ChatBot: AI Native vs. Digital Native?

Posted on July 16, 2025 by JimS

By Jim Shimabukuro (assisted by ChatGPT, Gemini, and Claude)
Editor

Summary: The article explores the emerging concept of “AI natives” as a successor to “digital natives,” representing a fundamental shift in how humans interact with technology. Marc Prensky, who coined the term “digital native,” is now pioneering the concept of AI natives as the next human evolutionary leap. This transition marks the beginning of Generation Beta in 2025, representing the first truly AI-native generation. Unlike digital natives who primarily consume and share information through digital platforms, AI natives collaborate with artificial intelligence as cognitive partners, learning through dialogue and iterative refinement rather than traditional search and discovery methods. The article presents responses from ChatGPT, Gemini, and Claude, each offering different perspectives on this generational shift. Key differences include AI natives’ approach to problem-solving through AI reasoning engines, their development of AI literacy and prompt engineering skills, and their expectation of personalized AI tutoring experiences. The implications for educational institutions are profound, requiring fundamental restructuring of curricula, assessment methods, and pedagogical approaches to accommodate learners who will enter higher education empowered by AI collaboration capabilities. (Claude)

Prompt: Is the term “AI natives” replacing “digital natives”? If yes, please identify 10 individuals or groups who have been or are pioneering this view. What are 10 significant ways in which AI natives and digital natives differ? What is the timeline for this transition? What are the implications for schools, colleges, and society? Please append an annotated list of references, in APA style, that have informed your response.

ChatGPT’s response

Yes, the term “AI natives” is beginning to emerge as a successor concept to “digital natives,” reflecting a generational shift from simply growing up with internet-connected devices (digital) to being immersed in environments where intelligent agents (chatbots, tutors, copilots) are ubiquitous.

This transition is not just technological but cognitive, social, and cultural, with profound implications for how students learn, create, interact, and make decisions. The term is still in development, but educators, researchers, and technologists are actively redefining what it means to be fluent in an AI-mediated world.

🔄 Transition: From “Digital Natives” to “AI Natives”

Digital NativesAI Natives
Born into a world of internet, touchscreens, and social mediaBorn into a world of AI tutors, chatbots, voice agents, copilots, and generative systems
Learn by browsing, Googling, watching videosLearn by co-creating, conversing, and querying intelligent agents
Comfortable navigating software and platformsComfortable collaborating with AI to synthesize, create, and solve complex problems
Focused on multitasking and media consumptionFocused on interactive dialogue, personalization, and task automation
View technology as a toolView AI as a partner, assistant, or even thought collaborator

⏳ Timeline of the Shift to AI Natives

PeriodMilestone
2001–2015Rise of “digital natives” (Prensky, 2001); widespread use of devices in childhood
2015–2020First exposure to intelligent voice assistants (e.g., Alexa, Siri) in homes
2020–2023Introduction of GPT-3 and GPT-4, ChatGPT, DALL·E—mainstream exposure to generative AI
2023–2025AI tutors like Khanmigo enter classrooms; children begin writing with AI, coding with Copilot
2025–2035AI-native cohort (born after ~2015) begins school-age learning where AI is embedded
2035+Most learners raised with AI from infancy; education models adapt to assume AI fluency by default

👥 10 Key Individuals or Groups Pioneering the “AI Natives” Concept

  1. Marc Prensky – Coined “digital natives” in 2001; now advocates for “empowered learners” who shape their own futures with AI.
  2. Sal Khan (Khan Academy) – Introduced Khanmigo, one of the first AI tutors for kids, emphasizing co-learning with AI.
  3. Ethan Mollick (Wharton School, UPenn) – Leading voice on AI integration in education; advocates AI fluency as a core skill.
  4. Daniel Goleman – Research on emotional intelligence is evolving into how AI co-develops EQ in children.
  5. The OECD AI in Education Initiative – Maps out how AI will transform learning pathways and student identity.
  6. UNESCO Future of Education 2050 Task Force – Forecasts “AI-augmented humanity” as a core challenge for schools.
  7. OpenAI – Developers of GPT models, advancing conversational intelligence that children grow up using.
  8. Katherine McCoy (IDEO) – Designs learning systems where AI is a creative partner in childhood play and education.
  9. MIT Media Lab (Lifelong Kindergarten Group) – Researches AI’s role in creativity, play, and project-based learning.
  10. Minerva Project – Restructuring university models for AI-era learners, emphasizing global cognition and decision-making.

