Elements in Generative AI in Education – serie

Generative artificial intelligence (AI) is becoming an integral part of children's lives, ranging from voice assistants and social robots to AI-generated storybooks. As children increasingly interact with these technologies, it is essential to consider their implications for developmental outcomes. This Element examines these implications across three interconnected domains: interaction, perception, and learning. A recurring theme across these domains is that children's engagement with AI both mirrors and diverges from their engagement with humans, positioning AI as a distinct yet potentially complementary source of experience, enrichment, and knowledge. Ultimately, the Element advances a framework for understanding the complex interplay among technology, children, and the social contexts that shape their development. This title is also available as Open Access on Cambridge Core.

Generative artificial intelligence (AI) is becoming an integral part of children's lives, ranging from voice assistants and social robots to AI-generated storybooks. As children increasingly interact with these technologies, it is essential to consider their implications for developmental outcomes. This Element examines these implications across three interconnected domains: interaction, perception, and learning. A recurring theme across these domains is that children's engagement with AI both mirrors and diverges from their engagement with humans, positioning AI as a distinct yet potentially complementary source of experience, enrichment, and knowledge. Ultimately, the Element advances a framework for understanding the complex interplay among technology, children, and the social contexts that shape their development. This title is also available as Open Access on Cambridge Core.

Generative AI is a disruptive technology that has the potential to transform many aspects of how computer science is taught. Like previous innovations such as high-level programming languages and block-based programming languages, generative AI lowers the technical expertise necessary to create working programs, bringing the power of computation to more people. The programming process is already changing as a result of its presence, even for expert programmers. It also poses significant challenges to educators around re-thinking assessment as some well-established approaches may no longer be viable. Many traditional programming assignments can be completed using generative AI tools with minimal effort, thus potentially undermining learning. In this Element, the authors explore both the opportunities and the challenges for computer science education resulting from the widespread availability of generative AI.

Generative AI is a disruptive technology that has the potential to transform many aspects of how computer science is taught. Like previous innovations such as high-level programming languages and block-based programming languages, generative AI lowers the technical expertise necessary to create working programs, bringing the power of computation to more people. The programming process is already changing as a result of its presence, even for expert programmers. It also poses significant challenges to educators around re-thinking assessment as some well-established approaches may no longer be viable. Many traditional programming assignments can be completed using generative AI tools with minimal effort, thus potentially undermining learning. In this Element, the authors explore both the opportunities and the challenges for computer science education resulting from the widespread availability of generative AI.

The development of Generative Artificial Intelligence (GenAI) has led to intense wonder, surprise, excitement, and concern within the language teaching profession. These tools offer the potential to assist language teachers in helping their learners achieve their language learning goals, and at the same time, risk disrupting language teaching and learning processes, the teaching profession, and possibly the instrumental needs to learn foreign languages. This Element provides an accessible introduction and guide to the use of GenAI for language teaching. It aims to facilitate language teachers' development of the professional knowledge and skills they need to use GenAI responsibly, ethically and effectively. The Element It is a valuable resource for pre-service and in-service language teachers of all experience levels. Each section includes helpful tips and questions for reflection to get teachers started with GenAI while ensuring they engage critically and responsibly with these tools. Evidence-informed approaches are promoted throughout the Element.

The development of Generative Artificial Intelligence (GenAI) has led to intense wonder, surprise, excitement, and concern within the language teaching profession. These tools offer the potential to assist language teachers in helping their learners achieve their language learning goals, and at the same time, risk disrupting language teaching and learning processes, the teaching profession, and possibly the instrumental needs to learn foreign languages. This Element provides an accessible introduction and guide to the use of GenAI for language teaching. It aims to facilitate language teachers' development of the professional knowledge and skills they need to use GenAI responsibly, ethically and effectively. The Element It is a valuable resource for pre-service and in-service language teachers of all experience levels. Each section includes helpful tips and questions for reflection to get teachers started with GenAI while ensuring they engage critically and responsibly with these tools. Evidence-informed approaches are promoted throughout the Element.

Generative AI (GenAI) technology is transforming the landscape of language teaching and learning and has attracted considerable attention from researchers and educators in the field of second language (L2) education. Research has shown that, when used appropriately, GenAI can support students throughout the writing process, provide high-quality feedback on written work, and facilitate the assessment of L2 writing. This Element presents five innovative topics that the co-authors have explored: (1) student-GenAI interaction during the writing process; (2) collaborative processing of GenAI-generated feedback; (3) GenAI-supported teacher feedback; (4) the potential of GenAI for L2 writing assessment; and (5) teacher education for the effective integration of GenAI in L2 writing instruction. By synthesizing current research and practical applications, this Element aims to inspire researchers, practitioners, and graduate students to further investigate the role of GenAI in L2 writing contexts.

