Participation research is concerned primarily with three overarching questions: What is the extent of participation? Who is participating? Why are certain people or groups participating either more or less, or not at all?
The integration of artificial intelligence (AI) in American education is rapidly accelerating, bringing both tremendous opportunities and risks. AI refers to computer systems that can perform tasks normally requiring human intelligence, such as visual perception, speech recognition, and decision-making. As AI capabilities advance, educators are exploring how these technologies can enhance teaching and learning. However, thoughtfully integrating AI in schools also requires grappling with ethical dilemmas and establishing proper regulatory oversight.
AI is already being applied in American classrooms in various ways. Intelligent tutoring systems provide personalized instruction and feedback for students. Machine learning algorithms help teachers track student progress and target interventions. Virtual assistants like Alexa act as a helpful aide for educators. AI also enables innovations in accessibility tools, classroom management, and assessment.
Proponents argue AI can make education more effective, engaging, and tailored to each child. AI tutors never get tired or impatient and can offer each student endless practice and scaffolding. Intelligent software can surface unique insights from educational data that human analysts would overlook. AI-enabled customization provides learning experiences matched to students' needs, interests, and abilities.
However, critics point out risks such as student overreliance on AI, biases creeping into algorithms, and improper use of student data. Thoughtful implementation requires protecting student privacy, ensuring transparency, and preserving teacher autonomy and oversight. AI should aim to enhance human teaching, not replace teachers. Wise integration will require ethical vigilance, ongoing regulation, and respect for the irreplaceable roles of educators.
Rapid progress in artificial intelligence research is unlocking new capabilities for enhancing teaching and learning. Key advancements include machine learning, natural language processing, computer vision, robotics, and specialized AI applications targeting health, finance, and other domains.
Machine learning algorithms enable computers to improve at tasks through experience without explicit programming. In education, machine learning powers AI tutors' ability to adapt to students and personalize instruction. Deep learning, a type of machine learning, is driving breakthroughs in areas like speech and image recognition through neural networks inspired by the human brain.
Natural language processing (NLP) allows computers to analyze and generate human language. NLP enables AI writing assistants to improve student essays by identifying errors and suggesting revisions. NLP also facilitates educational data mining to uncover insights for improving assessments and instruction.
Computer vision allows AI systems to interpret and analyze visual inputs like images and videos. In classrooms, computer vision can assess student engagement and participation, while vision-enabled robotics promotes interactive STEM learning. Computer vision is also revolutionizing accessibility tools for visually impaired students.
AI-powered robotics is enriching education by enabling more interactive learning experiences. Humanoid robots serve as tutors and peer learning companions, while more advanced robotics kits teach programming, design, and engineering. Robotics increases student engagement, promotes collaboration, and makes technical STEM concepts more tangible.
Specialized AI applications are also emerging, like finance platforms to improve financial literacy and health chatbots that answer student questions about nutrition, fitness, and mental wellbeing. These innovations demonstrate the versatility of AI-enabled solutions for advancing learning objectives.
Integrating AI technologies into America's schools raises important policy and governance considerations around responsible development and ethical use. Multiple government initiatives, task forces, and regulatory frameworks aim to provide oversight and guidance to ensure AI promotes educational equity and innovation.
In 2020, the U.S. Department of Education published five principles and several recommendations regarding the safe, ethical, and effective use of AI in education.* The principles emphasize promoting innovation and evidence-based learning strategies; protecting student privacy and security; enabling accessibility and equity; being transparent about when, where, and how AI technologies are used; and empowering educators with technology designed to augment—not replace—their work.
The U.S. government also convened a National AI Research Resource Task Force in 2020 consisting of experts from academia, research institutes, corporations, and other stakeholders. This group aims to facilitate collaborative efforts to foster AI advances in support of national priorities like education.**
While no comprehensive regulatory framework for educational AI yet exists, several multistakeholder initiatives have emerged involving academia, industry, government agencies, and civil society groups. For example, the Partnership on AI was formed to conduct research and recommend best practices around AI ethics, fairness, transparency, and inclusivity.* Such collaborative efforts help shape policies and governance models for responsible use of AI in learning.
Overall, ongoing policy development aims to stimulate AI innovation in American education while establishing safeguards against misuse. Striking the right balance can help realize AI's benefits while minimizing harms. More work remains to craft thoughtful policies and governance for AI integration in schools.
Artificial intelligence is poised to transform assessment and testing in American education in significant ways. AI-enabled assessments can reduce bias, provide richer insights into student knowledge, increase accessibility, and enable more individualized feedback and adaptive learning experiences. However, ethical risks around data privacy, transparency, and fairness must also be addressed.