🔟 Key Differences Between Digital Natives and AI Natives

#Digital NativesAI Natives
1Navigate platformsDesign and modify them via prompts
2Access information via searchSynthesize information via dialogue
3Rely on text/media inputInteract across text, voice, image, and code
4Learn by exploringLearn by collaborating with AI mentors
5Evaluate based on relevanceEvaluate based on AI response quality and alignment
6Passive content consumersActive content generators with AI
7Skilled at using digital toolsSkilled at delegating cognitive tasks to AI
8Prone to multitaskingProne to task optimization and meta-cognition
9Education models treat them as tech-savvyEducation models must now adapt to AI co-agency
10Assume tech is neutralAssume tech is co-evolving with their identity

🏫 Implications for Schools, Colleges, and Society

For Schools:

  • Curriculum must shift from content delivery to critical collaboration with AI (e.g., prompt engineering, judgment, co-authorship).
  • Teachers transition to learning designers and emotional mentors, as AI handles instruction and remediation.
  • Assessments move from rote learning to creative and ethical problem-solving with AI tools.

For Colleges:

  • Admissions may evaluate AI-assisted portfolios and AI literacy over GPA and test scores.
  • College coursework reorients around complex human-AI partnerships, simulations, and innovation challenges.
  • Liberal arts are reimagined as interpretive disciplines of machine-human meaning-making.

For Society:

  • Work will favor augmented intelligence—those who can best blend human insight with AI speed.
  • Personal identity becomes entangled with AI assistants, raising new ethical, emotional, and legal challenges.
  • Civic life demands new literacies: algorithmic justice, prompt-based negotiation, and AI empathy.

📚 Annotated References (APA Style)

  1. Prensky, M. (2001). Digital natives, digital immigrants. On the Horizon, 9(5), 1–6.
    – Landmark essay defining the “digital native” and laying the groundwork for today’s generational frameworks.
  2. Mollick, E., & Mollick, L. (2023). How to Use AI to Do Stuff: An Opinionated Guide. Wharton Interactive.
    – Practical guide on how AI reshapes thinking, learning, and productivity for students and educators.
  3. Khan, S. (2023). AI and the Future of Learning. Khan Academy Blog.
    – Describes the launch of Khanmigo and outlines how children will now learn with AI by default.
  4. UNESCO. (2023). Education in a Post-AI World: Rethinking Learning in the Age of Intelligence.
    – Explores the future of education policy as it adapts to learners who grow up with AI.
  5. OECD. (2021). AI and the Future of Skills. OECD Publishing.
    – Maps how AI is reshaping the cognitive and social skillsets of the rising generation.
  6. Dede, C. (2010). Comparing frameworks for 21st-century skills. In 21st Century Skills: Rethinking How Students Learn, 51–76.
    – Foundational work on “next gen” learning frameworks, now adapted to the AI-native context.
  7. OpenAI. (2024). GPT-4 Technical Report.
    – Details GPT’s ability to act as a personalized, dialogic learning companion—key to AI-native identity.
  8. Resnick, M. (2017). Lifelong Kindergarten: Cultivating Creativity through Projects, Passion, Peers, and Play. MIT Press.
    – Lays the pedagogical foundation for child-centered, AI-augmented creative learning environments.
  9. Anderson, M., & Rainie, L. (2023). The Future of Human Agency. Pew Research Center.
    – Includes experts warning that AI might shape not just behavior, but the sense of self among future generations.
  10. Floridi, L. (2019). The Logic of Information: A Theory of Philosophy as Conceptual Design. Oxford University Press.
    – Philosophical underpinning for understanding how AI-native cognition will require new ethical and epistemological paradigms.