Generative AI (GenAI) technology is transforming the landscape of language teaching and learning and has attracted considerable attention from researchers and educators in the field of second language (L2) education. Research has shown that, when used appropriately, GenAI can support students throughout the writing process, provide high-quality feedback on written work, and facilitate the assessment of L2 writing. This Element presents five innovative topics that the co-authors have explored: (1) student-GenAI interaction during the writing process; (2) collaborative processing of GenAI-generated feedback; (3) GenAI-supported teacher feedback; (4) the potential of GenAI for L2 writing assessment; and (5) teacher education for the effective integration of GenAI in L2 writing instruction. By synthesizing current research and practical applications, this Element aims to inspire researchers, practitioners, and graduate students to further investigate the role of GenAI in L2 writing contexts.

GenAI has rapidly and universally disrupted teaching, learning, and assessing with integrity. In this Element, the authors first orient readers to the academic integrity landscape and then examine historical technological disruptions to academic integrity for lessons learned. Readers are then transported to 2045 with a fictional depiction of the type of university that will unfortunately emerge if we continue our historical practice of layering technology onto a 20th century educational platform rather than reenvisioning academic integrity. After analyzing this possible future, the authors offer a multi-layered and balanced approach to the problem through developing guidelines, policies and procedures, rethinking assessment design, and cultivating humans who will act ethically, in the age of AI. Overall, this Element offers evidence-based strategies that instructors and institutions can adapt to their contexts, emphasizing that as humans, we have the agency to shape the integrity we want and deserve.

GenAI has rapidly and universally disrupted teaching, learning, and assessing with integrity. In this Element, the authors first orient readers to the academic integrity landscape and then examine historical technological disruptions to academic integrity for lessons learned. Readers are then transported to 2045 with a fictional depiction of the type of university that will unfortunately emerge if we continue our historical practice of layering technology onto a 20th century educational platform rather than reenvisioning academic integrity. After analyzing this possible future, the authors offer a multi-layered and balanced approach to the problem through developing guidelines, policies and procedures, rethinking assessment design, and cultivating humans who will act ethically, in the age of AI. Overall, this Element offers evidence-based strategies that instructors and institutions can adapt to their contexts, emphasizing that as humans, we have the agency to shape the integrity we want and deserve.

The rapid integration of generative AI (GenAI) tools into higher education (HE) presents both transformative opportunities and pressing challenges, particularly in English-medium education (EME) classrooms. While GenAI tools offer innovative possibilities for enhancing instruction, assessment, and learner autonomy, they also raise concerns about the erosion of meaningful language and content learning experiences through over-automation and excessive reliance on algorithmic output without involving students' thinking process. This Element offers a timely, practitioner-focused exploration of how GenAI tools can be thoughtfully integrated into both language and content-subject teaching while addressing key threats GenAI poses within EME contexts. The Element does not seek to promote the uncritical adoption of GenAI into HE but instead offers a pragmatic way forward that recognises the essential role of agentic teachers in supporting student content and language learning. This title is also available as Open Access on Cambridge Core.

The rapid integration of generative AI (GenAI) tools into higher education (HE) presents both transformative opportunities and pressing challenges, particularly in English-medium education (EME) classrooms. While GenAI tools offer innovative possibilities for enhancing instruction, assessment, and learner autonomy, they also raise concerns about the erosion of meaningful language and content learning experiences through over-automation and excessive reliance on algorithmic output without involving students' thinking process. This Element offers a timely, practitioner-focused exploration of how GenAI tools can be thoughtfully integrated into both language and content-subject teaching while addressing key threats GenAI poses within EME contexts. The Element does not seek to promote the uncritical adoption of GenAI into HE but instead offers a pragmatic way forward that recognises the essential role of agentic teachers in supporting student content and language learning. This title is also available as Open Access on Cambridge Core.

This Element examines how generative artificial intelligence (GenAI) is reshaping educator professional development across initial teacher education, in-service learning, and institutional policy. It addresses two related but distinct topics: professional learning about GenAI, in which teachers develop the knowledge, skills, and judgement needed to use these tools responsibly, and professional learning through GenAI, in which GenAI serves as a vehicle for learning about topics such as assessment, differentiation, and pedagogy. It argues that GenAI's educational significance lies less in automating production than in intensifying the need for professional judgement. It introduces practical quality gates for evaluating AI-assisted materials, a functional typology of GenAI tools, design principles for GenAI-enhanced professional learning, and inquiry approaches, including a GenAI Learning Log, that centre teacher agency. Mini-scenarios and checklists illustrate low-risk, job-embedded ways to build critical AI literacy responsibly and with attention to equity.

