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Navigating AI Integration: Case Studies and Best Practices in Educational Transformation
Abstract
This chapter explores the practical applications and effective methods of integrating artificial intelligence (AI) into various educational settings. It examines how educational institutions, ranging from K-12 to higher education, have successfully utilized AI to enhance teaching methods, strategies, and learning outcomes through the presentation of compelling case studies. In addition to theoretical frameworks, the chapter offers practical insights into the challenges faced, strategies employed, and lessons learned during the implementation of AI-enhanced teaching approaches. The adoption of AI in education can facilitate personalized learning journeys by tailoring instruction, materials, pacing, and resources to individual learners' needs and preferences. It also enables adaptive assessments and feedback systems that provide real-time feedback, identify areas for improvement, and contribute to more nuanced grading systems. The chapter highlights examples of AI-powered platforms, such as adaptive learning platforms, intelligent tutoring systems, smart content recommendation systems, and gamified learning paths, illustrating their effectiveness in meeting the unique requirements of students and promoting engagement and mastery. Furthermore, it discusses the importance of immediate and targeted feedback and individualized content structuring in adaptive learning environments. The chapter also explores AI-assessment tools, real-time feedback systems, learning analytics dashboards, and peer learning facilitation platforms as valuable resources for educators. By leveraging AI technologies, educational institutions can transform teaching and learning practices, promote personalized and adaptive learning, and ensure the alignment of AI-based systems with human values.
... Competencies are grouped around five blocks: knowledge about AI, what can be done with it, how it can be used, and how people perceive it. AI that integrates as a core element in all university curricula (Osman and Ahmed 2024). A good practice is the model by Southworth et al. (2023) or the formative dimensions of AI from the validated instrument by Hornberger, Bewersdorff, and Nerdel (2023). ...
... Additionally, leveraging AI can provide personalised learning experiences and thus transform traditional education into dynamic experiences that meet the real needs of each student (Pokrivcakova 2019;Riapina 2024). The opportunity to integrate AI technologies into curricula has the potential to transform the educational experience of students, fully leveraging the opportunities these technologies offer (Osman and Ahmed 2024). However, to achieve proper integration in academic environments, it is crucial to address ethical considerations such as privacy, algorithmic bias, and the balance between technology and human interaction (Riapina 2024). ...
The present research explores AI's impact on education among Mexican undergraduate students through a non- experimental, correlational, cross- sectional study. A validated public questionnaire was distributed to 840 students via Google Forms from February to May 2024. Analysis revealed significant AI exposure and use patterns, primarily influenced by mass media and personal connections. Psychometric evaluations showed strong internal consistency (Cronbach's alpha = 0.97), with PCA and clustering identifying two student profiles: a knowledgeable majority and an optimistic minority with lower formal knowledge. Significant correlations were found between AI familiarity and perceived educational impact. These findings underscore the need for integrating formal AI training into curricula to bridge the gap between enthusiasm and practical knowledge, promoting ethical and effective AI use in higher education. This study informs policy and practice for aligning AI technologies with
educational goals.
... This way, teachers can focus more of their time on interacting with students and supporting learning. The other role of AI is to support human intelligence and help people with learning tasks (Osman & Ahmed, 2024). By offering individualized feedback, detecting trends in data, and facilitating group learning, AI has the potential to raise the standard of instruction. ...
... Additionally, the program provided participants with a variety of viewpoints to clarify the connections between various concept representations. Overall, the use of AI for lesson planning has the potential to greatly increase the efficiency and effectiveness of education by enabling educators to plan lessons and other resources and providing students more easily and accurately with more personalized planning options (Fuchs, 2023;Osman & Ahmed, 2024;Zhai, 2022). ...
This study aimed to examine pre-service middle school mathematics teachers' purposes of using ChatGPT in lesson plan development. Phenomenology, one of the qualitative research designs, was applied in this study. A total of 56 third-grade pre-service middle school mathematics teachers, 43 females and 13 males were selected for the study. A questionnaire with open-ended questions was employed to gather data. First, pre-service teachers were asked to select one or more learning outcomes in the mathematics curriculum and prepare a lesson plan to address these outcomes using the ChatGPT program. Then, they completed the questionnaire. Content analysis was utilized in the current study to analyze the data. According to the results of the study, ChatGPT served as a valuable tool for pre-service mathematics teachers during lesson plan development. While not all participants utilized it for every subcategory, ChatGPT played a significant role in various aspects, including determining the subject scope, getting creative and interesting ideas, explaining math concepts, establishing relationships and the course flow, developing learning activities, problems, and evaluation tools, and even reviewing and improving the lesson plan. Moreover, pre-service teachers used ChatGPT to deepen their knowledge of mathematical concepts, learning strategies, and teaching models. However, it is important to remember that ChatGPT should be viewed as a support system, not a replacement for a teacher's knowledge and expertise.