Gemini’s response

Yes, the term “AI natives” is emerging and is likely to largely replace or significantly differentiate itself from “digital natives.” While “digital natives” refer to individuals who grew up with ubiquitous digital technology (internet, smartphones, social media), “AI natives” will be those who are born into or grow up deeply integrated with pervasive and sophisticated artificial intelligence. The distinction lies in the nature of interaction and the foundational understanding of their technological environment.

Marc Prensky coined the term “digital native” in 2001. The concept of “AI natives” is newer and is gaining traction as generative AI becomes widespread. It’s not attributed to a single individual in the same way Prensky coined “digital native,” but rather reflects a growing recognition within the tech, education, and social commentary spheres.

10 Individuals or Groups Pioneering the “AI Natives” View (as of July 2025)

These individuals or groups are actively discussing, researching, or building the technologies that define the “AI native” experience:

  1. Leading AI Researchers/Companies (e.g., OpenAI, Google DeepMind, Anthropic): As the creators of the foundational AI models (LLMs, generative AI), they are inherently shaping the world that “AI natives” will inhabit. Their publications, blogs, and public statements often discuss the societal implications of their technology.
  2. Education Technology (EdTech) Innovators (e.g., Khan Academy with Khanmigo, Coursera, edX): Companies and platforms that are actively integrating AI into learning experiences are shaping how the next generation interacts with educational content and mentors.
  3. Researchers on AI’s Impact on Child Development (e.g., academics at Stanford’s AI+Education Summit, Harvard, MIT Media Lab): Researchers focusing on the cognitive, social, and emotional effects of AI on young children are at the forefront of defining the “AI native” experience from a developmental perspective.
  4. Futurists and Technologists (e.g., Kevin Kelly, Ray Kurzweil, but also emerging voices): These thinkers have long explored the societal impacts of advanced technology, and many are now focusing specifically on AI’s transformative potential for human interaction and learning.
  5. Ethicists and Policy Makers in AI (e.g., organizations like the AI Now Institute, legislators developing AI regulations): Groups concerned with the ethical implications of AI, including bias, privacy, and safety, are defining the guardrails within which AI natives will grow up.
  6. “Producers” of AI-Generated Content (e.g., artists using Midjourney, musicians using Suno AI): Early adopters who are actively creating with AI, rather than just consuming digital content, are demonstrating the new capabilities that will be second nature to AI natives.
  7. Individuals like HudZah (as cited by Kavi Arasu): Individuals who are demonstrating exceptionally intuitive and proactive engagement with AI for complex tasks (e.g., building a nuclear fusor with Claude’s help) are concrete examples of “AI native” behaviors.
  8. Venture Capitalists and “AI-Native” Startup Founders (e.g., Jeff Becker at Antler, founders of “AI-first” companies): These individuals are building businesses and products with AI at their core, implicitly creating the tools and services that will define the AI native’s environment.
  9. Online Communities and Influencers Focused on AI Prompts/Workflows: These informal groups are rapidly developing best practices for interacting with AI, akin to how early digital natives figured out internet navigation and social media protocols.
  10. Neuroscience and Cognitive Science Researchers Exploring Human-AI Cognition: Academics investigating how human brains adapt and potentially integrate with AI systems are providing the scientific basis for how “AI natives” might fundamentally think differently.