AI allows for more granular, ongoing analysis of student responses, unlocking richer insights than conventional standardized tests. Researchers note AI techniques like natural language processing provide previously impossible levels of detail about learners' strengths and misconceptions.** AI-enhanced formative assessments support personalized learning by generating instant feedback showing students where they need to improve.
Proponents argue AI can reduce bias in high-stakes assessments and expand accessibility for diverse learners through features like text-to-speech. AI-powered adaptive testing also presents test questions adjusted based on students' responses, reducing test anxiety. Critics counter AI risks perpetuating biases from flawed training data. Transparency around AI integration in assessments is vital.
Educators have raised valid concerns around data privacy and ethical usage when applying AI in assessments. However, with thoughtful safeguards AI-driven assessment innovations could enhance equity, provide insights to strengthen instruction, and improve the learning experience. More research and collaborative policymaking is still required to realize AI's benefits in this domain while mitigating the risks.
While many teachers are excited by AI's potential, they also harbor concerns around issues like fairness, transparency, and preserving educator authority. Addressing these issues requires considering ethics and power dynamics in AI adoption. Pedagogical strategies should focus on augmenting human teaching rather than replacing teachers.
Surveys indicate the majority of teachers support incorporating AI in education.* However, most feel they lack training in how to effectively deploy these tools. Teachers worry about ethical issues like biased AI recommendations. Many stress the risks of over-automating education and view certain uses of AI as deprofessionalizing teaching.
AI integration strategies should empower teachers and address ethics proactively. Educators want transparency about when, where, and how AI is used in their classrooms. AI aimed at reducing teacher workload often backfires. Instead, human-AI collaboration works best. AI generates insights, while teachers interpret results using professional judgment to improve their practice.
Strong pedagogical frameworks recognize teaching as an inherently human endeavor. They leverage AI to enhance areas like data analysis, administrative tasks, and differentiated learning while preserving teacher oversight. With thoughtful design, AI can provide value-added functionality to classrooms without usurping teacher autonomy.
AI-enabled educational technologies are proliferating, aiming to enhance learning and teaching in areas like personalized instruction, virtual simulations, intelligent homework help, and data analytics. However, integrating these tools requires evaluating both benefits and risks, along with substantive educator involvement.
Personalized learning platforms apply techniques like machine learning and natural language processing to tailor lessons and assessments to each student's needs and interests. AI virtual tutors offer interactive practice and feedback. Simulated learning environments create immersive science, history, and vocational training experiences impossible in real life.
Homework help platformspowered by natural language processing assist students in real-time with homework questions across subjects and grade levels. AI data dashboards surface insights to help teachers identify learning gaps, gauge student engagement, and improve outcomes.
However, risks exist around student data privacy, hidden biases in algorithms, and potential overreliance on technology. Educators want transparency about the AI systems' capabilities and limitations to properly supervise their use. With educator input and ethical oversight, AI-enabled tools show immense promise for transforming learning for the better.
Artificial intelligence presents new opportunities to address variability in how different students learn and tailor education accordingly. AI-driven personalized learning tools are already demonstrating potential. However, careful design is essential to ensure algorithms promote fairness, equity, and inclusion when responding to diverse learner needs.
Students have diverse abilities, backgrounds, motivations, and learning styles. Conventional one-size-fits-all educational approaches often fail to meet all students' needs. But AI-powered solutions can help teachers differentiate instruction through characteristics like:
Adaptivity: AI tutors adjust teaching strategies based on real-time analysis of student responses and demonstrated mastery of concepts.
Personalization: Machine learning algorithms customize curriculum content, activities, and assessments to each learner's strengths, needs and interests.
Broad accessibility: Features like speech recognition, translation and text-to-speech support diverse learning needs.
However, biased algorithms risk perpetuating inequities. Transparency, thoughtful design and regular auditing of AI systems are imperative to uphold ethics. When implemented responsibly, AI holds immense potential to make learning more effective and accessible for all students.
Incorporating AI into student assessments brings opportunities to enhance measurement of learning, provide more timely feedback, and reduce bias. Researchers are exploring innovations including AI-enabled formative assessments, adaptive testing, data analytics, and improvements to summative assessments.
Formative assessments help teachers gauge student learning during instruction and provide ongoing feedback. AI techniques like natural language processing facilitate instant analysis of open-ended student responses and assignments, generating granular insights. These capabilities support timely pedagogical adjustments and differentiation.
AI-driven adaptive assessments adjust testing in real-time based on student responses. This reduces test anxiety and gives a more precise measure of student knowledge. Adaptive learning platforms apply similar personalization to provide customized learning activities and resources.
Large-scale assessments produce vast datasets that are cumbersome for humans to analyze. Machine learning algorithms help identify useful patterns and surface insights to strengthen curricula, teaching practices, and learning objectives.