... AI technologies enable the development of adaptive learning systems that can dynamically adjust the pace, content, and delivery of instruction based on individual learner characteristics, preferences, and progress. By providing personalized learning pathways, AI empowers students to take ownership of their learning journey and achieve their full potential [1], [2]. Furthermore, AI facilitates the implementation of data-driven decision-making processes in education. ...
The integration of Artificial Intelligence (AI) into education presents both challenges and opportunities for learning and teaching. This paper examines the multifaceted landscape of AI in education, analyzing the potential benefits and obstacles it brings to the forefront. The opportunities AI offers in education are extensive, ranging from personalized learning experiences and adaptive assessment to the augmentation of teaching tasks and administrative functions. However, alongside these opportunities, there are significant challenges to address, including issues of equity, privacy, and the ethical use of AI algorithms. Moreover, the successful integration of AI into educational settings requires overcoming technical hurdles, ensuring educator readiness, and fostering a culture of collaboration between humans and machines. This paper synthesizes current research and practice to provide insights into navigating the integration of AI in education effectively. By understanding the challenges and leveraging the opportunities, educators and policymakers can harness the potential of AI to enhance learning outcomes, promote equity, and prepare students for the demands of the future workforce.
Integrasi kecerdasan buatan (Artificial Intelligence/AI) dalam layanan bimbingan dan konseling sekolah menawarkan peluang transformatif untuk meningkatkan dukungan perkembangan dan pembimbingan bagi siswa. Penelitian ini mengkaji peran literasi digital dalam memfasilitasi adopsi teknologi AI oleh konselor sekolah. Dengan menggunakan tinjauan literatur sistematis, penelitian ini menyoroti bagaimana kompetensi digital (mulai dari penguasaan alat digital hingga pemahaman tentang privasi data) memungkinkan konselor untuk mengintegrasikan AI secara efektif ke dalam praktik mereka. Aplikasi utama AI yang diidentifikasi meliputi penilaian kebutuhan yang dipersonalisasi, analitik prediktif untuk dukungan siswa, serta pengembangan media konseling interaktif seperti chatbot dan lingkungan virtual. Hasil penelitian menunjukkan bahwa solusi berbasis AI secara signifikan dapat meningkatkan praktik inklusif, bimbingan akademik, kesejahteraan emosional, dan dukungan siswa yang dipersonalisasi. Meskipun menawarkan banyak manfaat, penelitian ini juga mengidentifikasi tantangan etis, seperti privasi data dan bias algoritmik, yang memerlukan pengembangan pedoman etis yang kuat. Selain itu, meskipun AI dapat melengkapi pekerjaan konselor sekolah dengan menyederhanakan tugas administratif dan menyediakan intervensi yang tepat waktu, elemen manusia tetap penting untuk mendukung siswa secara empati dan holistik.Penelitian ini berkontribusi pada diskursus yang berkembang mengenai AI dalam pendidikan dengan menekankan hubungan antara literasi digital dan adopsi AI dalam konseling sekolah. Penelitian ini mengisi kesenjangan riset dengan berfokus pada kebutuhan unik konselor yang harus menyeimbangkan integrasi teknologi dengan dukungan perkembangan dan emosional siswa. Temuan ini menegaskan pentingnya membekali konselor dengan keterampilan digital yang diperlukan untuk memanfaatkan potensi AI secara maksimal, sehingga meningkatkan kualitas dan jangkauan layanan konseling sekolah. Penelitian lanjutan diharapkan dapat berfokus pada evaluasi empiris alat AI dalam konteks konseling nyata serta pengembangan program pelatihan untuk meningkatkan literasi digital di kalangan konselor sekolah Kata Kunci: Ai Literacy; Konselor sekolah; Layanan Bimbingan dan konseling
Abstract—Inclusion in education is critical to achieving Sus- tainable Development Goal 4, which ensures equitable education for all. This study explores the application of decision tree algorithms to improve adaptive education, promoting inclusion and diversity in higher education. Decision trees were used to classify students into predefined categories based on various attributes such as gender, age, learning styles, and specific needs (for example, ADHD). The algorithm was trained on an experimental dataset and demonstrated high accuracy (95.94%) in classification. By dynamically adjusting educational content and accessibility options, this approach aims to provide per- sonalized learning experiences tailored to individual needs. The findings highlight the potential of AI-driven adaptive learning systems to improve educational outcomes and foster an inclusive environment by accommodating various learning preferences and requirements. This research contributes to a greater under- standing of how technology can be used to address educational inequities and suggests future directions to improve adaptive learning frameworks.