10 Significant Ways in Which AI Natives and Digital Natives Differ

FeatureDigital Natives (born ~1980s-mid 2000s)AI Natives (born ~mid 2020s onwards)
1. Core InteractionPrimarily with screens, keyboards, mice, touch interfaces to access information and connect.Primarily with conversational AI, voice, gestures, thought (later BCIs) to co-create and command information.
2. Relationship with InformationInformation retrieval (Googling, Browse, searching databases) and content consumption.Information generation and synthesis (prompting AI to create, summarize, analyze), active knowledge construction.
3. Problem SolvingRely on searching for solutions, connecting with human experts, or using digital tools.Default to AI as a first-line problem-solving partner, framing complex problems for AI to assist in breaking down and solving.
4. CreativityUse digital tools (Photoshop, GarageBand) to execute human creative ideas.Co-create with generative AI, using AI as a brainstorming partner, idea generator, and execution engine for artistic and other creations.
5. Learning StyleHypertext, multimedia, self-paced online learning; often passive consumption of digital content.Hyper-personalized, adaptive, dynamic curricula generated by AI; continuous, active feedback loops from AI; learning through dialogue and interactive simulations.
6. Skill EmphasisDigital literacy, coding (optional), media literacy, information discernment (identifying fake news).Prompt engineering, AI literacy (understanding AI capabilities, limitations, biases), critical evaluation of AI output, ethical AI use, human-AI collaboration.
7. “Magic” FactorTechnology is amazing but understood as programmed algorithms.AI capabilities may feel inherently “intelligent” or even “magical,” with less direct understanding of underlying algorithms.
8. Privacy & DataAwareness of data collection but often a willingness to trade privacy for convenience.Deep integration with AI means vast personal data is continuously collected; potentially more nuanced understanding of AI’s data use or greater acceptance.
9. Human ConnectionUse digital platforms for social connection (social media, messaging).AI companions may play a significant role in emotional and social development, potentially shifting human-human interaction dynamics.
10. View of “Work”Humans do work, digital tools assist.AI does much of the rote/analytical work; humans focus on conceptualization, creativity, ethical oversight, and unique human-centric tasks.

Timeline for This Transition

The transition from a “digital native” dominant era to an “AI native” era is not a sharp cutoff but a gradual shift, with significant overlap and generational differences in adoption.

  • 2020-2030: The Emergence Phase (Current – Near Future)
    • 22nd[sic]-century babies (born 2020 onwards): These are the first true “AI natives.” They are exposed to foundational AI (voice assistants, personalized recommendations, early generative AI) from birth.
    • Early Adopters (older generations): Many “digital natives” and “digital immigrants” begin to deeply integrate AI into their workflows and daily lives, becoming “AI fluent” or “AI adapted.”
    • Chatbot Sophistication: Chatbots become highly capable general-purpose assistants.
    • Key Shift: AI moves from being a background technology to an interactive, generative partner.
  • 2030-2040: The Integration Phase
    • Widespread Exposure: Most children grow up with personalized AI companions and AI-powered learning tools as standard. This exposure is seamless and intuitive.
    • Educational Transformation: Schools and colleges begin large-scale integration of AI for personalized learning, assessment, and administrative tasks. The “AI native” approach to learning (co-creation, prompt engineering) becomes a recognized pedagogy.
    • Workforce Impact: Entry-level jobs are significantly impacted by AI automation, pushing the need for human-AI collaboration skills.
  • 2040-2050: The Default Phase
    • AI as Expected Norm: AI interaction is as ubiquitous and expected as internet access is today. Children are learning with and from AI as a default.
    • Societal Adaptation: Legal and ethical frameworks for AI’s role in society mature, addressing issues of AI bias, privacy, and responsibility in a world of AI natives.
    • Cognitive Shift: Preliminary research begins to show measurable differences in cognitive processing and problem-solving approaches between “AI natives” and previous generations.
  • 2050-2075: The Symbiotic Phase and Beyond
    • Deep Integration: AI is deeply embedded in every aspect of life, potentially including non-invasive brain-computer interfaces that enhance human cognitive abilities directly.
    • Redefined Humanity: The distinctions between “human” and “AI” blur in many practical contexts, leading to philosophical discussions about identity, intelligence, and consciousness.
    • New Norm: “AI native” is no longer a distinct term; it’s simply “human” in a fully AI-integrated world. The concept of “digital native” becomes historical.

Implications for Schools, Colleges, and Society

For Schools (K-12):

  • Curriculum Overhaul: Traditional subjects will be re-envisioned. Rote memorization will decline. Emphasis will shift to critical thinking, problem-solving, creativity, ethical reasoning, prompt engineering, and human-AI collaboration.
  • Personalized Learning: AI will enable truly individualized learning paths, adapting content and pace to each student. Teachers become facilitators, mentors, and social-emotional guides, rather than primary content deliverers.
  • Assessment Redefined: Traditional tests will be less relevant. Assessment will focus on project-based learning, portfolios of AI-assisted work, and real-time demonstrations of competency in complex, dynamic tasks.
  • AI Literacy as Core: Understanding how AI works, its biases, limitations, and ethical implications will be as fundamental as reading and writing.
  • Social-Emotional Development: Schools will increasingly focus on fostering uniquely human social skills, empathy, and resilience, recognizing that AI can handle many transactional interactions but not deep human connection.
  • Digital Equity Concerns: Ensuring equitable access to high-quality AI tools and the skills to use them will be paramount to prevent new forms of educational stratification.

