Artificial Intelligence in Education. Promise and Implications for Teaching and Learning.
Abstract
Artificial intelligence (AI) is arguably the driving technological force of
the first half of this century, and will transform virtually every industry, if
not human endeavors at large. Businesses and governments worldwide
are pouring enormous sums of money into a very wide array of
implementations, and dozens of start-ups are being funded to the tune of
billions of dollars. It would be naive to think that AI will not have an impact on education—au contraire, the possibilities there are profound yet, for the time being, overhyped as well. This book attempts to provide the right balance between reality and hype, between true potential and wild extrapolations.
... School administrators face both enormous opportunities and significant obstacles when integrating artificial intelligence (AI) into their learning environments. The potential of AI technologies to revolutionize instructional approaches, improve learning outcomes, and accelerate administrative procedures is becoming more and more apparent as they develop quickly (Holmes et al., 2019). But this technology revolution also brings up important issues of moral application, fair access, and required changes to leadership styles (Eaton et al., 2022). ...
... -Resource Allocation and Infrastructure: According to Holmes et al. (2019), using AI technology usually necessitates a large financial investment in network capabilities, software, and hardware. In order to facilitate AI integration, school administrators must manage budgetary restrictions and make sure the required technology infrastructure is put in place. ...
... The framework that highlights the significance of giving everyone fair and impartial access to artificial intelligence (AI) was created by Holmes et al. (2019). According to this framework educational institutions should place a high priority on promoting and integrating AI technologies that are easily accessible to students from a variety of backgrounds, including those who come from low-income or disabled families. ...
This Master's thesis investigates the complex relationship between artificial intelligence (AI) and K-12 education, with particular focus on implications for school leadership. Through comprehensive literature review and qualitative analysis, the study examines critical themes including:
Development of robust professional development frameworks
Evolution of leadership competencies in AI-enhanced environments
Strategies for addressing educational disparities
Ethical considerations in AI adoption
The research synthesizes multiple theoretical frameworks including transformative leadership theory, TPACK, and connectivism to provide a nuanced perspective on AI integration in education. Key findings reveal significant shifts toward more comprehensive ethical frameworks, expanded leadership roles encompassing digital equity advocacy, and a deeper understanding of AI's potential to both exacerbate and bridge educational gaps.
The study advances the field by illuminating effective leadership strategies for AI integration, highlighting collaborative design processes and stakeholder engagement. It also identifies directions for future research, particularly in evaluating AI's long-term impacts on learning outcomes and developing equitable implementation strategies across diverse international contexts.
... • Technological advancements: There is a need for more sophisticated AI systems that are sensitive to the diverse needs of students. Research should focus on developing AI that can dynamically adjust to various learning styles and cultural backgrounds [75]. • Longitudinal studies: To better understand the long-term impacts of AI on education, longitudinal studies are required. ...
... • Longitudinal studies: To better understand the long-term impacts of AI on education, longitudinal studies are required. These studies will provide deeper insights into how AI technologies influence learning outcomes over time [75]. • Emotional and social support: Future AI systems should extend beyond academic support to include emotional and social dimensions. ...
This systematic literature review explores the integration of artificial intelligence (AI) technologies such as intelligent tutoring systems (ITS), machine learning, natural language processing, and adaptive learning platforms in university education. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we evaluated peer-reviewed articles, case studies, and government reports from 2015 onwards. The results demonstrate that AI technologies not only enhance personalized learning and educational outcomes but also streamline administrative functions, transforming educational practices. However, challenges such as ethical issues, data privacy, and algorithmic bias remain. The review underscores the importance of theoretical frameworks like constructivist learning theory and the TPACK framework for effective AI integration. Recommendations are provided for educators, administrators, and policymakers to ensure responsible AI use in university settings. This paper offers insights into the current capabilities and future prospects of AI in higher education, promoting ongoing research and strategic implementation.
... A logical result of an increase in 0.9 points in the engagement scores would implicate co-teaching with AI as improving student contribution compared to a totally traditional class. This agrees with (Holmes et al., 2019) in showing that AI technologies could maintain student interest and motivation with interactive, game-like learning experiences. ...