This Element examines how generative artificial intelligence (GenAI) is reshaping educator professional development across initial teacher education, in-service learning, and institutional policy. It addresses two related but distinct topics: professional learning about GenAI, in which teachers develop the knowledge, skills, and judgement needed to use these tools responsibly, and professional learning through GenAI, in which GenAI serves as a vehicle for learning about topics such as assessment, differentiation, and pedagogy. It argues that GenAI's educational significance lies less in automating production than in intensifying the need for professional judgement. It introduces practical quality gates for evaluating AI-assisted materials, a functional typology of GenAI tools, design principles for GenAI-enhanced professional learning, and inquiry approaches, including a GenAI Learning Log, that centre teacher agency. Mini-scenarios and checklists illustrate low-risk, job-embedded ways to build critical AI literacy responsibly and with attention to equity.

AI policy is proliferating in universities worldwide, yet AI policy research remains heavily descriptive, cataloguing institutional responses without examining the capacities required to make policy well. This Element argues that the missing capacity is AI policy literacy, the institutional capability to interrogate AI policy as text, discourse, and practice, and to attend to how authority, inequality, and time shape what governance can achieve. Drawing on policy sciences, applied linguistics, and decolonial thought, it develops a six-dimension framework for AI policy literacy. It also demonstrates that AI policies are simultaneously language policies, and that the absence of AI policy research in language education is creating deeper digital divides and knowledge injustices. Treating AI as cognitive infrastructure rather than a tool to be regulated, this Element proposes technodiversity as the orienting principle for governance aligned with universities' educational and social purposes. This title is also available as Open Access on Cambridge Core.

AI policy is proliferating in universities worldwide, yet AI policy research remains heavily descriptive, cataloguing institutional responses without examining the capacities required to make policy well. This Element argues that the missing capacity is AI policy literacy, the institutional capability to interrogate AI policy as text, discourse, and practice, and to attend to how authority, inequality, and time shape what governance can achieve. Drawing on policy sciences, applied linguistics, and decolonial thought, it develops a six-dimension framework for AI policy literacy. It also demonstrates that AI policies are simultaneously language policies, and that the absence of AI policy research in language education is creating deeper digital divides and knowledge injustices. Treating AI as cognitive infrastructure rather than a tool to be regulated, this Element proposes technodiversity as the orienting principle for governance aligned with universities' educational and social purposes. This title is also available as Open Access on Cambridge Core.

This Element explores how generative AI transforms education, not only by requiring new forms of literacy but by demanding a framework of autonomy, responsibility, and epistemic virtues. Current AI literacy frameworks emphasize technical proficiency, yet remain incomplete without addressing the normative risks of AI immaturity. This Element argues that education must promote AI maturity: the ability to use AI without succumbing to cognitive offloading, algorithmic dependence, or epistemic heteronomy. It identifies key forms of AI immaturity and reconceptualizes prompting as a dialogical practice paralleling the Socratic method - the art of questioning, iterating, and critically engaging AI. Finally, it introduces a framework of epistemic virtues that enables students and citizens to resist disempowerment and cultivate epistemic autonomy. By bridging philosophy and pedagogy, the Element shows how generative AI can become a medium of epistemic autonomy, offering concrete guidance for curricula, assessment, and classroom practice.

This Element explores how generative AI transforms education, not only by requiring new forms of literacy but by demanding a framework of autonomy, responsibility, and epistemic virtues. Current AI literacy frameworks emphasize technical proficiency, yet remain incomplete without addressing the normative risks of AI immaturity. This Element argues that education must promote AI maturity: the ability to use AI without succumbing to cognitive offloading, algorithmic dependence, or epistemic heteronomy. It identifies key forms of AI immaturity and reconceptualizes prompting as a dialogical practice paralleling the Socratic method - the art of questioning, iterating, and critically engaging AI. Finally, it introduces a framework of epistemic virtues that enables students and citizens to resist disempowerment and cultivate epistemic autonomy. By bridging philosophy and pedagogy, the Element shows how generative AI can become a medium of epistemic autonomy, offering concrete guidance for curricula, assessment, and classroom practice.

The dominance of commercial AI in education has created a paradox: while these tools promise transformation, they perpetuate dependency, financial barriers, and environmental unsustainability. This Element challenges the 'technical mystique', the misconception that AI development requires expertise beyond educators' reach, and demonstrates how teachers can become creators rather than consumers of educational technology. The authors chronicle their transformation from linguists and educators to AI developers without formal computer science training, illustrating how the technical barriers educators face are often more perceived than real. Through detailed case studies, including an AI-assisted Dialogue System (ADS) for multilingual nurse-patient training and assessment, they demonstrate how teachers can leverage AI coding assistants, browser-based models, and open-source resources to develop pedagogically tailored applications. This title is also available as open access on Cambridge Core.