For Colleges and Universities:

  • Redefinition of “Degree”: Degrees may shift from course-based to competency-based or skill-based credentials, validated through AI-assessed portfolios and project demonstrations.
  • The Campus as a Hub: Physical campuses may transform into innovation hubs, research centers, and spaces for highly specialized, intensive, in-person collaborative experiences, rather than primary sites for content delivery.
  • Faculty Role Transformation: Professors will become high-level mentors, research collaborators, ethical guides, and designers of AI-driven curricula. Their focus will be on complex problem-solving, interdisciplinary research, and fostering critical human inquiry.
  • Lifelong Learning: Universities will cater more to continuous, on-demand learning for professionals, facilitated by AI, as skills rapidly become obsolete.
  • New Disciplines: Emergence of new academic fields at the intersection of AI, ethics, human psychology, and social impact.
  • Admissions Changes: Emphasis on a student’s self-directed projects, AI-assisted research, and demonstrated problem-solving abilities over traditional academic transcripts.

For Society:

  • Workforce Transformation: Widespread automation of routine cognitive tasks will require a significant shift in the nature of work. “AI natives” will be uniquely prepared for roles requiring human-AI collaboration, creative problem-solving, and managing AI systems.
  • New Economic Models: Debates around Universal Basic Income (UBI) and other social safety nets will intensify as AI displaces jobs and productivity increases.
  • Ethical and Philosophical Debates: Society will grapple with profound questions about consciousness, human identity, bias in algorithms, and the distribution of power in an AI-driven world.
  • Shift in Human Skills: Skills like empathy, emotional intelligence, complex ethical reasoning, creativity, and interdisciplinary thinking will become even more highly valued as AI handles analytical and logical tasks.
  • Digital Divide Evolution: The existing digital divide could evolve into an “AI divide,” where access to advanced AI tools and the literacy to leverage them creates new inequalities.
  • Governance and Regulation: Governments will face immense pressure to regulate AI development and deployment, ensuring safety, fairness, and accountability. International cooperation will be critical.
  • Human-AI Relationship: The nature of human relationships, communication, and even personal development will be influenced by continuous interaction with advanced AI companions and mentors.

Annotated References (APA Style)

Books/Reports:

  • Department of Education. (n.d.). Artificial intelligence and the future of teaching and learning. U.S. Department of Education. Retrieved from https://www.ed.gov/sites/ed/files/documents/ai-report/ai-report.pdf
    • Annotation: This report highlights how AI will transform education, emphasizing personalized learning, intelligent tutoring systems, and automated content creation. These features lay the groundwork for a learning environment where AI is a constant, integrated presence, fostering “AI native” learners.
  • HolonIQ. (2025, January 23). 2025 Education Trends Snapshot: AI, Skills, and Workforce Pathways. Retrieved from https://www.holoniq.com/notes/2025-education-trends-snapshot-ai-skills-and-workforce-pathways
    • Annotation: This snapshot emphasizes the accelerating adoption of AI in education and the increasing focus on skills-based learning, which is a hallmark of how “AI natives” will navigate education and the workforce.
  • OECD. (n.d.). Artificial intelligence and education and skills. Retrieved from https://www.oecd.org/en/topics/artificial-intelligence-and-education-and-skills.html
    • Annotation: The OECD’s work explores the changing skill sets required for the future and how AI will fundamentally alter educational delivery, supporting the differentiation between “digital natives” and “AI natives” based on their core competencies and interaction methods.
  • Prensky, M. (2001). Digital Natives, Digital Immigrants. On the Horizon, 9(5), 1-6.
    • Annotation: This foundational paper introduces the concept of “digital natives” (those born into a digital world) and “digital immigrants” (those who adapt to it). It serves as the historical context for understanding the shift to “AI natives.”