AI also shows promise for improving summative assessments regarding accuracy, fairness, and accessibility. However, transparent use of AI in high-stakes assessments remains an active area of research and policy development.
AI innovations offer teachers growing opportunities for professional learning and classroom support. Intelligent teaching platforms provide personalized recommendations and resources to enrich lessons and improve practices. However, collaborative human-AI approaches recognize the continued importance of educator oversight.
Research shows most teachers have had minimal training in leveraging technology and lack support integrating new tools like AI-enabled classroom software, apps, and platforms. Intelligent teaching assistants aim to bridge this gap by analyzing teacher needs and offering targeted recommendations.
For example, AI coaching tools can identify instructional areas to strengthen and suggest personalized learning modules, lesson plans, discussion prompts, and multimedia to incorporate. Teachers maintain agency over what ideas to implement while benefiting from AI support.
Other AI teacher tools focus on simplifying administrative tasks like taking attendance and grading to allow educators more time engaging students. Some AI platforms also facilitate peer collaboration and mentoring at scale.
Overall, purposeful integration of AI in teacher professional development shows immense potential. But it must actively involve educators in design decisions and recognize the continued primacy of teachers' professional judgment and oversight.
Low student engagement remains a persistent problem challenging educators. Advocates believe AI could play a constructive role in creating more dynamic, personalized, and culturally-responsive learning experiences that heighten student engagement.
Research shows engaged students demonstrate higher achievement, satisfaction, and persistence in their studies. Yet, student boredom and low motivation are endemic in schools. Reasons include insufficient challenge, lack of autonomy, and inability to connect learning to personal interests and goals.
Proponents argue AI edtech tools can help address these issues through innovations like:
However, critics argue focusing overly on technology risks further disconnecting students from human relationships and hands-on learning. Students may also find AI disempowering or manipulative if used improperly. Balancing high-tech and high-touch strategies remains key.
AI technologies are providing new opportunities to enhance learning for the 14 percent of American students receiving special education services.* Advocates see potential for AI to facilitate more individualized, inclusive educational experiences for learners with diverse disabilities and challenges through applications like:
However, bias in data and algorithms risks further marginalizing students receiving special education services. Equitable, empowering AI integration relies on inclusive design practices and thoughtful auditing procedures. Assistive technologies should aim to increase disabled students' autonomy and dignity, not over-automate education.
Special education teachers also need comprehensive training to use AI tools effectively. With thoughtful implementation, AI could help America make progress towards more personalized, equitable education for all learners. But inclusive, ethical design remains paramount.
Many advocates believe applied conscientiously, AI could help advance educational equity for marginalized students. Intelligent systems can pinpoint inequities in course access, achievement, funding, and beyond. AI can also enable customized support to help each underserved learner thrive.
However, if biases and exclusions exist in the algorithms powering AI systems, they risk perpetuating discrimination against minorities. High-quality, representative training data is essential to prevent encoded biases. Continual auditing and human oversight of AI tools are also crucial safeguards.
Government initiatives are exploring how to harness AI responsibly to expand access and opportunity. For example, the National Science Foundation funded research examining how AI-enabled personalized learning could strengthen outcomes for rural students.** Such efforts underscore the possibilities but also the diligence required to ensure AI promotes equity.
While AI shows promise for advancing equitable, high-quality education, thoughtful policymaking and governance processes remain vital. Proactive, collaborative efforts engaging all stakeholders will help actualize benefits while mitigating risks.
Artificial intelligence is transforming possibilities for lifelong learning and workforce development. AI-powered apps, online courses, and intelligent skills training systems allow adults to continually gain new knowledge and adapt to evolving workplace needs.
A recent Pew Research study found 87 percent of Americans believe ongoing training and education will be essential to succeeding in the future economy.* Many predict AI and automation will disrupt employment, requiring workers to gain new skills throughout their careers.
In response, digital platforms are emerging that leverage AI to make continual upskilling more accessible and personalized. For example, AI writing enhancement tools help non-native English speakers strengthen business communication abilities. Intelligent tutoring systems allow workers to acquire new technical capabilities tailored to current industry needs.
However, thoughtfully designed policies and governance practices remain vital to ensuring AI-enabled training is empowering and equitable. AI risks automating education in disempowering ways if improperly deployed. But responsibly implemented, AI could make lifelong learning more effective and open professional pathways for diverse populations.
The explosion of digital data in education is catalyzing new opportunities for AI-enabled research and analytics. Machine learning algorithms help uncover insights from vast datasets that were previously inscrutable to human analysis alone. This supports data-driven improvements to curricula, assessments, teacher training, and educational policies.
For instance, natural language processing allows qualitative analysis of open-ended student responses to identify learning gaps. Network analysis of school systemic relationships surfaces areas to strengthen through a metadata approach. Predictive analytics forecasts the outcomes of potential policy changes to support evidence-based decision-making.
