in different occupations. Our study found diverse topics discussed including its use in schools, impact on exams, academic integrity concerns, and response accuracy evaluations. While most tweets were positive or neutral, concerns about integrity and response accuracy were evident. Analysis revealed sentiment and topic variations among users' occupations. These findings emphasize the opportunities and challenges of integrating ChatGPT in education, necessitating continued monitoring and informed policy-making for responsible utilization.

Martina Barbera, Amelia Rizzo

Although previous studies have investigated factors contributing to compulsive shopping, the specific role of alexithymia and its influence on emotional regulation in predicting this behavior remains underexplored. The study explores the link between alexithymia and compulsive shopping in young adults, focusing on whether emotional regulation difficulties predict problematic shopping behavior. A sample of 220 Italian young adults was assessed using the Toronto Alexithymia Scale (TAS-20) and the Shopping Behaviour Scale (SBS). Multiple regression analysis revealed that alexithymia's three dimensions explained 4.8% of the variance in compulsive shopping. Externally oriented thinking was the only significant predictor, while difficulty identifying and describing feelings were not. The findings suggest that individuals more focused on external realities are at higher risk for compulsive shopping. Improving emotional awareness and regulation may help reduce this behavior in young adults.

Xinmin Zhao, Kai Nan, Tongtong Xing

Background and Study Aim. In the context of increasing stress and declining health among female college students, there is an urgent need for effective methods to enhance their physical and mental well-being. The aim of this study is to analyze the impact of Baduanjin exercise on the physical fitness and mental health of female college students. Materials and methods. Sixty female college students at University were randomly selected from 150 volunteers to participate in this study. They were equally divided into an experimental group and a control group, with 30 students in each. All 60 participants completed the experiment. The study's protocol was conducted in accordance with ethical standards and was approved by the Institutional Review Board of University. Informed consent was obtained from all individual participants involved in the study. The experimental group received a 16-week intervention of traditional Baduanjin exercise, while the control group engaged in other unfixed sports activities synchronously. Results. After the experiment, the average weight of the experimental group decreased significantly from 52.41±6.35 kg to 50.06±5.46 kg (P<0.01). Body mass index, waist circumference, and other indicators also showed significant improvements. The step test index in the experimental group increased from 45.09±4.45 to 50.72±4.46, which was significantly different from the baseline (P<0.01). Improvements were noted in vital capacity, grip strength, and sit-up performance, all showing significant differences from baseline measures (P<0.01). The standing long jump and 800 m running performances in the experimental group showed significant improvement compared to pre-experiment measurements (P<0.05). Additionally, all mental health indicators in the experimental group demonstrated a downward trend, with significant differences in 10 indicators such as somatization, obsessive-compulsive symptoms, interpersonal sensitivity, and depression (P<0.05 or P<0.01). Moreover, there was a significant inter-group difference in motor skills and physical fitness between the experimental and control groups in the 800 m run (230.78±30.61 vs. 231.32±32.15) and standing long jump (1.81±0.33 vs. 1.78±0.42) after the intervention (P<0.05). Furthermore, significant differences were observed in mental excitement and participation in the experimental group before and after the Baduanjin exercise as assessed by Baduanjin’s Self-Perception Inventory (P<0.01). Conclusions. This study demonstrates that a 16-week program of traditional Baduanjin exercise significantly improves both physical and mental health parameters among female college students. These findings suggest that Baduanjin exercise can be an effective intervention for enhancing physical fitness and alleviating psychological distress in this population. The results underscore the potential of integrating traditional physical activities into health promotion strategies for young adults in educational settings.

Josenaide Alves da Silva, Geilsa Costa Santos Baptista, Nataélia Alves da Silva

A pesquisa é qualitativa e o objetivo propõe a análise da comunicação dos licenciandos para desenvolvimento de um ensino intercultural em aulas de ciências. Os envolvidos no trabalho foram dois licenciandos do curso de Ciências Agrárias, do Instituto Federal de Educação, Ciências e Tecnologia Baiano, do campus de Senhor do Bonfim-BA. Para coleta de dados, utilizou-se gravações em vídeos, procedendo a Análise de Conteúdo e a Estrutura de análise das classes comunicativas, para analisá-los. Este artigo apresenta resultados sobre as análises das aulas de ciências dos licenciandos, as quais direcionaram para o desenvolvimento da abordagem comunicativa dialógica, incluindo os saberes socioculturais dos estudantes e os saberes científicos, a partir de uma relação entre essas formas de conhecer. Considera-se que a abordagem comunicativa dialógica é um alicerce para os licenciandos ministrarem a prática de ciências contextualizada.