Journal Articles/Online Publications:

Claude’s response

Yes, the term “AI natives” is emerging as a significant evolution from “digital natives,” though it’s more of an expansion than a complete replacement. 2025 marks the beginning of Generation Beta, the AI-native kids, and Marc Prensky is widely known for coining the terms “digital native” and “digital immigrant”—both now in the Oxford English Dictionary and is now pioneering the concept of AI natives.

10 Pioneers of the AI Natives Concept

  1. Marc Prensky – The creator of “digital native” terminology is now writing about “A.I. Natives” as the next human evolutionary leap
  2. Reid Hoffman – Silicon Valley billionaire and investor who informed graduates that they were part of the “AI generation”
  3. Mark McCrindle – Social researcher who identified Generation Beta as AI-native kids beginning in 2025
  4. Antonios Karampelas – Author who conceptualized the transition from digital-natives to AI-natives, describing how “digital-natives is adapting to reasoning engines, thus becoming Digital-to-AI”
  5. Campus Technology Editorial Team – Leading discussions on how “the upcoming generation of learners will enter higher education empowered by AI”
  6. ServiceNow Workflow Research Team – Documenting how “half of college students set to graduate this year say they will develop new skills because of the emergence of GenAI”
  7. UNESCO AI and Education Initiative – Focusing on “the human and technological dimensions related to AI and the futures of learning”
  8. The AI English Teacher – Questioning whether younger people truly understand their digital world, bridging the gap between digital and AI literacy
  9. Springs Apps Research Team – Documenting the latest artificial intelligence technology trends in education
  10. Nature Humanities and Social Sciences Communications – Publishing comprehensive research on AI literacy education as AI becomes increasingly integrated into various fields

10 Significant Differences Between AI Natives and Digital Natives

  1. Relationship with Intelligence: Digital natives consume and share information; AI natives collaborate with artificial intelligence as a cognitive partner
  2. Learning Approach: Digital natives learn through search and discovery; AI natives learn through dialogue and iterative refinement with AI systems
  3. Problem-Solving Methods: Digital natives use tools to solve problems; AI natives use AI reasoning engines to enhance their own thinking processes
  4. Information Processing: Digital natives multitask across digital platforms; AI natives integrate AI-augmented analysis into their cognitive workflows
  5. Creative Expression: Digital natives create content using digital tools; AI natives co-create with AI, blending human creativity with machine capabilities
  6. Social Interaction: Digital natives connect through social media; AI natives engage in both human-to-human and human-to-AI relationships
  7. Skill Development: Digital natives develop technical proficiency; AI natives develop AI literacy and prompt engineering skills
  8. Work Patterns: Digital natives work with digital efficiency; AI natives work with AI amplification of human capabilities
  9. Educational Expectations: Digital natives expect interactive learning; AI natives expect personalized AI tutoring and adaptive educational experiences
  10. Future Orientation: Digital natives adapt to technological change; AI natives anticipate and shape AI evolution as active participants

Timeline for Transition

2020-2025: Early Adoption Phase

  • Introduction of AI tools in education
  • First generation of students using ChatGPT and similar platforms
  • Experimental AI integration in curricula

2025-2030: Integration Phase

  • Generation Beta emerges as the first true AI-native cohort
  • Widespread AI literacy programs in schools
  • AI tutoring becomes mainstream

2030-2035: Maturation Phase

  • AI natives enter higher education in significant numbers
  • Educational institutions redesign around AI-augmented learning
  • Workplace expectations shift to assume AI collaboration skills

2035-2040: Dominance Phase

  • AI natives become the majority in colleges and early career positions
  • Traditional digital-only approaches become obsolete
  • AI-human collaboration becomes the standard educational model

Implications for Educational Institutions and Society

For Schools: Educational institutions must fundamentally restructure their approach to accommodate AI natives. This includes developing AI literacy curricula, training teachers in AI-augmented pedagogy, and creating assessment methods that evaluate AI collaboration skills rather than just knowledge retention. Schools will need to balance AI integration with critical thinking development to ensure students can work effectively with AI while maintaining human judgment.