However, while Big Data analysis holds promise, thoughtful governance of educational AI research remains vital. Areas requiring vigilance include data privacy, preventing biases, and maintaining transparency. With responsible practices, collaborative human-AI research initiatives could significantly advance teaching practices, programs, and policies.
The integration of artificial intelligence in US education is expected to continue accelerating in the years ahead. Several emerging developments will shape AI's evolving role, including creating human-centered algorithms, democratizing access, enhancing government oversight, and strengthening digital citizenship education.
Many researchers argue the most powerful applications will combine AI and human strengths through collaborative approaches. Developing algorithms that align with human values and ethics is crucial. Similarly, democratizing access to quality AI training data and tools will help ensure underserved communities benefit.
Government oversight will continue growing to stimulate innovation responsibly. Policies should aim to provide guidance and guardrails without stifling progress. schools also need to strengthen digital literacy curricula preparing students to navigate an AI-enabled world.
The future of artificial intelligence in education will depend on people's collective priorities, policies, and values. With wisdom and vigilance, AI could positively transform learning experiences. But we must proactively shape AI's continued integration to promote empowerment over automation.
The integration of artificial intelligence in education raises immense possibilities along with risks that require vigilant governance and policymaking. Realizing the benefits of AI while mitigating ethical perils will rely on human-centered design practices and collaborative solutions engaging all stakeholders. Although AI offers tools to enhance learning, teachers must maintain their vital roles guiding education's human dimensions. With concerted efforts, AI could make American education more equitable, effective and empowering for all. But ultimately, transformative change will depend most on our shared vision, values and moral imagination regarding how to best prepare young people for the future.
Participation research is concerned primarily with three overarching questions: What is the extent of participation? Who is participating? Why are certain people or groups participating either more or less, or not at all?
A quick review of international policy documents reveals the importance of adult learning in supporting the wellbeing of nations and individuals.
This article focuses on the wider benefits of adult learning, covering a mix of personal and social (external) effects. The emphasis is on the effects of adult learning on health and civic engagement.
The expansion of post-compulsory education and training has been one of the most striking recent changes in the education systems of more developed societies.
Education increasingly appears to be the natural form of learning in modern life within a contemporary Western mindset.
Generally absent until now from the International Encyclopedia Education, the theme adult learning and health is taking momentum in many countries as well as in international multilateral networks (UNESCO, 1997; US Dept. of Health, 2000; ICAE, 2001; WHO, 2005b).
Adult education and training have been focal points of labor market studies in developed economies since the 1960s.
The late President of Tanzania said in 1976 that, ‘‘Adult education is a highly political activity. Politicians are sometimes more aware of this fact than educators, and therefore they do not always welcome real adult education’’ (Nyerere, 1976).
It is important to understand that the idea of civil society has been defined and deployed in different ways at different times, depending on what kind of social and political struggles were being fought, and won or lost.
The expansion of higher education across Organisation for Economic Co-Operation and Development (OECD) countries at the end of the twentieth century represented one of the most significant aspects of change in higher education in recent times.
Older learning has become a major focus in international and national government’s educational and social policies.
Prior learning assessment (PLA) is a topic that has emerged as a central aspect of the policy and practice of lifelong learning around the world.
Popular adult education is one of those slippery concepts within the field of education that is used to denote different educational aims, ideas, approaches, activities, and programs around the world.
Since lifelong learning systems embodying various policies are emerging in a variety of unique contexts, they represent very different perspectives from country to country.
Hundreds of millions of people throughout the world visit museums, galleries, zoos, aquariums, nature and science centers, and historic sites. This article discusses museums as sites of adult learning.
The learning city is an ideal, rather than a description of any actual place or places. It is an aspiration for the way the city might be better managed and manage itself in a complex world characterized by terms such as global and knowledge economy.
For many hundreds of thousands of people around the world, learning through their trade union has been an important, and occasionally, pivotal experience in their development and understanding as workers and citizens.
Adult learning is a wide-ranging phenomenon and accordingly its financing. It engages individuals, organizations, communities, and nations.
In the field of adult education, it is possible to identify two separate areas of research, theory, and practice that are often related: citizenship education and immigrant education. Generally speaking, citizenship education aims at preparing individuals to become citizens in a particular political community.
The last few years have seen growing interest in the field of adult literacy education, also known as adult basic education and adult literacy and numeracy, with increased attention at national and international levels.
Two recent European Community policy documents on adult education raise the need for a changing role of adult basic and vocational based learning (VBL) in the context of the evolving knowledge society and knowledge economy (EC, 2006, 2007).
In collections of university libraries worldwide, one can locate relatively small sections of volumes pertinent to the history of adult education.