Kaska Porayska-Pomsta

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.

Anvit Sinha, Shruti Goyal, Zachary Sy et al.

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.

Charles Koutcheme, Nicola Dainese, Sami Sarsa et al.

The emergence of large language models (LLMs) has sparked enormous interest due to their potential application across a range of educational tasks. For example, recent work in programming education has used LLMs to generate learning resources, improve error messages, and provide feedback on code. However, one factor that limits progress within the field is that much of the research uses bespoke datasets and different evaluation metrics, making direct comparisons between results unreliable. Thus, there is a pressing need for standardization and benchmarks that facilitate the equitable comparison of competing approaches. One task where LLMs show great promise is program repair, which can be used to provide debugging support and next-step hints to students. In this article, we propose a novel educational program repair benchmark. We curate two high-quality publicly available programming datasets, present a unified evaluation procedure introducing a novel evaluation metric rouge@k for approximating the quality of repairs, and evaluate a set of five recent models to establish baseline performance.

Sven Jacobs, Steffen Jaschke

This study investigates the application of large language models, specifically GPT-4, to enhance programming education. The research outlines the design of a web application that uses GPT-4 to provide feedback on programming tasks, without giving away the solution. A web application for working on programming tasks was developed for the study and evaluated with 51 students over the course of one semester. The results show that most of the feedback generated by GPT-4 effectively addressed code errors. However, challenges with incorrect suggestions and hallucinated issues indicate the need for further improvements.

Shuaishuai Mi, Jianqiang Ye, Yan Li et al.

This study developed and validated an instrument to investigate senior school students’ understanding of electrostatics and provide a cognitive diagnostic assessment of their strengths and weaknesses on the related concepts (e.g., electric charge). The instrument included 20 four-tier multiple-choice items and the development process is organized around two activities: the development of the instrument and its validation. The development step defined the secondary concepts and designed the items using the misconceptions related to them. In the validation step, the instrument was applied to 1850 senior high school students from nine schools in two provinces in China, and the collected data were analyzed using the CDM package in R language. This step ensures that the diagnostic reports represent students’ conceptual understanding reliably and validly by selecting the best model, analyzing item quality, overall test reliability, and the instrument’s structure. The instrument can provide the percentage of students in the test population who possess certain combinations of concepts, the percentage of students in the test population possessing individual concepts, and the fine-grained size of concept proficiency information, which can be integrated as one completed report to issue to students, teachers, and parents to demonstrate students’ status of conceptual understanding related to electrostatics. In addition, the construct induced from the diagnostic results can also be aggregated to the classroom, schools for instruction planning or low-stakes decision making, or infer a learning sequence.

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.

Cansu Koyuturk, Mona Yavari, Emily Theophilou et al.

Educational chatbots come with a promise of interactive and personalized learning experiences, yet their development has been limited by the restricted free interaction capabilities of available platforms and the difficulty of encoding knowledge in a suitable format. Recent advances in language learning models with zero-shot learning capabilities, such as ChatGPT, suggest a new possibility for developing educational chatbots using a prompt-based approach. We present a case study with a simple system that enables mixed-turn chatbot interactions and discuss the insights and preliminary guidelines obtained from initial tests. We examine ChatGPT's ability to pursue multiple interconnected learning objectives, adapt the educational activity to users' characteristics, such as culture, age, and level of education, and its ability to use diverse educational strategies and conversational styles. Although the results are encouraging, challenges are posed by the limited history maintained for the conversation and the highly structured form of responses by ChatGPT, as well as their variability, which can lead to an unexpected switch of the chatbot's role from a teacher to a therapist. We provide some initial guidelines to address these issues and to facilitate the development of effective educational chatbots.

Fereniki Panagopoulou, Christina Parpoula, Kostas Karpouzis

Artificial intelligence has evolved enormously over the last two decades, becoming mainstream in different scientific domains including education, where so far, it is mainly utilized to enhance administrative and intelligent tutoring systems services and academic support. ChatGPT, an artificial intelligence-based chatbot, developed by OpenAI and released in November 2022, has rapidly gained attention from the entire international community for its impressive performance in generating comprehensive, systematic, and informative human-like responses to user input through natural language processing. Inevitably, it has also rapidly posed several challenges, opportunities, and potential issues and concerns raised regarding its use across various scientific disciplines. This paper aims to discuss the legal and ethical implications arising from this new technology, identify potential use cases, and enrich our understanding of Generative AI, such as ChatGPT, and its capabilities in education.