Index Terms—adaptive education, Smart Learning Platforms, educational innovation, higher education, decision trees, inclusion and diversity.
AI-enhanced micro-credentials can optimize learning pathways, improve access to education, and boost workforce skills, according to this report. It addresses traditional education delivery model issues and an increasing requirement for lifelong learning. An AI expert system tool called EDMENTUM validates credentials and customizes learning routes in the quasi-experimental study on AI-enhanced micro-credentials and learning efficiency. Discussing micro-credentials history and integration with AI, case examples show their practical use. The document also emphasizes linking education with industrial demands and addressing fairness, access, data privacy, and security issues for policy and practice. AI-enhanced micro-credentials have many benefits, but careful design and execution are needed to maintain data transparency, trust, and management.
In inclusive education, students with different needs learn in the same context. With the advancement of artificial intelligence (AI) technologies, it is expected that they will contribute further to an inclusive learning environment that meets the individual needs of diverse learners. However, in Japan, we did not find any studies exploring current needs in an actual special needs context. In this study, we used the learning and evidence analysis framework (LEAF) as a learning analytics-enhanced learning environment and employed Active Reading as an example learning task to investigate the challenges and possibilities of applying AI to inclusive education in the future. Two students who attended a resource room formed the context. We investigated learning logs in the LEAF system while each student executed a given learning task. We detected specific learning behaviors from the logs and explored the challenges and future potential of learning with AI technology, considering human involvement in orchestrating inclusive educational practices.
So far, this pandemic has severely affected the education sector. As education undergoes a brilliant transformation with advancing technology, the digital acquisition of knowledge has yet to find widespread use - virtual exams. Faraway Proctoring offers several advantages of using manual and primarily based technology. Although this allows students to take an exam in any field with specific technical requirements, it eliminates the need for physical research centers. It is cost-effective and easy to plan, which can be challenging to manage, especially during aggressive trials. Finally, the paper discusses the performance characteristics of different styles of web-based inspection systems, along with their limitations and challenges.
Artificial Intelligence (AI) is changing the way we interact with technology and the way we live our lives. It has the potential to revolutionize the field of education, especially teacher education. This research paper aims to investigate the role of AI in teacher education, including its potential benefits, drawbacks, and challenges. The paper explores how AI can improve the quality of teacher education, enhance teachers' skills, and facilitate personalized learning. The research also highlights the ethical, social, and cultural implications of AI in teacher education. The paper concludes that AI has the potential to transform teacher education, but it requires careful implementation and ethical considerations.
In this intellectual work, the clinical and educational aspects of dentistry were confronted with practical applications of artificial intelligence (AI). The aim was to provide an up-to-date overview of the upcoming changes and a brief analysis of the influential advancements in the use of AI in dental education since 2020. In addition, this review provides a guide for a dental curriculum update for undergraduate and postgraduate education in the context of advances in AI applications and their impact on dentistry. Unsurprisingly, most dental educators have limited knowledge and skills to assess AI applications, as they were not trained to do so. Also, AI technology has evolved exponentially in recent years. Factual reliability and opportunities with OpenAI Inc.’s ChatGPT are considered critical inflection points in the era of generative AI. Updating curricula at dental institutions is inevitable as advanced deep-learning approaches take over the clinical areas of dentistry and reshape diagnostics, treatment planning, management, and telemedicine screening. With recent advances in AI language models, communication with patients will change, and the foundations of dental education, including essay, thesis, or scientific paper writing, will need to adapt. However, there is a growing concern about its ethical and legal implications, and further consensus is needed for the safe and responsible implementation of AI in dental education.