Artificial Intelligence is fast changing the face of education, acting almost as a co-teacher in enhancing personalized learning experiences. The role of AI in the classroom has been underlined in this article, emphasizing how it can adapt to individual learning styles, real-time assessment of the progress students makes, and customized instructional support. This mixed-method study questioned quantitative data from schools using the AI tools and gathered qualitative feedback from teachers and students. Striking among these was the improvement in engagement and academic performance. Accordingly, the average test scores increased by as high as 15%, while the trend of student participation continued to increase, with as high as 78% of the teachers reporting increased levels of engagement. AI is complementing teaching and preparing students for the world that they will encounter, which demands digital literacy. This will enable the educators, upon embracing the technology, to ensure that no single student misses out on a leaning need and that the learning environment is effective and inclusive. The contribution of this research adds to the growing compilation of research papers on AI in Education, providing significant information to policymakers and educators interested in improving teaching and learning results.
Жасанды интеллект (ЖИ) білім беру саласында төңкеріс жасап, оқыту мен оқудың жаңа тәсілдерін ұсынады. Бұл мақалада ЖИ-дің білім беру процесіндегі рөлі мен оның тиімділігін арттырудағы мүмкіндіктері қарастырылады. ЖИ оқушылардың қажеттіліктеріне бейімделген персонализирленген оқу бағдарламаларын ұсыну арқылы оқыту сапасын жақсартуға мүмкіндік береді. Сонымен қатар, автоматтандырылған бағалау, мәтіндерді талдау және интерактивті тапсырмаларды құру ЖИ-дің негізгі артықшылықтары болып табылады.Мақалада ЖИ-дің қолдану салалары, оның ішінде қашықтықтан оқыту, оқушылардың оқу жетістіктерін бақылау және инклюзивті білім беру процестеріндегі үлесі талқыланады. ЖИ технологиялары мұғалімдерге жүктемені азайтып, шығармашылық және стратегиялық мәселелерге назар аударуға мүмкіндік береді. Дегенмен, этикалық мәселелер мен технологиялық шектеулер ЖИ-ді білім беру жүйесіне енгізу барысында маңызды факторлар болып табылады. Бұл зерттеу ЖИ-дің білім беру саласындағы ықпалы мен болашағын тереңірек түсінуге бағытталған.
Бұл мақалада бастауыш білім беру жүйесінде жасанды интеллекттің (ЖИ) қолдану мүмкіндіктері қарастырылады. ЖИ технологиялары оқыту процесін жекелендіруге, оқушылардың танымдық қабілеттерін дамытуға және оқу нәтижелерін жақсартуға бағытталған. Мақалада ЖИ құралдарының бастауыш сынып оқушыларының оқу процесіне ықпалы және оны қолданудың тиімді әдістері талданады. Сонымен қатар, ЖИ-дың сыни ойлау, шығармашылық және өз бетінше оқу қабілеттерін дамытудағы рөлі сипатталады. ЖИ білім беру саласына енгізілуі оқушылардың оқуға деген ынтасын арттырып, олардың білім алу сапасын жақсартуға ықпал етеді.
Бұл мақалада бастауыш сынып оқушыларын оқытудағы инновациялық тәсілдер мен олардың білім беру жүйесіндегі маңыздылығы қарастырылады. Оқыту процесінде қолданылатын заманауи әдістер оқушылардың танымдық қабілеттерін арттырып, шығармашылық және сыни ойлау дағдыларын дамытуға ықпал етеді. Мақалада оқыту тәжірибесінде интерактивті технологияларды, жобалық оқыту әдістерін және ойын элементтерін қолданудың тиімділігі талқыланған. Инновациялық тәсілдер оқушылардың оқуға деген қызығушылығын арттырып, олардың оқу нәтижелерін жақсартуға мүмкіндік береді.
Бұл мақалада функционалдық сауаттылықты дамытуда жасанды интеллекттің (ЖИ) мүмкіндіктері қарастырылады. ЖИ технологиялары оқу процесін жекелендіруге, білім алушылардың танымдық қабілеттерін арттыруға және оқу нәтижелерін жақсартуға мүмкіндік береді. Мақалада ЖИ-дың негізгі құралдары, олардың функционалдық сауаттылықты дамытудағы рөлі мен тиімділігі талданған. Сонымен қатар, ЖИ-ды қолданудың заманауи тәсілдері мен педагогикалық мүмкіндіктері сипатталады. ЖИ-дың білім беру саласына енгізілуі оқушылардың сыни ойлау, талдау және мәселелерді шешу дағдыларын жетілдіруге ықпал етеді.