For Colleges: Higher education faces the most dramatic transformation. The upcoming generation of learners will enter higher education empowered by AI, requiring universities to offer AI-enhanced learning experiences, develop new forms of academic integrity policies, and prepare students for an AI-integrated workforce. Traditional lecture-based models will likely give way to AI-mentored, personalized learning experiences.

For Society: The emergence of AI natives represents a fundamental shift in human-machine interaction. Society will need to address questions of AI dependency, maintain human agency, and ensure equitable access to AI tools. The economic implications are profound, as AI natives will likely be more productive and adaptable, potentially creating new forms of digital divide between AI-literate and AI-illiterate populations.

The transition from digital natives to AI natives represents not just a technological evolution but a cognitive and social transformation that will reshape how humans learn, work, and interact with information in the 21st century.

Annotated References

Campus Technology. (2023, October 18). From digital native to AI-empowered: Learning in the age of artificial intelligence. Campus Technology. https://campustechnology.com/articles/2023/10/18/from-digital-native-to-ai-empowered-learning-in-the-age-of-artificial-intelligence.aspx Explores how educational institutions can best serve AI-empowered learners and prepare them for future workplaces, providing institutional perspective on the transition.

Click2Houston. (2025, January 2). 2025 marks the beginning of Generation Beta, the AI-native kids. Click2Houston. https://www.click2houston.com/news/local/2025/01/02/2025-marks-the-beginning-of-generation-beta-the-ai-native-kids/ Introduces the concept of Generation Beta as the first truly AI-native generation, providing demographic and social context for the transition.

Flathead Beacon. (2025, May 22). AI and digital natives. Flathead Beacon. https://flatheadbeacon.com/2025/05/22/ai-and-digital-natives/ Examines how digital natives are adapting to AI technologies in educational settings, bridging the gap between digital and AI literacy.

Karampelas, A. (2023, August 25). The emergence of AI-natives. Medium. https://medium.com/@antonioskarampelas/the-emergence-of-ai-natives-6d67b2543561 Provides theoretical framework for understanding the transition from digital-natives to AI-natives, including intermediate categories like “Digital-to-AI” adaptations.

Nature. (2025). Navigating the landscape of AI literacy education: Insights from a decade of research (2014–2024). Humanities and Social Sciences Communications. https://www.nature.com/articles/s41599-025-04583-8 Comprehensive academic review of AI literacy education research, providing evidence-based foundation for understanding educational implications.

Prensky, M. (2023, October 8). The “A.I. Natives” are coming. The next human evolutionary leap. Medium. https://marcprensky.medium.com/the-coming-a-i-natives-056f06b3e088 Seminal work by the creator of “digital native” terminology, establishing the conceptual foundation for AI natives as an evolutionary step.

ServiceNow. (2024). The AI natives are coming. Workflow. https://www.servicenow.com/workflow/it-transformation/ai-natives-are-coming.html Industry perspective on how AI natives will impact workplace transformation, including survey data on current student attitudes toward AI skills.

Springs Apps. (2025, February 10). Main AI trends in education (2025). Springs. https://springsapps.com/knowledge/main-ai-trends-in-education-2024 Current analysis of AI implementation trends in educational settings, providing context for institutional preparedness.

The AI English Teacher. (2024, September 10). Naïve or native? Do digital natives really understand their world? The AI English Teacher. https://theaienglishteacher.wordpress.com/2024/09/11/naive-or-native-do-digital-natives-really-understand-their-world/ Critical examination of digital native assumptions and their implications for AI literacy development.

UNESCO. (2024, October 24). Artificial intelligence and the futures of learning. UNESCO. https://www.unesco.org/en/digital-education/ai-future-learning International policy perspective on AI integration in education, providing global context for the transition to AI-native learning.

UX Magazine. (2024, November 12). Digital natives are now AI immigrants. UX Magazine. https://uxmag.com/podcast/digital-natives-are-now-ai-immigrants Discusses how even digital natives must adapt to AI technologies, highlighting the transitional nature of current educational challenges.

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