p>Engineering education is constantly evolving to keep up with the latest technological developments and meet the changing needs of the engineering industry. One promising development in this field is the use of generative artificial intelligence technology, such as the ChatGPT conversational agent. ChatGPT has the potential to offer personalized and effective learning experiences by providing students with customized feedback and explanations, as well as creating realistic virtual simulations for hands-on learning. However, it is important to also consider the limitations of this technology. ChatGPT and other generative AI systems are only as good as their training data and may perpetuate biases or even generate and spread misinformation. Additionally, the use of generative AI in education raises ethical concerns such as the potential for unethical or dishonest use by students and the potential unemployment of humans who are made redundant by technology. While the current state of generative AI technology represented by ChatGPT is impressive but flawed, it is only a preview of what is to come. It is important for engineering educators to understand the implications of this technology and study how to adapt the engineering education ecosystem to ensure that the next generation of engineers can take advantage of the benefits offered by generative AI while minimizing any negative consequences.</p
This study provides an overview of research on teachers' use of artificial intelligence (AI) applications and machine learning methods to analyze teachers' data. Our analysis showed that AI offers teachers several opportunities for improved planning (e.g., by defining students' needs and familiarizing teachers with such needs), implementation (e.g., through immediate feedback and teacher intervention), and assessment (e.g., through automated essay scoring) of their teaching. We also found that teachers have various roles in the development of AI technology. These roles include acting as models for training AI algorithms and participating in AI development by checking the accuracy of AI automated assessment systems. Our findings further underlined several challenges in AI implementation in teaching practice, which provide guidelines for developing the field.
A Smart Campus is a miniature of a Smart City with a more demanding framework that enables learning, social interaction and creativity. To ensure a Smart Campus uninterruptible secure operation, a key requirement is that daily routines and activities are performed protected in an environment monitored unobtrusively by a robust surveillance system. The various components that compose such an environment, buildings, labs, public spaces, smart lighting, smart parking, or even smart traffic lights, require us to focus on surveillance systems, and recognize which detection activities to establish. In this paper, we perform a comparative assessment in the area of surveillance systems for Smart Campuses. A proposed taxonomy for IoT-enabled Smart Campus unfold five research dimensions: (1) physical infrastructure; (2) enabling technologies; (3) software analytics; (4) system security; and (5) research methodology. By applying this taxonomy and by adopting a weighted scoring model on the surveyed systems, we first present the state-of-the-art, and then we make a comparative assessment and classify the systems. We extract valuable conclusions and inferences from this classification, providing insights and directions towards required services offered by surveillance systems for Smart Campus.
This survey is an updated and improved version of the previous one published in 2013 in this journal with the title “data mining in education”. It reviews in a comprehensible and very general way how Educational Data Mining and Learning Analytics have been applied over educational data. In the last decade, this research area has evolved enormously and a wide range of related terms are now used in the bibliography such as Academic Analytics, Institutional Analytics, Teaching Analytics, Data‐Driven Education, Data‐Driven Decision‐Making in Education, Big Data in Education, and Educational Data Science. This paper provides the current state of the art by reviewing the main publications, the key milestones, the knowledge discovery cycle, the main educational environments, the specific tools, the free available datasets, the most used methods, the main objectives, and the future trends in this research area.
This article is categorized under:
Application Areas > Education and Learning
Owing to the widespread distribution of open‐source robotic software and cheaper hardware, design education in architecture and engineering is evolving to emphasize interactive and dynamic geometries, using new digital media and technologies. However, ethnic minority groups are still underrepresented in technology‐driven changes in architecture, and their career is at risk in the growing tech‐related job markets of the architecture, construction, and engineering (ACE) industry. In this context, the paper addresses college‐level pedagogical issues and learning performance related to introductory robotics education in architectural design studios for the underrepresented minority (URM) students. This article presents the organization of curriculum activities, technical experiments in learning, and teaching experiences in the largest Hispanic‐serving institution (HSI) in the U.S. The academic projects presented exhibit both technical challenges and achievements for URM students to build design competency and tool fluency in a streamlined prototyping of responsive kinetic building fabrication. The effectiveness of computer‐aided design (CAD) technologies (3D printing, sensing, microcontrollers, etc.) for the advancement of minority‐serving design education is discussed to promote design‐learning possibilities and interdisciplinary collaborations associated with science and engineering. Expectations from course objectives are compared with final outcomes to suggest efficient learning strategies and teaching solutions of kinetic architecture.
This paper explores the phenomena of the emergence of the use of artificial intelligence in teaching and learning in higher education. It investigates educational implications of emerging technologies on the way students learn and how institutions teach and evolve. Recent technological advancements and the increasing speed of adopting new technologies in higher education are explored in order to predict the future nature of higher education in a world where artificial intelligence is part of the fabric of our universities. We pinpoint some challenges for institutions of higher education and student learning in the adoption of these technologies for teaching, learning, student support, and administration and explore further directions for research.