Бұл мақалада бастауыш білім беру жүйесінде жасанды интеллект (ЖИ) технологияларын қолданудың тиімділігі мен маңыздылығы қарастырылады. ЖИ құралдары білім беру процесін жекелендіруге, оқушылардың танымдық қабілеттерін арттыруға және олардың білім алу сапасын жақсартуға бағытталған. Мақалада ЖИ технологиялары арқылы оқыту әдістерінің инновациялық аспектілері, сонымен қатар олардың бастауыш сынып оқушыларының шығармашылық және сыни ойлау дағдыларын дамытудағы рөлі талданады. ЖИ-дың білім беру саласындағы заманауи үлгілері оқушылардың оқуға деген қызығушылығын арттыруға және олардың оқу нәтижелерін жақсартуға ықпал етеді.
The integration of Artificial Intelligence (AI) in lifelong learning presents unprecedented opportunities to revolutionize education by enhancing accessibility, personalization, and sustainability. This paper explores the multifaceted role of AI in creating adaptable learning environments that cater to diverse learner needs while promoting sustainable practices in education. Through AI-driven tools such as adaptive learning platforms, intelligent tutoring systems, and data analytics, the study highlights the potential for scalable solutions that democratize learning opportunities globally. Moreover, it examines the sustainability dimension, emphasizing how AI can optimize resource use, reduce educational disparities, and foster eco-friendly practices. The findings suggest that AI's transformative potential can address critical challenges in lifelong learning, making education more inclusive and sustainable for a rapidly evolving world.
The integration of artificial intelligence (AI) into distance learning presents a transformative opportunity for the sustainable development of continuing adult education. This research explores the multifaceted impacts of AI on distance learning frameworks, emphasizing its potential to enhance personalization, engagement, and accessibility. Through a comprehensive literature review, case studies, and qualitative interviews with educators and learners, this study identifies the key AI technologies driving these changes, including natural language processing, machine learning, and predictive analytics. The findings highlight that AI not only facilitates individualized learning experiences but also supports the principles of sustainable education by promoting lifelong learning and reducing educational inequalities. Furthermore, the study discusses the challenges and ethical considerations inherent in deploying AI in educational contexts. The results suggest that while AI holds significant promise for improving educational outcomes, careful implementation and continuous evaluation are essential to ensure its benefits are equitably distributed. This research provides valuable insights for educators, policymakers, and technologists aiming to leverage AI for the advancement of sustainable distance learning in adult education.
Diversity has long been the norm in German schools. The educational
system, however, has not yet succeeded in providing all students
with equal educational opportunities: In Germany, more than in
almost any other country, learning success is dependent on a
student’s social background.
Consequently, the call for personalisation of the learning experience,
i.e. providing individual support to students, is frequently heard.
In real life, however, this approach entails major challenges, both for
students who will have to learn self-guided learning, and teachers
who will need to support them. It still remains unclear whether
technology can help in these efforts, for instance by diagnosing a
student’s level of knowledge, selecting learning content or adapting
it to the respective student. Technology-enhanced personalised
learning is not yet common in Germany, which is why we have
tasked scientists with summarising the current status of international
research on the matter.
Purpose:
Improved access to technology in the radiation therapy (RT) workforce education has resulted in opportunities for innovative patient education methods. This study investigated the impact of a newly developed education tool using the Virtual Environment for Radiotherapy Training (VERT) system on patients' RT knowledge and anxiety.
Method:
Breast cancer patients were recruited into a control group (CG) (n = 18) who underwent the standard pre-RT education package at a targeted cancer therapy centre, followed by a VERT group (VG) (n = 19). VG patients attended a VERT-based education session detailing RT immobilisation, planning and treatment. All patients completed questionnaires at four time points throughout their treatment, with survey sub-sections on RT knowledge, experience and anxiety.
Results:
For both groups, anxiety levels were highest at time point 1(T1 after initial radiation oncologist consultation) (CG, 41.2; VG, 43.1), with a gradual decrease observed thereafter at time points before simulation, at the beginning of treatment and at the end of treatment (p > 0.05). The VG's RT knowledge scores were statistically significantly higher than those of the CG scores at all time points following VERT education (p < 0.05).
Conclusion:
This study reports the high value of VERT breast cancer-targeted education programs in improving RT knowledge and perhaps decreasing patient anxiety. Continued efforts are required to improve patients' accessibility to VERT in Australia, and to better understand the effect of VERT's unique educational features on patients' emotional and physical needs throughout their RT.
Introduction
When on board H.M.S. ‘Beagle,’ as naturalist, I was much struck with certain facts in the distribution of the inhabitants of South America, and in the geological relations of the present to the past inhabitants of that continent. These facts seemed to me...
Analyzing the quality of classroom talk is central to educational research and improvement efforts. In particular, the presence of authentic teacher questions, where answers are not predetermined by the teacher, helps constitute and serves as a marker of productive classroom discourse. Further, authentic questions can be cultivated to improve teaching effectiveness and consequently student achievement. Unfortunately, current methods to measure question authenticity do not scale because they rely on human observations or coding of teacher discourse. To address this challenge, we set out to use automatic speech recognition, natural language processing, and machine learning to train computers to detect authentic questions in real-world classrooms automatically. Our methods were iteratively refined using classroom audio and human-coded observational data from two sources: (a) a large archival database of text transcripts of 451 observations from 112 classrooms; and (b) a newly collected sample of 132 high-quality audio recordings from 27 classrooms, obtained under technical constraints that anticipate large-scale automated data collection and analysis. Correlations between human-coded and computer-coded authenticity at the classroom level were sufficiently high (r = .602 for archival transcripts and .687 for audio recordings) to provide a valuable complement to human coding in research efforts.
Virtual Reality(VR) is still an emerging technology in terms of recognizing its full potential in education and specifically in teacher education. A key issue of a VR-based approach for teacher training, is the level of presence along with the empathy inflicted on the trainees that will allow them to experience realistic and emotion-rich classroom situations in a virtual environment. This paper describes an experiment that aims to assess the influence of the graphical realism of a virtual classroom to the levels of presence and development of empathy skills for trainee teachers. Moreover, a second objective is to investigate whether there are significant differences between training in a VR classroom and a real physical classroom and how this affects the trainee teacher. The overall conclusion of the experiment is that the design of the VR classroom environment influenced the levels of immersion and presence. Moreover, according to the results there are serious indications that the VR system provided users the immersion necessary for the development of embodied thinking skills and thus of empathy in relation to multiculturalism.
As the development of virtual reality (VR) and simulation technologies have progressed, so has their incorporation into graduate medical education, especially within surgical specialties. The attention on duty-hour restrictions, the emphasis on patient safety, as well as the advancement of complex surgical techniques, all contribute to the increasing use and utility of virtual reality simulation in neurosurgical training. Additionally, residency programs have sought quantitative measures of competency to achieve the ACGME milestones, and simulation software generally provides detailed proficiency and performance reports for the user, which could be implemented as an evaluative tool throughout training. This brief chapter will overview developments in virtual reality simulation within neurosurgery and their competency-directed use in graduate medical education. Other chapters within this textbook will review specific technologies in more detail.
Artificial intelligence (AI) algorithms, particularly deep learning, have demonstrated remarkable progress in image-recognition tasks. Methods ranging from convolutional neural networks to variational autoencoders have found myriad applications in the medical image analysis field, propelling it forward at a rapid pace. Historically, in radiology practice, trained physicians visually assessed medical images for the detection, characterization and monitoring of diseases. AI methods excel at automatically recognizing complex patterns in imaging data and providing quantitative, rather than qualitative, assessments of radiographic characteristics. In this Opinion article, we establish a general understanding of AI methods, particularly those pertaining to image-based tasks. We explore how these methods could impact multiple facets of radiology, with a general focus on applications in oncology, and demonstrate ways in which these methods are advancing the field. Finally, we discuss the challenges facing clinical implementation and provide our perspective on how the domain could be advanced. Full text: https://rdcu.be/O1xz
This chapter describes a design strategy for blending virtual reality (VR) with an immersive multi-user virtual environment (MUVE) curriculum developed by the EcoLearn design team at Harvard University for middle school students to learn ecosystems science. The EcoMUVE Pond middle grades curriculum focuses on the potential of immersive authentic simulations for teaching ecosystems science concepts, scientific inquiry (collaborative and individual), and complex causality. The curriculum is inquiry-based; students investigate research questions by exploring the virtual ecosystem and collecting data from a variety of sources over time, assuming roles as ecosystems scientists. The implications of blending in VR for EcoMUVE’s technical characteristics, user-interface, learning objectives, and classroom implementation are discussed. Then, research questions for comparisons between the VR version and the “Classic” version are described. The chapter concludes with generalizable design heuristics for blending MUVE-based curricula with head-mounted display immersion.