Through a comprehensive analysis of using natural language processing in information retrieval, we compared the effects with the various natural language techniques for information retrieval precision in this paper. This is for the tasks of more suitable as well as accurate results of natural language processing.
In recent years, learner models have emerged from the research laboratory and research classrooms into the wider world. Learner
models are now embedded in real world applications which can claim to have thousands, or even hundreds of thousands, of users.
Probabilistic models for skill assessment are playing a key role in these advanced learning environments. In this paper, we
review the learner models that have played the largest roles in the success of these learning environments, and also the latest
advances in the modeling and assessment of learner skills. We conclude by discussing related advancements in modeling other
key constructs such as learner motivation, emotional and attentional state, meta-cognition and self-regulated learning, group
learning, and the recent movement towards open and shared learner models.
KeywordsStudent models–Learner models–Probabilistic models–Bayesian Networks–IRT–Model tracing–POKS–Bayesian Knowledge Tracing–Intelligent Tutoring System–Learning environments–Cognitive modeling
This book systematically reviews a broad range of cases in education that utilize cutting-edge AI technologies. Furthermore, it introduces readers to the latest findings on the scope of AI in education, so as to inspire researchers from non-technological fields (e.g. education, psychology and neuroscience) to solve education problems using the latest AI techniques. It also showcases a number of established AI systems and products that have been employed for education. Lastly, the book discusses how AI can offer an enabling technology for critical aspects of education, typically including the learner, content, strategy, tools and environment, and what breakthroughs and advances the future holds.
The book provides an essential resource for researchers, students and industrial practitioners interested and engaged in the fields of AI and education. It also offers a convenient handbook for non-professional readers who need a primer on AI in education, and who want to gain a deeper understanding of emerging trends in this domain.
This book brings together the fields of artificial intelligence (often known as A.I.) and inclusive education in order to speculate on the future of teaching and learning in increasingly diverse social, cultural, emotional, and linguistic educational contexts. This book addresses a pressing need to understand how future educational practices can promote equity and equality, while at the same time adopting A.I. systems that are oriented towards automation, standardisation and efficiency. The contributions in this edited volume appeal to scholars and students with an interest in forming a critical understanding of the development of A.I. for education, as well as an interest in how the processes of inclusive education might be shaped by future technologies. Grounded in theoretical engagement, establishing key challenges for future practice, and outlining the latest research, this book offers a comprehensive overview of the complex issues arising from the convergence of A.I. technologies and the necessity of developing inclusive teaching and learning.
To date, there has been little in the way of direct association between research and practice in these domains: A.I. has been a predominantly technical field of research and development, and while intelligent computer systems and ‘smart’ software are being increasingly applied in many areas of industry, economics, social life, and education itself, a specific engagement with the agenda of inclusion appears lacking. Although such technology offers exciting possibilities for education, including software that is designed to ‘personalise’ learning or adapt to learner behaviours, these developments are accompanied by growing concerns about the in-built biases involved in machine learning techniques driven by ‘big data’.
The inclusion of computer technology in education has led to increased attention for personalized learning and instruction. By means of personalized learning, or adaptive learning, learners are given instruction and support directly, adjusted to their cognitive and noncognitive needs. This chapter aims at giving an overview of the current research that addresses advanced technologies, models, and approaches to establish personalized learning, instruction, and performance. In order to provide this, relevant learner and learning characteristics need to be measured or inferred and incorporated in learner models. These learner models provide the basis from which personalization can occur and have to be considered as the core of personalized learning environments. In order to provide dynamic personalized learning, learner models need to be adjusted and updated with new information about the learner's knowledge, affective states, and behavior. To do so, the fields of artificial intelligence and educational data mining provide advanced technologies that can be applied for fine-grained learner modeling. First, the field of artificial intelligence in education has largely supported the development of intelligent tutoring systems. Second, educational data mining is indispensable for providing information about the learning process and learner behavior. The integration of artificial intelligence and educational data mining in the learner modeling research provides a firm basis for effectiveness research on personalized systems. This chapter is concluded with the call for educational technologists to use advanced technologies as a method to support personalized learning and not as a goal when developing adaptive learning environments. © Springer Science+Business Media New York 2014. All rights reserved.
The initial vision for intelligent tutoring systems involved powerful, multi-faceted systems that would leverage rich models of students and pedagogies to create complex learning interactions. But the intelligent tutoring systems used at scale today are much simpler. In this article, I present hypotheses on the factors underlying this development, and discuss the potential of educational data mining driving human decision-making as an alternate paradigm for online learning, focusing on intelligence amplification rather than artificial intelligence.
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